4809 lines
363 KiB
YAML
4809 lines
363 KiB
YAML
interactions:
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '108'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
encoding_format: base64
|
|
input:
|
|
- 'Sales report Q1: Revenue was $100,000.'
|
|
model: qwen3-embedding:4b
|
|
uri: http://localhost:11434/v1/embeddings
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
data:
|
|
- embedding: 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
|
|
index: 0
|
|
object: embedding
|
|
model: qwen3-embedding:4b
|
|
object: list
|
|
usage:
|
|
prompt_tokens: 17
|
|
total_tokens: 17
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '108'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
encoding_format: base64
|
|
input:
|
|
- 'Sales report Q2: Revenue was $150,000.'
|
|
model: qwen3-embedding:4b
|
|
uri: http://localhost:11434/v1/embeddings
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
data:
|
|
- embedding: 7Z4puDO8P7yDO7i88AsdPZOY3bnreRI9PExEPcTEhD1Vj3M8DjQnurWNtbzIaly8ikqkO6wYx7z32aw8WewYvEC+rLqScOC5bI+IPEOyhbuVs2i7RBBuPb2dxzwjaYy9lEsovOi71by/zMW8OazxvDxR27zDC0U8h4sivbRq3LxxlCc9FnOAvKJYJjt8ZGm6KAnkPDmAz7uJRwQ8jvaOu61cGzy6YbC8twG7PFpbCjnPOje85ISCPDtT1Tvewas7OaimvEEEB73Pce66Hxp0ulgAXb3BM128cSy1PK74gTy7Yss6TBwrO9aZF70Vp1W8ZZ0cvJjPQzyN5gm90idrO2YxCLx+H7y8vFKvuhvlxbxfPyK7urZuPOLzkTxw0aY8etFLvMun2rsUMvE8JCd2vNMpRLyIobI87cyoOs6nUDwz6FA8TozruwJPTjxlXUs8gREhPOhmybqW1Su8EAZhu/JZB70eJBy8GIjFO0CYqDzvR+M7ZsN9uxKKVDwka707MQakvDCrH7ylwKE6roW5O61Xubl0j3Y7Y/EfveQrrLwp9+u80rQcvJbw07khKZ08eH9uO93uWzz9cZW8ovSwOizTIT1O1kY8UU2avA67BDwQX5e87lGRPBX1drpKvZS7zMAuvOoTET1AI+87IjOEOyY2+DvCaiU8To9/u6WzpLrPrqq8u6G9PLVob7xdJ6U7DSWtPJIcMzoBk0a9KipRvD1bRrxUmrk5VzCAvKAxwTxla7W6AXfqum4GXjxrUP+75VZXvLZW6zvWqdc7IFaiOw1LXDyBr6A7zEpSPA6qNryRaES7DpbEPBMQSztdk5I8mdHPu9zRHjxmkAK8KS13PDUtJrxfxVU88tG0u6MLbj10lA66F9GVOytxgLwU5L48Q6iJvI1yEzyVEg48e3kcO+smnLwnSjW89yOBvGnZcjyyOJK87WMLvLwin7vzeKe8SweTOiLLDTyvoHO64fF8O7OnbTyv+Sq6v65DvDxUt7sFvmy7SeAXuw9qGrwCeBY764gCPDsYkjxbdEG8JBjCu1YAMjtDt4q8YPs0vLPjyDu3t6s82I0APEf1W7uxmxW6BhhDuxd0aryu0f+7o5BmPFXJezyY3gq8hfxWPDQd87zrCm+8HMQRvPVWtrvj/cG7Tvs2vBvFl7tLOiI8Biw0PY6wizxjZjq6uD9SPD0U5rssbf+8ZGOJPFMR8LtBhdM7ZPJQPJM5rTs+IjE9/E2FPPmsH7z6PEa8oOSwvE9EGT2G9ni8eKivOw+dILuaYo68I/yePOXSArwBPz86zEG8u29wLTyBDp68iiutu6qlqTuk/dO7wGkvvKKCLDu77608bp2zOkNoYLyJbGo7WuEDPNkYCjwkYiG7y5WoOvW8gDyIv6k7Oa+VvBN9pLxkibI5bPjYvPXGtDyOnWU8sqNEPBuFiTtrCZS8uFiqunwjrLyL9ki7JkCtvOkqajztzei70koKPHgo3zuDOvU7QS2APFwRybzSONw8REPYvD6YzbyWIKO8KxTdPOiMHz1kbEA6MXxEvJyggDsfnN46/ANUvQR/Fjw94Ok6hB6bu7PWKT31gUI8D8lLvKfmLrzXH+K8GKiTPEzNTruh6mA7mUKfOseAXzyx70k6gxa8vPFx5zzlPwE8aG2PvaUxFbysQdO6dfFzu9vNXjv2zjM7pRJbO+LCCbx6B867M28EvbPtubybXHI6l9OmvEFn5zlF4bu7X8lIvKSmjzm7jro7zIypu6nglTzwLZU8IO/fPPg50jxXdhS9s9xUu4CHV7zhIoI7mLqGvKzNVjxTCY08qGGju77SfLraKby8WWuEvM5nH72ZyQm9GxbYvJ99qjubxec8eIYcvR+bprwKjE284ZmEvPHaYDz+xB+8d+OZvMoTezx4tne7wn4UPHo1ED00nvm72fRXugPHlDyAHVO8TB0IvOFQgrxbZUy7ImmaO3zABj1uNFg8mgPqvLEfFzw43b27+eI+u1Q2FL0qPw+8PsAavHk4GTqkvtK8THa7POrKHjwkY1o8QCm2PIEqQ7zlCeI7beu8uw2opLyazTK9vAMgvF3s1zzEkEY9WkE2O5o4e7yWeN86hEGqOoCOQ719qtk856Eju+pOBTws8Ze8tgrLvMfEBbwpI0a9huNTvUekvLyl0cW8JAWJvKSWKzuoRp87Q/UNO/yQCLzi1Zo7xN/DO9jVmDsbqK67m2LavL5BObz1s4g7w9CMvDEUVrx5qLY8lKN6PFCCDLzxNYm6tK+5vBQJybuRv2Y8XXIjvN3LELwS/DK8fJ4cvOA+FDwETXm7HQGrPE+8BzzlnHy8EFQlPTlCP7zWY5g8Fv5XPE8xKr3e4Qg8EyyJvCiiLjxsZgu7QZyfvKtVvTsgztE79Wt5PDHUHD3E1z88Iyp9OxFHBjzsGJ28IAwBvIrbxLzAfBe4X6eHPHv25Lu0a5S8PKaEPOB5T72D88U8TluTPPzCtrzmKVS811OZvFNt87zHe6Q8d0g2PKvSXLzGXMC7pVcwOxYruzx5C5C8kgp8PN1OHD0yuUq8ljK0u6jo1DwgoG88kvcuPYP6qjz4maQ8wfrfPGrrNLxHuZ07W8DJPJZykzwVAwc8tSlKvPKprjuvqA49lkYbvSUkTbxQCM88ugOjuKCbSrrMwcu8ykcRux3VijxJws08n1LTuttYoLy+Vze86YPtPEAwJTx1TtG768QyO3hRyLsXTG08UMe9PI4RSDxHfMi65VufvO1g0Dz6Yje8gLe/vMUIMbznaAs9fHoBvQpRmDsqHaI77JR6vCtSKDw5AAG9yKgiPa+1HDs4n1+8TQwWPGRBN7wEpxm8jPmVPOGNKj24Oh49vnRzvYez27zJzOQ82xksO9tHl7x9KN87BgdUvLrnoruApm283ySvvJTY+Dx8cg48WsKSu4FORr3j5Wu8GdxjPKU8Lj3yCpS8SA1EPKur+bzTiyo99VgyPXwIE7oHtYQ8d6M3POkqnTsxa8Q8wKHFu8S6HLylp0E8P7eCPN4au7z1ROA84yzYu+xhLzsBqOy5KKqXuwVubjwdxWS8baGnO1H7C7q+Bia7Xs0rO7tmiLxAgBU9ZtS8OpMwOzy+49O73BN+vEATLjzY6QM8vjlXOwzCJb1NXJs8vldTPM8j9TvxVrO8qC0mPPgegzy0bpi7FbKUPEOQ67y8Qpa7/rz3u1tMwbwt3nO8tD2MPGIuMLzWo446aEufO+aGbrw2ohw80okjvNexqTyi1Vw8vJSYPG9CD7wlkU48gMS+vN9yYjytSIw8t+JtPKP5dzskJw05pdaSO4QKyrvUpgW8KyMvvMe4Fr1qbwg7ZKXNvJ3qL7x446a7Pl+KPKnoDbxhMh09jiCwPBpYrDtWXek84j8TvK3LhTyvqxe8A9IRvZhu1Ltzkao8GbSnPORvwzy6kOw7E42GPIosK7vFNNw7E7uqvIc5U7yyQRU9qld8vI0MLz0H95+7VGwRvPI9CbyfJ7u8nCp2PGqzlbyd/Ru7hWwMvA25cjzrkV48CWngvAPVvbxNNJE7yc9jPIWnRr3ZxMa7rrlOPBnBGr3uw7Y8BEMfPKMbKLrwMsM7mkS0vCLmUjtVYNO73fL3vCXbQjycqrG8DwNgNybyiLtRhou8x7v6uvQtljsSpGs8w9mYPFhBj7w+85o8gMy9POn7vDzHpIo8YkDau3QQ/LxEb6M7MSD1O7auWTwvwVe8RR5Hu8Qr9zywYHm8QY0sPNahujp+r548GDouuS81gTzQ7xK83RPaPOHUZDzfQGQ88IeXO3iXvDzicpC8CL6vvDEJm7uNi6283ytwvLMpurxyAEO8jBkzOrlyULw/phy8wuI1uxhpLbzEAZq7h+p6PMh9mTxZQJE7uL5LvDvEdjnDrEI8HWw+PUrrM7sY4Bm5OHOpuwZ0CT35NLA6Ym1GuxsAIr0zjV48yItMvEzhmrwqUyG88577uzZGsjvFEVM8Eq71vLqw6DxiLl075c9evHLEzTo9A5O8ElXGvI66gzvgbBA8ICwMPNEeEr27wOc8phm8PIotv7vf9x49Eba6Ou3Sjrse8ao70oXCvILxgbtA1jK91C21OwYziTyUmpG8zlecvMa2mztlCrm8ZsWCPI2tlDt6oAO8e2QXuwff4zu4e8S8537DO3PDNjzFB5M7szkdvEZsADuaJWE8Y9dcPI7azjllU1g8PeNOvelmsLweG947msPDvIbiBz2IA6o8tLi3vDCT7rtzJaU8S9PjPHiYFL28wEm90jaEva8+ybzbZ8Y81u9rvPkBmTxAvBc9fidNPA9ZObytu4Y8E9v/u/72n7s3Duy6ULEKPS+qY72OfQa9uJZcvEEfRbxvJ0G8DWHSPM3GGryojFk8MdMVvAnwuzwQIrG73lOEPMicB7yduJo80goJvKISpzzVlLe8I9Iqu1S9uTwGK+m7Ny6CvBI0zLwcNks8kDBWvNcTKb3fC+U8HoWKvTRDXr0O9AK9cG03vH1QfrqEUCs9CSA8vDXoFr3rm788/kOhPE5OiTzFlLy8wElIPMYwjjw5FSU93NobvRPoUT2Www881ktpPDStrTuXCwc98xu5PHwPlDtr+eY7YJCkOqIul7vQSqG707eoOl94vbo+tYY89w1out/0Gj26n6u88b0SPe9W+TxIs2M99VUuPLhzojwhLpw8sjflvKzHHzwP7VQ7CdjqPCtVG7u/nrS80l8VPXjaZzxCWlC88kVbPb3oKjxDF848L+3nvDyPxbovkQ293qrePPhIFrxxX0W839pCvAD2kzzV2pU8EI8UvXNzOTy+luC8OOTFuzOZ8DwlBz+8A/XGPLxOBD2z+6w8mMn7u2yvqrluew88cCb3O1Jx+jp3SH28a5+xvNCvo7wRGDc8Hr0gvG5fobxiNu06h8n1vE1MMbwiQjW7CcU0PGyxi7yEM7+7bOmuPGH/mbxGWLG7LMn6vD+7sbyM1Qa8GqWzvF9uPbpciYm8p4tcvMa4VDwgBXI6KLopPZMTND2llMg8+qZzu0i7trpkgLY6qdZzuy8nFz0wfUC81Ix6PA4XHz0KAus8Dl3xO5oUMLsp+OY8FUrvvBXQQT0e3sk8XaKsvEqYAjwf8Yk88FcgvZ7PQjxqjs08m0bbvDYw+bvYnR48nRbBPJrVljwgcxa9fS5kPL9EtLrmjAo8FYEyvYh537zvArY7U/aevJMwlrwzFhK9mpIEPNNSpjzcdYY8TnmAPKIw+TyvaxG9fThFPIHZTDzuPCG8v+uHvEgG6Lx4PL47/u/XvHLQQDyU1Ai8NjtSO40ilbxELwi8peDlu7TrKzyhzY48ybm9vMfwUTxltwE9o2fKO9+RTDv5D308UZhDup4CD7y9XT669PkivfF3Cj0B1cQ8BV5XPJvomrwXh3S8ShdTvBwRkzyMJaM7213FuxpWi7w8NYw7ub3iPOH6HLwnf5+8K1GAvMPujLxk93G8fYwhPc7qDDzcQyG9vZK+PHSnMLvMb4I8lMBEu91YKTxJQoI7qjowvOekHjviCq28nYCCvG9ZjDx2QFw7jlKFPPMtqrwsnua8E0Ofuq32czvnlv87r60KuiP0QjxXkbC7XrUdPD46mLogqaO7/Tx+vNiptTxJiam7MxTwPDrQALwtOPU8ToRcPHzJz7vPsmE8Nw+ZPG2a47xeiuW8EGqavO0R8Dw79cy8AgLgvGvhAzt0KkE8/5PZvJ9XW7xHmSK9T6eGPGsYg7u7Ob68vuSoPEjvF7xEoUa8bga7PNi5BDwi+PK6sWjWvE2yT7s+tDK85soUO2eaHLt69pC8DVA9PFsPxrynHV482nWmPPbiFLuI8ZS80EVavXCbRLvBPUw8lJiku8VoZTy/lRi70Le/vNParTwwewW7oqdiPSoig7srtUG56Zz0vKOfdzwF5mm9et2AvAop27vHObE8W2/Vu4RNGDybwuc7+RraPM8m8Dvw9FY8WlcHvH8zKbxVNs68gpsZvcu08DxJ8gy8djiDuwWqOTzJCRG9go1WvGmGODuujoM9uaHQPLBXQjyTI6y8m+4JPdnC5DtOHz28dwnEPLbH+DzeZXY8y7oTvbcyIzyTVGc7urQdPLrtL7wRkvY8dDNovOHW8rq2fGQ9MdStvNHJyTzOOvk8TbU0vWty6rvWjdC7yrbJvFhmfTu7dfg6oBsXvIdbFzy/49U8ha+OO7BUlTwb7Og7YmRFPODNBT3jbC08aZGYu4IZzjxhOFC8t8zDO7S1PbvN7P68Soo+vLYD9Lwhbr87Mn3ou0Rb8btuA026AFkOPcdXHDxpDWM8RKgevc5mPrvoOmg7e8OxO/0Q87xf1n88pgsHuqxVJj1jExW8bkxVvE0ahDwb+7c8d10bvVXpCb31PFq8BLm1PEteSbzRygQ8bTSZvJnR37w0Di69Jzq7O0VQ1Lt3nvG73fugvGH/PLsGR+w8U5s6PKYlxTtLpPg7Up98PZgyKjtR5IQ82R+7O01qyzxkMUu97eKRu2NPtDwCVqI7uKf4PKjeorvjO5w7X3uyPMa5ojx8kxC9Rq9rvIBgmzoBWRy8ExYqvEsCBD2vX0u8G9EFPb58UbvivJg8L2c5OxaBYTrU/8M72eJWPPAD+bzBfQA9V9SzPDo02TsU8lU7u2pbOzeLyjvLuC89n6DrOkizG716YYE8mxIqu3qDYTyDI+a7mvMRu1lAMbpEfP08F1AMPZCEsTx9TrY8sO60vN32XLv3DOE8OkIjver6Ab1DV6g7wjxAvPizkDxMrma743Zxux81zLu8mqS8IJEMPQwC1jwnBeQ7J7mKvGukEj0gTp+7rYGavDUXHL1PqmK7lVq5OzfQTLtdeZe8oaeuvK58IbwcwMg8hK/4PCqvgrwnwga9AwmTPGAvqjyoEqS77umcuonfYDxrB5i8j7tfvC/uozxEVZO7kIkzPHL+ILw9kg89u/MdvA84fLsi/QO8V4XNvOT7u7yBCg289kgIvZKhCz2bLgO8Xp1KO2YYOz0wG/c89rCYO4XD1zs+S807jRpnPJwyyjuRfDQ7WYYxvVLMZrvoItI5CC1UPBEuxrzz3TM8CINdO/FSBrz63uQ78AA7vNWoHbsooI67rYmOPDiRrzl/9bk8I7SPvP3uQTx3m+m7nthlPFu4VDy24ZA8qvGHPIxfAr2hgFa8kGmUOwSBBb3WX2m7EBQAvTp6kbwmr3K85awXvCias7z0rHy8t9i3u59KMLyYiB48FskSPDQOa7zSm4i8Z+eyPORKCLxX9k674rXcupvK6Dznw7+8yrZOPYVJLDzbTmu8QhyKO7eieDwX80C61BjmvAkflLyivH28HfvIPFlPxrzsAxA8VocbPeGiWrwcnYG8An4jPGpsWrsxZ6g8aaLvPEKeAzp2b1w7JziCuqlBED2fOLM7sT0sPFEIBz0S3+U8V5pdOzTSGb3XwbW7UQwlvQwibDx4gh88TaSSu4pE9jzwA4K8Ua8gPKzXgTzV7JU8KYqHvLNqjbvCbJQ8GiKVOzTBO7wnbgU8lqOkvKvhAL1qkqC7Xx2TvD+CEj3ifo28jp4VvDcjuTtmThG9SB7FuBDln7yrpCU89Euqu7CakjxxmBe9kO8cPBtApjtCySC8r+mdvDPkXDwZ2Qg8Hv9SPJqoxzuYzSU80TzTuz+wWrzwxxm9wrIRuwcswrtFg5A8vmK3PKsh1jzrHgA9vuz2uquh3bzv6cE8v1JqPErTyLsl5NW7xVvyvN7zHLwjSEo8xulRvKPLA70p+bU8NpAtvKK/2buiS+88S4YvvH5IED2ZoUm8QTLZPBbb1Tzz+xo6cKh1Owx83LyILH08RzH4PDbbpjsHzeO8Ef6Zu8LIAr2tdO68v5KZvAn1cDzcvlu81125OtVzRTtJJXM89JQLPD8EIb0WjPU8/L4AvIbGjLkscDA8EcqRu37637zMpcq8So/APHmLLjxJSBq9auSEPD/MHjuNa7m5+GHOvLYsNjxwjPA8sPWjPLsjyTub9Aa9QHHSOwSMGzyFhxa8t8WXO8U1AzxUiAQ9KE3WvNhYAj0fsR286awtPDIpuzzc78e6AGJNvJxQtDtzjFC845mnvKrViTt/qhW9ga34u8XLRT1RXso8qrgoPHLRnDt2PDU6I1rsvM1+ubwv76i8LBVWPZQDOL3XNmU7lAIMO0wCmzzt4KA7HpHSPIUh6ry8AFo86QmJOnxEJ71ZmyG8tR/Qu/G5Hj1x3MY8TtaAu4EBPzwzrca8bCUavOit+TtnEwE8neuHvDr+RLw7QaY8f0jyu/3oJDxmwiQ8ysCPu9AKMTmMYbk8QQWwvKsQgzzdEUU84TmGPIOEkLqC7um45nHMPBjYaTzdwEy8ZYngvF7gu7xXk7w8wYyHPGYp0jy9OES866ymuuQiRzyjwr880A7tPGAHrDusuEM874GWvFuhY7xqM8c8dlKsuzAqcDzWKzU7NhbHvP/0zDuiVd+8rxShPJjotrzpjr483npmPaPt1zuhF0K8QcyZPKvxBjsQnSc97taRPBhJ1bt0Ias7QQTGuxUfPjveXz499xnTvLOrmbnet0m8P7m9OzOqzbwnEKG7fQtTPKtJbzxyWEm8QIGZvOiu/TvN1Tg9NErWvEBqEbwmLes8mFWhPGAxET0LEOK7r+zCPLMNw7xndpS89iGRupiwtDvSXp26XJOUu5NBPzywymy5DsAIO/01prykpoc8owwfvUPbID25CAW9rUMwO/bqzDzGUse8l8C8uqZRCTyi2eg8siWBPMi/rjp57Ri8nng1vFldbTylG4W8HVmZvHh0Br3jsvc7OMCqu7R3bDwmUPK8EInnuSDoXjyDPMG7h0ujPIGz57xf8/87SvY/PEu/pbuDuL46MytUvPffGb2qqqA789sbPY9AUzyc69q7z+b5PBFkELwfm+Y8JwhXO6ChszsgNyg8LDmUvEFMa7w9XFW8aUpLvF/OsTx9wvy7uNPIPEIZxrv7DMW8/ppoPAOl3rzXiey7h2cDvE2Yo7ywTMU6uAU1vA8l7jxCO+e7Ya+auwN63Ds5Z9w8ItibO74tHjx8n7w7V8ZnuwYq3jveDtc7In7IvDGsNzybHEa9tN3VPEX1WTs6lxW9mTYVPDnR5TtrCBq9pwsnu1b3Er1GPgy8HTwwPGT9FT1j//+78W7qOyULaDyIf+k7MreTPHtSzDyzfUg8vpojPERjcDyMiJ88jBZDPF9wozv9C9E7DmsjOzZPsjvHOxU8aoJiu9R1TzsaZbA8boc/vDiFET3swSu9GBJdvJkW5ryxKmy8wQlhPGlUt7wrim281iDQOmycLDyBNoi7F2VyPBIdBby1ehw8Pkr0Ojc7ODwhwi49irROu5X/1juJXhy7ecFOPFzmwbq5ySu9RXz2PDq/Abtqe3Y8AZaLvESIQbiYIri8QpfSvNGgCb0k5vy6P/rAujM0EL0eXBW8WbkBPN9JFb38Zoi8sBUAvCOwrjwDubU7n4yyO8sUuLxSbQW984Fou3xRyDy0GyS6i1tBuhpL9TzTiRk8zsP9vDTJoDxh5jI8oaA7PKVwY7yeOUe8Q02KO+JmdbzsQnO8fUzlPFygVTqSGqW8Ed+Iu5b/7jwIlM68NrQePBkSzrtqAmc5KYMPvGiKx7yk5DU8QDS7vIxZ4blhwXc86PjnOoixTDyXvr88Hys2vFJkIT39PFQ9NH+VvMgb3Ts8VWO6SJijPOOhDb1HHqQ8uOMGvT1f0Lr8fhq9wateuiQ6kjz77907HeYFvQOvmrw759o8kgrhvAt48rvWPMI8o0YaO5Z3STwLQhu9BK0BvfFHSDzAMAi9Z8XbPOHBrrzHnm49VK9avH9/LDwxmf886Z6gPO/PnDx9WwW9vd+IvOJMQLv2Wbk7AHVJu+UHPTwcUoS79VSevE5SzLya9jk78yNUOv75uTtWPWo7yTT1vJ5SQbwpE0487/R/OidpBLyw0BO7Ldn9Oil6uDxdPNC7mAzhuz+VHr238D060Z1kuywuIDoCFtu8lRq9OxdYHbyRJNg8GVwHPAV5wDzqx4U6exOWuyjxZ7xjCwU9jxuUPKvJpzynqdc8EJzfPNL2nDylI6E8NxQAPe9BDL08wAu9CnAZvJvdNz2PeC07VdYmO1Q0CbwKOjG8+ZdlvJJrRzwGUrO6+iYjvBTZQL0nMeO8UCynPHaGtrtnys+8XFTIvG6ia7osPdm853XHubC7gjzbcr26Pik4OwAM7LzCT0W7zxmUPKcnpjzGi6m8DOEcPLMy5ryeuuK6DdIcPAFUnjwdAzE8duDquxrf2juCHgS8UY5+PEIn2Tv1qUk7OWeAOtd24DzEjNG7NKWhO4Zml7yQDNG6gQC1PGTj/jjydR68Q7qOu18BFr2nV8W8fqGNPPQ7drywr5G7UYWRO1tahbx2oLS8XwrWvKYALbxhKpM7way6vHntUjx+MwG8tPLvPCzoHjxmRoe8esGpPBb927wAk/y8HN17vDhtZDzTlt87oqW7vBBrE7w6L428WLV7O7oHeDvi7QS9AjdsvJHdDrxYz108XrmEOiND8LqKxIc73M8cPV+ncbyZXlC7Qgc9O0UZwbsWisW80atGO1Ld8zsphou8nSheOyXyg7uEv+I86WgnPAMbPDzhgPY7u+TXOzdcUT1gLDG8t6nku2hsdTyXMNi7W6MMvIbZZDwmJh49VlG9utdcQDy04Jq822h+PIvZ5ztRmYE7mVAFPKalmryP4o886ghIPLOPfrzEs7a8aAo/vC/7FLzOVDm7SiIKvEgXZDs82NM8VinmO41QKDxcYbo6/vq5vKb68Dwb57o7jE1Vu9p02bwiuee8zaG5PAf+jbs2dA+7fUtdu7WRMbyQUKq8GzGovPmh0DqtCfc8IATwPBPRp7sbcf68A6MjvddRX7x2YZ286qgbPK37d7r42g87jnVrvKm2WDtjfIu8GcYJPcjRCrztoBy9hE9avIOMHTvSyws76yyBPLiHaruBSqG8h6yfvGVu/rrJ5LU8G+3fvIk5i7ts3F68+qJkPXR/nbpW18W8CDEUvF0EN7y12kM7RbaiPOKkpDy10My8lTcGvW25nzwAFsK7zNkSO10XmrwL2qW80rM9PMyRk7tRxAC9jQPYvKmRRDx+2pK6V+CeuAV4uzz0Uy+6dswDveyZtLwtPfm83jitOYuYHL123wq9iZLGu1x3X7zdjhm8MJ2sPI9Ba7u2cyI8EYeYO4Pw9Dzu88c72L45vDoBvjzHlZo7eU+FPLQOtrxW4ys9WFZDPWsLrDypymy8QiXFPHln3zsDgw+8+clNu3jb1Dy2BYK7Ng6ZPDOicjzwZDG9tCG9vKI9Hj1u7P48oKUFPJYuQL0JlwO7RbanvBDWxbzF+g+8XtSVvCQqcrwlYwE8ve5Yu6XaqTtx/Y+7Mpv7uubZh7zg8m28k2K6O45m7bsNOei8cIuLPFGrUzx2+vU8JkdKvKiSBT0SoU+8foQSPAZt+TyvCwM81pjtPIxIAT1kTWG792ZvvFj7Lrynjjm9CABau4EnxrxGK1U92lAJPSDtdTyywYq6y3QtO8JZKbxLwBa9hgNwO8gxVLwDiqY72GTCPKOGBrwKRoI8zT88vRfRfDy+H5i8yCUQvRmS7TutCjo9ezOLvPTQFbzdqWo8iyJbu2UaDD0gG8G8VH3+Ootl5LwxIno8mUYtPJkryjteVtk88Mfcu9xjIbs1UnW7QQ3gPG5rKTwsPf48acwtvP7wx7twzzm8S200PNzuEbuMJdq8riSsuqrd6Lw0YKg7h6yDvMd4vTx+QBi9gXUgvbggBb3icuC8Ft0KPNgxdLzot6+8T1YlvFBDE724u947yMc5vft61Ttb07i8ssd9vBULsrygD5S85iI+PPeNoTtsz6q71wECPbMXXbyCFX+8kffjO3znqzy5dXY6uwvWO+lYzjukZcE8N9iKPAHYr7vfws+7ghkGPXCNkrzP1ho82ElLvM+wFDzv/u274qD5vC6CpLx2JHI6D5+CvGbZmrxUsiu8I84YvbjZHbwr3R66SFLWvOymujpI26G80OjbPGE5hrzhSCi8z6ZouBT12ruUqWg8zZrfuxYWhTyajzo8Z620OLjMeD1giqc7FBAZPOE2ZzzBfg28CxaLu3kCz7xJ84a8na4CvOihybvtMKq8EncLPK6IaLynbfi6o4BmvMrQhDurfea7G3GPO1+4dLwk7KE8pSrKu1mxUL04ihe8Po6xu0pxF723Xdo7X17RO1gYCjvPIE89eGw+PNy1wDxmrpA6TGXHu9DJGTwUP208XgrvvKwj2Tww+xk9KB2hu25B6LzXG/M88Q6SueaJHTsFrAI9QqvcuvitC7zAt9S7jbeuvMmlY7xffOM7NWL6vF1oVroNeVe83e2FPBOyq7yk5Wm8T6sGvaXUVrzECTM8B15rO1plmrtzV/W88eUsPMNP8js2ME07tEKhvAsoQrwB0Cq8DpkFPNLCMLzp74W7vARKO1hkrLt/7T08TBA4O96G0zv3UyO8k6P0vKr5PLw2fUW8AXDmPARNebxQ0TI712P6u61EPzyJ3qI7xLGvvNSDB7ykVqe8zcxbPOPb9ruWi5K60oG5vHbkNjzRA3G8G5KRvJmLKb1z/T27HvLbPCdPJT2tpIk8dDNUPJ8DAr0nkYy8ldeFPNpQp7xqnje81fgMvYVjDDwamKy7qKzTPBVe/Ls3agq9WnJPPI0+ijygkOk8eR/tO17DjTzPf1e7Q17WvBN+yTr04fU7f8jkO6vYYjz3SU08vQCcOx3llLvXzeG7nQjrvD62tbyZ0Zc9HZoCPAupiLxzZ6K8hcN/PDWuzDzH1eO7EBYGPYCVubyIdfU8XY8NvBbmsbzJR0w8sVTRvGLpUjzAwcc7na9yO/EekTulIUW8caekvErF0Tusk6g8CQvivPOgsjx01oU7zRj9vGlkIL0UbiQ8L9xYu4wTNzxtrlQ9LCrVuiJt/bw/Bw+6jak5PGOmJLzfg3+8S+Y6vK0Y1jv9Cw87zEf5vEkcBj0ZXOG7wgYkPOXsYrsKxVK7rst/vK/N6zsp1Fq9y9HxOyTzv7z5Diw8fvzKPDm5XDxHLIg8OWLku9abSDxz+5u8UZwNPcFhFL3nWmw8THn4OyB7Orw1InC8g8CYO8FUazwF64u87gUxPIvLwrw9MYW8fmZ2O+v39btfqlW8ohEpvO9EGLz/ZpC8Mbc0PGYYCz3IEEW8NCHnOxosa7tBeNG8fwCDvKRtYj1SOAi93WrfvEoVATvZA0E8vSvzPMr1+ztPebw7dS5sO6vE0rlVgVS80euLvNvujjz/AYU8aQNYO144k7yxA8s8C/1DPOg+4zu9u5k7dfmaPH3Ojbrx0Gw80KW1O+VvPbwVRpM7+yIMvDFv8LyFVJW8O6LjvNEOrTqBYnE85JtnvPqWF7yUxJI8KObKuw5g2jsDwwo9INN+O3pBsrw/PwK9VfsGvdywETy6g2G6rMljPEUqWLwYj2G8q0aqO85qB7raYyA89l26vKXkkLw6wPA80rUTO/9HAL1wLtg8quV+vMBIAL0AyDk8OdIoPJvI0bpU1wu7FNzrPD1lVzxwG6M8vb+dvH6hqDucIDK80maJvIFgg7zy6rM7Z+uUPPXBsbs1d2y7PtAQPQi8V7weJc28uBHQPFw3izzyDYQ8tyC1vB9OGr2I4GC77+s+vA==
|
|
index: 0
|
|
object: embedding
|
|
model: qwen3-embedding:4b
|
|
object: list
|
|
usage:
|
|
prompt_tokens: 17
|
|
total_tokens: 17
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '108'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
encoding_format: base64
|
|
input:
|
|
- 'Sales report Q3: Revenue was $200,000.'
|
|
model: qwen3-embedding:4b
|
|
uri: http://localhost:11434/v1/embeddings
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
data:
|
|
- embedding: WUyhuMd+aroavbm8fOuwPLxgILoV8jk9CBxvPRcmRT2GsWg8F/CXu0Ck2LxE2Oi6HLaUO9EjzbyPFqM8IhN4vNapI7wPbXk7AU9qPNV0PrvStD2791ksPeyu3jxjEYy9HCltvItLF701Isq8WgzsvDxKs7yRl/e7XtsHvVXID73LHh49pJQovHmZ9blLepC5YmwUPMrmqruXX6g7PZF+vLMpTDw9ZSO8peaDPBc+UTxzMwW8UcK0O5tJNDyYq6q5+CV+vONKy7yMlDK7szgPO476gL12RJa8eEarPJSIHDykZI66c5CAuuCGPL3Zreu7OTPIu8qfhTr1mQ29LMFgu783CrwXhqm8TwgPuxsloLyjkQY8RHeUPCYH3jwvwpm6dAu5vPAI/DhUb/M8hAthvJsRgLzeN6k8SWqIOm4U5TuhXj48P5+juvOBUTsuAr876ZIWPDqNlztjeoO8rFUGu7iA1bznbv27+nC8O5a8/DxJeE48UnY2O2ipfzyK7y48ohZnvDOcB7zOlhi7A507O2WER7y6oiu6S1AMvYOk17yvE++8HVFivBqlCzrYRdQ8gK5AO9zToTxbZHu89Znuu8geHz3KNT88S02Lu7toGzxyP8W8VX2sPHl4AzuIWRK8kBdVvLbSBD3bWgO32gt0u/bNJzwrKic7WHSmukSsHDwcRqi7V8fUPPhzZrz1vMC6o0rhPJ+Q3rva82a9WE9ju2NPFbyrDB47p/ulvF4R8Tze/Hg6cAgcPMxIQjw+j+K7YgJmvFSCz7qVJNg7oSjsOxbgTzwX8sK6krNoPBw3Jrxm4kc5qrmMPH+dlDlq5iU8sL0SvL84kDwSaxK7QRiXPN7xT7xwt2Y8g52FvCisGj1t5Cs71SM7PIEGR7wQ+fM8ihhUvKRIijuOlkI8z6YYPGcfqbxpjhu8Ao2pvINZczxgo7i8k/3iu6F1vLswebW8XtUNvAGn5jq343u7JnX0O7GTjzyXdKS7bSx7vJ8nyLshyJi7CfBeOpcJXbypb7Y7eNRDPPortDznG2q8mvArvGJwjLtob0C8eW2Yu+OcNTyfXrQ8hCPwO+ADCbsS3ji7kwzPu3gQCrxbHh+7qQUXPL3yHTsKpfa7Lq3iPGrgubyFd5m8VVbbu+hWJrsOOBK8A5BYvMF+nrufTJs7lP8zPewzrzwt7L67lTyGPCZ8z7sPIem8J5uNPBf6VrwbgRM8tSO+O5VLEjyXjPA89dCqPDOE8Ltw1sK6eNQivDozKz2SGKa8VsQPu3EG7riTt8G8yBiqPNSzmLx4nwm7hL6ouwBpozz8/li89JoavAZDNDv/c1u8+LwnvAS2pjs1ork8Ug4pOwO2Jbw2p3o6EDK8uS0JUjyfm5i7DFUmvGf3+Ts6Pdw6bf5SvCQN4bzcEh86JpftvEvK8jpB7/w7mw3qO8JnGDzC7Li8D0DcOcRmnLyyL/O7XcmIvOBnSzxiV0y88QNWO74njrupfyA80++YPF/P2bxbzdc8/YOIvEDW8rz5UE68eswTPdWI+Dyf1Co857u2vIECSbkxn7W4SjJYvdi4lTxq9oM7VwO/uiY7Dz2bnS08Z2CxO3taMLwetWG8a93XPBFg9Dsbk+Y6FbK8OhhGEDznImI7Wj+mvHXI2zxW1iA8UvKJvSvCNbw7BsE7Dt4ivL7MIDx/cCq6+lffOt+CrbtCKEg7+0IevSpksrwrUFE86fOYvJl4pDu+8AG8FEYUvFMMqLvoXDM77hiwu0oSfjxd4Wc8UE7zPOtt2zyl7xS9hT8GvJ8pf7vVUCs8JdwPvPxxBz2WFgs89FEXvPdSfrqtuVC8BdQlvLYxN73/uOy85Y7MvCWBCTzJTPE80+EEvRjmcbwlL3y8MBakvKRdnjyNvkO8/VmKu0ek6jsdEHG8Kh4MPNtOAz0+e6G82ypouzbshTx3LXS7CyxhvBC4mbxcJm67luacOz/F5DzWa5u72a4EvR2zjjwwEgA8c2vfOjVCJL1gs4S7rxICvCCkhbwAdwK9SjK6PF/xTzlyHmg889H8PPNPm7yAQcI8IOaAuw2jt7wM0ia9+zsTvJ+/xzzVVCc9zV2Lu2fstLzjCiW8vICfO+0ZML3/Jb48d2tju1wDtLpSAv28Tc3HvC/7G7zImj692AZRvRdpCr3Ix/q8wSimvJDMRrxz3H48G3cyPC0/Frx+2F08BLC6O+jtBDztxG06WcDWvDFslDt9ZvI7A5Seu6SXIryby1o8m5hDPC8Hd7zs8XK8Tm7CvLPm0bus0rw8ZLxuuwwCP7zCVpu8kXRTuwDrejyE4n28Uh5UPPbJ3Ts8UL+8LLUmPRI39ruwIuo8Ru0kPAN/Eb3QCuM7KCclvAm9rzx5DHY7u4SDvC7p6DpX6gS6NI8vO59dPD25EV47rengu8qL77uji4o7g27fu3aCxryScpE8UBTGPGjzbLwgwSG8xHcqPDoVMr3a6Yc8PoMMPRHI6btvjJC8Ed81vJYAHr0o4Lk8o9ZpPM0oOru/W6+7J5wHO9V9FDyp9Ze8duOWO4tQzTwyUoO8+5lVvJgi0DsmQsM8CMpePQ0FyjwAYtA8tYkSPfDtGrzq0FI5c+PCPEwoijx6vxI8bpGRvLGI6DutLLc8gQ4kvbJjNLykL708hYWOPDGLiTtnVFW86zwLO6I6cjyoIes88iO1u7cp1bvCYxq8WxkTPfcCgDsyjYm8bgTmuvJVGrwJF4k8iSViPMrDIzy1GVy7g2vuvO+Wkzw3TOa7Wm5vvJqyabyC6QA9J5sbvf9beDn9kly7LCuKvNtbjTxeJwy9SakvPetSGDwkqHy8pAp5PAn5hrtnG8C7ajoNPIpBJj2lTpE8/4xWvZva27zr2ng8t6lWuCE1JbwcAMI7LLbXu5AHLbww3py8P3v8vCR9vjzFKlA8k9pUu8UAOb13Pm68Y2WfPCWtHz2bjB+89IsoPLegf7xiNuY8AlFFPUYVDTxj+qs7Vmo2PHIlIDzuWs08REQTvJj/0jtZMsc8oltLPBkG7rze49A8fcFzvIdpMDsCCzq7HayHu6nmeTxpKSu8quaMPBOEDTslvFa8D3Oou96+Irzt0/k8ZXQ0PLRbUDyYsFC71kmJuyX8JzxkpEw8LxqQOxPLGL0Nnr883MxWPETFTzzFxJK87NCTO4ZI0TwoyBg8V3LMPEf7ibzt3HQ7tq0yvE/O3rwOemq8iQKsPDDnk7ypj3C8f9oWPDeDgrzcRzQ8bCkTvKQIQzy8epY7NYadPMNRkbwE8Ko8gRV9vFcqwTwyrYo8KosDPRmLuTvVNwm8etUiO1pYEbzGZQa8AdfBvJl7CL1qy4a7ivVLvKLeCrvDvJW6nLeYPLGLwzpXdRY9Lr/aPONiEDzUtII851wQvANEvzxfTpy8fOobvVeTu7wTjUQ8gIa6PHgZ1zwP9EE8RcAGPLOsNLyRJok7qS5ivG6zI7yVePQ8yeHivDpS2zxf2QK89s+HPM14K7x9GPu8Cz4Ku6yH4rypn5+7I+LAvE+0UDwDQuK7cy8BvUy9Dr13B048ZcODPPcGN705UhW8FF/7OyQUD705Pvo8I5vhOuvEMjyMFC08SY5CvHJdmbsW1qC7MnfQvLF8jjz20Ci8NW7hu8Mma7vG8Ca8wvqQu/N2tboRHTo71TQSu23AIDtRY8g8a/oLPVAD2jxdccc8KxbNu8pI/LwA2oc8+nh2PPGglDwIik+8hRqIvEnbvTyaS147mMvKu675uDsSqAY9pCYwvCHGxzytZoe8qBWSPAh9tDyf3wg8W4k/PD1YkTyvkru8NLIkvTTSlrzI2PK6H1MXvB+KqrzGqIK8wFc7PCD9f7wJj9a7qiRju7/dFLy6nWQ62eKoPMzTgTwJsaY7N2+fu+h6LLw84zI8DDZKPfexszvhtiw6MWqFvJbU0TyA4og88UuYu71//byhH1Y7wG13vITKprzgjtG70zmou07bRjzSvEs7Av2BvNHx2jykbfe6j8qbvMYUCbq/Jxa8fsihvO950Tt9l3I8EDo5PIt6Bb2IUVs8ZjqfPLq5jjucxhg9FcqPPNEOxzsmnz078P6kvIDrL7rRdQS9aZtfPFjXPDzvi2e8OfCNvBFjDTtxwq28NadUPNArDbzPle679hB9vMhtozy64pu8KvNRPLU8mjw8Wt87ns02vFElVDvTG+Q7z5ukPJHLlDvwLXm6LAFxvS9GLbxeMG08WGuAvCYuxDxeeoY8j7iovJkCg7p4vdQ8/J1nPBFvIr2t9Ta96YORvcDu9rx3lQQ9VXyQvKSdhTxyRT89aru7PKvngrt15cO7iV7Mu56BBTqjoOQ7UXwFPYuPf73skLu8UiiJvLZ3lrzB1TW8PrL5PASghDvQtAQ8rjrDu150MTyQxPc7bHHZPP9PO7oJ6ZY8eG30uZV2xDwum868qMUoupc/uTzEH5y8povfvEUNCL0g51M8lidAvNl7D73Iot8850qKvdMdO73WpCi9VkpbvK80O7pWwUI9Mr3LvE4o/7z1Tj88BZvzPPbUFzyRVL68RQKBO/ECsTwFFSg9mmEVvZUxLj3miDk8z3aWO85hJzx+Z7c8JQyxPNe5ZLyE+Zm6p/6FPEpzYLtRWXu7X3iSObpusTtAhJs8KmUTu9t2ND1Rr8u8FSEePVA0mTySoDw9MsXauWnYLzy4s788jpe+vNzIbjtczd060tRpPOdscTpWo52899MKPdezFTxIdy+8kLFtPUk8tbpil3Y8ejgzvWW2N7v7EEm8K7btPKObhbii5ha8ARatvGRAgzzpXH47HEyjvCGmqDynaIi88EUlPIhduDwMe6u7d2mvPAs5Cz3SyxU8RiWivMHKO7wRTqg6VnI9Oq+rjjqlzd28efG9vM4yS7kUzFI8SWWDPBys1ryUGYU7NKnFvJUTzbwYrAU8WEyfO+MJkLxRIDO8vyavPGaSbbwJ1A+7l6XSvJA8qLwerPU7aa+ivP6cyjqSRR+80xGhvEVncbsGQU68/gEdPXgRDT0i7TQ8m0ocvDhQzru9u+g7fNhOvAO0iTxhRB28do4BPLZaID3GiyQ90LhSPB3GAbyuS7w8iv/SvHroUz1y85U8xO9JvNOFBDy871c8TvQAvejCWDyVAGg8hKDRvLRXzjugHNE8RQcBPVQx7jwslh+9l6dOu3twDrzrs/o7Z5YovRhSyryq2wY7Qt/YvORNL7wDdt+8/OwAvKUQnzscnJ48jIuRPIL1Ej0luQa9B+E4O2ToADysRZq7hDOZvKsUtLwI0M47WfvHvA/iKzwR5X28zUyPPH6S47s+tZe7EEy8u5DE1TvrxJE8XbkavDLA4zzEJ5o8XDB0u+ctuzvXbLE7ZWRxOwXkgLyYleI6Jmo+vY9hHj17JiU8KSfJPFJosbzGjsG8/wmZvEwdGjpln3Q8IZQoO8XWkbxWZg48uJnDPOXerrzWWhK5cwcbvA3hkrwEli289MgaPTFkfTw+KTO97k+oPKQSerxsNJc8nd+SOxrphTzfSHK6M8J4vAXcxjiBBoa8+G0ovKuLiDyuAHg8M5w4OeIblbyKC9S8XpmOu3AmqLt5neY78HAYvES7mjvLdhk7FFIqvGn9Cbwpds87i3tDvGkyBD3dmMu7NsucPI8vVjwRDhs9mLQAPLd2qbuljJk8ZfG5PEgjxrzVJP68kQanvL2Y+zyx5Qy8K6sLvViHnzt/lVY89rYivaVnmrtAsEK9E/acPIp0V7xr41m8ezo5PEewx7wLxNq7C5RUO5aCPDk0VGW7a5TXvNyvDTo+Wum8r8aaOzRoVrpgoxa8nqcSPMR8Eb2bWFI8rzlzPIre9Tq23ta8NBVDvTKfA7ycyHI8ZZJCumXlXTwI/aQ7wDjYvKECdTysbQ282yBDPUeivjsnIwo8z0OwvCbi6TvJmlu9s7ODvNs3Ebx7fes86ui7u2VfpDygzgu7H88NPeGmirsn3xQ7FtfguqzbzrwzQry89R71vKeONDwWWl680On7u0g6mzvzaRi9f1auvAjvnbjGKF49rhppPPhgqDw37Bm8qB4bPN+VLDuBwtW8QhftPKPP1jxtsd07ABcIvTs4JzwvCu676KcyPOxNIbu6cEk9zqC0vCsekrs6aXQ9+uEGvX0qID1rphA9/WksvSTBfDvZzsc73vHdvG+b/TuoDUA8euE0vP1KmLrJfts8DqErO0Bn6Dx3J6g78ySRPMq5xDyafWo8r33nuisSID3tj4a8CLELPH2XgLmfjbq89Pg/uypa0Lz4CSY6em2ROwU/sLx8O267wqysPEdmDTuOnJo8Sz7uvHlFpTsZiUc71bZFPFPtCLwXFzc8a/ISPEGWNT1UJls7v5kPvIhQkTx4Ipg84budvI6C07yQzs27uhqgPO4SBrzM/sO6CfKpvNhsN72TkRu9QOj6O4xcjDvW3ui7tXR9vKC70Ls9LdU89/FWPPWqiDuYNHg7699nPcY0KTyami486DPOuphJHzwsFiS9DEieu/3tED01wlM8RJeQPIdUHTsbLku6+Mk9O+DuujunadO8klUgvAhkE7pQnrC7y8shvBpE/Ty4x0K8acoWPQcgx7r4yQk9g/slPDr0U7zVd2O7IrdbPNwRfrxhdiY9nVCGPOpmfjyO1Mc7jnYauyUfPTwtDTk9LHYTvBiOCr0b2Qg8x1UUPG2/uDwIsAg8BIjrO7gM+TsF+AU9rSSxPOX1gjw4gvI8VP/NvIjPJbwTR408VKYKvajsvbwoWxy7Xm+Cu61v1DynKMK7lLDXOvEBjLsUCk28u7wzPVSNDT3QfLs7nyCfus9iND0G6Rk8JeoovO40IL2d31i8MUBbPJjwnTpBJhy7UZA3vEYM+btss/Q8H/LAPPr70bxGZyi9VkabPMx4fTxT5Ng6YlcTPNDX5DzvRUW8qudFuox3xDwqPUu7gY0OPCJKA7waszs9JA+pO096u7uxcBA8U3fjvFqRtbx2/Wm7R7cpvew2+TxqDrS75cWuObqUyTxSLxI9hQQZvO77CTzcwEU8qA0SPAGQBTzs/dK6QzYBvSI95DsCXxk64S0Pu5dDsLxGLNI7tWBhPAwRULtWYYM863+KvEFuDbw5UHO8RaCyPLsmFTyyWMA74M+JvPbBgjywk2a8gPMhPMR9mTwkAbw8KEEGPDW9Bb1OSpe8/IdcOcNTBL1IcB282wq4vFETqryjFBy8tHT1u14++7xe6UC835WGvLl7iLwQg4o8HUCXPPEdFLyJceC8twuyPIQTsLuZzIa7xPKlu4tU7DwKuDq9NKoqPSPRiTxpwFi8nJOeumwKljw86Am70ZnJvHJUXrzU/Me8lu3XPAu8srxa7Og704ljPTdvDbwQVaS8l0bCO4IKkLyDPBI8apnCPPc7hTylORo7yEbOu2SM2zw7ozI8vx9UPJYXED3OKcU85qiIPLpz4rzFIi28c+XovETDiTyofK48PrMLvAIeBD30KD687Sg/OwrKpzytrDc8cvEVvKojhbzYCpg8ISEdu2oV2joLJgg8+8jZvJkGMb0nR0W76uAGvZeKFz1Q9c+8zB/bu9eZIDuITgi9Jp00PBO8Eb37R3c73i89PJ55lTyX1/u8vm5OPKISBjwDLze8FduFvPa1Lzyn2XQ8so1gPANrVDw2/rk8KIVgvAdtC7wNVgK9LYq3O4UWbrzQvGA8/7aXPANvBz0FZyo9+xKeO/U42LxOC6E8rPYFPBvaijvzyb68XzwBvfcchrwW28A7KIa9OxjzNLyn8uY8ejalu/CNirxYSAI9Z1nQu8ccDD0gbke7et3fPOKDuzxI2TW6Tt0dPJqJgLxdz0Q7YIXoPLFO4zsTzgG9Ex8CvMZj7byPV7+82sPqvH/ZVzzrOoe7ZgkjPMFZvzs4Gok8yYVWPGcBJr0F0+c8Vj87u5nvozvXal48+x6dOyZkXrzy3/C87dPNPCw6Ujsbo968LHRCPP/ff7qrU0e85VicvCbgXzx7kLI8Oft2PMN+0DsWIRW918RsuuuHqzvbQLW8DMdYPHga8jqh2jc9ExPtvP7xdDysqNC72Gg0Oy3vDzyhoas7r4sVvDKrUzxg2Pq7lHqku5YYlbtbciy9yyMXvOVdED3y8rA8nSRdPJuJODvrpKU5qY8RvcC9s7tEzwS9jdhhPRWgHb2lVOy545o5u5blKzxX3Rg8jbVTPGAz2LyZGm88nH9Outy8JL3VYWW8vZ47vPjZ/TyFThU9m5huvBtDvTxB0YG8uHMSu6YlgDy+4tE8beiNvFdKDLwqe8w8vGSDu1IFujyOaHk82jPkuBYR1TvaDLQ8N7+yvLyLNTxoYss77cvIO3lXs7vOnNe6tjimPONdszxJQge8RrDuvKnC5LxwKG08+lvWPH0bmDygz0+8OjTGu/VeljsU1MM8JhoKPZWEpbozDXk8Nim9uRLek7sJ2dA8LIluu6E/2Dsa/Cc8nmknvc6AmbvPzYW80yi9PHAJ3bxNHzM8eYRUPWm7KDzfCJy86R64O11DGDuktB09Cq4CPH5zq7kIWau7KlbjuEyd3buhIUg9gunsvGjGRjsR5tY75VI5PDkXvrxfzqu7EKgzPPHMQzzaKaW7eaEhvOZCNzxoNS09NJoWvHmEOLzqa248xCitOzFO3TxvKTY8J2K5PKs9l7xvIIy70nJkOj8k5zt+N6k6yB9xuyEbVjwYRN+7gOlWOyhQMLxSLKo8qEZ+vdD5GD2jzya9+476uzRs8TwNLu+8Fw3lu/AY4jwkaxI9lRAQPM0fVzt6qcq7fVvCvMQvAzzPgOG8nsjovHOi6LwPEnW7T+euukgijjvgjAS9BR40PNFQ7DvcV6C8CYKBPDI2XrzyevA7ofyOPITKBzssLbu7RXG6vFB007w39DI8OqGePICzKjoTk3a8Bq0aPat33LtyNcU8beYHO5ahjDst58E8V+EfvFr7KrzO6K07CxB3vCQFCT27ePa7blb5PMk1FLwdDui8ZeIxPO1AurxHwZi741VavOvGlbyNu6M8nUKFvIApoTyCiR+8IHi4uw1jmjzEpvM87E5WO9y0RTtlbkY7x9AwvBVnFTukg0m7P+olvAWmIjzZt0C9wmGIPKTO2bu7nAK9cgElPHo5HDwRZQO9+A/Mu4VuEL35dfu7szGhPJ46Hj0cjre6aAgAPAQLpzwE/R88ZkUzPM+4ETwL3s480Xv9O57iTDxUXAQ8WhK4PGIpHjw+dGU8CyphvAWVkjxBbpU8Vuvwuyj3Mjv7pPY8c/48PGzWDT0yaie9kEcEvO+gxLyMkzq8IASaPGEDv7yd3Ye8/O2PO9mDEjyScse7tnfkO91qVzp4tg07hMURPMlcOzycnhs9+2oDu2IsKjv2+VE7okKnPKJvzbuB2rm8XFYZPTqdBTyoQHo8JCOFvAJP5ruZbKW869novKs9Ib3cu5m8xHHUuxtr+Lx48Xa8arS4ubfY9rzoGtC8h22JOyCEIj1pc5A84/9tPIGQ+LyN2Bi9sFMbPPFmsTzp48M6k3BFvMlf0jwhBnQ8S+jMvObE/bq+GvU7jhTIt383hbzz54K8LXvYOuOws7wjdAi959rQPBuX5bpFObq8UAwVu3oFtzyoA3S8bNqvPJ2FMrsWzRE8aGr4u/2dzbybcfE7k3gEvXLlErxUGYE8baTSOkFR4TyOyAM8z3YlvFdb8zxMOSc9N59BvBHUpzseOQG84338uoDbHr0Xn5Y7BW/hvLhyzzsiBg291DviuxXYrDycGHw8FJDXvMF6t7y+eUo9AN64vMDlYbuI5IQ8LSzYOziHODwY5A29ZyoPvZ0O8Ty7Iga9sO3/PMcOlryxo309+flDvIuA3zuEO0E9hOczPFuv2Txfcgi9tbLWvOZRCTmHQBw8smPqOg7RojwhSzE7IFM2vJXa0ryIHcE7DR6Bu3sihjwk2ia6GYcjvUijKrzcPQ080RdROv+hoLtzS4E6JvWSPK+rBj1WGsu7YeN+O9+8UL1mdYc7wJxMuwMqoDwOpuG85uBmOy+DxLt/9KY8Isf1OxiJDD0jBfk6OdgCvLMnhrwQLNo8HzyPPBucDzsvxow834XZPM+PVjsJi8Y8zYcfPXFHEb2ZOT692BKhu4bqJD30Wg88/rDfO+eT9Lsp4V85Vq4Tu+vuFTsgbse7duSEO8DXML2zcE68cDMjPGKUL7xTVu685v3bvBLBLLw30dm8PiFoOjcPvDwopSO8XlVJPICaHLz+Zzo8QOo2PFdaDjyxq9m7Gc3RPAb+1LwLt1K8vZORPH42pDz6uZE8NGgTvNOpuruNOcu7Nlqquxr+gjsTziw89Ik5Oyt5/DyrRVK8oghJPNxjn7xMtUe8nzi1PHF/prvX8ZW7YaeQu8okwbxktAW8JTWfPI+parznKmO88aOLPMnp0LzN+4W8Uyn0vLuPa7sQ6qo74wKbvKyNdjt9fZW8lMgPPacuorv3X1+8OyEBPbamh7xkWxO90qwvvIdB5zyXP1s8E3TOvHfghjuT93e8BoIIvPCjmTr6Key8T0C2u5tOBrxWiVM7lrX3O2bLtLxUWQ46HRMHPURqQLy6bem5jUGRO+5w17oWkp28uzc0O7iYjDwSI028wiFQO1SfkLudraI8uZKePHoMjTiRDXo8eRThO9X5UT3hia27MWgAPD1NFjx+QNu7dFG5u6vQvzyJogs9Q6ZEPP45wzsOC9S8Tok0PAxxnjtGT8M4CMJMPONkHbuLnow7typAPILnVbztZ9C8qHo+vGG0x7zyO+66vbZZu13d6LrdZMM8ct3QOErK1jzAnbO56a7+vFm3Ij1lrAU7+K5Fu628s7y+CQm9NBA+PMYHqDqQkXM7x34fvM9SRLzbn5S84sHTvJd5Uzy9rAE9FuGOPH5NKrxpGhq9KOvavNfNbrzv/hi8tyKWPCeaLzvBovk7kTJSvGEFHryn85S8LIC7PNxk+rtO/Vq92drLvNYGFjvljG48egWsPPQsljrNcKG8xPCeum6zoDuRe6Q8hEDDu/sJITz6/H28f+RrPezlprt7K8G8EiguOw9tH7zyMbW7GxPzPK2IWTw0Yea8EYOvvMyfhztZ6we8Kj+tu49jfrzC1gG9vkwnvCEpvboiKs+83cTYvJ0YMzzvs8c79F5EPIPGpjydjge8gLvIvIWat7xwrRS9isXXO2btQr0ANKO8MZ6JOkc7drpWjQ+8twmUPJexBbyJTR08pK+oPHPQAT0dNxe7qHWcvHNe4jydUXi7puTCPNlvnryxJq08aAEaPd58tzu1ube7Ju7RPGJeGjyEg1y8abPAO1BIgjyVCwQ8mUgDPabNfjwtYe68RUqcu/iM7zyziPw8X+dtPNgJ5rxC35O7WyeLvE+vi7yqxRG83mlLvFjor7y86fg6LXdmu9wDoTrrzSi8n+VxO5CSU7yJR328p7sXvC5Ku7v9eum8ZMBePIanzjzkkd485fUwvIfN7zx0n8m8KM5GPOov6jzXxMs7hwBoPNirsTzlrSq8Umy7vAjUgbx82ti8tx4NOgiYQr3HnzE9lIOsPJCfOjywQz+8xjuZO+gHlLom4Pm888grPDiLQ7yfnR27x4jFO7uEZrya3M081uAfvdkvvDwHNKK8VZUTvcnGmjsp8xg9PGa0vOjbQ7zazBA88LU1vE3aAz19Zpy8F0VLvPVyCb2Rvq087D5wvPzhjzs82eM8TaQqvHPN7Lpfw0s8ZTIBPVoDZjtAZ+w8m9aCvAi6SbxLajq8V1LwO/G5STzWRJO8ztuMO8dP4rzB0HY7Ge62vEr1gTyJrwa9rsxDvTp7u7yF6uO89fHpOhIkgrz3d4S8tkFqvBzBJL09ZtI7Wn1TvXBoATxMRfG8XIlsvMGBwrzT6ku7q1jcOyALdjxvoea7nq7dPBKRLbxNZOq89CZJuiifALvVB6+6qi6RO9MmnDqNE8c8aSLZPBzLybtEegW8eMAIPdUGkby6EJk8YLTwvKJncTwo7BS8jxPUvJnekryIq5+73ZsmvFXVnLy34R28cOKYvC1wyTuNszY6N4L7vNcwzDsp4Pi8f6zEPDpM1rsrgn67NjVNvPV9qbtbdjE7cAulvIhxczsGt4w7CAg4vPSWfD2c9Jk8HCMyPAeRHjx3+5i8fjZmvNT8Eb2c+wi9MQfSu5QChTu+Lba8PhD/O8Uk5bx3Cne8l9qnvJPrVTz0BlW84VjKO9WJc7yaLiQ8hEfoOvkaJ71VO6W8cTxdvDrER70Y4707XqEDPCA/l7vUlj09JU+aPAGlpDwul7A78qZRvL85SruX4348M+ISvQlZmTwXzuk8YmLXOzVjobzK0Jg8Qf41PLn3D7z4zMc8XC1RvOEUlrxsdwU8hmehvA5agbyQxgg82y7fvN3xnruf/Qi8v1U3PKogIrwmogy872Q8vV9g9rxD2tQ8jouyuVNAVDuFPMe8aqTpO1T9h7vCY+i6JDBBvCEWgruSHSS8wu8LPIUVA7yg6uq7CJ9aPNfpAzuQZ148g4JAPJRk3rpau0a8H+eMvFl7NruYAze8ryR3PLFDHbx+Ihy8B4X0u5I2bDy9cEk6uqa3vOAFuDsPhou8tK7ePBQtJrukx4u8xhCdvD8Fjjzi0oU6aI0VvPBU/byOHpG6a/msPIASDz0LdmE8hPZLPM4YD72OJ3G8ipOvO1qk6bv8bo+7QA/7vNF9srvGU1K7ytGgPMvOPbz68q68WduyPFr+Qzxm17c8dmyTPN6evzxxhzu7WhJdvIU4RLoMe0k71HltuoL9Wzw3Uo08/JyGusdAYryw6Sy64ymyvBq9nrwNgIY93ZaUPEmeA72A+Wq8pBM8POfImDyalBO8FHrjPCNb+bxgpwo9p+qAOw13Crwmaus7KedQvH+aFLuM+uM7mzo7OzQKArvPm6K7nfXdvKSjCzwJhiM8q8O+vHgiAD1lTEI8YDgZvTtFDL0tf3U8z32dtjBuezwX5TU9nC/YOV9u7bx9QNC665XMOxWkjrwRPUS8OSpivDX/VrpqyrM7SkzrvAWgzjx17DG6AgUtPJDOwrtga1a7/F3lvNAnFDxIIx+98KY0u0sXAL0hI+Y7LYr+PAwg+Dq4xY48Ac3CubFOQzy6ipu88wkePSQnybz7OSU7eHZEu3qHzrx2yj+7yGdbu9EGrTx2kp68MmKLPMxJv7su4u26dx8qOyGP+jrGhYy8EdeOO+HoILzuN528NXw6O2Y3Fz1rjhe8yjd9u49WAjzB4S68wT7GvCSKQz2D1cG8RK7ovFQXybtHcuo7EXDOPHgzqjxBrvo7bgbmOzWWk7t3hX28BJubvJS0SDvMFhg866MjOoMcZ7zUOfI8iJmhPBAOzzuTduC77qXEPCRp6DtsKsg8S5c/PIWnFTv3VVQ8fVoFvMVi8LzF3d66kKzNvCFoGDtBn5k8o3e/vJYdrDk4mrU7Px/NunX7sjy9DfE8/LXouiR6NbwExE+9A2IIvS8DdTz74Gy8aEcIPGzbrbxIhS+8gJaEPFixB7r+T7U8riiTvO6xBr3p4IE8p+l3uhVbkLxTO7E88qVBvCtX/LwDiUQ6hP+ZPFNRn7t9yOK7QVItPIZ8Hzy4YvM86fzXvCoHYzsyqZa7q4kJvHRrxLxjfGI5lYRQPGILNrxw/8A4DDMTPQBRpbz+eL28ZaSSPFEeBDyxFSc8v2y7vEzdAb1dmZ66Vr6lvA==
|
|
index: 0
|
|
object: embedding
|
|
model: qwen3-embedding:4b
|
|
object: list
|
|
usage:
|
|
prompt_tokens: 17
|
|
total_tokens: 17
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '9681'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
messages:
|
|
- content: |-
|
|
# Analysis
|
|
|
|
You answer questions over a document knowledge base. Two common workflows:
|
|
|
|
- **`analysis_search → analysis_cite → answer`** when the answer is grounded on specific document content. Call `analysis_cite` with the supporting chunk_ids before writing the answer.
|
|
- **`analysis_execute_code → answer`** when the answer is a count, aggregation, listing, or structural computation over the corpus (e.g. "how many documents?", "average page count"). No `analysis_cite` is needed when no specific chunks support the answer.
|
|
|
|
You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation.
|
|
|
|
## Tools
|
|
|
|
### analysis_execute_code
|
|
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
|
|
|
Inside the code, these functions are available (use `await`):
|
|
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
|
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
|
|
|
Available modules: `json`, `re`, `math`, `pathlib`
|
|
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
|
|
|
### analysis_search
|
|
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
|
|
|
### analysis_cite
|
|
Register the chunk IDs that ground your answer. **You must call `analysis_cite` before writing any final answer that uses retrieved evidence — search results, items.jsonl rows, toc.json nodes, or content.txt content.** Skipping `analysis_cite` leaves the answer ungrounded and is treated as a failure.
|
|
|
|
`analysis_cite` is **not** required when your answer is a corpus-level computation that doesn't draw on specific chunks — counts, aggregations, listings, averages across documents. Don't fabricate citations for these.
|
|
|
|
Chunk IDs come from two places:
|
|
- The `chunk_id` field on `search` / `await search(...)` results
|
|
- The `chunk_ids` field on `items.jsonl` rows / `toc.json` nodes (when you ground via direct file reads)
|
|
|
|
Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs.
|
|
|
|
## Document Filesystem (inside execute_code)
|
|
|
|
All documents are mounted as a virtual filesystem at `/documents/`:
|
|
|
|
```
|
|
/documents/{document_id}/
|
|
metadata.json # {"id", "title", "uri", "created_at"}
|
|
content.txt # Full document text
|
|
items.jsonl # Structured items (one JSON object per line)
|
|
toc.json # Section tree derived from heading_level
|
|
```
|
|
|
|
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora.
|
|
|
|
### Reading files
|
|
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
|
|
|
```python
|
|
from pathlib import Path
|
|
import json
|
|
|
|
# Discover documents
|
|
for doc_dir in Path('/documents').iterdir():
|
|
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
|
print(meta['title'])
|
|
|
|
# Read full text
|
|
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
|
|
|
# Read and parse items
|
|
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
|
item = json.loads(line)
|
|
if item['label'] == 'table':
|
|
print(item['text'][:200])
|
|
```
|
|
|
|
### metadata.json
|
|
Document metadata: `id`, `title`, `uri`, `created_at`.
|
|
|
|
### content.txt
|
|
Full text content. Use for regex or keyword search across a whole document.
|
|
|
|
### items.jsonl
|
|
Structured document items. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file.
|
|
|
|
Each row carries:
|
|
- `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results
|
|
- `label`: item type — one of `"section_header"`, `"text"`, `"table"`, `"list_item"`, `"caption"`, `"formula"`, `"picture"`, `"code"`, `"footnote"`
|
|
- `text`: rendered content (tables are markdown with `|` columns)
|
|
- `page_numbers`: list of page numbers where the item appears
|
|
- `chunk_ids`: chunks that contain this item — pass to `analysis_cite()` to ground an answer that read this item directly
|
|
- `heading_level`: H-level for `section_header` rows; `0` on non-header rows
|
|
|
|
### toc.json
|
|
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `analysis_cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `tree: []` for docs with no headers.
|
|
|
|
### Cross-referencing search results with items
|
|
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in `items.jsonl`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index.
|
|
|
|
## Strategy
|
|
|
|
1. Search first.
|
|
2. Identify the chunk_ids from the search results that support your answer and call `analysis_cite` with them. Then write a concise answer.
|
|
3. Reach for `analysis_execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
|
4. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents.
|
|
5. Before writing your final response, call `analysis_cite` with the chunk_ids that ground your answer.
|
|
|
|
You MUST call `analysis_cite` with at least one chunk ID before producing your final answer **when your answer is grounded on retrieved evidence**. Skip `analysis_cite` in two cases: (a) you are refusing for lack of information, or (b) your answer is a corpus-level computation (count, aggregation, listing) that doesn't draw on specific chunks. In those cases do **not** fabricate citations.
|
|
|
|
## Important
|
|
|
|
- Variables persist between `analysis_execute_code` calls — you can search in one call and process results in the next
|
|
- Use `print()` to output results — the output is your only feedback
|
|
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `analysis_search → analysis_cite`.
|
|
- Use `await` for all async functions inside `analysis_execute_code` (`search`, `list_documents`)
|
|
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
|
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `analysis_cite` tool separately to register citations. `cite{...}` markdown-style inline references do nothing; only an actual `analysis_cite` tool call registers a citation.
|
|
- **Before you write your final answer, invoke the `analysis_cite` tool with the supporting chunk_ids.** This is the last tool call before answering whenever your answer draws on retrieved evidence.
|
|
role: system
|
|
- content: What is the total revenue across all quarterly reports?
|
|
role: user
|
|
model: gpt-oss
|
|
reasoning_effort: high
|
|
stream: false
|
|
temperature: 0.3
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: Search the knowledge base for evidence to analyze.
|
|
name: analysis_search
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
limit:
|
|
anyOf:
|
|
- type: integer
|
|
- type: 'null'
|
|
default: null
|
|
query:
|
|
type: string
|
|
required:
|
|
- query
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Execute Python against the sandboxed document filesystem.
|
|
name: analysis_execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Register exact retrieved chunk IDs as citations for the answer.
|
|
name: analysis_cite
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
chunk_ids:
|
|
items:
|
|
type: string
|
|
type: array
|
|
required:
|
|
- chunk_ids
|
|
type: object
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: |+
|
|
The user asks: "What is the total revenue across all quarterly reports?" We need to interpret what documents are in the knowledge base. The question likely refers to a set of quarterly report documents that contain revenue figures. We need to compute the sum of revenue across all quarterly reports.
|
|
|
|
We must determine how many documents exist, which ones are quarterly reports, and extract revenue numbers from each. Then sum them up.
|
|
|
|
The user didn't specify a particular document or context; it's a corpus-level computation: "total revenue across all quarterly reports". This is a calculation that aggregates data across multiple documents. According to the instructions: "No `analysis_cite` is needed when no specific chunks support the answer." But we need to compute this sum, which requires reading content from each relevant document. That is a corpus-level computation. The instruction says: "You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation." So if we aggregate across many documents and produce a single number, that is a corpus-level computation. We should not provide citations? But the answer might be considered derived from specific chunks. However, the instruction says "Don't fabricate citations for corpus-level computation." That means we can compute sum without citing each chunk individually. But maybe we need to cite some evidence that revenue appears in documents? The question: "What is the total revenue across all quarterly reports?" It's a numeric answer. We might compute it by scanning all documents, but we don't have to provide citations for each chunk. The instruction says no `analysis_cite` needed when no specific chunks support the answer. But here the answer is derived from many chunks; but it's still corpus-level computation. So we can skip citing.
|
|
|
|
But we need to actually compute the sum. We must search for quarterly reports and revenue numbers. Let's think: There might be documents with "Quarterly Report" in title or content, containing revenue figures. We need to find all such documents. Then parse revenue numbers from each. The revenue could be expressed as "$X million", "Revenue: $Y", etc.
|
|
|
|
We need to decide how to locate quarterly reports. Approach:
|
|
|
|
- Use `analysis_execute_code` to search for documents with 'Quarterly Report' in title or content. We can use the `search` function inside code, but we might also use `list_documents()` and filter by title containing "Quarterly Report". But maybe there are many documents; better to use search.
|
|
|
|
We need to find all documents that contain revenue figures. But likely each quarterly report has a revenue figure. The question: "total revenue across all quarterly reports" implies sum of revenue numbers from each quarter. So we need to extract the revenue number from each quarterly report document.
|
|
|
|
Potential approach:
|
|
|
|
- Use `search` with query "Quarterly Report" and maybe "Revenue". That will return chunks containing those terms. But we might get many results, including non-quarterly reports that mention revenue. We need to filter by documents that are quarterly reports. Maybe the metadata or title contains 'Q1', 'Q2', etc.
|
|
|
|
- Alternatively, search for pattern like "Revenue" and then check if document is a quarterly report (maybe via title containing "Quarterly Report").
|
|
|
|
Better approach: Use `list_documents()` to get all docs. For each doc, read its metadata.title; if it contains "Quarterly Report", parse revenue from content or items.
|
|
|
|
But we need to know the file structure. The documents are in `/documents/{document_id}/`. Each has metadata.json with title and uri. We can list them. Then for each doc whose title matches /quarterly report/i, read its content.txt and find revenue numbers.
|
|
|
|
We need a regex that captures revenue amounts. Revenue could be expressed as "$123 million" or "Revenue: $123M". Could also be numeric like "Revenue 123,456,789".
|
|
|
|
We need to parse these numbers into numeric values (maybe in millions). But we don't know the units; maybe all are in USD and millions.
|
|
|
|
Better approach: Use regex that captures patterns like:
|
|
|
|
- "$\d{1,3}(,\d{3})*(\.\d+)?(M|B)?" or "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?"
|
|
|
|
But we need to decide how to interpret units. The question likely expects a sum in the same unit as reported.
|
|
|
|
We might find revenue numbers like "$1,234,567" or "$12.3M". We can convert all to numeric values (in dollars). For M, multiply by 1e6; for B, multiply by 1e9.
|
|
|
|
But we need to decide how to sum them: If some are in millions and others in billions, we unify to dollars.
|
|
|
|
Alternatively, maybe the revenue numbers are all in millions. But we can't assume.
|
|
|
|
We can parse each number string, detect if it ends with 'M' or 'B', then convert accordingly.
|
|
|
|
Also consider that some documents might have multiple revenue figures (like revenue for different segments). We need to sum only the main revenue figure per document. How to identify? Maybe it's the first occurrence of "Revenue" in the document.
|
|
|
|
Simplify: For each quarterly report doc, find all matches of pattern "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?" and take the first match. That should capture main revenue figure.
|
|
|
|
But we need to be careful with formatting like "$1,234,567" (no M/B). Then treat as dollars.
|
|
|
|
Also consider "Revenue: 123,456,789". We'll parse numeric part.
|
|
|
|
We also need to handle numbers that include commas and decimal points. We can remove commas then convert to float.
|
|
|
|
Edge cases: Some documents might have revenue expressed in thousands or millions; but we can't know. But likely they use M for million.
|
|
|
|
Ok.
|
|
|
|
Implementation plan:
|
|
|
|
- Use `analysis_execute_code` with async code.
|
|
|
|
Pseudo-code:
|
|
|
|
```
|
|
import json, re
|
|
from pathlib import Path
|
|
|
|
# Get list of docs
|
|
docs = await list_documents()
|
|
quarterly_docs = []
|
|
for doc in docs:
|
|
title = doc.get('title', '')
|
|
if 'Quarterly Report' in title or 'Q1' in title or 'Q2' in title or 'Q3' in title or 'Q4' in title:
|
|
quarterly_docs.append(doc)
|
|
|
|
# For each doc, read content.txt
|
|
total_revenue = 0.0
|
|
for doc in quarterly_docs:
|
|
path = Path(f'/documents/{doc["id"]}/content.txt')
|
|
text = path.read_text()
|
|
# find revenue patterns
|
|
matches = re.findall(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', text, flags=re.IGNORECASE)
|
|
if not matches:
|
|
continue
|
|
# take first match
|
|
num_str, unit = matches[0]
|
|
# remove commas
|
|
num_val = float(num_str.replace(',', ''))
|
|
if unit.lower() == 'm':
|
|
num_val *= 1_000_000
|
|
elif unit.lower() == 'b':
|
|
num_val *= 1_000_000_000
|
|
total_revenue += num_val
|
|
|
|
print(total_revenue)
|
|
```
|
|
|
|
But we need to ensure we capture numbers like "$12.3M" where the number part includes decimal.
|
|
|
|
Our regex: r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' works for that.
|
|
|
|
However, if there's no unit but a comma separated number like "Revenue: 1,234,567", then unit will be empty string. We treat as dollars.
|
|
|
|
Also need to handle numbers with decimal and maybe M/B after decimal e.g., "$12.34M".
|
|
|
|
Ok.
|
|
|
|
But we also need to consider that some documents might have revenue in thousands or millions but no unit; we can't know. But likely they use M for million.
|
|
|
|
We should sum all revenue values as numeric dollars.
|
|
|
|
After computing total, print the number. We may want to format with commas and maybe show as integer if it's whole number.
|
|
|
|
But the answer expects a numeric value. We'll output the sum in plain number.
|
|
|
|
Potential issues: The regex might find multiple matches per doc; we only take first. But what if revenue appears multiple times? Might be fine.
|
|
|
|
Also, some docs may have "Revenue" but not quarterly report; but we filter by title containing "Quarterly Report". That should reduce noise.
|
|
|
|
But maybe the title doesn't contain "Quarterly Report", but the document is a quarterly report with a different naming scheme. Could be like "Q1 2023 Financial Statement". But we might miss them. We can broaden search: include titles containing 'Q1', 'Q2', 'Q3', 'Q4' and maybe 'Financial Report'. But that could bring in other documents.
|
|
|
|
Better approach: Use `search` for query "Quarterly Report" to get chunk IDs, then find which docs those belong to. Then we can use the doc ids from search results. That may be more precise.
|
|
|
|
But we need to sum revenue across all quarterly reports; we might want to include all such docs. But if some are not labeled as 'Quarterly Report' but still contain revenue, we might miss them.
|
|
|
|
We could also search for "Revenue" and then filter by doc id that appears in a set of known quarterly report ids from previous search. That is complex.
|
|
|
|
Simplify: Use list_documents to get all docs; for each doc, check if title contains 'Quarterly Report' or matches pattern like r'\bQ[1-4]\b'. But we might also include "Annual Report" which we don't want. So maybe filter out titles containing 'Annual'.
|
|
|
|
Better approach: For each doc, read content and see if it has a heading "Quarterly Report". We could parse toc.json for sections titled "Quarterly Report".
|
|
|
|
But that is too complex.
|
|
|
|
Given the instruction to compute sum across all quarterly reports, we can assume that documents with title containing 'Quarterly Report' are the ones. That should be fine.
|
|
|
|
Ok.
|
|
|
|
We need to run code and print total revenue.
|
|
|
|
Let's implement in analysis_execute_code.
|
|
|
|
Also, we might want to handle potential errors: missing content.txt etc.
|
|
|
|
We'll use Path.read_text() inside try/except.
|
|
|
|
Also, we may want to convert total_revenue to integer if it's whole number. But we can just print as float with no decimals if it's an integer.
|
|
|
|
Ok.
|
|
|
|
Let's write code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report''
|
|
in title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, flags=re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try
|
|
searching items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit
|
|
= matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_qzkuh2up
|
|
index: 0
|
|
type: function
|
|
created: 1784300473
|
|
id: chatcmpl-515
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 2787
|
|
prompt_tokens: 2144
|
|
total_tokens: 4931
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '22156'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
messages:
|
|
- content: |-
|
|
# Analysis
|
|
|
|
You answer questions over a document knowledge base. Two common workflows:
|
|
|
|
- **`analysis_search → analysis_cite → answer`** when the answer is grounded on specific document content. Call `analysis_cite` with the supporting chunk_ids before writing the answer.
|
|
- **`analysis_execute_code → answer`** when the answer is a count, aggregation, listing, or structural computation over the corpus (e.g. "how many documents?", "average page count"). No `analysis_cite` is needed when no specific chunks support the answer.
|
|
|
|
You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation.
|
|
|
|
## Tools
|
|
|
|
### analysis_execute_code
|
|
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
|
|
|
Inside the code, these functions are available (use `await`):
|
|
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
|
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
|
|
|
Available modules: `json`, `re`, `math`, `pathlib`
|
|
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
|
|
|
### analysis_search
|
|
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
|
|
|
### analysis_cite
|
|
Register the chunk IDs that ground your answer. **You must call `analysis_cite` before writing any final answer that uses retrieved evidence — search results, items.jsonl rows, toc.json nodes, or content.txt content.** Skipping `analysis_cite` leaves the answer ungrounded and is treated as a failure.
|
|
|
|
`analysis_cite` is **not** required when your answer is a corpus-level computation that doesn't draw on specific chunks — counts, aggregations, listings, averages across documents. Don't fabricate citations for these.
|
|
|
|
Chunk IDs come from two places:
|
|
- The `chunk_id` field on `search` / `await search(...)` results
|
|
- The `chunk_ids` field on `items.jsonl` rows / `toc.json` nodes (when you ground via direct file reads)
|
|
|
|
Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs.
|
|
|
|
## Document Filesystem (inside execute_code)
|
|
|
|
All documents are mounted as a virtual filesystem at `/documents/`:
|
|
|
|
```
|
|
/documents/{document_id}/
|
|
metadata.json # {"id", "title", "uri", "created_at"}
|
|
content.txt # Full document text
|
|
items.jsonl # Structured items (one JSON object per line)
|
|
toc.json # Section tree derived from heading_level
|
|
```
|
|
|
|
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora.
|
|
|
|
### Reading files
|
|
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
|
|
|
```python
|
|
from pathlib import Path
|
|
import json
|
|
|
|
# Discover documents
|
|
for doc_dir in Path('/documents').iterdir():
|
|
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
|
print(meta['title'])
|
|
|
|
# Read full text
|
|
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
|
|
|
# Read and parse items
|
|
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
|
item = json.loads(line)
|
|
if item['label'] == 'table':
|
|
print(item['text'][:200])
|
|
```
|
|
|
|
### metadata.json
|
|
Document metadata: `id`, `title`, `uri`, `created_at`.
|
|
|
|
### content.txt
|
|
Full text content. Use for regex or keyword search across a whole document.
|
|
|
|
### items.jsonl
|
|
Structured document items. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file.
|
|
|
|
Each row carries:
|
|
- `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results
|
|
- `label`: item type — one of `"section_header"`, `"text"`, `"table"`, `"list_item"`, `"caption"`, `"formula"`, `"picture"`, `"code"`, `"footnote"`
|
|
- `text`: rendered content (tables are markdown with `|` columns)
|
|
- `page_numbers`: list of page numbers where the item appears
|
|
- `chunk_ids`: chunks that contain this item — pass to `analysis_cite()` to ground an answer that read this item directly
|
|
- `heading_level`: H-level for `section_header` rows; `0` on non-header rows
|
|
|
|
### toc.json
|
|
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `analysis_cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `tree: []` for docs with no headers.
|
|
|
|
### Cross-referencing search results with items
|
|
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in `items.jsonl`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index.
|
|
|
|
## Strategy
|
|
|
|
1. Search first.
|
|
2. Identify the chunk_ids from the search results that support your answer and call `analysis_cite` with them. Then write a concise answer.
|
|
3. Reach for `analysis_execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
|
4. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents.
|
|
5. Before writing your final response, call `analysis_cite` with the chunk_ids that ground your answer.
|
|
|
|
You MUST call `analysis_cite` with at least one chunk ID before producing your final answer **when your answer is grounded on retrieved evidence**. Skip `analysis_cite` in two cases: (a) you are refusing for lack of information, or (b) your answer is a corpus-level computation (count, aggregation, listing) that doesn't draw on specific chunks. In those cases do **not** fabricate citations.
|
|
|
|
## Important
|
|
|
|
- Variables persist between `analysis_execute_code` calls — you can search in one call and process results in the next
|
|
- Use `print()` to output results — the output is your only feedback
|
|
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `analysis_search → analysis_cite`.
|
|
- Use `await` for all async functions inside `analysis_execute_code` (`search`, `list_documents`)
|
|
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
|
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `analysis_cite` tool separately to register citations. `cite{...}` markdown-style inline references do nothing; only an actual `analysis_cite` tool call registers a citation.
|
|
- **Before you write your final answer, invoke the `analysis_cite` tool with the supporting chunk_ids.** This is the last tool call before answering whenever your answer draws on retrieved evidence.
|
|
role: system
|
|
- content: What is the total revenue across all quarterly reports?
|
|
role: user
|
|
- content: null
|
|
reasoning: |+
|
|
The user asks: "What is the total revenue across all quarterly reports?" We need to interpret what documents are in the knowledge base. The question likely refers to a set of quarterly report documents that contain revenue figures. We need to compute the sum of revenue across all quarterly reports.
|
|
|
|
We must determine how many documents exist, which ones are quarterly reports, and extract revenue numbers from each. Then sum them up.
|
|
|
|
The user didn't specify a particular document or context; it's a corpus-level computation: "total revenue across all quarterly reports". This is a calculation that aggregates data across multiple documents. According to the instructions: "No `analysis_cite` is needed when no specific chunks support the answer." But we need to compute this sum, which requires reading content from each relevant document. That is a corpus-level computation. The instruction says: "You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation." So if we aggregate across many documents and produce a single number, that is a corpus-level computation. We should not provide citations? But the answer might be considered derived from specific chunks. However, the instruction says "Don't fabricate citations for corpus-level computation." That means we can compute sum without citing each chunk individually. But maybe we need to cite some evidence that revenue appears in documents? The question: "What is the total revenue across all quarterly reports?" It's a numeric answer. We might compute it by scanning all documents, but we don't have to provide citations for each chunk. The instruction says no `analysis_cite` needed when no specific chunks support the answer. But here the answer is derived from many chunks; but it's still corpus-level computation. So we can skip citing.
|
|
|
|
But we need to actually compute the sum. We must search for quarterly reports and revenue numbers. Let's think: There might be documents with "Quarterly Report" in title or content, containing revenue figures. We need to find all such documents. Then parse revenue numbers from each. The revenue could be expressed as "$X million", "Revenue: $Y", etc.
|
|
|
|
We need to decide how to locate quarterly reports. Approach:
|
|
|
|
- Use `analysis_execute_code` to search for documents with 'Quarterly Report' in title or content. We can use the `search` function inside code, but we might also use `list_documents()` and filter by title containing "Quarterly Report". But maybe there are many documents; better to use search.
|
|
|
|
We need to find all documents that contain revenue figures. But likely each quarterly report has a revenue figure. The question: "total revenue across all quarterly reports" implies sum of revenue numbers from each quarter. So we need to extract the revenue number from each quarterly report document.
|
|
|
|
Potential approach:
|
|
|
|
- Use `search` with query "Quarterly Report" and maybe "Revenue". That will return chunks containing those terms. But we might get many results, including non-quarterly reports that mention revenue. We need to filter by documents that are quarterly reports. Maybe the metadata or title contains 'Q1', 'Q2', etc.
|
|
|
|
- Alternatively, search for pattern like "Revenue" and then check if document is a quarterly report (maybe via title containing "Quarterly Report").
|
|
|
|
Better approach: Use `list_documents()` to get all docs. For each doc, read its metadata.title; if it contains "Quarterly Report", parse revenue from content or items.
|
|
|
|
But we need to know the file structure. The documents are in `/documents/{document_id}/`. Each has metadata.json with title and uri. We can list them. Then for each doc whose title matches /quarterly report/i, read its content.txt and find revenue numbers.
|
|
|
|
We need a regex that captures revenue amounts. Revenue could be expressed as "$123 million" or "Revenue: $123M". Could also be numeric like "Revenue 123,456,789".
|
|
|
|
We need to parse these numbers into numeric values (maybe in millions). But we don't know the units; maybe all are in USD and millions.
|
|
|
|
Better approach: Use regex that captures patterns like:
|
|
|
|
- "$\d{1,3}(,\d{3})*(\.\d+)?(M|B)?" or "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?"
|
|
|
|
But we need to decide how to interpret units. The question likely expects a sum in the same unit as reported.
|
|
|
|
We might find revenue numbers like "$1,234,567" or "$12.3M". We can convert all to numeric values (in dollars). For M, multiply by 1e6; for B, multiply by 1e9.
|
|
|
|
But we need to decide how to sum them: If some are in millions and others in billions, we unify to dollars.
|
|
|
|
Alternatively, maybe the revenue numbers are all in millions. But we can't assume.
|
|
|
|
We can parse each number string, detect if it ends with 'M' or 'B', then convert accordingly.
|
|
|
|
Also consider that some documents might have multiple revenue figures (like revenue for different segments). We need to sum only the main revenue figure per document. How to identify? Maybe it's the first occurrence of "Revenue" in the document.
|
|
|
|
Simplify: For each quarterly report doc, find all matches of pattern "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?" and take the first match. That should capture main revenue figure.
|
|
|
|
But we need to be careful with formatting like "$1,234,567" (no M/B). Then treat as dollars.
|
|
|
|
Also consider "Revenue: 123,456,789". We'll parse numeric part.
|
|
|
|
We also need to handle numbers that include commas and decimal points. We can remove commas then convert to float.
|
|
|
|
Edge cases: Some documents might have revenue expressed in thousands or millions; but we can't know. But likely they use M for million.
|
|
|
|
Ok.
|
|
|
|
Implementation plan:
|
|
|
|
- Use `analysis_execute_code` with async code.
|
|
|
|
Pseudo-code:
|
|
|
|
```
|
|
import json, re
|
|
from pathlib import Path
|
|
|
|
# Get list of docs
|
|
docs = await list_documents()
|
|
quarterly_docs = []
|
|
for doc in docs:
|
|
title = doc.get('title', '')
|
|
if 'Quarterly Report' in title or 'Q1' in title or 'Q2' in title or 'Q3' in title or 'Q4' in title:
|
|
quarterly_docs.append(doc)
|
|
|
|
# For each doc, read content.txt
|
|
total_revenue = 0.0
|
|
for doc in quarterly_docs:
|
|
path = Path(f'/documents/{doc["id"]}/content.txt')
|
|
text = path.read_text()
|
|
# find revenue patterns
|
|
matches = re.findall(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', text, flags=re.IGNORECASE)
|
|
if not matches:
|
|
continue
|
|
# take first match
|
|
num_str, unit = matches[0]
|
|
# remove commas
|
|
num_val = float(num_str.replace(',', ''))
|
|
if unit.lower() == 'm':
|
|
num_val *= 1_000_000
|
|
elif unit.lower() == 'b':
|
|
num_val *= 1_000_000_000
|
|
total_revenue += num_val
|
|
|
|
print(total_revenue)
|
|
```
|
|
|
|
But we need to ensure we capture numbers like "$12.3M" where the number part includes decimal.
|
|
|
|
Our regex: r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' works for that.
|
|
|
|
However, if there's no unit but a comma separated number like "Revenue: 1,234,567", then unit will be empty string. We treat as dollars.
|
|
|
|
Also need to handle numbers with decimal and maybe M/B after decimal e.g., "$12.34M".
|
|
|
|
Ok.
|
|
|
|
But we also need to consider that some documents might have revenue in thousands or millions but no unit; we can't know. But likely they use M for million.
|
|
|
|
We should sum all revenue values as numeric dollars.
|
|
|
|
After computing total, print the number. We may want to format with commas and maybe show as integer if it's whole number.
|
|
|
|
But the answer expects a numeric value. We'll output the sum in plain number.
|
|
|
|
Potential issues: The regex might find multiple matches per doc; we only take first. But what if revenue appears multiple times? Might be fine.
|
|
|
|
Also, some docs may have "Revenue" but not quarterly report; but we filter by title containing "Quarterly Report". That should reduce noise.
|
|
|
|
But maybe the title doesn't contain "Quarterly Report", but the document is a quarterly report with a different naming scheme. Could be like "Q1 2023 Financial Statement". But we might miss them. We can broaden search: include titles containing 'Q1', 'Q2', 'Q3', 'Q4' and maybe 'Financial Report'. But that could bring in other documents.
|
|
|
|
Better approach: Use `search` for query "Quarterly Report" to get chunk IDs, then find which docs those belong to. Then we can use the doc ids from search results. That may be more precise.
|
|
|
|
But we need to sum revenue across all quarterly reports; we might want to include all such docs. But if some are not labeled as 'Quarterly Report' but still contain revenue, we might miss them.
|
|
|
|
We could also search for "Revenue" and then filter by doc id that appears in a set of known quarterly report ids from previous search. That is complex.
|
|
|
|
Simplify: Use list_documents to get all docs; for each doc, check if title contains 'Quarterly Report' or matches pattern like r'\bQ[1-4]\b'. But we might also include "Annual Report" which we don't want. So maybe filter out titles containing 'Annual'.
|
|
|
|
Better approach: For each doc, read content and see if it has a heading "Quarterly Report". We could parse toc.json for sections titled "Quarterly Report".
|
|
|
|
But that is too complex.
|
|
|
|
Given the instruction to compute sum across all quarterly reports, we can assume that documents with title containing 'Quarterly Report' are the ones. That should be fine.
|
|
|
|
Ok.
|
|
|
|
We need to run code and print total revenue.
|
|
|
|
Let's implement in analysis_execute_code.
|
|
|
|
Also, we might want to handle potential errors: missing content.txt etc.
|
|
|
|
We'll use Path.read_text() inside try/except.
|
|
|
|
Also, we may want to convert total_revenue to integer if it's whole number. But we can just print as float with no decimals if it's an integer.
|
|
|
|
Ok.
|
|
|
|
Let's write code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, flags=re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_qzkuh2up
|
|
type: function
|
|
- content: "Error: TypeError: re.search() takes no keyword arguments\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_qzkuh2up
|
|
model: gpt-oss
|
|
reasoning_effort: high
|
|
stream: false
|
|
temperature: 0.3
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: Search the knowledge base for evidence to analyze.
|
|
name: analysis_search
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
limit:
|
|
anyOf:
|
|
- type: integer
|
|
- type: 'null'
|
|
default: null
|
|
query:
|
|
type: string
|
|
required:
|
|
- query
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Execute Python against the sandboxed document filesystem.
|
|
name: analysis_execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Register exact retrieved chunk IDs as citations for the answer.
|
|
name: analysis_cite
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
chunk_ids:
|
|
items:
|
|
type: string
|
|
type: array
|
|
required:
|
|
- chunk_ids
|
|
type: object
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: |+
|
|
The error indicates that `re.search` doesn't accept keyword arguments. In Python, you can pass flags as positional argument or use `re.compile`. Let's adjust.
|
|
|
|
We used:
|
|
|
|
```
|
|
if 'Quarterly Report' in title or re.search(r"\bQ[1-4]\b", title, flags=re.IGNORECASE):
|
|
```
|
|
|
|
But re.search signature is `re.search(pattern, string[, flags])`, so flags must be positional after string. So we need to pass as third argument.
|
|
|
|
Let's correct code: `re.search(r"\bQ[1-4]\b", title, re.IGNORECASE)`.
|
|
|
|
Also, we used `re.compile` for revenue_pattern with flags; that's fine.
|
|
|
|
Let's update code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report''
|
|
in title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try
|
|
searching items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit
|
|
= matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_hnlx62rm
|
|
index: 0
|
|
type: function
|
|
created: 1784300501
|
|
id: chatcmpl-961
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 771
|
|
prompt_tokens: 4960
|
|
total_tokens: 5731
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '25387'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
messages:
|
|
- content: |-
|
|
# Analysis
|
|
|
|
You answer questions over a document knowledge base. Two common workflows:
|
|
|
|
- **`analysis_search → analysis_cite → answer`** when the answer is grounded on specific document content. Call `analysis_cite` with the supporting chunk_ids before writing the answer.
|
|
- **`analysis_execute_code → answer`** when the answer is a count, aggregation, listing, or structural computation over the corpus (e.g. "how many documents?", "average page count"). No `analysis_cite` is needed when no specific chunks support the answer.
|
|
|
|
You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation.
|
|
|
|
## Tools
|
|
|
|
### analysis_execute_code
|
|
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
|
|
|
Inside the code, these functions are available (use `await`):
|
|
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
|
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
|
|
|
Available modules: `json`, `re`, `math`, `pathlib`
|
|
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
|
|
|
### analysis_search
|
|
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
|
|
|
### analysis_cite
|
|
Register the chunk IDs that ground your answer. **You must call `analysis_cite` before writing any final answer that uses retrieved evidence — search results, items.jsonl rows, toc.json nodes, or content.txt content.** Skipping `analysis_cite` leaves the answer ungrounded and is treated as a failure.
|
|
|
|
`analysis_cite` is **not** required when your answer is a corpus-level computation that doesn't draw on specific chunks — counts, aggregations, listings, averages across documents. Don't fabricate citations for these.
|
|
|
|
Chunk IDs come from two places:
|
|
- The `chunk_id` field on `search` / `await search(...)` results
|
|
- The `chunk_ids` field on `items.jsonl` rows / `toc.json` nodes (when you ground via direct file reads)
|
|
|
|
Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs.
|
|
|
|
## Document Filesystem (inside execute_code)
|
|
|
|
All documents are mounted as a virtual filesystem at `/documents/`:
|
|
|
|
```
|
|
/documents/{document_id}/
|
|
metadata.json # {"id", "title", "uri", "created_at"}
|
|
content.txt # Full document text
|
|
items.jsonl # Structured items (one JSON object per line)
|
|
toc.json # Section tree derived from heading_level
|
|
```
|
|
|
|
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora.
|
|
|
|
### Reading files
|
|
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
|
|
|
```python
|
|
from pathlib import Path
|
|
import json
|
|
|
|
# Discover documents
|
|
for doc_dir in Path('/documents').iterdir():
|
|
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
|
print(meta['title'])
|
|
|
|
# Read full text
|
|
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
|
|
|
# Read and parse items
|
|
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
|
item = json.loads(line)
|
|
if item['label'] == 'table':
|
|
print(item['text'][:200])
|
|
```
|
|
|
|
### metadata.json
|
|
Document metadata: `id`, `title`, `uri`, `created_at`.
|
|
|
|
### content.txt
|
|
Full text content. Use for regex or keyword search across a whole document.
|
|
|
|
### items.jsonl
|
|
Structured document items. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file.
|
|
|
|
Each row carries:
|
|
- `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results
|
|
- `label`: item type — one of `"section_header"`, `"text"`, `"table"`, `"list_item"`, `"caption"`, `"formula"`, `"picture"`, `"code"`, `"footnote"`
|
|
- `text`: rendered content (tables are markdown with `|` columns)
|
|
- `page_numbers`: list of page numbers where the item appears
|
|
- `chunk_ids`: chunks that contain this item — pass to `analysis_cite()` to ground an answer that read this item directly
|
|
- `heading_level`: H-level for `section_header` rows; `0` on non-header rows
|
|
|
|
### toc.json
|
|
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `analysis_cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `tree: []` for docs with no headers.
|
|
|
|
### Cross-referencing search results with items
|
|
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in `items.jsonl`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index.
|
|
|
|
## Strategy
|
|
|
|
1. Search first.
|
|
2. Identify the chunk_ids from the search results that support your answer and call `analysis_cite` with them. Then write a concise answer.
|
|
3. Reach for `analysis_execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
|
4. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents.
|
|
5. Before writing your final response, call `analysis_cite` with the chunk_ids that ground your answer.
|
|
|
|
You MUST call `analysis_cite` with at least one chunk ID before producing your final answer **when your answer is grounded on retrieved evidence**. Skip `analysis_cite` in two cases: (a) you are refusing for lack of information, or (b) your answer is a corpus-level computation (count, aggregation, listing) that doesn't draw on specific chunks. In those cases do **not** fabricate citations.
|
|
|
|
## Important
|
|
|
|
- Variables persist between `analysis_execute_code` calls — you can search in one call and process results in the next
|
|
- Use `print()` to output results — the output is your only feedback
|
|
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `analysis_search → analysis_cite`.
|
|
- Use `await` for all async functions inside `analysis_execute_code` (`search`, `list_documents`)
|
|
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
|
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `analysis_cite` tool separately to register citations. `cite{...}` markdown-style inline references do nothing; only an actual `analysis_cite` tool call registers a citation.
|
|
- **Before you write your final answer, invoke the `analysis_cite` tool with the supporting chunk_ids.** This is the last tool call before answering whenever your answer draws on retrieved evidence.
|
|
role: system
|
|
- content: What is the total revenue across all quarterly reports?
|
|
role: user
|
|
- content: null
|
|
reasoning: |+
|
|
The user asks: "What is the total revenue across all quarterly reports?" We need to interpret what documents are in the knowledge base. The question likely refers to a set of quarterly report documents that contain revenue figures. We need to compute the sum of revenue across all quarterly reports.
|
|
|
|
We must determine how many documents exist, which ones are quarterly reports, and extract revenue numbers from each. Then sum them up.
|
|
|
|
The user didn't specify a particular document or context; it's a corpus-level computation: "total revenue across all quarterly reports". This is a calculation that aggregates data across multiple documents. According to the instructions: "No `analysis_cite` is needed when no specific chunks support the answer." But we need to compute this sum, which requires reading content from each relevant document. That is a corpus-level computation. The instruction says: "You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation." So if we aggregate across many documents and produce a single number, that is a corpus-level computation. We should not provide citations? But the answer might be considered derived from specific chunks. However, the instruction says "Don't fabricate citations for corpus-level computation." That means we can compute sum without citing each chunk individually. But maybe we need to cite some evidence that revenue appears in documents? The question: "What is the total revenue across all quarterly reports?" It's a numeric answer. We might compute it by scanning all documents, but we don't have to provide citations for each chunk. The instruction says no `analysis_cite` needed when no specific chunks support the answer. But here the answer is derived from many chunks; but it's still corpus-level computation. So we can skip citing.
|
|
|
|
But we need to actually compute the sum. We must search for quarterly reports and revenue numbers. Let's think: There might be documents with "Quarterly Report" in title or content, containing revenue figures. We need to find all such documents. Then parse revenue numbers from each. The revenue could be expressed as "$X million", "Revenue: $Y", etc.
|
|
|
|
We need to decide how to locate quarterly reports. Approach:
|
|
|
|
- Use `analysis_execute_code` to search for documents with 'Quarterly Report' in title or content. We can use the `search` function inside code, but we might also use `list_documents()` and filter by title containing "Quarterly Report". But maybe there are many documents; better to use search.
|
|
|
|
We need to find all documents that contain revenue figures. But likely each quarterly report has a revenue figure. The question: "total revenue across all quarterly reports" implies sum of revenue numbers from each quarter. So we need to extract the revenue number from each quarterly report document.
|
|
|
|
Potential approach:
|
|
|
|
- Use `search` with query "Quarterly Report" and maybe "Revenue". That will return chunks containing those terms. But we might get many results, including non-quarterly reports that mention revenue. We need to filter by documents that are quarterly reports. Maybe the metadata or title contains 'Q1', 'Q2', etc.
|
|
|
|
- Alternatively, search for pattern like "Revenue" and then check if document is a quarterly report (maybe via title containing "Quarterly Report").
|
|
|
|
Better approach: Use `list_documents()` to get all docs. For each doc, read its metadata.title; if it contains "Quarterly Report", parse revenue from content or items.
|
|
|
|
But we need to know the file structure. The documents are in `/documents/{document_id}/`. Each has metadata.json with title and uri. We can list them. Then for each doc whose title matches /quarterly report/i, read its content.txt and find revenue numbers.
|
|
|
|
We need a regex that captures revenue amounts. Revenue could be expressed as "$123 million" or "Revenue: $123M". Could also be numeric like "Revenue 123,456,789".
|
|
|
|
We need to parse these numbers into numeric values (maybe in millions). But we don't know the units; maybe all are in USD and millions.
|
|
|
|
Better approach: Use regex that captures patterns like:
|
|
|
|
- "$\d{1,3}(,\d{3})*(\.\d+)?(M|B)?" or "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?"
|
|
|
|
But we need to decide how to interpret units. The question likely expects a sum in the same unit as reported.
|
|
|
|
We might find revenue numbers like "$1,234,567" or "$12.3M". We can convert all to numeric values (in dollars). For M, multiply by 1e6; for B, multiply by 1e9.
|
|
|
|
But we need to decide how to sum them: If some are in millions and others in billions, we unify to dollars.
|
|
|
|
Alternatively, maybe the revenue numbers are all in millions. But we can't assume.
|
|
|
|
We can parse each number string, detect if it ends with 'M' or 'B', then convert accordingly.
|
|
|
|
Also consider that some documents might have multiple revenue figures (like revenue for different segments). We need to sum only the main revenue figure per document. How to identify? Maybe it's the first occurrence of "Revenue" in the document.
|
|
|
|
Simplify: For each quarterly report doc, find all matches of pattern "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?" and take the first match. That should capture main revenue figure.
|
|
|
|
But we need to be careful with formatting like "$1,234,567" (no M/B). Then treat as dollars.
|
|
|
|
Also consider "Revenue: 123,456,789". We'll parse numeric part.
|
|
|
|
We also need to handle numbers that include commas and decimal points. We can remove commas then convert to float.
|
|
|
|
Edge cases: Some documents might have revenue expressed in thousands or millions; but we can't know. But likely they use M for million.
|
|
|
|
Ok.
|
|
|
|
Implementation plan:
|
|
|
|
- Use `analysis_execute_code` with async code.
|
|
|
|
Pseudo-code:
|
|
|
|
```
|
|
import json, re
|
|
from pathlib import Path
|
|
|
|
# Get list of docs
|
|
docs = await list_documents()
|
|
quarterly_docs = []
|
|
for doc in docs:
|
|
title = doc.get('title', '')
|
|
if 'Quarterly Report' in title or 'Q1' in title or 'Q2' in title or 'Q3' in title or 'Q4' in title:
|
|
quarterly_docs.append(doc)
|
|
|
|
# For each doc, read content.txt
|
|
total_revenue = 0.0
|
|
for doc in quarterly_docs:
|
|
path = Path(f'/documents/{doc["id"]}/content.txt')
|
|
text = path.read_text()
|
|
# find revenue patterns
|
|
matches = re.findall(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', text, flags=re.IGNORECASE)
|
|
if not matches:
|
|
continue
|
|
# take first match
|
|
num_str, unit = matches[0]
|
|
# remove commas
|
|
num_val = float(num_str.replace(',', ''))
|
|
if unit.lower() == 'm':
|
|
num_val *= 1_000_000
|
|
elif unit.lower() == 'b':
|
|
num_val *= 1_000_000_000
|
|
total_revenue += num_val
|
|
|
|
print(total_revenue)
|
|
```
|
|
|
|
But we need to ensure we capture numbers like "$12.3M" where the number part includes decimal.
|
|
|
|
Our regex: r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' works for that.
|
|
|
|
However, if there's no unit but a comma separated number like "Revenue: 1,234,567", then unit will be empty string. We treat as dollars.
|
|
|
|
Also need to handle numbers with decimal and maybe M/B after decimal e.g., "$12.34M".
|
|
|
|
Ok.
|
|
|
|
But we also need to consider that some documents might have revenue in thousands or millions but no unit; we can't know. But likely they use M for million.
|
|
|
|
We should sum all revenue values as numeric dollars.
|
|
|
|
After computing total, print the number. We may want to format with commas and maybe show as integer if it's whole number.
|
|
|
|
But the answer expects a numeric value. We'll output the sum in plain number.
|
|
|
|
Potential issues: The regex might find multiple matches per doc; we only take first. But what if revenue appears multiple times? Might be fine.
|
|
|
|
Also, some docs may have "Revenue" but not quarterly report; but we filter by title containing "Quarterly Report". That should reduce noise.
|
|
|
|
But maybe the title doesn't contain "Quarterly Report", but the document is a quarterly report with a different naming scheme. Could be like "Q1 2023 Financial Statement". But we might miss them. We can broaden search: include titles containing 'Q1', 'Q2', 'Q3', 'Q4' and maybe 'Financial Report'. But that could bring in other documents.
|
|
|
|
Better approach: Use `search` for query "Quarterly Report" to get chunk IDs, then find which docs those belong to. Then we can use the doc ids from search results. That may be more precise.
|
|
|
|
But we need to sum revenue across all quarterly reports; we might want to include all such docs. But if some are not labeled as 'Quarterly Report' but still contain revenue, we might miss them.
|
|
|
|
We could also search for "Revenue" and then filter by doc id that appears in a set of known quarterly report ids from previous search. That is complex.
|
|
|
|
Simplify: Use list_documents to get all docs; for each doc, check if title contains 'Quarterly Report' or matches pattern like r'\bQ[1-4]\b'. But we might also include "Annual Report" which we don't want. So maybe filter out titles containing 'Annual'.
|
|
|
|
Better approach: For each doc, read content and see if it has a heading "Quarterly Report". We could parse toc.json for sections titled "Quarterly Report".
|
|
|
|
But that is too complex.
|
|
|
|
Given the instruction to compute sum across all quarterly reports, we can assume that documents with title containing 'Quarterly Report' are the ones. That should be fine.
|
|
|
|
Ok.
|
|
|
|
We need to run code and print total revenue.
|
|
|
|
Let's implement in analysis_execute_code.
|
|
|
|
Also, we might want to handle potential errors: missing content.txt etc.
|
|
|
|
We'll use Path.read_text() inside try/except.
|
|
|
|
Also, we may want to convert total_revenue to integer if it's whole number. But we can just print as float with no decimals if it's an integer.
|
|
|
|
Ok.
|
|
|
|
Let's write code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, flags=re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_qzkuh2up
|
|
type: function
|
|
- content: "Error: TypeError: re.search() takes no keyword arguments\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_qzkuh2up
|
|
- content: null
|
|
reasoning: |+
|
|
The error indicates that `re.search` doesn't accept keyword arguments. In Python, you can pass flags as positional argument or use `re.compile`. Let's adjust.
|
|
|
|
We used:
|
|
|
|
```
|
|
if 'Quarterly Report' in title or re.search(r"\bQ[1-4]\b", title, flags=re.IGNORECASE):
|
|
```
|
|
|
|
But re.search signature is `re.search(pattern, string[, flags])`, so flags must be positional after string. So we need to pass as third argument.
|
|
|
|
Let's correct code: `re.search(r"\bQ[1-4]\b", title, re.IGNORECASE)`.
|
|
|
|
Also, we used `re.compile` for revenue_pattern with flags; that's fine.
|
|
|
|
Let's update code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_hnlx62rm
|
|
type: function
|
|
- content: |
|
|
Error: TypeError: re.compile expected at most 2 arguments, got 1
|
|
|
|
Output: Found 3 quarterly report documents.
|
|
role: tool
|
|
tool_call_id: call_hnlx62rm
|
|
model: gpt-oss
|
|
reasoning_effort: high
|
|
stream: false
|
|
temperature: 0.3
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: Search the knowledge base for evidence to analyze.
|
|
name: analysis_search
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
limit:
|
|
anyOf:
|
|
- type: integer
|
|
- type: 'null'
|
|
default: null
|
|
query:
|
|
type: string
|
|
required:
|
|
- query
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Execute Python against the sandboxed document filesystem.
|
|
name: analysis_execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Register exact retrieved chunk IDs as citations for the answer.
|
|
name: analysis_cite
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
chunk_ids:
|
|
items:
|
|
type: string
|
|
type: array
|
|
required:
|
|
- chunk_ids
|
|
type: object
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: |+
|
|
The error indicates that `re.compile` expects at most two arguments. We passed flags as keyword argument; but in Python's re.compile signature is `re.compile(pattern, flags=0)`. So we should pass flags as second positional argument or use keyword 'flags' correctly? Actually re.compile accepts flags as optional positional argument: `re.compile(pattern[, flags])`. But the error says "got 1" meaning it thinks only one argument was passed. Wait, we wrote:
|
|
|
|
```
|
|
revenue_pattern = re.compile(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', flags=re.IGNORECASE)
|
|
```
|
|
|
|
But re.compile signature doesn't accept keyword arguments; you must pass flags as second positional argument: `re.compile(pattern, re.IGNORECASE)`. So we need to change.
|
|
|
|
Let's update code accordingly.
|
|
|
|
Also, we used `print` statements inside the loop; that's fine.
|
|
|
|
Let's correct.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report''
|
|
in title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try
|
|
searching items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit
|
|
= matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_ipebddpq
|
|
index: 0
|
|
type: function
|
|
created: 1784300530
|
|
id: chatcmpl-222
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 815
|
|
prompt_tokens: 5770
|
|
total_tokens: 6585
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '29046'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
messages:
|
|
- content: |-
|
|
# Analysis
|
|
|
|
You answer questions over a document knowledge base. Two common workflows:
|
|
|
|
- **`analysis_search → analysis_cite → answer`** when the answer is grounded on specific document content. Call `analysis_cite` with the supporting chunk_ids before writing the answer.
|
|
- **`analysis_execute_code → answer`** when the answer is a count, aggregation, listing, or structural computation over the corpus (e.g. "how many documents?", "average page count"). No `analysis_cite` is needed when no specific chunks support the answer.
|
|
|
|
You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation.
|
|
|
|
## Tools
|
|
|
|
### analysis_execute_code
|
|
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
|
|
|
Inside the code, these functions are available (use `await`):
|
|
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
|
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
|
|
|
Available modules: `json`, `re`, `math`, `pathlib`
|
|
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
|
|
|
### analysis_search
|
|
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
|
|
|
### analysis_cite
|
|
Register the chunk IDs that ground your answer. **You must call `analysis_cite` before writing any final answer that uses retrieved evidence — search results, items.jsonl rows, toc.json nodes, or content.txt content.** Skipping `analysis_cite` leaves the answer ungrounded and is treated as a failure.
|
|
|
|
`analysis_cite` is **not** required when your answer is a corpus-level computation that doesn't draw on specific chunks — counts, aggregations, listings, averages across documents. Don't fabricate citations for these.
|
|
|
|
Chunk IDs come from two places:
|
|
- The `chunk_id` field on `search` / `await search(...)` results
|
|
- The `chunk_ids` field on `items.jsonl` rows / `toc.json` nodes (when you ground via direct file reads)
|
|
|
|
Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs.
|
|
|
|
## Document Filesystem (inside execute_code)
|
|
|
|
All documents are mounted as a virtual filesystem at `/documents/`:
|
|
|
|
```
|
|
/documents/{document_id}/
|
|
metadata.json # {"id", "title", "uri", "created_at"}
|
|
content.txt # Full document text
|
|
items.jsonl # Structured items (one JSON object per line)
|
|
toc.json # Section tree derived from heading_level
|
|
```
|
|
|
|
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora.
|
|
|
|
### Reading files
|
|
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
|
|
|
```python
|
|
from pathlib import Path
|
|
import json
|
|
|
|
# Discover documents
|
|
for doc_dir in Path('/documents').iterdir():
|
|
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
|
print(meta['title'])
|
|
|
|
# Read full text
|
|
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
|
|
|
# Read and parse items
|
|
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
|
item = json.loads(line)
|
|
if item['label'] == 'table':
|
|
print(item['text'][:200])
|
|
```
|
|
|
|
### metadata.json
|
|
Document metadata: `id`, `title`, `uri`, `created_at`.
|
|
|
|
### content.txt
|
|
Full text content. Use for regex or keyword search across a whole document.
|
|
|
|
### items.jsonl
|
|
Structured document items. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file.
|
|
|
|
Each row carries:
|
|
- `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results
|
|
- `label`: item type — one of `"section_header"`, `"text"`, `"table"`, `"list_item"`, `"caption"`, `"formula"`, `"picture"`, `"code"`, `"footnote"`
|
|
- `text`: rendered content (tables are markdown with `|` columns)
|
|
- `page_numbers`: list of page numbers where the item appears
|
|
- `chunk_ids`: chunks that contain this item — pass to `analysis_cite()` to ground an answer that read this item directly
|
|
- `heading_level`: H-level for `section_header` rows; `0` on non-header rows
|
|
|
|
### toc.json
|
|
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `analysis_cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `tree: []` for docs with no headers.
|
|
|
|
### Cross-referencing search results with items
|
|
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in `items.jsonl`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index.
|
|
|
|
## Strategy
|
|
|
|
1. Search first.
|
|
2. Identify the chunk_ids from the search results that support your answer and call `analysis_cite` with them. Then write a concise answer.
|
|
3. Reach for `analysis_execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
|
4. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents.
|
|
5. Before writing your final response, call `analysis_cite` with the chunk_ids that ground your answer.
|
|
|
|
You MUST call `analysis_cite` with at least one chunk ID before producing your final answer **when your answer is grounded on retrieved evidence**. Skip `analysis_cite` in two cases: (a) you are refusing for lack of information, or (b) your answer is a corpus-level computation (count, aggregation, listing) that doesn't draw on specific chunks. In those cases do **not** fabricate citations.
|
|
|
|
## Important
|
|
|
|
- Variables persist between `analysis_execute_code` calls — you can search in one call and process results in the next
|
|
- Use `print()` to output results — the output is your only feedback
|
|
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `analysis_search → analysis_cite`.
|
|
- Use `await` for all async functions inside `analysis_execute_code` (`search`, `list_documents`)
|
|
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
|
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `analysis_cite` tool separately to register citations. `cite{...}` markdown-style inline references do nothing; only an actual `analysis_cite` tool call registers a citation.
|
|
- **Before you write your final answer, invoke the `analysis_cite` tool with the supporting chunk_ids.** This is the last tool call before answering whenever your answer draws on retrieved evidence.
|
|
role: system
|
|
- content: What is the total revenue across all quarterly reports?
|
|
role: user
|
|
- content: null
|
|
reasoning: |+
|
|
The user asks: "What is the total revenue across all quarterly reports?" We need to interpret what documents are in the knowledge base. The question likely refers to a set of quarterly report documents that contain revenue figures. We need to compute the sum of revenue across all quarterly reports.
|
|
|
|
We must determine how many documents exist, which ones are quarterly reports, and extract revenue numbers from each. Then sum them up.
|
|
|
|
The user didn't specify a particular document or context; it's a corpus-level computation: "total revenue across all quarterly reports". This is a calculation that aggregates data across multiple documents. According to the instructions: "No `analysis_cite` is needed when no specific chunks support the answer." But we need to compute this sum, which requires reading content from each relevant document. That is a corpus-level computation. The instruction says: "You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation." So if we aggregate across many documents and produce a single number, that is a corpus-level computation. We should not provide citations? But the answer might be considered derived from specific chunks. However, the instruction says "Don't fabricate citations for corpus-level computation." That means we can compute sum without citing each chunk individually. But maybe we need to cite some evidence that revenue appears in documents? The question: "What is the total revenue across all quarterly reports?" It's a numeric answer. We might compute it by scanning all documents, but we don't have to provide citations for each chunk. The instruction says no `analysis_cite` needed when no specific chunks support the answer. But here the answer is derived from many chunks; but it's still corpus-level computation. So we can skip citing.
|
|
|
|
But we need to actually compute the sum. We must search for quarterly reports and revenue numbers. Let's think: There might be documents with "Quarterly Report" in title or content, containing revenue figures. We need to find all such documents. Then parse revenue numbers from each. The revenue could be expressed as "$X million", "Revenue: $Y", etc.
|
|
|
|
We need to decide how to locate quarterly reports. Approach:
|
|
|
|
- Use `analysis_execute_code` to search for documents with 'Quarterly Report' in title or content. We can use the `search` function inside code, but we might also use `list_documents()` and filter by title containing "Quarterly Report". But maybe there are many documents; better to use search.
|
|
|
|
We need to find all documents that contain revenue figures. But likely each quarterly report has a revenue figure. The question: "total revenue across all quarterly reports" implies sum of revenue numbers from each quarter. So we need to extract the revenue number from each quarterly report document.
|
|
|
|
Potential approach:
|
|
|
|
- Use `search` with query "Quarterly Report" and maybe "Revenue". That will return chunks containing those terms. But we might get many results, including non-quarterly reports that mention revenue. We need to filter by documents that are quarterly reports. Maybe the metadata or title contains 'Q1', 'Q2', etc.
|
|
|
|
- Alternatively, search for pattern like "Revenue" and then check if document is a quarterly report (maybe via title containing "Quarterly Report").
|
|
|
|
Better approach: Use `list_documents()` to get all docs. For each doc, read its metadata.title; if it contains "Quarterly Report", parse revenue from content or items.
|
|
|
|
But we need to know the file structure. The documents are in `/documents/{document_id}/`. Each has metadata.json with title and uri. We can list them. Then for each doc whose title matches /quarterly report/i, read its content.txt and find revenue numbers.
|
|
|
|
We need a regex that captures revenue amounts. Revenue could be expressed as "$123 million" or "Revenue: $123M". Could also be numeric like "Revenue 123,456,789".
|
|
|
|
We need to parse these numbers into numeric values (maybe in millions). But we don't know the units; maybe all are in USD and millions.
|
|
|
|
Better approach: Use regex that captures patterns like:
|
|
|
|
- "$\d{1,3}(,\d{3})*(\.\d+)?(M|B)?" or "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?"
|
|
|
|
But we need to decide how to interpret units. The question likely expects a sum in the same unit as reported.
|
|
|
|
We might find revenue numbers like "$1,234,567" or "$12.3M". We can convert all to numeric values (in dollars). For M, multiply by 1e6; for B, multiply by 1e9.
|
|
|
|
But we need to decide how to sum them: If some are in millions and others in billions, we unify to dollars.
|
|
|
|
Alternatively, maybe the revenue numbers are all in millions. But we can't assume.
|
|
|
|
We can parse each number string, detect if it ends with 'M' or 'B', then convert accordingly.
|
|
|
|
Also consider that some documents might have multiple revenue figures (like revenue for different segments). We need to sum only the main revenue figure per document. How to identify? Maybe it's the first occurrence of "Revenue" in the document.
|
|
|
|
Simplify: For each quarterly report doc, find all matches of pattern "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?" and take the first match. That should capture main revenue figure.
|
|
|
|
But we need to be careful with formatting like "$1,234,567" (no M/B). Then treat as dollars.
|
|
|
|
Also consider "Revenue: 123,456,789". We'll parse numeric part.
|
|
|
|
We also need to handle numbers that include commas and decimal points. We can remove commas then convert to float.
|
|
|
|
Edge cases: Some documents might have revenue expressed in thousands or millions; but we can't know. But likely they use M for million.
|
|
|
|
Ok.
|
|
|
|
Implementation plan:
|
|
|
|
- Use `analysis_execute_code` with async code.
|
|
|
|
Pseudo-code:
|
|
|
|
```
|
|
import json, re
|
|
from pathlib import Path
|
|
|
|
# Get list of docs
|
|
docs = await list_documents()
|
|
quarterly_docs = []
|
|
for doc in docs:
|
|
title = doc.get('title', '')
|
|
if 'Quarterly Report' in title or 'Q1' in title or 'Q2' in title or 'Q3' in title or 'Q4' in title:
|
|
quarterly_docs.append(doc)
|
|
|
|
# For each doc, read content.txt
|
|
total_revenue = 0.0
|
|
for doc in quarterly_docs:
|
|
path = Path(f'/documents/{doc["id"]}/content.txt')
|
|
text = path.read_text()
|
|
# find revenue patterns
|
|
matches = re.findall(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', text, flags=re.IGNORECASE)
|
|
if not matches:
|
|
continue
|
|
# take first match
|
|
num_str, unit = matches[0]
|
|
# remove commas
|
|
num_val = float(num_str.replace(',', ''))
|
|
if unit.lower() == 'm':
|
|
num_val *= 1_000_000
|
|
elif unit.lower() == 'b':
|
|
num_val *= 1_000_000_000
|
|
total_revenue += num_val
|
|
|
|
print(total_revenue)
|
|
```
|
|
|
|
But we need to ensure we capture numbers like "$12.3M" where the number part includes decimal.
|
|
|
|
Our regex: r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' works for that.
|
|
|
|
However, if there's no unit but a comma separated number like "Revenue: 1,234,567", then unit will be empty string. We treat as dollars.
|
|
|
|
Also need to handle numbers with decimal and maybe M/B after decimal e.g., "$12.34M".
|
|
|
|
Ok.
|
|
|
|
But we also need to consider that some documents might have revenue in thousands or millions but no unit; we can't know. But likely they use M for million.
|
|
|
|
We should sum all revenue values as numeric dollars.
|
|
|
|
After computing total, print the number. We may want to format with commas and maybe show as integer if it's whole number.
|
|
|
|
But the answer expects a numeric value. We'll output the sum in plain number.
|
|
|
|
Potential issues: The regex might find multiple matches per doc; we only take first. But what if revenue appears multiple times? Might be fine.
|
|
|
|
Also, some docs may have "Revenue" but not quarterly report; but we filter by title containing "Quarterly Report". That should reduce noise.
|
|
|
|
But maybe the title doesn't contain "Quarterly Report", but the document is a quarterly report with a different naming scheme. Could be like "Q1 2023 Financial Statement". But we might miss them. We can broaden search: include titles containing 'Q1', 'Q2', 'Q3', 'Q4' and maybe 'Financial Report'. But that could bring in other documents.
|
|
|
|
Better approach: Use `search` for query "Quarterly Report" to get chunk IDs, then find which docs those belong to. Then we can use the doc ids from search results. That may be more precise.
|
|
|
|
But we need to sum revenue across all quarterly reports; we might want to include all such docs. But if some are not labeled as 'Quarterly Report' but still contain revenue, we might miss them.
|
|
|
|
We could also search for "Revenue" and then filter by doc id that appears in a set of known quarterly report ids from previous search. That is complex.
|
|
|
|
Simplify: Use list_documents to get all docs; for each doc, check if title contains 'Quarterly Report' or matches pattern like r'\bQ[1-4]\b'. But we might also include "Annual Report" which we don't want. So maybe filter out titles containing 'Annual'.
|
|
|
|
Better approach: For each doc, read content and see if it has a heading "Quarterly Report". We could parse toc.json for sections titled "Quarterly Report".
|
|
|
|
But that is too complex.
|
|
|
|
Given the instruction to compute sum across all quarterly reports, we can assume that documents with title containing 'Quarterly Report' are the ones. That should be fine.
|
|
|
|
Ok.
|
|
|
|
We need to run code and print total revenue.
|
|
|
|
Let's implement in analysis_execute_code.
|
|
|
|
Also, we might want to handle potential errors: missing content.txt etc.
|
|
|
|
We'll use Path.read_text() inside try/except.
|
|
|
|
Also, we may want to convert total_revenue to integer if it's whole number. But we can just print as float with no decimals if it's an integer.
|
|
|
|
Ok.
|
|
|
|
Let's write code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, flags=re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_qzkuh2up
|
|
type: function
|
|
- content: "Error: TypeError: re.search() takes no keyword arguments\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_qzkuh2up
|
|
- content: null
|
|
reasoning: |+
|
|
The error indicates that `re.search` doesn't accept keyword arguments. In Python, you can pass flags as positional argument or use `re.compile`. Let's adjust.
|
|
|
|
We used:
|
|
|
|
```
|
|
if 'Quarterly Report' in title or re.search(r"\bQ[1-4]\b", title, flags=re.IGNORECASE):
|
|
```
|
|
|
|
But re.search signature is `re.search(pattern, string[, flags])`, so flags must be positional after string. So we need to pass as third argument.
|
|
|
|
Let's correct code: `re.search(r"\bQ[1-4]\b", title, re.IGNORECASE)`.
|
|
|
|
Also, we used `re.compile` for revenue_pattern with flags; that's fine.
|
|
|
|
Let's update code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_hnlx62rm
|
|
type: function
|
|
- content: |
|
|
Error: TypeError: re.compile expected at most 2 arguments, got 1
|
|
|
|
Output: Found 3 quarterly report documents.
|
|
role: tool
|
|
tool_call_id: call_hnlx62rm
|
|
- content: null
|
|
reasoning: |+
|
|
The error indicates that `re.compile` expects at most two arguments. We passed flags as keyword argument; but in Python's re.compile signature is `re.compile(pattern, flags=0)`. So we should pass flags as second positional argument or use keyword 'flags' correctly? Actually re.compile accepts flags as optional positional argument: `re.compile(pattern[, flags])`. But the error says "got 1" meaning it thinks only one argument was passed. Wait, we wrote:
|
|
|
|
```
|
|
revenue_pattern = re.compile(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', flags=re.IGNORECASE)
|
|
```
|
|
|
|
But re.compile signature doesn't accept keyword arguments; you must pass flags as second positional argument: `re.compile(pattern, re.IGNORECASE)`. So we need to change.
|
|
|
|
Let's update code accordingly.
|
|
|
|
Also, we used `print` statements inside the loop; that's fine.
|
|
|
|
Let's correct.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_ipebddpq
|
|
type: function
|
|
- content: |
|
|
Found 3 quarterly report documents.
|
|
No revenue found in Q1 Report (5427fb0e-24ab-4dc5-a8c5-6f4fe7f29e7d)
|
|
No revenue found in Q2 Report (a0d9d838-2b7c-4785-8c13-1eab81c52a90)
|
|
No revenue found in Q3 Report (515faf6e-3758-45bd-89fb-552c3c8a159e)
|
|
Total revenue across all quarterly reports: 0.0
|
|
role: tool
|
|
tool_call_id: call_ipebddpq
|
|
model: gpt-oss
|
|
reasoning_effort: high
|
|
stream: false
|
|
temperature: 0.3
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: Search the knowledge base for evidence to analyze.
|
|
name: analysis_search
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
limit:
|
|
anyOf:
|
|
- type: integer
|
|
- type: 'null'
|
|
default: null
|
|
query:
|
|
type: string
|
|
required:
|
|
- query
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Execute Python against the sandboxed document filesystem.
|
|
name: analysis_execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Register exact retrieved chunk IDs as citations for the answer.
|
|
name: analysis_cite
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
chunk_ids:
|
|
items:
|
|
type: string
|
|
type: array
|
|
required:
|
|
- chunk_ids
|
|
type: object
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: |+
|
|
The script found 3 documents titled "Q1 Report", "Q2 Report", "Q3 Report". It didn't find any revenue in them.
|
|
|
|
Maybe the revenue is not labeled with "Revenue" but something else like "Net Sales" or "Total Revenue". We need to broaden search. Let's inspect these documents' content to see how revenue is expressed.
|
|
|
|
We can read each document's content and print some lines around where revenue might be. But we don't know where it appears.
|
|
|
|
Let's open the content of one doc, e.g., Q1 Report. We'll use Path.read_text() and maybe show first 2000 characters or search for "Revenue" or "Sales".
|
|
|
|
But we need to parse revenue numbers. The revenue may appear in a table. In items.jsonl, there might be tables with revenue figures.
|
|
|
|
We can examine the items.jsonl of Q1 Report to see if there's any numeric values that could represent revenue. But we need to identify which is revenue.
|
|
|
|
Alternatively, maybe the documents are not named "Quarterly Report" but something else; maybe they are part of a larger set like "Financial Statement Q1 2023".
|
|
|
|
We might need to search for all documents containing "Revenue" and then filter those that have quarterly report context. But we need to sum across all quarterly reports, so we should include any document that is a quarterly report.
|
|
|
|
But the script didn't find revenue in these three docs. Maybe revenue appears in tables but not captured by our regex because it's inside a table with markdown syntax like "| Revenue | $1,234,567 |". Our regex might still catch it if "Revenue" appears before number. But maybe the text is "Total Revenue:" or "Total Revenue".
|
|
|
|
Our regex only matches "Revenue" exactly; we could broaden to match "Total Revenue", "Net Revenue", etc.
|
|
|
|
Let's modify regex: r'(?:Total\s+)?Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' but also allow "Net Revenue". So maybe pattern: r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b.*?[:\-]?\s*\$?([0-9.,]+)([MmBb])?'.
|
|
|
|
But we need to capture number after colon or dash. Let's design a regex that matches "Revenue" optionally preceded by "Total" or "Net", then optional whitespace, then maybe colon/dash, then optional whitespace, then $ sign, then numeric part with optional unit.
|
|
|
|
Simplify: r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b.*?[:\-]?\s*\$?([0-9.,]+)([MmBb])?'.
|
|
|
|
But we need to capture the number. The pattern uses a capturing group for number and unit. But we also need to ensure that the colon/dash is matched before number; but we can use non-greedy match between 'Revenue' and number: `.*?` will consume minimal characters until next part. That should work.
|
|
|
|
Let's test this regex on some sample strings:
|
|
|
|
- "Total Revenue: $12.3M" -> matches group 1 = "12.3", group2="M".
|
|
- "Net Revenue - $5,000,000" -> group1="5,000,000", group2="".
|
|
- "Revenue: $1234567" -> group1="1234567", group2="".
|
|
- "Revenue: 123.45B" -> group1="123.45", group2="B".
|
|
|
|
Ok.
|
|
|
|
Let's update regex accordingly.
|
|
|
|
Also, we might need to parse numbers that include commas and decimals.
|
|
|
|
We also should consider that the number may be preceded by "$" or not. We handle that.
|
|
|
|
Now let's modify code: use new pattern.
|
|
|
|
Also, maybe revenue is in a table with markdown like "| Revenue | $12.3M |". In that case, our regex might still match because "Revenue:" appears? Actually there might be no colon; but we can adjust to allow optional colon/dash after "Revenue" or not. But the number may appear after a pipe symbol.
|
|
|
|
We could also search for patterns like r'(?i)Revenue\s*[:\-]?\s*\$?([0-9.,]+)([MmBb])?' which is similar but we added optional preceding words.
|
|
|
|
But maybe the revenue appears in a table row like "Revenue | $12.3M", so there is no colon or dash. Our regex would not match because it expects `[:\-]?` after "Revenue". But we can modify to allow any non-digit characters between "Revenue" and number: e.g., `\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*?\$?([0-9,.]+)([MmBb])?`. That would match.
|
|
|
|
Let's design pattern:
|
|
|
|
```
|
|
re.compile(r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*\$?([0-9.,]+)([MmBb])?', re.IGNORECASE)
|
|
```
|
|
|
|
This matches "Revenue" optionally preceded by "Total" or "Net", then any characters that are not digits (including colon, dash, pipe, space), then optional `$`, then number.
|
|
|
|
But if there is a digit before the number? For example "Revenue: $12.3M" -> after "Revenue:" we have space and "$". The pattern `[^0-9]*` will match ":" and space until it sees a digit or $; but we also allow `$?`. Actually we need to ensure that the `$` is optional before number. So pattern: `\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*\$?([0-9.,]+)([MmBb])?`.
|
|
|
|
But if there is no colon/dash, but a pipe symbol like "Revenue | $12.3M", then after "Revenue" we have space and "|" which is not digit; `[^0-9]*` will match that.
|
|
|
|
Ok.
|
|
|
|
Let's update regex accordingly.
|
|
|
|
Also, we should consider that the number might be in thousands or millions with no unit. We'll treat as dollars.
|
|
|
|
Now let's modify code accordingly.
|
|
|
|
Additionally, maybe revenue appears in items.jsonl but not in content.txt; our script already tries to parse items if no matches found in text. That may catch some.
|
|
|
|
But we need to ensure we read all lines of items.jsonl and search for "Revenue" inside each item.text. But the regex will apply to that too.
|
|
|
|
Ok.
|
|
|
|
Let's update code with new pattern.
|
|
|
|
Also, we might want to print the revenue value for debugging.
|
|
|
|
Let's run again.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report''
|
|
in title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''(?i)\\b(?:Total\\s+|Net\\s+)?Revenue\\b[^0-9]*\\$?([0-9.,]+)([MmBb])?'', re.IGNORECASE)\n\ntotal_revenue
|
|
= 0.0\nfor doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try
|
|
searching items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit
|
|
= matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_1kz75zth
|
|
index: 0
|
|
type: function
|
|
created: 1784300601
|
|
id: chatcmpl-746
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 2007
|
|
prompt_tokens: 6719
|
|
total_tokens: 8726
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '37183'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
messages:
|
|
- content: |-
|
|
# Analysis
|
|
|
|
You answer questions over a document knowledge base. Two common workflows:
|
|
|
|
- **`analysis_search → analysis_cite → answer`** when the answer is grounded on specific document content. Call `analysis_cite` with the supporting chunk_ids before writing the answer.
|
|
- **`analysis_execute_code → answer`** when the answer is a count, aggregation, listing, or structural computation over the corpus (e.g. "how many documents?", "average page count"). No `analysis_cite` is needed when no specific chunks support the answer.
|
|
|
|
You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation.
|
|
|
|
## Tools
|
|
|
|
### analysis_execute_code
|
|
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
|
|
|
Inside the code, these functions are available (use `await`):
|
|
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
|
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
|
|
|
Available modules: `json`, `re`, `math`, `pathlib`
|
|
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
|
|
|
### analysis_search
|
|
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
|
|
|
### analysis_cite
|
|
Register the chunk IDs that ground your answer. **You must call `analysis_cite` before writing any final answer that uses retrieved evidence — search results, items.jsonl rows, toc.json nodes, or content.txt content.** Skipping `analysis_cite` leaves the answer ungrounded and is treated as a failure.
|
|
|
|
`analysis_cite` is **not** required when your answer is a corpus-level computation that doesn't draw on specific chunks — counts, aggregations, listings, averages across documents. Don't fabricate citations for these.
|
|
|
|
Chunk IDs come from two places:
|
|
- The `chunk_id` field on `search` / `await search(...)` results
|
|
- The `chunk_ids` field on `items.jsonl` rows / `toc.json` nodes (when you ground via direct file reads)
|
|
|
|
Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs.
|
|
|
|
## Document Filesystem (inside execute_code)
|
|
|
|
All documents are mounted as a virtual filesystem at `/documents/`:
|
|
|
|
```
|
|
/documents/{document_id}/
|
|
metadata.json # {"id", "title", "uri", "created_at"}
|
|
content.txt # Full document text
|
|
items.jsonl # Structured items (one JSON object per line)
|
|
toc.json # Section tree derived from heading_level
|
|
```
|
|
|
|
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora.
|
|
|
|
### Reading files
|
|
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
|
|
|
```python
|
|
from pathlib import Path
|
|
import json
|
|
|
|
# Discover documents
|
|
for doc_dir in Path('/documents').iterdir():
|
|
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
|
print(meta['title'])
|
|
|
|
# Read full text
|
|
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
|
|
|
# Read and parse items
|
|
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
|
item = json.loads(line)
|
|
if item['label'] == 'table':
|
|
print(item['text'][:200])
|
|
```
|
|
|
|
### metadata.json
|
|
Document metadata: `id`, `title`, `uri`, `created_at`.
|
|
|
|
### content.txt
|
|
Full text content. Use for regex or keyword search across a whole document.
|
|
|
|
### items.jsonl
|
|
Structured document items. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file.
|
|
|
|
Each row carries:
|
|
- `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results
|
|
- `label`: item type — one of `"section_header"`, `"text"`, `"table"`, `"list_item"`, `"caption"`, `"formula"`, `"picture"`, `"code"`, `"footnote"`
|
|
- `text`: rendered content (tables are markdown with `|` columns)
|
|
- `page_numbers`: list of page numbers where the item appears
|
|
- `chunk_ids`: chunks that contain this item — pass to `analysis_cite()` to ground an answer that read this item directly
|
|
- `heading_level`: H-level for `section_header` rows; `0` on non-header rows
|
|
|
|
### toc.json
|
|
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `analysis_cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `tree: []` for docs with no headers.
|
|
|
|
### Cross-referencing search results with items
|
|
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in `items.jsonl`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index.
|
|
|
|
## Strategy
|
|
|
|
1. Search first.
|
|
2. Identify the chunk_ids from the search results that support your answer and call `analysis_cite` with them. Then write a concise answer.
|
|
3. Reach for `analysis_execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
|
4. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents.
|
|
5. Before writing your final response, call `analysis_cite` with the chunk_ids that ground your answer.
|
|
|
|
You MUST call `analysis_cite` with at least one chunk ID before producing your final answer **when your answer is grounded on retrieved evidence**. Skip `analysis_cite` in two cases: (a) you are refusing for lack of information, or (b) your answer is a corpus-level computation (count, aggregation, listing) that doesn't draw on specific chunks. In those cases do **not** fabricate citations.
|
|
|
|
## Important
|
|
|
|
- Variables persist between `analysis_execute_code` calls — you can search in one call and process results in the next
|
|
- Use `print()` to output results — the output is your only feedback
|
|
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `analysis_search → analysis_cite`.
|
|
- Use `await` for all async functions inside `analysis_execute_code` (`search`, `list_documents`)
|
|
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
|
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `analysis_cite` tool separately to register citations. `cite{...}` markdown-style inline references do nothing; only an actual `analysis_cite` tool call registers a citation.
|
|
- **Before you write your final answer, invoke the `analysis_cite` tool with the supporting chunk_ids.** This is the last tool call before answering whenever your answer draws on retrieved evidence.
|
|
role: system
|
|
- content: What is the total revenue across all quarterly reports?
|
|
role: user
|
|
- content: null
|
|
reasoning: |+
|
|
The user asks: "What is the total revenue across all quarterly reports?" We need to interpret what documents are in the knowledge base. The question likely refers to a set of quarterly report documents that contain revenue figures. We need to compute the sum of revenue across all quarterly reports.
|
|
|
|
We must determine how many documents exist, which ones are quarterly reports, and extract revenue numbers from each. Then sum them up.
|
|
|
|
The user didn't specify a particular document or context; it's a corpus-level computation: "total revenue across all quarterly reports". This is a calculation that aggregates data across multiple documents. According to the instructions: "No `analysis_cite` is needed when no specific chunks support the answer." But we need to compute this sum, which requires reading content from each relevant document. That is a corpus-level computation. The instruction says: "You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation." So if we aggregate across many documents and produce a single number, that is a corpus-level computation. We should not provide citations? But the answer might be considered derived from specific chunks. However, the instruction says "Don't fabricate citations for corpus-level computation." That means we can compute sum without citing each chunk individually. But maybe we need to cite some evidence that revenue appears in documents? The question: "What is the total revenue across all quarterly reports?" It's a numeric answer. We might compute it by scanning all documents, but we don't have to provide citations for each chunk. The instruction says no `analysis_cite` needed when no specific chunks support the answer. But here the answer is derived from many chunks; but it's still corpus-level computation. So we can skip citing.
|
|
|
|
But we need to actually compute the sum. We must search for quarterly reports and revenue numbers. Let's think: There might be documents with "Quarterly Report" in title or content, containing revenue figures. We need to find all such documents. Then parse revenue numbers from each. The revenue could be expressed as "$X million", "Revenue: $Y", etc.
|
|
|
|
We need to decide how to locate quarterly reports. Approach:
|
|
|
|
- Use `analysis_execute_code` to search for documents with 'Quarterly Report' in title or content. We can use the `search` function inside code, but we might also use `list_documents()` and filter by title containing "Quarterly Report". But maybe there are many documents; better to use search.
|
|
|
|
We need to find all documents that contain revenue figures. But likely each quarterly report has a revenue figure. The question: "total revenue across all quarterly reports" implies sum of revenue numbers from each quarter. So we need to extract the revenue number from each quarterly report document.
|
|
|
|
Potential approach:
|
|
|
|
- Use `search` with query "Quarterly Report" and maybe "Revenue". That will return chunks containing those terms. But we might get many results, including non-quarterly reports that mention revenue. We need to filter by documents that are quarterly reports. Maybe the metadata or title contains 'Q1', 'Q2', etc.
|
|
|
|
- Alternatively, search for pattern like "Revenue" and then check if document is a quarterly report (maybe via title containing "Quarterly Report").
|
|
|
|
Better approach: Use `list_documents()` to get all docs. For each doc, read its metadata.title; if it contains "Quarterly Report", parse revenue from content or items.
|
|
|
|
But we need to know the file structure. The documents are in `/documents/{document_id}/`. Each has metadata.json with title and uri. We can list them. Then for each doc whose title matches /quarterly report/i, read its content.txt and find revenue numbers.
|
|
|
|
We need a regex that captures revenue amounts. Revenue could be expressed as "$123 million" or "Revenue: $123M". Could also be numeric like "Revenue 123,456,789".
|
|
|
|
We need to parse these numbers into numeric values (maybe in millions). But we don't know the units; maybe all are in USD and millions.
|
|
|
|
Better approach: Use regex that captures patterns like:
|
|
|
|
- "$\d{1,3}(,\d{3})*(\.\d+)?(M|B)?" or "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?"
|
|
|
|
But we need to decide how to interpret units. The question likely expects a sum in the same unit as reported.
|
|
|
|
We might find revenue numbers like "$1,234,567" or "$12.3M". We can convert all to numeric values (in dollars). For M, multiply by 1e6; for B, multiply by 1e9.
|
|
|
|
But we need to decide how to sum them: If some are in millions and others in billions, we unify to dollars.
|
|
|
|
Alternatively, maybe the revenue numbers are all in millions. But we can't assume.
|
|
|
|
We can parse each number string, detect if it ends with 'M' or 'B', then convert accordingly.
|
|
|
|
Also consider that some documents might have multiple revenue figures (like revenue for different segments). We need to sum only the main revenue figure per document. How to identify? Maybe it's the first occurrence of "Revenue" in the document.
|
|
|
|
Simplify: For each quarterly report doc, find all matches of pattern "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?" and take the first match. That should capture main revenue figure.
|
|
|
|
But we need to be careful with formatting like "$1,234,567" (no M/B). Then treat as dollars.
|
|
|
|
Also consider "Revenue: 123,456,789". We'll parse numeric part.
|
|
|
|
We also need to handle numbers that include commas and decimal points. We can remove commas then convert to float.
|
|
|
|
Edge cases: Some documents might have revenue expressed in thousands or millions; but we can't know. But likely they use M for million.
|
|
|
|
Ok.
|
|
|
|
Implementation plan:
|
|
|
|
- Use `analysis_execute_code` with async code.
|
|
|
|
Pseudo-code:
|
|
|
|
```
|
|
import json, re
|
|
from pathlib import Path
|
|
|
|
# Get list of docs
|
|
docs = await list_documents()
|
|
quarterly_docs = []
|
|
for doc in docs:
|
|
title = doc.get('title', '')
|
|
if 'Quarterly Report' in title or 'Q1' in title or 'Q2' in title or 'Q3' in title or 'Q4' in title:
|
|
quarterly_docs.append(doc)
|
|
|
|
# For each doc, read content.txt
|
|
total_revenue = 0.0
|
|
for doc in quarterly_docs:
|
|
path = Path(f'/documents/{doc["id"]}/content.txt')
|
|
text = path.read_text()
|
|
# find revenue patterns
|
|
matches = re.findall(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', text, flags=re.IGNORECASE)
|
|
if not matches:
|
|
continue
|
|
# take first match
|
|
num_str, unit = matches[0]
|
|
# remove commas
|
|
num_val = float(num_str.replace(',', ''))
|
|
if unit.lower() == 'm':
|
|
num_val *= 1_000_000
|
|
elif unit.lower() == 'b':
|
|
num_val *= 1_000_000_000
|
|
total_revenue += num_val
|
|
|
|
print(total_revenue)
|
|
```
|
|
|
|
But we need to ensure we capture numbers like "$12.3M" where the number part includes decimal.
|
|
|
|
Our regex: r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' works for that.
|
|
|
|
However, if there's no unit but a comma separated number like "Revenue: 1,234,567", then unit will be empty string. We treat as dollars.
|
|
|
|
Also need to handle numbers with decimal and maybe M/B after decimal e.g., "$12.34M".
|
|
|
|
Ok.
|
|
|
|
But we also need to consider that some documents might have revenue in thousands or millions but no unit; we can't know. But likely they use M for million.
|
|
|
|
We should sum all revenue values as numeric dollars.
|
|
|
|
After computing total, print the number. We may want to format with commas and maybe show as integer if it's whole number.
|
|
|
|
But the answer expects a numeric value. We'll output the sum in plain number.
|
|
|
|
Potential issues: The regex might find multiple matches per doc; we only take first. But what if revenue appears multiple times? Might be fine.
|
|
|
|
Also, some docs may have "Revenue" but not quarterly report; but we filter by title containing "Quarterly Report". That should reduce noise.
|
|
|
|
But maybe the title doesn't contain "Quarterly Report", but the document is a quarterly report with a different naming scheme. Could be like "Q1 2023 Financial Statement". But we might miss them. We can broaden search: include titles containing 'Q1', 'Q2', 'Q3', 'Q4' and maybe 'Financial Report'. But that could bring in other documents.
|
|
|
|
Better approach: Use `search` for query "Quarterly Report" to get chunk IDs, then find which docs those belong to. Then we can use the doc ids from search results. That may be more precise.
|
|
|
|
But we need to sum revenue across all quarterly reports; we might want to include all such docs. But if some are not labeled as 'Quarterly Report' but still contain revenue, we might miss them.
|
|
|
|
We could also search for "Revenue" and then filter by doc id that appears in a set of known quarterly report ids from previous search. That is complex.
|
|
|
|
Simplify: Use list_documents to get all docs; for each doc, check if title contains 'Quarterly Report' or matches pattern like r'\bQ[1-4]\b'. But we might also include "Annual Report" which we don't want. So maybe filter out titles containing 'Annual'.
|
|
|
|
Better approach: For each doc, read content and see if it has a heading "Quarterly Report". We could parse toc.json for sections titled "Quarterly Report".
|
|
|
|
But that is too complex.
|
|
|
|
Given the instruction to compute sum across all quarterly reports, we can assume that documents with title containing 'Quarterly Report' are the ones. That should be fine.
|
|
|
|
Ok.
|
|
|
|
We need to run code and print total revenue.
|
|
|
|
Let's implement in analysis_execute_code.
|
|
|
|
Also, we might want to handle potential errors: missing content.txt etc.
|
|
|
|
We'll use Path.read_text() inside try/except.
|
|
|
|
Also, we may want to convert total_revenue to integer if it's whole number. But we can just print as float with no decimals if it's an integer.
|
|
|
|
Ok.
|
|
|
|
Let's write code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, flags=re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_qzkuh2up
|
|
type: function
|
|
- content: "Error: TypeError: re.search() takes no keyword arguments\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_qzkuh2up
|
|
- content: null
|
|
reasoning: |+
|
|
The error indicates that `re.search` doesn't accept keyword arguments. In Python, you can pass flags as positional argument or use `re.compile`. Let's adjust.
|
|
|
|
We used:
|
|
|
|
```
|
|
if 'Quarterly Report' in title or re.search(r"\bQ[1-4]\b", title, flags=re.IGNORECASE):
|
|
```
|
|
|
|
But re.search signature is `re.search(pattern, string[, flags])`, so flags must be positional after string. So we need to pass as third argument.
|
|
|
|
Let's correct code: `re.search(r"\bQ[1-4]\b", title, re.IGNORECASE)`.
|
|
|
|
Also, we used `re.compile` for revenue_pattern with flags; that's fine.
|
|
|
|
Let's update code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_hnlx62rm
|
|
type: function
|
|
- content: |
|
|
Error: TypeError: re.compile expected at most 2 arguments, got 1
|
|
|
|
Output: Found 3 quarterly report documents.
|
|
role: tool
|
|
tool_call_id: call_hnlx62rm
|
|
- content: null
|
|
reasoning: |+
|
|
The error indicates that `re.compile` expects at most two arguments. We passed flags as keyword argument; but in Python's re.compile signature is `re.compile(pattern, flags=0)`. So we should pass flags as second positional argument or use keyword 'flags' correctly? Actually re.compile accepts flags as optional positional argument: `re.compile(pattern[, flags])`. But the error says "got 1" meaning it thinks only one argument was passed. Wait, we wrote:
|
|
|
|
```
|
|
revenue_pattern = re.compile(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', flags=re.IGNORECASE)
|
|
```
|
|
|
|
But re.compile signature doesn't accept keyword arguments; you must pass flags as second positional argument: `re.compile(pattern, re.IGNORECASE)`. So we need to change.
|
|
|
|
Let's update code accordingly.
|
|
|
|
Also, we used `print` statements inside the loop; that's fine.
|
|
|
|
Let's correct.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_ipebddpq
|
|
type: function
|
|
- content: |
|
|
Found 3 quarterly report documents.
|
|
No revenue found in Q1 Report (5427fb0e-24ab-4dc5-a8c5-6f4fe7f29e7d)
|
|
No revenue found in Q2 Report (a0d9d838-2b7c-4785-8c13-1eab81c52a90)
|
|
No revenue found in Q3 Report (515faf6e-3758-45bd-89fb-552c3c8a159e)
|
|
Total revenue across all quarterly reports: 0.0
|
|
role: tool
|
|
tool_call_id: call_ipebddpq
|
|
- content: null
|
|
reasoning: |+
|
|
The script found 3 documents titled "Q1 Report", "Q2 Report", "Q3 Report". It didn't find any revenue in them.
|
|
|
|
Maybe the revenue is not labeled with "Revenue" but something else like "Net Sales" or "Total Revenue". We need to broaden search. Let's inspect these documents' content to see how revenue is expressed.
|
|
|
|
We can read each document's content and print some lines around where revenue might be. But we don't know where it appears.
|
|
|
|
Let's open the content of one doc, e.g., Q1 Report. We'll use Path.read_text() and maybe show first 2000 characters or search for "Revenue" or "Sales".
|
|
|
|
But we need to parse revenue numbers. The revenue may appear in a table. In items.jsonl, there might be tables with revenue figures.
|
|
|
|
We can examine the items.jsonl of Q1 Report to see if there's any numeric values that could represent revenue. But we need to identify which is revenue.
|
|
|
|
Alternatively, maybe the documents are not named "Quarterly Report" but something else; maybe they are part of a larger set like "Financial Statement Q1 2023".
|
|
|
|
We might need to search for all documents containing "Revenue" and then filter those that have quarterly report context. But we need to sum across all quarterly reports, so we should include any document that is a quarterly report.
|
|
|
|
But the script didn't find revenue in these three docs. Maybe revenue appears in tables but not captured by our regex because it's inside a table with markdown syntax like "| Revenue | $1,234,567 |". Our regex might still catch it if "Revenue" appears before number. But maybe the text is "Total Revenue:" or "Total Revenue".
|
|
|
|
Our regex only matches "Revenue" exactly; we could broaden to match "Total Revenue", "Net Revenue", etc.
|
|
|
|
Let's modify regex: r'(?:Total\s+)?Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' but also allow "Net Revenue". So maybe pattern: r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b.*?[:\-]?\s*\$?([0-9.,]+)([MmBb])?'.
|
|
|
|
But we need to capture number after colon or dash. Let's design a regex that matches "Revenue" optionally preceded by "Total" or "Net", then optional whitespace, then maybe colon/dash, then optional whitespace, then $ sign, then numeric part with optional unit.
|
|
|
|
Simplify: r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b.*?[:\-]?\s*\$?([0-9.,]+)([MmBb])?'.
|
|
|
|
But we need to capture the number. The pattern uses a capturing group for number and unit. But we also need to ensure that the colon/dash is matched before number; but we can use non-greedy match between 'Revenue' and number: `.*?` will consume minimal characters until next part. That should work.
|
|
|
|
Let's test this regex on some sample strings:
|
|
|
|
- "Total Revenue: $12.3M" -> matches group 1 = "12.3", group2="M".
|
|
- "Net Revenue - $5,000,000" -> group1="5,000,000", group2="".
|
|
- "Revenue: $1234567" -> group1="1234567", group2="".
|
|
- "Revenue: 123.45B" -> group1="123.45", group2="B".
|
|
|
|
Ok.
|
|
|
|
Let's update regex accordingly.
|
|
|
|
Also, we might need to parse numbers that include commas and decimals.
|
|
|
|
We also should consider that the number may be preceded by "$" or not. We handle that.
|
|
|
|
Now let's modify code: use new pattern.
|
|
|
|
Also, maybe revenue is in a table with markdown like "| Revenue | $12.3M |". In that case, our regex might still match because "Revenue:" appears? Actually there might be no colon; but we can adjust to allow optional colon/dash after "Revenue" or not. But the number may appear after a pipe symbol.
|
|
|
|
We could also search for patterns like r'(?i)Revenue\s*[:\-]?\s*\$?([0-9.,]+)([MmBb])?' which is similar but we added optional preceding words.
|
|
|
|
But maybe the revenue appears in a table row like "Revenue | $12.3M", so there is no colon or dash. Our regex would not match because it expects `[:\-]?` after "Revenue". But we can modify to allow any non-digit characters between "Revenue" and number: e.g., `\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*?\$?([0-9,.]+)([MmBb])?`. That would match.
|
|
|
|
Let's design pattern:
|
|
|
|
```
|
|
re.compile(r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*\$?([0-9.,]+)([MmBb])?', re.IGNORECASE)
|
|
```
|
|
|
|
This matches "Revenue" optionally preceded by "Total" or "Net", then any characters that are not digits (including colon, dash, pipe, space), then optional `$`, then number.
|
|
|
|
But if there is a digit before the number? For example "Revenue: $12.3M" -> after "Revenue:" we have space and "$". The pattern `[^0-9]*` will match ":" and space until it sees a digit or $; but we also allow `$?`. Actually we need to ensure that the `$` is optional before number. So pattern: `\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*\$?([0-9.,]+)([MmBb])?`.
|
|
|
|
But if there is no colon/dash, but a pipe symbol like "Revenue | $12.3M", then after "Revenue" we have space and "|" which is not digit; `[^0-9]*` will match that.
|
|
|
|
Ok.
|
|
|
|
Let's update regex accordingly.
|
|
|
|
Also, we should consider that the number might be in thousands or millions with no unit. We'll treat as dollars.
|
|
|
|
Now let's modify code accordingly.
|
|
|
|
Additionally, maybe revenue appears in items.jsonl but not in content.txt; our script already tries to parse items if no matches found in text. That may catch some.
|
|
|
|
But we need to ensure we read all lines of items.jsonl and search for "Revenue" inside each item.text. But the regex will apply to that too.
|
|
|
|
Ok.
|
|
|
|
Let's update code with new pattern.
|
|
|
|
Also, we might want to print the revenue value for debugging.
|
|
|
|
Let's run again.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''(?i)\\b(?:Total\\s+|Net\\s+)?Revenue\\b[^0-9]*\\$?([0-9.,]+)([MmBb])?'', re.IGNORECASE)\n\ntotal_revenue
|
|
= 0.0\nfor doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_1kz75zth
|
|
type: function
|
|
- content: "Error: TimeoutError: time limit exceeded: 128.590520458s > 60s\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_1kz75zth
|
|
model: gpt-oss
|
|
reasoning_effort: high
|
|
stream: false
|
|
temperature: 0.3
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: Search the knowledge base for evidence to analyze.
|
|
name: analysis_search
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
limit:
|
|
anyOf:
|
|
- type: integer
|
|
- type: 'null'
|
|
default: null
|
|
query:
|
|
type: string
|
|
required:
|
|
- query
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Execute Python against the sandboxed document filesystem.
|
|
name: analysis_execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Register exact retrieved chunk IDs as citations for the answer.
|
|
name: analysis_cite
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
chunk_ids:
|
|
items:
|
|
type: string
|
|
type: array
|
|
required:
|
|
- chunk_ids
|
|
type: object
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: |+
|
|
The script timed out after 60 seconds; maybe there are many documents or reading large files is slow. We need to optimize.
|
|
|
|
We found only 3 quarterly docs, but reading content.txt for each might be fine. But the code may have blocked due to something else. The timeout indicates that the entire execution took >128 seconds (the limit). That suggests that maybe our script attempted to read many documents or did a heavy operation like searching items.jsonl lines which could be large.
|
|
|
|
But we only processed 3 docs, so why would it take long? Maybe reading content.txt for each doc is fine. But perhaps the code didn't finish due to some infinite loop? Let's check: The code loops over quarterly_docs (3). Inside each, it reads content_path.read_text() - that's fine. Then matches = revenue_pattern.findall(text) - fine.
|
|
|
|
If no matches, then try items.jsonl reading lines and iterating. That might be heavy if items.jsonl is huge. But only 3 docs; maybe each has many items? But still not that large.
|
|
|
|
The code may have hung due to some error like recursion or something else. Let's see the output: It says "Error: TimeoutError: time limit exceeded: 128.590520458s > 60s". That means the entire function didn't finish within 60 seconds, but we had no prints before that? Actually we printed "Found X quarterly report documents." at start; maybe that printed but not shown because of timeout? The output shows nothing except error.
|
|
|
|
Wait, the output shows only:
|
|
|
|
```
|
|
Error: TimeoutError: time limit exceeded: 128.590520458s > 60s
|
|
```
|
|
|
|
So it didn't print anything else. That means the code crashed before printing "Found X quarterly report documents." maybe due to an earlier error like reading list_documents? But we printed after retrieving docs.
|
|
|
|
But the output shows nothing else, meaning that the script didn't even reach the first print. So the problem might be with `await list_documents()` or something else. Let's test a minimal code: just call await list_documents() and print length.
|
|
|
|
Let's run a simpler snippet to see if list_documents works.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"docs = await list_documents()\nprint(''Number of docs:'', len(docs))\n"}'
|
|
name: analysis_execute_code
|
|
id: call_yyp7tcdy
|
|
index: 0
|
|
type: function
|
|
created: 1784300619
|
|
id: chatcmpl-210
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 484
|
|
prompt_tokens: 8762
|
|
total_tokens: 9246
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '39651'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
messages:
|
|
- content: |-
|
|
# Analysis
|
|
|
|
You answer questions over a document knowledge base. Two common workflows:
|
|
|
|
- **`analysis_search → analysis_cite → answer`** when the answer is grounded on specific document content. Call `analysis_cite` with the supporting chunk_ids before writing the answer.
|
|
- **`analysis_execute_code → answer`** when the answer is a count, aggregation, listing, or structural computation over the corpus (e.g. "how many documents?", "average page count"). No `analysis_cite` is needed when no specific chunks support the answer.
|
|
|
|
You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation.
|
|
|
|
## Tools
|
|
|
|
### analysis_execute_code
|
|
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
|
|
|
Inside the code, these functions are available (use `await`):
|
|
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
|
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
|
|
|
Available modules: `json`, `re`, `math`, `pathlib`
|
|
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
|
|
|
### analysis_search
|
|
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
|
|
|
### analysis_cite
|
|
Register the chunk IDs that ground your answer. **You must call `analysis_cite` before writing any final answer that uses retrieved evidence — search results, items.jsonl rows, toc.json nodes, or content.txt content.** Skipping `analysis_cite` leaves the answer ungrounded and is treated as a failure.
|
|
|
|
`analysis_cite` is **not** required when your answer is a corpus-level computation that doesn't draw on specific chunks — counts, aggregations, listings, averages across documents. Don't fabricate citations for these.
|
|
|
|
Chunk IDs come from two places:
|
|
- The `chunk_id` field on `search` / `await search(...)` results
|
|
- The `chunk_ids` field on `items.jsonl` rows / `toc.json` nodes (when you ground via direct file reads)
|
|
|
|
Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs.
|
|
|
|
## Document Filesystem (inside execute_code)
|
|
|
|
All documents are mounted as a virtual filesystem at `/documents/`:
|
|
|
|
```
|
|
/documents/{document_id}/
|
|
metadata.json # {"id", "title", "uri", "created_at"}
|
|
content.txt # Full document text
|
|
items.jsonl # Structured items (one JSON object per line)
|
|
toc.json # Section tree derived from heading_level
|
|
```
|
|
|
|
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora.
|
|
|
|
### Reading files
|
|
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
|
|
|
```python
|
|
from pathlib import Path
|
|
import json
|
|
|
|
# Discover documents
|
|
for doc_dir in Path('/documents').iterdir():
|
|
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
|
print(meta['title'])
|
|
|
|
# Read full text
|
|
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
|
|
|
# Read and parse items
|
|
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
|
item = json.loads(line)
|
|
if item['label'] == 'table':
|
|
print(item['text'][:200])
|
|
```
|
|
|
|
### metadata.json
|
|
Document metadata: `id`, `title`, `uri`, `created_at`.
|
|
|
|
### content.txt
|
|
Full text content. Use for regex or keyword search across a whole document.
|
|
|
|
### items.jsonl
|
|
Structured document items. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file.
|
|
|
|
Each row carries:
|
|
- `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results
|
|
- `label`: item type — one of `"section_header"`, `"text"`, `"table"`, `"list_item"`, `"caption"`, `"formula"`, `"picture"`, `"code"`, `"footnote"`
|
|
- `text`: rendered content (tables are markdown with `|` columns)
|
|
- `page_numbers`: list of page numbers where the item appears
|
|
- `chunk_ids`: chunks that contain this item — pass to `analysis_cite()` to ground an answer that read this item directly
|
|
- `heading_level`: H-level for `section_header` rows; `0` on non-header rows
|
|
|
|
### toc.json
|
|
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `analysis_cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `tree: []` for docs with no headers.
|
|
|
|
### Cross-referencing search results with items
|
|
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in `items.jsonl`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index.
|
|
|
|
## Strategy
|
|
|
|
1. Search first.
|
|
2. Identify the chunk_ids from the search results that support your answer and call `analysis_cite` with them. Then write a concise answer.
|
|
3. Reach for `analysis_execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
|
4. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents.
|
|
5. Before writing your final response, call `analysis_cite` with the chunk_ids that ground your answer.
|
|
|
|
You MUST call `analysis_cite` with at least one chunk ID before producing your final answer **when your answer is grounded on retrieved evidence**. Skip `analysis_cite` in two cases: (a) you are refusing for lack of information, or (b) your answer is a corpus-level computation (count, aggregation, listing) that doesn't draw on specific chunks. In those cases do **not** fabricate citations.
|
|
|
|
## Important
|
|
|
|
- Variables persist between `analysis_execute_code` calls — you can search in one call and process results in the next
|
|
- Use `print()` to output results — the output is your only feedback
|
|
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `analysis_search → analysis_cite`.
|
|
- Use `await` for all async functions inside `analysis_execute_code` (`search`, `list_documents`)
|
|
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
|
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `analysis_cite` tool separately to register citations. `cite{...}` markdown-style inline references do nothing; only an actual `analysis_cite` tool call registers a citation.
|
|
- **Before you write your final answer, invoke the `analysis_cite` tool with the supporting chunk_ids.** This is the last tool call before answering whenever your answer draws on retrieved evidence.
|
|
role: system
|
|
- content: What is the total revenue across all quarterly reports?
|
|
role: user
|
|
- content: null
|
|
reasoning: |+
|
|
The user asks: "What is the total revenue across all quarterly reports?" We need to interpret what documents are in the knowledge base. The question likely refers to a set of quarterly report documents that contain revenue figures. We need to compute the sum of revenue across all quarterly reports.
|
|
|
|
We must determine how many documents exist, which ones are quarterly reports, and extract revenue numbers from each. Then sum them up.
|
|
|
|
The user didn't specify a particular document or context; it's a corpus-level computation: "total revenue across all quarterly reports". This is a calculation that aggregates data across multiple documents. According to the instructions: "No `analysis_cite` is needed when no specific chunks support the answer." But we need to compute this sum, which requires reading content from each relevant document. That is a corpus-level computation. The instruction says: "You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation." So if we aggregate across many documents and produce a single number, that is a corpus-level computation. We should not provide citations? But the answer might be considered derived from specific chunks. However, the instruction says "Don't fabricate citations for corpus-level computation." That means we can compute sum without citing each chunk individually. But maybe we need to cite some evidence that revenue appears in documents? The question: "What is the total revenue across all quarterly reports?" It's a numeric answer. We might compute it by scanning all documents, but we don't have to provide citations for each chunk. The instruction says no `analysis_cite` needed when no specific chunks support the answer. But here the answer is derived from many chunks; but it's still corpus-level computation. So we can skip citing.
|
|
|
|
But we need to actually compute the sum. We must search for quarterly reports and revenue numbers. Let's think: There might be documents with "Quarterly Report" in title or content, containing revenue figures. We need to find all such documents. Then parse revenue numbers from each. The revenue could be expressed as "$X million", "Revenue: $Y", etc.
|
|
|
|
We need to decide how to locate quarterly reports. Approach:
|
|
|
|
- Use `analysis_execute_code` to search for documents with 'Quarterly Report' in title or content. We can use the `search` function inside code, but we might also use `list_documents()` and filter by title containing "Quarterly Report". But maybe there are many documents; better to use search.
|
|
|
|
We need to find all documents that contain revenue figures. But likely each quarterly report has a revenue figure. The question: "total revenue across all quarterly reports" implies sum of revenue numbers from each quarter. So we need to extract the revenue number from each quarterly report document.
|
|
|
|
Potential approach:
|
|
|
|
- Use `search` with query "Quarterly Report" and maybe "Revenue". That will return chunks containing those terms. But we might get many results, including non-quarterly reports that mention revenue. We need to filter by documents that are quarterly reports. Maybe the metadata or title contains 'Q1', 'Q2', etc.
|
|
|
|
- Alternatively, search for pattern like "Revenue" and then check if document is a quarterly report (maybe via title containing "Quarterly Report").
|
|
|
|
Better approach: Use `list_documents()` to get all docs. For each doc, read its metadata.title; if it contains "Quarterly Report", parse revenue from content or items.
|
|
|
|
But we need to know the file structure. The documents are in `/documents/{document_id}/`. Each has metadata.json with title and uri. We can list them. Then for each doc whose title matches /quarterly report/i, read its content.txt and find revenue numbers.
|
|
|
|
We need a regex that captures revenue amounts. Revenue could be expressed as "$123 million" or "Revenue: $123M". Could also be numeric like "Revenue 123,456,789".
|
|
|
|
We need to parse these numbers into numeric values (maybe in millions). But we don't know the units; maybe all are in USD and millions.
|
|
|
|
Better approach: Use regex that captures patterns like:
|
|
|
|
- "$\d{1,3}(,\d{3})*(\.\d+)?(M|B)?" or "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?"
|
|
|
|
But we need to decide how to interpret units. The question likely expects a sum in the same unit as reported.
|
|
|
|
We might find revenue numbers like "$1,234,567" or "$12.3M". We can convert all to numeric values (in dollars). For M, multiply by 1e6; for B, multiply by 1e9.
|
|
|
|
But we need to decide how to sum them: If some are in millions and others in billions, we unify to dollars.
|
|
|
|
Alternatively, maybe the revenue numbers are all in millions. But we can't assume.
|
|
|
|
We can parse each number string, detect if it ends with 'M' or 'B', then convert accordingly.
|
|
|
|
Also consider that some documents might have multiple revenue figures (like revenue for different segments). We need to sum only the main revenue figure per document. How to identify? Maybe it's the first occurrence of "Revenue" in the document.
|
|
|
|
Simplify: For each quarterly report doc, find all matches of pattern "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?" and take the first match. That should capture main revenue figure.
|
|
|
|
But we need to be careful with formatting like "$1,234,567" (no M/B). Then treat as dollars.
|
|
|
|
Also consider "Revenue: 123,456,789". We'll parse numeric part.
|
|
|
|
We also need to handle numbers that include commas and decimal points. We can remove commas then convert to float.
|
|
|
|
Edge cases: Some documents might have revenue expressed in thousands or millions; but we can't know. But likely they use M for million.
|
|
|
|
Ok.
|
|
|
|
Implementation plan:
|
|
|
|
- Use `analysis_execute_code` with async code.
|
|
|
|
Pseudo-code:
|
|
|
|
```
|
|
import json, re
|
|
from pathlib import Path
|
|
|
|
# Get list of docs
|
|
docs = await list_documents()
|
|
quarterly_docs = []
|
|
for doc in docs:
|
|
title = doc.get('title', '')
|
|
if 'Quarterly Report' in title or 'Q1' in title or 'Q2' in title or 'Q3' in title or 'Q4' in title:
|
|
quarterly_docs.append(doc)
|
|
|
|
# For each doc, read content.txt
|
|
total_revenue = 0.0
|
|
for doc in quarterly_docs:
|
|
path = Path(f'/documents/{doc["id"]}/content.txt')
|
|
text = path.read_text()
|
|
# find revenue patterns
|
|
matches = re.findall(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', text, flags=re.IGNORECASE)
|
|
if not matches:
|
|
continue
|
|
# take first match
|
|
num_str, unit = matches[0]
|
|
# remove commas
|
|
num_val = float(num_str.replace(',', ''))
|
|
if unit.lower() == 'm':
|
|
num_val *= 1_000_000
|
|
elif unit.lower() == 'b':
|
|
num_val *= 1_000_000_000
|
|
total_revenue += num_val
|
|
|
|
print(total_revenue)
|
|
```
|
|
|
|
But we need to ensure we capture numbers like "$12.3M" where the number part includes decimal.
|
|
|
|
Our regex: r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' works for that.
|
|
|
|
However, if there's no unit but a comma separated number like "Revenue: 1,234,567", then unit will be empty string. We treat as dollars.
|
|
|
|
Also need to handle numbers with decimal and maybe M/B after decimal e.g., "$12.34M".
|
|
|
|
Ok.
|
|
|
|
But we also need to consider that some documents might have revenue in thousands or millions but no unit; we can't know. But likely they use M for million.
|
|
|
|
We should sum all revenue values as numeric dollars.
|
|
|
|
After computing total, print the number. We may want to format with commas and maybe show as integer if it's whole number.
|
|
|
|
But the answer expects a numeric value. We'll output the sum in plain number.
|
|
|
|
Potential issues: The regex might find multiple matches per doc; we only take first. But what if revenue appears multiple times? Might be fine.
|
|
|
|
Also, some docs may have "Revenue" but not quarterly report; but we filter by title containing "Quarterly Report". That should reduce noise.
|
|
|
|
But maybe the title doesn't contain "Quarterly Report", but the document is a quarterly report with a different naming scheme. Could be like "Q1 2023 Financial Statement". But we might miss them. We can broaden search: include titles containing 'Q1', 'Q2', 'Q3', 'Q4' and maybe 'Financial Report'. But that could bring in other documents.
|
|
|
|
Better approach: Use `search` for query "Quarterly Report" to get chunk IDs, then find which docs those belong to. Then we can use the doc ids from search results. That may be more precise.
|
|
|
|
But we need to sum revenue across all quarterly reports; we might want to include all such docs. But if some are not labeled as 'Quarterly Report' but still contain revenue, we might miss them.
|
|
|
|
We could also search for "Revenue" and then filter by doc id that appears in a set of known quarterly report ids from previous search. That is complex.
|
|
|
|
Simplify: Use list_documents to get all docs; for each doc, check if title contains 'Quarterly Report' or matches pattern like r'\bQ[1-4]\b'. But we might also include "Annual Report" which we don't want. So maybe filter out titles containing 'Annual'.
|
|
|
|
Better approach: For each doc, read content and see if it has a heading "Quarterly Report". We could parse toc.json for sections titled "Quarterly Report".
|
|
|
|
But that is too complex.
|
|
|
|
Given the instruction to compute sum across all quarterly reports, we can assume that documents with title containing 'Quarterly Report' are the ones. That should be fine.
|
|
|
|
Ok.
|
|
|
|
We need to run code and print total revenue.
|
|
|
|
Let's implement in analysis_execute_code.
|
|
|
|
Also, we might want to handle potential errors: missing content.txt etc.
|
|
|
|
We'll use Path.read_text() inside try/except.
|
|
|
|
Also, we may want to convert total_revenue to integer if it's whole number. But we can just print as float with no decimals if it's an integer.
|
|
|
|
Ok.
|
|
|
|
Let's write code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, flags=re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_qzkuh2up
|
|
type: function
|
|
- content: "Error: TypeError: re.search() takes no keyword arguments\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_qzkuh2up
|
|
- content: null
|
|
reasoning: |+
|
|
The error indicates that `re.search` doesn't accept keyword arguments. In Python, you can pass flags as positional argument or use `re.compile`. Let's adjust.
|
|
|
|
We used:
|
|
|
|
```
|
|
if 'Quarterly Report' in title or re.search(r"\bQ[1-4]\b", title, flags=re.IGNORECASE):
|
|
```
|
|
|
|
But re.search signature is `re.search(pattern, string[, flags])`, so flags must be positional after string. So we need to pass as third argument.
|
|
|
|
Let's correct code: `re.search(r"\bQ[1-4]\b", title, re.IGNORECASE)`.
|
|
|
|
Also, we used `re.compile` for revenue_pattern with flags; that's fine.
|
|
|
|
Let's update code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_hnlx62rm
|
|
type: function
|
|
- content: |
|
|
Error: TypeError: re.compile expected at most 2 arguments, got 1
|
|
|
|
Output: Found 3 quarterly report documents.
|
|
role: tool
|
|
tool_call_id: call_hnlx62rm
|
|
- content: null
|
|
reasoning: |+
|
|
The error indicates that `re.compile` expects at most two arguments. We passed flags as keyword argument; but in Python's re.compile signature is `re.compile(pattern, flags=0)`. So we should pass flags as second positional argument or use keyword 'flags' correctly? Actually re.compile accepts flags as optional positional argument: `re.compile(pattern[, flags])`. But the error says "got 1" meaning it thinks only one argument was passed. Wait, we wrote:
|
|
|
|
```
|
|
revenue_pattern = re.compile(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', flags=re.IGNORECASE)
|
|
```
|
|
|
|
But re.compile signature doesn't accept keyword arguments; you must pass flags as second positional argument: `re.compile(pattern, re.IGNORECASE)`. So we need to change.
|
|
|
|
Let's update code accordingly.
|
|
|
|
Also, we used `print` statements inside the loop; that's fine.
|
|
|
|
Let's correct.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_ipebddpq
|
|
type: function
|
|
- content: |
|
|
Found 3 quarterly report documents.
|
|
No revenue found in Q1 Report (5427fb0e-24ab-4dc5-a8c5-6f4fe7f29e7d)
|
|
No revenue found in Q2 Report (a0d9d838-2b7c-4785-8c13-1eab81c52a90)
|
|
No revenue found in Q3 Report (515faf6e-3758-45bd-89fb-552c3c8a159e)
|
|
Total revenue across all quarterly reports: 0.0
|
|
role: tool
|
|
tool_call_id: call_ipebddpq
|
|
- content: null
|
|
reasoning: |+
|
|
The script found 3 documents titled "Q1 Report", "Q2 Report", "Q3 Report". It didn't find any revenue in them.
|
|
|
|
Maybe the revenue is not labeled with "Revenue" but something else like "Net Sales" or "Total Revenue". We need to broaden search. Let's inspect these documents' content to see how revenue is expressed.
|
|
|
|
We can read each document's content and print some lines around where revenue might be. But we don't know where it appears.
|
|
|
|
Let's open the content of one doc, e.g., Q1 Report. We'll use Path.read_text() and maybe show first 2000 characters or search for "Revenue" or "Sales".
|
|
|
|
But we need to parse revenue numbers. The revenue may appear in a table. In items.jsonl, there might be tables with revenue figures.
|
|
|
|
We can examine the items.jsonl of Q1 Report to see if there's any numeric values that could represent revenue. But we need to identify which is revenue.
|
|
|
|
Alternatively, maybe the documents are not named "Quarterly Report" but something else; maybe they are part of a larger set like "Financial Statement Q1 2023".
|
|
|
|
We might need to search for all documents containing "Revenue" and then filter those that have quarterly report context. But we need to sum across all quarterly reports, so we should include any document that is a quarterly report.
|
|
|
|
But the script didn't find revenue in these three docs. Maybe revenue appears in tables but not captured by our regex because it's inside a table with markdown syntax like "| Revenue | $1,234,567 |". Our regex might still catch it if "Revenue" appears before number. But maybe the text is "Total Revenue:" or "Total Revenue".
|
|
|
|
Our regex only matches "Revenue" exactly; we could broaden to match "Total Revenue", "Net Revenue", etc.
|
|
|
|
Let's modify regex: r'(?:Total\s+)?Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' but also allow "Net Revenue". So maybe pattern: r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b.*?[:\-]?\s*\$?([0-9.,]+)([MmBb])?'.
|
|
|
|
But we need to capture number after colon or dash. Let's design a regex that matches "Revenue" optionally preceded by "Total" or "Net", then optional whitespace, then maybe colon/dash, then optional whitespace, then $ sign, then numeric part with optional unit.
|
|
|
|
Simplify: r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b.*?[:\-]?\s*\$?([0-9.,]+)([MmBb])?'.
|
|
|
|
But we need to capture the number. The pattern uses a capturing group for number and unit. But we also need to ensure that the colon/dash is matched before number; but we can use non-greedy match between 'Revenue' and number: `.*?` will consume minimal characters until next part. That should work.
|
|
|
|
Let's test this regex on some sample strings:
|
|
|
|
- "Total Revenue: $12.3M" -> matches group 1 = "12.3", group2="M".
|
|
- "Net Revenue - $5,000,000" -> group1="5,000,000", group2="".
|
|
- "Revenue: $1234567" -> group1="1234567", group2="".
|
|
- "Revenue: 123.45B" -> group1="123.45", group2="B".
|
|
|
|
Ok.
|
|
|
|
Let's update regex accordingly.
|
|
|
|
Also, we might need to parse numbers that include commas and decimals.
|
|
|
|
We also should consider that the number may be preceded by "$" or not. We handle that.
|
|
|
|
Now let's modify code: use new pattern.
|
|
|
|
Also, maybe revenue is in a table with markdown like "| Revenue | $12.3M |". In that case, our regex might still match because "Revenue:" appears? Actually there might be no colon; but we can adjust to allow optional colon/dash after "Revenue" or not. But the number may appear after a pipe symbol.
|
|
|
|
We could also search for patterns like r'(?i)Revenue\s*[:\-]?\s*\$?([0-9.,]+)([MmBb])?' which is similar but we added optional preceding words.
|
|
|
|
But maybe the revenue appears in a table row like "Revenue | $12.3M", so there is no colon or dash. Our regex would not match because it expects `[:\-]?` after "Revenue". But we can modify to allow any non-digit characters between "Revenue" and number: e.g., `\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*?\$?([0-9,.]+)([MmBb])?`. That would match.
|
|
|
|
Let's design pattern:
|
|
|
|
```
|
|
re.compile(r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*\$?([0-9.,]+)([MmBb])?', re.IGNORECASE)
|
|
```
|
|
|
|
This matches "Revenue" optionally preceded by "Total" or "Net", then any characters that are not digits (including colon, dash, pipe, space), then optional `$`, then number.
|
|
|
|
But if there is a digit before the number? For example "Revenue: $12.3M" -> after "Revenue:" we have space and "$". The pattern `[^0-9]*` will match ":" and space until it sees a digit or $; but we also allow `$?`. Actually we need to ensure that the `$` is optional before number. So pattern: `\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*\$?([0-9.,]+)([MmBb])?`.
|
|
|
|
But if there is no colon/dash, but a pipe symbol like "Revenue | $12.3M", then after "Revenue" we have space and "|" which is not digit; `[^0-9]*` will match that.
|
|
|
|
Ok.
|
|
|
|
Let's update regex accordingly.
|
|
|
|
Also, we should consider that the number might be in thousands or millions with no unit. We'll treat as dollars.
|
|
|
|
Now let's modify code accordingly.
|
|
|
|
Additionally, maybe revenue appears in items.jsonl but not in content.txt; our script already tries to parse items if no matches found in text. That may catch some.
|
|
|
|
But we need to ensure we read all lines of items.jsonl and search for "Revenue" inside each item.text. But the regex will apply to that too.
|
|
|
|
Ok.
|
|
|
|
Let's update code with new pattern.
|
|
|
|
Also, we might want to print the revenue value for debugging.
|
|
|
|
Let's run again.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''(?i)\\b(?:Total\\s+|Net\\s+)?Revenue\\b[^0-9]*\\$?([0-9.,]+)([MmBb])?'', re.IGNORECASE)\n\ntotal_revenue
|
|
= 0.0\nfor doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_1kz75zth
|
|
type: function
|
|
- content: "Error: TimeoutError: time limit exceeded: 128.590520458s > 60s\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_1kz75zth
|
|
- content: null
|
|
reasoning: |+
|
|
The script timed out after 60 seconds; maybe there are many documents or reading large files is slow. We need to optimize.
|
|
|
|
We found only 3 quarterly docs, but reading content.txt for each might be fine. But the code may have blocked due to something else. The timeout indicates that the entire execution took >128 seconds (the limit). That suggests that maybe our script attempted to read many documents or did a heavy operation like searching items.jsonl lines which could be large.
|
|
|
|
But we only processed 3 docs, so why would it take long? Maybe reading content.txt for each doc is fine. But perhaps the code didn't finish due to some infinite loop? Let's check: The code loops over quarterly_docs (3). Inside each, it reads content_path.read_text() - that's fine. Then matches = revenue_pattern.findall(text) - fine.
|
|
|
|
If no matches, then try items.jsonl reading lines and iterating. That might be heavy if items.jsonl is huge. But only 3 docs; maybe each has many items? But still not that large.
|
|
|
|
The code may have hung due to some error like recursion or something else. Let's see the output: It says "Error: TimeoutError: time limit exceeded: 128.590520458s > 60s". That means the entire function didn't finish within 60 seconds, but we had no prints before that? Actually we printed "Found X quarterly report documents." at start; maybe that printed but not shown because of timeout? The output shows nothing except error.
|
|
|
|
Wait, the output shows only:
|
|
|
|
```
|
|
Error: TimeoutError: time limit exceeded: 128.590520458s > 60s
|
|
```
|
|
|
|
So it didn't print anything else. That means the code crashed before printing "Found X quarterly report documents." maybe due to an earlier error like reading list_documents? But we printed after retrieving docs.
|
|
|
|
But the output shows nothing else, meaning that the script didn't even reach the first print. So the problem might be with `await list_documents()` or something else. Let's test a minimal code: just call await list_documents() and print length.
|
|
|
|
Let's run a simpler snippet to see if list_documents works.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"docs = await list_documents()\nprint(''Number of docs:'', len(docs))\n"}'
|
|
name: analysis_execute_code
|
|
id: call_yyp7tcdy
|
|
type: function
|
|
- content: "Error: TimeoutError: time limit exceeded: 146.704897417s > 60s\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_yyp7tcdy
|
|
model: gpt-oss
|
|
reasoning_effort: high
|
|
stream: false
|
|
temperature: 0.3
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: Search the knowledge base for evidence to analyze.
|
|
name: analysis_search
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
limit:
|
|
anyOf:
|
|
- type: integer
|
|
- type: 'null'
|
|
default: null
|
|
query:
|
|
type: string
|
|
required:
|
|
- query
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Execute Python against the sandboxed document filesystem.
|
|
name: analysis_execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Register exact retrieved chunk IDs as citations for the answer.
|
|
name: analysis_cite
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
chunk_ids:
|
|
items:
|
|
type: string
|
|
type: array
|
|
required:
|
|
- chunk_ids
|
|
type: object
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: |+
|
|
It timed out again. That suggests that calling `list_documents()` is slow or blocked. Maybe the corpus is huge and listing all documents takes too long.
|
|
|
|
We need to avoid listing all docs. Instead, we can use search queries to find relevant docs. The search function likely returns results sorted by relevance across the entire corpus but limited in number (limit). We can query for "Quarterly Report" with a limit of maybe 1000 or something. That will return hits from documents containing that phrase.
|
|
|
|
But we need to get unique document IDs from those hits. Then we can process each doc individually.
|
|
|
|
Alternatively, we can use search for "Revenue" and then filter by docs that have quarterly report context. But we want all quarterly reports; maybe the corpus has many but not too many.
|
|
|
|
We cannot list all documents due to performance constraints. So we need to use search.
|
|
|
|
Let's try a search query: `analysis_search` with query "Quarterly Report". That will return results across the corpus, likely limited to 10 by default. But we can set limit=1000 maybe? The tool signature: analysis_search(query: string, limit: number | null). We can call it with limit=200 or something.
|
|
|
|
We need to get chunk IDs from search results; but we also want doc ids. Each result includes document_id and other fields. So we can collect unique document_ids.
|
|
|
|
Then for each doc id, read content.txt etc.
|
|
|
|
But we might not get all quarterly reports if the query is too narrow. But it's likely fine.
|
|
|
|
Alternatively, we could search for "Q1" or "Quarterly Report". But let's start with "Quarterly Report".
|
|
|
|
Let's call analysis_search with limit=200.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"Quarterly Report","limit":200}'
|
|
name: analysis_search
|
|
id: call_lk5136v9
|
|
index: 0
|
|
type: function
|
|
created: 1784300634
|
|
id: chatcmpl-822
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 374
|
|
prompt_tokens: 9282
|
|
total_tokens: 9656
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '86'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
encoding_format: base64
|
|
input:
|
|
- Quarterly Report
|
|
model: qwen3-embedding:4b
|
|
uri: http://localhost:11434/v1/embeddings
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
data:
|
|
- embedding: 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
|
|
index: 0
|
|
object: embedding
|
|
model: qwen3-embedding:4b
|
|
object: list
|
|
usage:
|
|
prompt_tokens: 4
|
|
total_tokens: 4
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '42019'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
messages:
|
|
- content: |-
|
|
# Analysis
|
|
|
|
You answer questions over a document knowledge base. Two common workflows:
|
|
|
|
- **`analysis_search → analysis_cite → answer`** when the answer is grounded on specific document content. Call `analysis_cite` with the supporting chunk_ids before writing the answer.
|
|
- **`analysis_execute_code → answer`** when the answer is a count, aggregation, listing, or structural computation over the corpus (e.g. "how many documents?", "average page count"). No `analysis_cite` is needed when no specific chunks support the answer.
|
|
|
|
You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation.
|
|
|
|
## Tools
|
|
|
|
### analysis_execute_code
|
|
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
|
|
|
Inside the code, these functions are available (use `await`):
|
|
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
|
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
|
|
|
Available modules: `json`, `re`, `math`, `pathlib`
|
|
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
|
|
|
### analysis_search
|
|
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
|
|
|
### analysis_cite
|
|
Register the chunk IDs that ground your answer. **You must call `analysis_cite` before writing any final answer that uses retrieved evidence — search results, items.jsonl rows, toc.json nodes, or content.txt content.** Skipping `analysis_cite` leaves the answer ungrounded and is treated as a failure.
|
|
|
|
`analysis_cite` is **not** required when your answer is a corpus-level computation that doesn't draw on specific chunks — counts, aggregations, listings, averages across documents. Don't fabricate citations for these.
|
|
|
|
Chunk IDs come from two places:
|
|
- The `chunk_id` field on `search` / `await search(...)` results
|
|
- The `chunk_ids` field on `items.jsonl` rows / `toc.json` nodes (when you ground via direct file reads)
|
|
|
|
Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs.
|
|
|
|
## Document Filesystem (inside execute_code)
|
|
|
|
All documents are mounted as a virtual filesystem at `/documents/`:
|
|
|
|
```
|
|
/documents/{document_id}/
|
|
metadata.json # {"id", "title", "uri", "created_at"}
|
|
content.txt # Full document text
|
|
items.jsonl # Structured items (one JSON object per line)
|
|
toc.json # Section tree derived from heading_level
|
|
```
|
|
|
|
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora.
|
|
|
|
### Reading files
|
|
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
|
|
|
```python
|
|
from pathlib import Path
|
|
import json
|
|
|
|
# Discover documents
|
|
for doc_dir in Path('/documents').iterdir():
|
|
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
|
print(meta['title'])
|
|
|
|
# Read full text
|
|
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
|
|
|
# Read and parse items
|
|
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
|
item = json.loads(line)
|
|
if item['label'] == 'table':
|
|
print(item['text'][:200])
|
|
```
|
|
|
|
### metadata.json
|
|
Document metadata: `id`, `title`, `uri`, `created_at`.
|
|
|
|
### content.txt
|
|
Full text content. Use for regex or keyword search across a whole document.
|
|
|
|
### items.jsonl
|
|
Structured document items. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file.
|
|
|
|
Each row carries:
|
|
- `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results
|
|
- `label`: item type — one of `"section_header"`, `"text"`, `"table"`, `"list_item"`, `"caption"`, `"formula"`, `"picture"`, `"code"`, `"footnote"`
|
|
- `text`: rendered content (tables are markdown with `|` columns)
|
|
- `page_numbers`: list of page numbers where the item appears
|
|
- `chunk_ids`: chunks that contain this item — pass to `analysis_cite()` to ground an answer that read this item directly
|
|
- `heading_level`: H-level for `section_header` rows; `0` on non-header rows
|
|
|
|
### toc.json
|
|
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `analysis_cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `tree: []` for docs with no headers.
|
|
|
|
### Cross-referencing search results with items
|
|
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in `items.jsonl`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index.
|
|
|
|
## Strategy
|
|
|
|
1. Search first.
|
|
2. Identify the chunk_ids from the search results that support your answer and call `analysis_cite` with them. Then write a concise answer.
|
|
3. Reach for `analysis_execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
|
4. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents.
|
|
5. Before writing your final response, call `analysis_cite` with the chunk_ids that ground your answer.
|
|
|
|
You MUST call `analysis_cite` with at least one chunk ID before producing your final answer **when your answer is grounded on retrieved evidence**. Skip `analysis_cite` in two cases: (a) you are refusing for lack of information, or (b) your answer is a corpus-level computation (count, aggregation, listing) that doesn't draw on specific chunks. In those cases do **not** fabricate citations.
|
|
|
|
## Important
|
|
|
|
- Variables persist between `analysis_execute_code` calls — you can search in one call and process results in the next
|
|
- Use `print()` to output results — the output is your only feedback
|
|
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `analysis_search → analysis_cite`.
|
|
- Use `await` for all async functions inside `analysis_execute_code` (`search`, `list_documents`)
|
|
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
|
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `analysis_cite` tool separately to register citations. `cite{...}` markdown-style inline references do nothing; only an actual `analysis_cite` tool call registers a citation.
|
|
- **Before you write your final answer, invoke the `analysis_cite` tool with the supporting chunk_ids.** This is the last tool call before answering whenever your answer draws on retrieved evidence.
|
|
role: system
|
|
- content: What is the total revenue across all quarterly reports?
|
|
role: user
|
|
- content: null
|
|
reasoning: |+
|
|
The user asks: "What is the total revenue across all quarterly reports?" We need to interpret what documents are in the knowledge base. The question likely refers to a set of quarterly report documents that contain revenue figures. We need to compute the sum of revenue across all quarterly reports.
|
|
|
|
We must determine how many documents exist, which ones are quarterly reports, and extract revenue numbers from each. Then sum them up.
|
|
|
|
The user didn't specify a particular document or context; it's a corpus-level computation: "total revenue across all quarterly reports". This is a calculation that aggregates data across multiple documents. According to the instructions: "No `analysis_cite` is needed when no specific chunks support the answer." But we need to compute this sum, which requires reading content from each relevant document. That is a corpus-level computation. The instruction says: "You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation." So if we aggregate across many documents and produce a single number, that is a corpus-level computation. We should not provide citations? But the answer might be considered derived from specific chunks. However, the instruction says "Don't fabricate citations for corpus-level computation." That means we can compute sum without citing each chunk individually. But maybe we need to cite some evidence that revenue appears in documents? The question: "What is the total revenue across all quarterly reports?" It's a numeric answer. We might compute it by scanning all documents, but we don't have to provide citations for each chunk. The instruction says no `analysis_cite` needed when no specific chunks support the answer. But here the answer is derived from many chunks; but it's still corpus-level computation. So we can skip citing.
|
|
|
|
But we need to actually compute the sum. We must search for quarterly reports and revenue numbers. Let's think: There might be documents with "Quarterly Report" in title or content, containing revenue figures. We need to find all such documents. Then parse revenue numbers from each. The revenue could be expressed as "$X million", "Revenue: $Y", etc.
|
|
|
|
We need to decide how to locate quarterly reports. Approach:
|
|
|
|
- Use `analysis_execute_code` to search for documents with 'Quarterly Report' in title or content. We can use the `search` function inside code, but we might also use `list_documents()` and filter by title containing "Quarterly Report". But maybe there are many documents; better to use search.
|
|
|
|
We need to find all documents that contain revenue figures. But likely each quarterly report has a revenue figure. The question: "total revenue across all quarterly reports" implies sum of revenue numbers from each quarter. So we need to extract the revenue number from each quarterly report document.
|
|
|
|
Potential approach:
|
|
|
|
- Use `search` with query "Quarterly Report" and maybe "Revenue". That will return chunks containing those terms. But we might get many results, including non-quarterly reports that mention revenue. We need to filter by documents that are quarterly reports. Maybe the metadata or title contains 'Q1', 'Q2', etc.
|
|
|
|
- Alternatively, search for pattern like "Revenue" and then check if document is a quarterly report (maybe via title containing "Quarterly Report").
|
|
|
|
Better approach: Use `list_documents()` to get all docs. For each doc, read its metadata.title; if it contains "Quarterly Report", parse revenue from content or items.
|
|
|
|
But we need to know the file structure. The documents are in `/documents/{document_id}/`. Each has metadata.json with title and uri. We can list them. Then for each doc whose title matches /quarterly report/i, read its content.txt and find revenue numbers.
|
|
|
|
We need a regex that captures revenue amounts. Revenue could be expressed as "$123 million" or "Revenue: $123M". Could also be numeric like "Revenue 123,456,789".
|
|
|
|
We need to parse these numbers into numeric values (maybe in millions). But we don't know the units; maybe all are in USD and millions.
|
|
|
|
Better approach: Use regex that captures patterns like:
|
|
|
|
- "$\d{1,3}(,\d{3})*(\.\d+)?(M|B)?" or "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?"
|
|
|
|
But we need to decide how to interpret units. The question likely expects a sum in the same unit as reported.
|
|
|
|
We might find revenue numbers like "$1,234,567" or "$12.3M". We can convert all to numeric values (in dollars). For M, multiply by 1e6; for B, multiply by 1e9.
|
|
|
|
But we need to decide how to sum them: If some are in millions and others in billions, we unify to dollars.
|
|
|
|
Alternatively, maybe the revenue numbers are all in millions. But we can't assume.
|
|
|
|
We can parse each number string, detect if it ends with 'M' or 'B', then convert accordingly.
|
|
|
|
Also consider that some documents might have multiple revenue figures (like revenue for different segments). We need to sum only the main revenue figure per document. How to identify? Maybe it's the first occurrence of "Revenue" in the document.
|
|
|
|
Simplify: For each quarterly report doc, find all matches of pattern "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?" and take the first match. That should capture main revenue figure.
|
|
|
|
But we need to be careful with formatting like "$1,234,567" (no M/B). Then treat as dollars.
|
|
|
|
Also consider "Revenue: 123,456,789". We'll parse numeric part.
|
|
|
|
We also need to handle numbers that include commas and decimal points. We can remove commas then convert to float.
|
|
|
|
Edge cases: Some documents might have revenue expressed in thousands or millions; but we can't know. But likely they use M for million.
|
|
|
|
Ok.
|
|
|
|
Implementation plan:
|
|
|
|
- Use `analysis_execute_code` with async code.
|
|
|
|
Pseudo-code:
|
|
|
|
```
|
|
import json, re
|
|
from pathlib import Path
|
|
|
|
# Get list of docs
|
|
docs = await list_documents()
|
|
quarterly_docs = []
|
|
for doc in docs:
|
|
title = doc.get('title', '')
|
|
if 'Quarterly Report' in title or 'Q1' in title or 'Q2' in title or 'Q3' in title or 'Q4' in title:
|
|
quarterly_docs.append(doc)
|
|
|
|
# For each doc, read content.txt
|
|
total_revenue = 0.0
|
|
for doc in quarterly_docs:
|
|
path = Path(f'/documents/{doc["id"]}/content.txt')
|
|
text = path.read_text()
|
|
# find revenue patterns
|
|
matches = re.findall(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', text, flags=re.IGNORECASE)
|
|
if not matches:
|
|
continue
|
|
# take first match
|
|
num_str, unit = matches[0]
|
|
# remove commas
|
|
num_val = float(num_str.replace(',', ''))
|
|
if unit.lower() == 'm':
|
|
num_val *= 1_000_000
|
|
elif unit.lower() == 'b':
|
|
num_val *= 1_000_000_000
|
|
total_revenue += num_val
|
|
|
|
print(total_revenue)
|
|
```
|
|
|
|
But we need to ensure we capture numbers like "$12.3M" where the number part includes decimal.
|
|
|
|
Our regex: r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' works for that.
|
|
|
|
However, if there's no unit but a comma separated number like "Revenue: 1,234,567", then unit will be empty string. We treat as dollars.
|
|
|
|
Also need to handle numbers with decimal and maybe M/B after decimal e.g., "$12.34M".
|
|
|
|
Ok.
|
|
|
|
But we also need to consider that some documents might have revenue in thousands or millions but no unit; we can't know. But likely they use M for million.
|
|
|
|
We should sum all revenue values as numeric dollars.
|
|
|
|
After computing total, print the number. We may want to format with commas and maybe show as integer if it's whole number.
|
|
|
|
But the answer expects a numeric value. We'll output the sum in plain number.
|
|
|
|
Potential issues: The regex might find multiple matches per doc; we only take first. But what if revenue appears multiple times? Might be fine.
|
|
|
|
Also, some docs may have "Revenue" but not quarterly report; but we filter by title containing "Quarterly Report". That should reduce noise.
|
|
|
|
But maybe the title doesn't contain "Quarterly Report", but the document is a quarterly report with a different naming scheme. Could be like "Q1 2023 Financial Statement". But we might miss them. We can broaden search: include titles containing 'Q1', 'Q2', 'Q3', 'Q4' and maybe 'Financial Report'. But that could bring in other documents.
|
|
|
|
Better approach: Use `search` for query "Quarterly Report" to get chunk IDs, then find which docs those belong to. Then we can use the doc ids from search results. That may be more precise.
|
|
|
|
But we need to sum revenue across all quarterly reports; we might want to include all such docs. But if some are not labeled as 'Quarterly Report' but still contain revenue, we might miss them.
|
|
|
|
We could also search for "Revenue" and then filter by doc id that appears in a set of known quarterly report ids from previous search. That is complex.
|
|
|
|
Simplify: Use list_documents to get all docs; for each doc, check if title contains 'Quarterly Report' or matches pattern like r'\bQ[1-4]\b'. But we might also include "Annual Report" which we don't want. So maybe filter out titles containing 'Annual'.
|
|
|
|
Better approach: For each doc, read content and see if it has a heading "Quarterly Report". We could parse toc.json for sections titled "Quarterly Report".
|
|
|
|
But that is too complex.
|
|
|
|
Given the instruction to compute sum across all quarterly reports, we can assume that documents with title containing 'Quarterly Report' are the ones. That should be fine.
|
|
|
|
Ok.
|
|
|
|
We need to run code and print total revenue.
|
|
|
|
Let's implement in analysis_execute_code.
|
|
|
|
Also, we might want to handle potential errors: missing content.txt etc.
|
|
|
|
We'll use Path.read_text() inside try/except.
|
|
|
|
Also, we may want to convert total_revenue to integer if it's whole number. But we can just print as float with no decimals if it's an integer.
|
|
|
|
Ok.
|
|
|
|
Let's write code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, flags=re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_qzkuh2up
|
|
type: function
|
|
- content: "Error: TypeError: re.search() takes no keyword arguments\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_qzkuh2up
|
|
- content: null
|
|
reasoning: |+
|
|
The error indicates that `re.search` doesn't accept keyword arguments. In Python, you can pass flags as positional argument or use `re.compile`. Let's adjust.
|
|
|
|
We used:
|
|
|
|
```
|
|
if 'Quarterly Report' in title or re.search(r"\bQ[1-4]\b", title, flags=re.IGNORECASE):
|
|
```
|
|
|
|
But re.search signature is `re.search(pattern, string[, flags])`, so flags must be positional after string. So we need to pass as third argument.
|
|
|
|
Let's correct code: `re.search(r"\bQ[1-4]\b", title, re.IGNORECASE)`.
|
|
|
|
Also, we used `re.compile` for revenue_pattern with flags; that's fine.
|
|
|
|
Let's update code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_hnlx62rm
|
|
type: function
|
|
- content: |
|
|
Error: TypeError: re.compile expected at most 2 arguments, got 1
|
|
|
|
Output: Found 3 quarterly report documents.
|
|
role: tool
|
|
tool_call_id: call_hnlx62rm
|
|
- content: null
|
|
reasoning: |+
|
|
The error indicates that `re.compile` expects at most two arguments. We passed flags as keyword argument; but in Python's re.compile signature is `re.compile(pattern, flags=0)`. So we should pass flags as second positional argument or use keyword 'flags' correctly? Actually re.compile accepts flags as optional positional argument: `re.compile(pattern[, flags])`. But the error says "got 1" meaning it thinks only one argument was passed. Wait, we wrote:
|
|
|
|
```
|
|
revenue_pattern = re.compile(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', flags=re.IGNORECASE)
|
|
```
|
|
|
|
But re.compile signature doesn't accept keyword arguments; you must pass flags as second positional argument: `re.compile(pattern, re.IGNORECASE)`. So we need to change.
|
|
|
|
Let's update code accordingly.
|
|
|
|
Also, we used `print` statements inside the loop; that's fine.
|
|
|
|
Let's correct.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_ipebddpq
|
|
type: function
|
|
- content: |
|
|
Found 3 quarterly report documents.
|
|
No revenue found in Q1 Report (5427fb0e-24ab-4dc5-a8c5-6f4fe7f29e7d)
|
|
No revenue found in Q2 Report (a0d9d838-2b7c-4785-8c13-1eab81c52a90)
|
|
No revenue found in Q3 Report (515faf6e-3758-45bd-89fb-552c3c8a159e)
|
|
Total revenue across all quarterly reports: 0.0
|
|
role: tool
|
|
tool_call_id: call_ipebddpq
|
|
- content: null
|
|
reasoning: |+
|
|
The script found 3 documents titled "Q1 Report", "Q2 Report", "Q3 Report". It didn't find any revenue in them.
|
|
|
|
Maybe the revenue is not labeled with "Revenue" but something else like "Net Sales" or "Total Revenue". We need to broaden search. Let's inspect these documents' content to see how revenue is expressed.
|
|
|
|
We can read each document's content and print some lines around where revenue might be. But we don't know where it appears.
|
|
|
|
Let's open the content of one doc, e.g., Q1 Report. We'll use Path.read_text() and maybe show first 2000 characters or search for "Revenue" or "Sales".
|
|
|
|
But we need to parse revenue numbers. The revenue may appear in a table. In items.jsonl, there might be tables with revenue figures.
|
|
|
|
We can examine the items.jsonl of Q1 Report to see if there's any numeric values that could represent revenue. But we need to identify which is revenue.
|
|
|
|
Alternatively, maybe the documents are not named "Quarterly Report" but something else; maybe they are part of a larger set like "Financial Statement Q1 2023".
|
|
|
|
We might need to search for all documents containing "Revenue" and then filter those that have quarterly report context. But we need to sum across all quarterly reports, so we should include any document that is a quarterly report.
|
|
|
|
But the script didn't find revenue in these three docs. Maybe revenue appears in tables but not captured by our regex because it's inside a table with markdown syntax like "| Revenue | $1,234,567 |". Our regex might still catch it if "Revenue" appears before number. But maybe the text is "Total Revenue:" or "Total Revenue".
|
|
|
|
Our regex only matches "Revenue" exactly; we could broaden to match "Total Revenue", "Net Revenue", etc.
|
|
|
|
Let's modify regex: r'(?:Total\s+)?Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' but also allow "Net Revenue". So maybe pattern: r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b.*?[:\-]?\s*\$?([0-9.,]+)([MmBb])?'.
|
|
|
|
But we need to capture number after colon or dash. Let's design a regex that matches "Revenue" optionally preceded by "Total" or "Net", then optional whitespace, then maybe colon/dash, then optional whitespace, then $ sign, then numeric part with optional unit.
|
|
|
|
Simplify: r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b.*?[:\-]?\s*\$?([0-9.,]+)([MmBb])?'.
|
|
|
|
But we need to capture the number. The pattern uses a capturing group for number and unit. But we also need to ensure that the colon/dash is matched before number; but we can use non-greedy match between 'Revenue' and number: `.*?` will consume minimal characters until next part. That should work.
|
|
|
|
Let's test this regex on some sample strings:
|
|
|
|
- "Total Revenue: $12.3M" -> matches group 1 = "12.3", group2="M".
|
|
- "Net Revenue - $5,000,000" -> group1="5,000,000", group2="".
|
|
- "Revenue: $1234567" -> group1="1234567", group2="".
|
|
- "Revenue: 123.45B" -> group1="123.45", group2="B".
|
|
|
|
Ok.
|
|
|
|
Let's update regex accordingly.
|
|
|
|
Also, we might need to parse numbers that include commas and decimals.
|
|
|
|
We also should consider that the number may be preceded by "$" or not. We handle that.
|
|
|
|
Now let's modify code: use new pattern.
|
|
|
|
Also, maybe revenue is in a table with markdown like "| Revenue | $12.3M |". In that case, our regex might still match because "Revenue:" appears? Actually there might be no colon; but we can adjust to allow optional colon/dash after "Revenue" or not. But the number may appear after a pipe symbol.
|
|
|
|
We could also search for patterns like r'(?i)Revenue\s*[:\-]?\s*\$?([0-9.,]+)([MmBb])?' which is similar but we added optional preceding words.
|
|
|
|
But maybe the revenue appears in a table row like "Revenue | $12.3M", so there is no colon or dash. Our regex would not match because it expects `[:\-]?` after "Revenue". But we can modify to allow any non-digit characters between "Revenue" and number: e.g., `\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*?\$?([0-9,.]+)([MmBb])?`. That would match.
|
|
|
|
Let's design pattern:
|
|
|
|
```
|
|
re.compile(r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*\$?([0-9.,]+)([MmBb])?', re.IGNORECASE)
|
|
```
|
|
|
|
This matches "Revenue" optionally preceded by "Total" or "Net", then any characters that are not digits (including colon, dash, pipe, space), then optional `$`, then number.
|
|
|
|
But if there is a digit before the number? For example "Revenue: $12.3M" -> after "Revenue:" we have space and "$". The pattern `[^0-9]*` will match ":" and space until it sees a digit or $; but we also allow `$?`. Actually we need to ensure that the `$` is optional before number. So pattern: `\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*\$?([0-9.,]+)([MmBb])?`.
|
|
|
|
But if there is no colon/dash, but a pipe symbol like "Revenue | $12.3M", then after "Revenue" we have space and "|" which is not digit; `[^0-9]*` will match that.
|
|
|
|
Ok.
|
|
|
|
Let's update regex accordingly.
|
|
|
|
Also, we should consider that the number might be in thousands or millions with no unit. We'll treat as dollars.
|
|
|
|
Now let's modify code accordingly.
|
|
|
|
Additionally, maybe revenue appears in items.jsonl but not in content.txt; our script already tries to parse items if no matches found in text. That may catch some.
|
|
|
|
But we need to ensure we read all lines of items.jsonl and search for "Revenue" inside each item.text. But the regex will apply to that too.
|
|
|
|
Ok.
|
|
|
|
Let's update code with new pattern.
|
|
|
|
Also, we might want to print the revenue value for debugging.
|
|
|
|
Let's run again.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''(?i)\\b(?:Total\\s+|Net\\s+)?Revenue\\b[^0-9]*\\$?([0-9.,]+)([MmBb])?'', re.IGNORECASE)\n\ntotal_revenue
|
|
= 0.0\nfor doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_1kz75zth
|
|
type: function
|
|
- content: "Error: TimeoutError: time limit exceeded: 128.590520458s > 60s\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_1kz75zth
|
|
- content: null
|
|
reasoning: |+
|
|
The script timed out after 60 seconds; maybe there are many documents or reading large files is slow. We need to optimize.
|
|
|
|
We found only 3 quarterly docs, but reading content.txt for each might be fine. But the code may have blocked due to something else. The timeout indicates that the entire execution took >128 seconds (the limit). That suggests that maybe our script attempted to read many documents or did a heavy operation like searching items.jsonl lines which could be large.
|
|
|
|
But we only processed 3 docs, so why would it take long? Maybe reading content.txt for each doc is fine. But perhaps the code didn't finish due to some infinite loop? Let's check: The code loops over quarterly_docs (3). Inside each, it reads content_path.read_text() - that's fine. Then matches = revenue_pattern.findall(text) - fine.
|
|
|
|
If no matches, then try items.jsonl reading lines and iterating. That might be heavy if items.jsonl is huge. But only 3 docs; maybe each has many items? But still not that large.
|
|
|
|
The code may have hung due to some error like recursion or something else. Let's see the output: It says "Error: TimeoutError: time limit exceeded: 128.590520458s > 60s". That means the entire function didn't finish within 60 seconds, but we had no prints before that? Actually we printed "Found X quarterly report documents." at start; maybe that printed but not shown because of timeout? The output shows nothing except error.
|
|
|
|
Wait, the output shows only:
|
|
|
|
```
|
|
Error: TimeoutError: time limit exceeded: 128.590520458s > 60s
|
|
```
|
|
|
|
So it didn't print anything else. That means the code crashed before printing "Found X quarterly report documents." maybe due to an earlier error like reading list_documents? But we printed after retrieving docs.
|
|
|
|
But the output shows nothing else, meaning that the script didn't even reach the first print. So the problem might be with `await list_documents()` or something else. Let's test a minimal code: just call await list_documents() and print length.
|
|
|
|
Let's run a simpler snippet to see if list_documents works.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"docs = await list_documents()\nprint(''Number of docs:'', len(docs))\n"}'
|
|
name: analysis_execute_code
|
|
id: call_yyp7tcdy
|
|
type: function
|
|
- content: "Error: TimeoutError: time limit exceeded: 146.704897417s > 60s\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_yyp7tcdy
|
|
- content: null
|
|
reasoning: |+
|
|
It timed out again. That suggests that calling `list_documents()` is slow or blocked. Maybe the corpus is huge and listing all documents takes too long.
|
|
|
|
We need to avoid listing all docs. Instead, we can use search queries to find relevant docs. The search function likely returns results sorted by relevance across the entire corpus but limited in number (limit). We can query for "Quarterly Report" with a limit of maybe 1000 or something. That will return hits from documents containing that phrase.
|
|
|
|
But we need to get unique document IDs from those hits. Then we can process each doc individually.
|
|
|
|
Alternatively, we can use search for "Revenue" and then filter by docs that have quarterly report context. But we want all quarterly reports; maybe the corpus has many but not too many.
|
|
|
|
We cannot list all documents due to performance constraints. So we need to use search.
|
|
|
|
Let's try a search query: `analysis_search` with query "Quarterly Report". That will return results across the corpus, likely limited to 10 by default. But we can set limit=1000 maybe? The tool signature: analysis_search(query: string, limit: number | null). We can call it with limit=200 or something.
|
|
|
|
We need to get chunk IDs from search results; but we also want doc ids. Each result includes document_id and other fields. So we can collect unique document_ids.
|
|
|
|
Then for each doc id, read content.txt etc.
|
|
|
|
But we might not get all quarterly reports if the query is too narrow. But it's likely fine.
|
|
|
|
Alternatively, we could search for "Q1" or "Quarterly Report". But let's start with "Quarterly Report".
|
|
|
|
Let's call analysis_search with limit=200.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"Quarterly Report","limit":200}'
|
|
name: analysis_search
|
|
id: call_lk5136v9
|
|
type: function
|
|
- content: |-
|
|
[a1352ff9-a8ba-4a1d-9ee2-de89e0fc1ccd] [rank 1 of 3]
|
|
Source: "Q1 Report"
|
|
Type: text
|
|
Content:
|
|
Sales report Q1: Revenue was $100,000.
|
|
|
|
---
|
|
|
|
[05fd1668-3a01-4679-a3c5-cb55ac62d0aa] [rank 2 of 3]
|
|
Source: "Q3 Report"
|
|
Type: text
|
|
Content:
|
|
Sales report Q3: Revenue was $200,000.
|
|
|
|
---
|
|
|
|
[513adcea-e401-432d-bcde-cbd024cda5f4] [rank 3 of 3]
|
|
Source: "Q2 Report"
|
|
Type: text
|
|
Content:
|
|
Sales report Q2: Revenue was $150,000.
|
|
role: tool
|
|
tool_call_id: call_lk5136v9
|
|
model: gpt-oss
|
|
reasoning_effort: high
|
|
stream: false
|
|
temperature: 0.3
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: Search the knowledge base for evidence to analyze.
|
|
name: analysis_search
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
limit:
|
|
anyOf:
|
|
- type: integer
|
|
- type: 'null'
|
|
default: null
|
|
query:
|
|
type: string
|
|
required:
|
|
- query
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Execute Python against the sandboxed document filesystem.
|
|
name: analysis_execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Register exact retrieved chunk IDs as citations for the answer.
|
|
name: analysis_cite
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
chunk_ids:
|
|
items:
|
|
type: string
|
|
type: array
|
|
required:
|
|
- chunk_ids
|
|
type: object
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-type:
|
|
- application/json
|
|
transfer-encoding:
|
|
- chunked
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: tool_calls
|
|
index: 0
|
|
message:
|
|
content: ''
|
|
reasoning: |+
|
|
The search returned 3 results with doc IDs:
|
|
|
|
- a1352ff9-a8ba-4a1d-9ee2-de89e0fc1ccd (Source "Q1 Report")
|
|
- 05fd1668-3a01-4679-a3c5-cb55ac62d0aa (Source "Q3 Report")
|
|
- 513adcea-e401-432d-bcde-cbd024cda5f4 (Source "Q2 Report")
|
|
|
|
The content shows revenue amounts: $100,000; $200,000; $150,000.
|
|
|
|
Thus the total revenue across all quarterly reports is $450,000. That seems straightforward.
|
|
|
|
But we need to confirm that these are indeed all quarterly reports. The search returned only 3 results. There might be more but maybe not. But if there were more, they'd appear in search results. But we set limit=200; but the search returned only 3 hits. So likely those are the only quarterly reports.
|
|
|
|
Thus total revenue = $100k + $150k + $200k = $450k.
|
|
|
|
We can compute that easily.
|
|
|
|
But we need to provide answer with citations. We should cite the chunk IDs from the search results: a1352ff9-a8ba-4a1d-9ee2-de89e0fc1ccd, 05fd1668-3a01-4679-a3c5-cb55ac62d0aa, 513adcea-e401-432d-bcde-cbd024cda5f4.
|
|
|
|
But we need to call analysis_cite with these chunk_ids before writing answer. The chunk IDs are the ones from search results: those three IDs.
|
|
|
|
We should confirm that each ID is a chunk ID. In search results, they appear as `[a1352ff9-a8ba-4a1d-9ee2-de89e0fc1ccd]`. That looks like a chunk ID. So we can cite them.
|
|
|
|
Thus answer: $450,000.
|
|
|
|
We must not fabricate citations; we have these three chunks that support the revenue figures.
|
|
|
|
Now we need to call analysis_cite with chunk_ids list containing those IDs.
|
|
|
|
Then write final answer: "The total revenue across all quarterly reports is $450,000."
|
|
|
|
Let's do that.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"chunk_ids":["a1352ff9-a8ba-4a1d-9ee2-de89e0fc1ccd","05fd1668-3a01-4679-a3c5-cb55ac62d0aa","513adcea-e401-432d-bcde-cbd024cda5f4"]}'
|
|
name: analysis_cite
|
|
id: call_5xm0fvg7
|
|
index: 0
|
|
type: function
|
|
created: 1784300656
|
|
id: chatcmpl-240
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 570
|
|
prompt_tokens: 9840
|
|
total_tokens: 10410
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
- request:
|
|
headers:
|
|
accept:
|
|
- application/json
|
|
accept-encoding:
|
|
- gzip, deflate, zstd
|
|
connection:
|
|
- keep-alive
|
|
content-length:
|
|
- '44045'
|
|
content-type:
|
|
- application/json
|
|
host:
|
|
- localhost:11434
|
|
method: POST
|
|
parsed_body:
|
|
messages:
|
|
- content: |-
|
|
# Analysis
|
|
|
|
You answer questions over a document knowledge base. Two common workflows:
|
|
|
|
- **`analysis_search → analysis_cite → answer`** when the answer is grounded on specific document content. Call `analysis_cite` with the supporting chunk_ids before writing the answer.
|
|
- **`analysis_execute_code → answer`** when the answer is a count, aggregation, listing, or structural computation over the corpus (e.g. "how many documents?", "average page count"). No `analysis_cite` is needed when no specific chunks support the answer.
|
|
|
|
You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation.
|
|
|
|
## Tools
|
|
|
|
### analysis_execute_code
|
|
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
|
|
|
Inside the code, these functions are available (use `await`):
|
|
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
|
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
|
|
|
Available modules: `json`, `re`, `math`, `pathlib`
|
|
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
|
|
|
### analysis_search
|
|
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
|
|
|
### analysis_cite
|
|
Register the chunk IDs that ground your answer. **You must call `analysis_cite` before writing any final answer that uses retrieved evidence — search results, items.jsonl rows, toc.json nodes, or content.txt content.** Skipping `analysis_cite` leaves the answer ungrounded and is treated as a failure.
|
|
|
|
`analysis_cite` is **not** required when your answer is a corpus-level computation that doesn't draw on specific chunks — counts, aggregations, listings, averages across documents. Don't fabricate citations for these.
|
|
|
|
Chunk IDs come from two places:
|
|
- The `chunk_id` field on `search` / `await search(...)` results
|
|
- The `chunk_ids` field on `items.jsonl` rows / `toc.json` nodes (when you ground via direct file reads)
|
|
|
|
Do NOT cite `self_ref` (`#/texts/N` style refs), `position`, or any other identifier-shaped field. They are not chunk IDs and the tool will reject them. Copy chunk IDs verbatim — they are opaque UUIDs.
|
|
|
|
## Document Filesystem (inside execute_code)
|
|
|
|
All documents are mounted as a virtual filesystem at `/documents/`:
|
|
|
|
```
|
|
/documents/{document_id}/
|
|
metadata.json # {"id", "title", "uri", "created_at"}
|
|
content.txt # Full document text
|
|
items.jsonl # Structured items (one JSON object per line)
|
|
toc.json # Section tree derived from heading_level
|
|
```
|
|
|
|
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids and match against `uri` / `title` — that's a single call to the host. Iterating `/documents/` and reading every `metadata.json` works too but is much slower on portal-scale corpora.
|
|
|
|
### Reading files
|
|
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
|
|
|
```python
|
|
from pathlib import Path
|
|
import json
|
|
|
|
# Discover documents
|
|
for doc_dir in Path('/documents').iterdir():
|
|
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
|
print(meta['title'])
|
|
|
|
# Read full text
|
|
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
|
|
|
# Read and parse items
|
|
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
|
item = json.loads(line)
|
|
if item['label'] == 'table':
|
|
print(item['text'][:200])
|
|
```
|
|
|
|
### metadata.json
|
|
Document metadata: `id`, `title`, `uri`, `created_at`.
|
|
|
|
### content.txt
|
|
Full text content. Use for regex or keyword search across a whole document.
|
|
|
|
### items.jsonl
|
|
Structured document items. One JSON object per line. The row's **line index** is the item's position — `item_range` values in `toc.json` are line-slice bounds into this file.
|
|
|
|
Each row carries:
|
|
- `self_ref`: item reference (e.g. `"#/texts/5"`, `"#/tables/0"`) — used to cross-reference with `doc_item_refs` from search results
|
|
- `label`: item type — one of `"section_header"`, `"text"`, `"table"`, `"list_item"`, `"caption"`, `"formula"`, `"picture"`, `"code"`, `"footnote"`
|
|
- `text`: rendered content (tables are markdown with `|` columns)
|
|
- `page_numbers`: list of page numbers where the item appears
|
|
- `chunk_ids`: chunks that contain this item — pass to `analysis_cite()` to ground an answer that read this item directly
|
|
- `heading_level`: H-level for `section_header` rows; `0` on non-header rows
|
|
|
|
### toc.json
|
|
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, page_numbers, item_range: [start, end_exclusive], chunk_ids, children}`. `item_range` is a line slice into `items.jsonl` — `items[start:end]`. `chunk_ids` aggregates the citable chunks across all items in the section — pass directly to `analysis_cite()` to ground a section-scoped answer without a corpus-wide `search()` call. `tree: []` for docs with no headers.
|
|
|
|
### Cross-referencing search results with items
|
|
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in `items.jsonl`. To find which section a hit lives in: locate the item by `self_ref`, take its line index, and walk `toc.json` to find the deepest node whose `item_range` contains that index.
|
|
|
|
## Strategy
|
|
|
|
1. Search first.
|
|
2. Identify the chunk_ids from the search results that support your answer and call `analysis_cite` with them. Then write a concise answer.
|
|
3. Reach for `analysis_execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
|
4. For questions about a *known document's* structure ("which section contains X", "list the sections of doc Y", "summarise section Z"), read `/documents/{id}/toc.json` first. Each node carries `item_range` (a slice into `items.jsonl`) and `chunk_ids` (citable). Prefer this over `search()` for in-document navigation — `search()` ranks across the whole corpus and can return chunks from unrelated documents.
|
|
5. Before writing your final response, call `analysis_cite` with the chunk_ids that ground your answer.
|
|
|
|
You MUST call `analysis_cite` with at least one chunk ID before producing your final answer **when your answer is grounded on retrieved evidence**. Skip `analysis_cite` in two cases: (a) you are refusing for lack of information, or (b) your answer is a corpus-level computation (count, aggregation, listing) that doesn't draw on specific chunks. In those cases do **not** fabricate citations.
|
|
|
|
## Important
|
|
|
|
- Variables persist between `analysis_execute_code` calls — you can search in one call and process results in the next
|
|
- Use `print()` to output results — the output is your only feedback
|
|
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `analysis_search → analysis_cite`.
|
|
- Use `await` for all async functions inside `analysis_execute_code` (`search`, `list_documents`)
|
|
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
|
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `analysis_cite` tool separately to register citations. `cite{...}` markdown-style inline references do nothing; only an actual `analysis_cite` tool call registers a citation.
|
|
- **Before you write your final answer, invoke the `analysis_cite` tool with the supporting chunk_ids.** This is the last tool call before answering whenever your answer draws on retrieved evidence.
|
|
role: system
|
|
- content: What is the total revenue across all quarterly reports?
|
|
role: user
|
|
- content: null
|
|
reasoning: |+
|
|
The user asks: "What is the total revenue across all quarterly reports?" We need to interpret what documents are in the knowledge base. The question likely refers to a set of quarterly report documents that contain revenue figures. We need to compute the sum of revenue across all quarterly reports.
|
|
|
|
We must determine how many documents exist, which ones are quarterly reports, and extract revenue numbers from each. Then sum them up.
|
|
|
|
The user didn't specify a particular document or context; it's a corpus-level computation: "total revenue across all quarterly reports". This is a calculation that aggregates data across multiple documents. According to the instructions: "No `analysis_cite` is needed when no specific chunks support the answer." But we need to compute this sum, which requires reading content from each relevant document. That is a corpus-level computation. The instruction says: "You can mix the two. The rule: cite when grounded on retrieved evidence; don't fabricate citations for corpus-level computation." So if we aggregate across many documents and produce a single number, that is a corpus-level computation. We should not provide citations? But the answer might be considered derived from specific chunks. However, the instruction says "Don't fabricate citations for corpus-level computation." That means we can compute sum without citing each chunk individually. But maybe we need to cite some evidence that revenue appears in documents? The question: "What is the total revenue across all quarterly reports?" It's a numeric answer. We might compute it by scanning all documents, but we don't have to provide citations for each chunk. The instruction says no `analysis_cite` needed when no specific chunks support the answer. But here the answer is derived from many chunks; but it's still corpus-level computation. So we can skip citing.
|
|
|
|
But we need to actually compute the sum. We must search for quarterly reports and revenue numbers. Let's think: There might be documents with "Quarterly Report" in title or content, containing revenue figures. We need to find all such documents. Then parse revenue numbers from each. The revenue could be expressed as "$X million", "Revenue: $Y", etc.
|
|
|
|
We need to decide how to locate quarterly reports. Approach:
|
|
|
|
- Use `analysis_execute_code` to search for documents with 'Quarterly Report' in title or content. We can use the `search` function inside code, but we might also use `list_documents()` and filter by title containing "Quarterly Report". But maybe there are many documents; better to use search.
|
|
|
|
We need to find all documents that contain revenue figures. But likely each quarterly report has a revenue figure. The question: "total revenue across all quarterly reports" implies sum of revenue numbers from each quarter. So we need to extract the revenue number from each quarterly report document.
|
|
|
|
Potential approach:
|
|
|
|
- Use `search` with query "Quarterly Report" and maybe "Revenue". That will return chunks containing those terms. But we might get many results, including non-quarterly reports that mention revenue. We need to filter by documents that are quarterly reports. Maybe the metadata or title contains 'Q1', 'Q2', etc.
|
|
|
|
- Alternatively, search for pattern like "Revenue" and then check if document is a quarterly report (maybe via title containing "Quarterly Report").
|
|
|
|
Better approach: Use `list_documents()` to get all docs. For each doc, read its metadata.title; if it contains "Quarterly Report", parse revenue from content or items.
|
|
|
|
But we need to know the file structure. The documents are in `/documents/{document_id}/`. Each has metadata.json with title and uri. We can list them. Then for each doc whose title matches /quarterly report/i, read its content.txt and find revenue numbers.
|
|
|
|
We need a regex that captures revenue amounts. Revenue could be expressed as "$123 million" or "Revenue: $123M". Could also be numeric like "Revenue 123,456,789".
|
|
|
|
We need to parse these numbers into numeric values (maybe in millions). But we don't know the units; maybe all are in USD and millions.
|
|
|
|
Better approach: Use regex that captures patterns like:
|
|
|
|
- "$\d{1,3}(,\d{3})*(\.\d+)?(M|B)?" or "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?"
|
|
|
|
But we need to decide how to interpret units. The question likely expects a sum in the same unit as reported.
|
|
|
|
We might find revenue numbers like "$1,234,567" or "$12.3M". We can convert all to numeric values (in dollars). For M, multiply by 1e6; for B, multiply by 1e9.
|
|
|
|
But we need to decide how to sum them: If some are in millions and others in billions, we unify to dollars.
|
|
|
|
Alternatively, maybe the revenue numbers are all in millions. But we can't assume.
|
|
|
|
We can parse each number string, detect if it ends with 'M' or 'B', then convert accordingly.
|
|
|
|
Also consider that some documents might have multiple revenue figures (like revenue for different segments). We need to sum only the main revenue figure per document. How to identify? Maybe it's the first occurrence of "Revenue" in the document.
|
|
|
|
Simplify: For each quarterly report doc, find all matches of pattern "Revenue\s*[:\-]?\s*\$?(\d[\d,.]*)([MmBb])?" and take the first match. That should capture main revenue figure.
|
|
|
|
But we need to be careful with formatting like "$1,234,567" (no M/B). Then treat as dollars.
|
|
|
|
Also consider "Revenue: 123,456,789". We'll parse numeric part.
|
|
|
|
We also need to handle numbers that include commas and decimal points. We can remove commas then convert to float.
|
|
|
|
Edge cases: Some documents might have revenue expressed in thousands or millions; but we can't know. But likely they use M for million.
|
|
|
|
Ok.
|
|
|
|
Implementation plan:
|
|
|
|
- Use `analysis_execute_code` with async code.
|
|
|
|
Pseudo-code:
|
|
|
|
```
|
|
import json, re
|
|
from pathlib import Path
|
|
|
|
# Get list of docs
|
|
docs = await list_documents()
|
|
quarterly_docs = []
|
|
for doc in docs:
|
|
title = doc.get('title', '')
|
|
if 'Quarterly Report' in title or 'Q1' in title or 'Q2' in title or 'Q3' in title or 'Q4' in title:
|
|
quarterly_docs.append(doc)
|
|
|
|
# For each doc, read content.txt
|
|
total_revenue = 0.0
|
|
for doc in quarterly_docs:
|
|
path = Path(f'/documents/{doc["id"]}/content.txt')
|
|
text = path.read_text()
|
|
# find revenue patterns
|
|
matches = re.findall(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', text, flags=re.IGNORECASE)
|
|
if not matches:
|
|
continue
|
|
# take first match
|
|
num_str, unit = matches[0]
|
|
# remove commas
|
|
num_val = float(num_str.replace(',', ''))
|
|
if unit.lower() == 'm':
|
|
num_val *= 1_000_000
|
|
elif unit.lower() == 'b':
|
|
num_val *= 1_000_000_000
|
|
total_revenue += num_val
|
|
|
|
print(total_revenue)
|
|
```
|
|
|
|
But we need to ensure we capture numbers like "$12.3M" where the number part includes decimal.
|
|
|
|
Our regex: r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' works for that.
|
|
|
|
However, if there's no unit but a comma separated number like "Revenue: 1,234,567", then unit will be empty string. We treat as dollars.
|
|
|
|
Also need to handle numbers with decimal and maybe M/B after decimal e.g., "$12.34M".
|
|
|
|
Ok.
|
|
|
|
But we also need to consider that some documents might have revenue in thousands or millions but no unit; we can't know. But likely they use M for million.
|
|
|
|
We should sum all revenue values as numeric dollars.
|
|
|
|
After computing total, print the number. We may want to format with commas and maybe show as integer if it's whole number.
|
|
|
|
But the answer expects a numeric value. We'll output the sum in plain number.
|
|
|
|
Potential issues: The regex might find multiple matches per doc; we only take first. But what if revenue appears multiple times? Might be fine.
|
|
|
|
Also, some docs may have "Revenue" but not quarterly report; but we filter by title containing "Quarterly Report". That should reduce noise.
|
|
|
|
But maybe the title doesn't contain "Quarterly Report", but the document is a quarterly report with a different naming scheme. Could be like "Q1 2023 Financial Statement". But we might miss them. We can broaden search: include titles containing 'Q1', 'Q2', 'Q3', 'Q4' and maybe 'Financial Report'. But that could bring in other documents.
|
|
|
|
Better approach: Use `search` for query "Quarterly Report" to get chunk IDs, then find which docs those belong to. Then we can use the doc ids from search results. That may be more precise.
|
|
|
|
But we need to sum revenue across all quarterly reports; we might want to include all such docs. But if some are not labeled as 'Quarterly Report' but still contain revenue, we might miss them.
|
|
|
|
We could also search for "Revenue" and then filter by doc id that appears in a set of known quarterly report ids from previous search. That is complex.
|
|
|
|
Simplify: Use list_documents to get all docs; for each doc, check if title contains 'Quarterly Report' or matches pattern like r'\bQ[1-4]\b'. But we might also include "Annual Report" which we don't want. So maybe filter out titles containing 'Annual'.
|
|
|
|
Better approach: For each doc, read content and see if it has a heading "Quarterly Report". We could parse toc.json for sections titled "Quarterly Report".
|
|
|
|
But that is too complex.
|
|
|
|
Given the instruction to compute sum across all quarterly reports, we can assume that documents with title containing 'Quarterly Report' are the ones. That should be fine.
|
|
|
|
Ok.
|
|
|
|
We need to run code and print total revenue.
|
|
|
|
Let's implement in analysis_execute_code.
|
|
|
|
Also, we might want to handle potential errors: missing content.txt etc.
|
|
|
|
We'll use Path.read_text() inside try/except.
|
|
|
|
Also, we may want to convert total_revenue to integer if it's whole number. But we can just print as float with no decimals if it's an integer.
|
|
|
|
Ok.
|
|
|
|
Let's write code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, flags=re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_qzkuh2up
|
|
type: function
|
|
- content: "Error: TypeError: re.search() takes no keyword arguments\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_qzkuh2up
|
|
- content: null
|
|
reasoning: |+
|
|
The error indicates that `re.search` doesn't accept keyword arguments. In Python, you can pass flags as positional argument or use `re.compile`. Let's adjust.
|
|
|
|
We used:
|
|
|
|
```
|
|
if 'Quarterly Report' in title or re.search(r"\bQ[1-4]\b", title, flags=re.IGNORECASE):
|
|
```
|
|
|
|
But re.search signature is `re.search(pattern, string[, flags])`, so flags must be positional after string. So we need to pass as third argument.
|
|
|
|
Let's correct code: `re.search(r"\bQ[1-4]\b", title, re.IGNORECASE)`.
|
|
|
|
Also, we used `re.compile` for revenue_pattern with flags; that's fine.
|
|
|
|
Let's update code accordingly.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', flags=re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_hnlx62rm
|
|
type: function
|
|
- content: |
|
|
Error: TypeError: re.compile expected at most 2 arguments, got 1
|
|
|
|
Output: Found 3 quarterly report documents.
|
|
role: tool
|
|
tool_call_id: call_hnlx62rm
|
|
- content: null
|
|
reasoning: |+
|
|
The error indicates that `re.compile` expects at most two arguments. We passed flags as keyword argument; but in Python's re.compile signature is `re.compile(pattern, flags=0)`. So we should pass flags as second positional argument or use keyword 'flags' correctly? Actually re.compile accepts flags as optional positional argument: `re.compile(pattern[, flags])`. But the error says "got 1" meaning it thinks only one argument was passed. Wait, we wrote:
|
|
|
|
```
|
|
revenue_pattern = re.compile(r'Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?', flags=re.IGNORECASE)
|
|
```
|
|
|
|
But re.compile signature doesn't accept keyword arguments; you must pass flags as second positional argument: `re.compile(pattern, re.IGNORECASE)`. So we need to change.
|
|
|
|
Let's update code accordingly.
|
|
|
|
Also, we used `print` statements inside the loop; that's fine.
|
|
|
|
Let's correct.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''Revenue\\s*[:\\-]?\\s*\\$?([\\d,.]+)([MmBb])?'', re.IGNORECASE)\n\ntotal_revenue = 0.0\nfor
|
|
doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_ipebddpq
|
|
type: function
|
|
- content: |
|
|
Found 3 quarterly report documents.
|
|
No revenue found in Q1 Report (5427fb0e-24ab-4dc5-a8c5-6f4fe7f29e7d)
|
|
No revenue found in Q2 Report (a0d9d838-2b7c-4785-8c13-1eab81c52a90)
|
|
No revenue found in Q3 Report (515faf6e-3758-45bd-89fb-552c3c8a159e)
|
|
Total revenue across all quarterly reports: 0.0
|
|
role: tool
|
|
tool_call_id: call_ipebddpq
|
|
- content: null
|
|
reasoning: |+
|
|
The script found 3 documents titled "Q1 Report", "Q2 Report", "Q3 Report". It didn't find any revenue in them.
|
|
|
|
Maybe the revenue is not labeled with "Revenue" but something else like "Net Sales" or "Total Revenue". We need to broaden search. Let's inspect these documents' content to see how revenue is expressed.
|
|
|
|
We can read each document's content and print some lines around where revenue might be. But we don't know where it appears.
|
|
|
|
Let's open the content of one doc, e.g., Q1 Report. We'll use Path.read_text() and maybe show first 2000 characters or search for "Revenue" or "Sales".
|
|
|
|
But we need to parse revenue numbers. The revenue may appear in a table. In items.jsonl, there might be tables with revenue figures.
|
|
|
|
We can examine the items.jsonl of Q1 Report to see if there's any numeric values that could represent revenue. But we need to identify which is revenue.
|
|
|
|
Alternatively, maybe the documents are not named "Quarterly Report" but something else; maybe they are part of a larger set like "Financial Statement Q1 2023".
|
|
|
|
We might need to search for all documents containing "Revenue" and then filter those that have quarterly report context. But we need to sum across all quarterly reports, so we should include any document that is a quarterly report.
|
|
|
|
But the script didn't find revenue in these three docs. Maybe revenue appears in tables but not captured by our regex because it's inside a table with markdown syntax like "| Revenue | $1,234,567 |". Our regex might still catch it if "Revenue" appears before number. But maybe the text is "Total Revenue:" or "Total Revenue".
|
|
|
|
Our regex only matches "Revenue" exactly; we could broaden to match "Total Revenue", "Net Revenue", etc.
|
|
|
|
Let's modify regex: r'(?:Total\s+)?Revenue\s*[:\-]?\s*\$?([\d,.]+)([MmBb])?' but also allow "Net Revenue". So maybe pattern: r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b.*?[:\-]?\s*\$?([0-9.,]+)([MmBb])?'.
|
|
|
|
But we need to capture number after colon or dash. Let's design a regex that matches "Revenue" optionally preceded by "Total" or "Net", then optional whitespace, then maybe colon/dash, then optional whitespace, then $ sign, then numeric part with optional unit.
|
|
|
|
Simplify: r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b.*?[:\-]?\s*\$?([0-9.,]+)([MmBb])?'.
|
|
|
|
But we need to capture the number. The pattern uses a capturing group for number and unit. But we also need to ensure that the colon/dash is matched before number; but we can use non-greedy match between 'Revenue' and number: `.*?` will consume minimal characters until next part. That should work.
|
|
|
|
Let's test this regex on some sample strings:
|
|
|
|
- "Total Revenue: $12.3M" -> matches group 1 = "12.3", group2="M".
|
|
- "Net Revenue - $5,000,000" -> group1="5,000,000", group2="".
|
|
- "Revenue: $1234567" -> group1="1234567", group2="".
|
|
- "Revenue: 123.45B" -> group1="123.45", group2="B".
|
|
|
|
Ok.
|
|
|
|
Let's update regex accordingly.
|
|
|
|
Also, we might need to parse numbers that include commas and decimals.
|
|
|
|
We also should consider that the number may be preceded by "$" or not. We handle that.
|
|
|
|
Now let's modify code: use new pattern.
|
|
|
|
Also, maybe revenue is in a table with markdown like "| Revenue | $12.3M |". In that case, our regex might still match because "Revenue:" appears? Actually there might be no colon; but we can adjust to allow optional colon/dash after "Revenue" or not. But the number may appear after a pipe symbol.
|
|
|
|
We could also search for patterns like r'(?i)Revenue\s*[:\-]?\s*\$?([0-9.,]+)([MmBb])?' which is similar but we added optional preceding words.
|
|
|
|
But maybe the revenue appears in a table row like "Revenue | $12.3M", so there is no colon or dash. Our regex would not match because it expects `[:\-]?` after "Revenue". But we can modify to allow any non-digit characters between "Revenue" and number: e.g., `\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*?\$?([0-9,.]+)([MmBb])?`. That would match.
|
|
|
|
Let's design pattern:
|
|
|
|
```
|
|
re.compile(r'(?i)\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*\$?([0-9.,]+)([MmBb])?', re.IGNORECASE)
|
|
```
|
|
|
|
This matches "Revenue" optionally preceded by "Total" or "Net", then any characters that are not digits (including colon, dash, pipe, space), then optional `$`, then number.
|
|
|
|
But if there is a digit before the number? For example "Revenue: $12.3M" -> after "Revenue:" we have space and "$". The pattern `[^0-9]*` will match ":" and space until it sees a digit or $; but we also allow `$?`. Actually we need to ensure that the `$` is optional before number. So pattern: `\b(?:Total\s+|Net\s+)?Revenue\b[^0-9]*\$?([0-9.,]+)([MmBb])?`.
|
|
|
|
But if there is no colon/dash, but a pipe symbol like "Revenue | $12.3M", then after "Revenue" we have space and "|" which is not digit; `[^0-9]*` will match that.
|
|
|
|
Ok.
|
|
|
|
Let's update regex accordingly.
|
|
|
|
Also, we should consider that the number might be in thousands or millions with no unit. We'll treat as dollars.
|
|
|
|
Now let's modify code accordingly.
|
|
|
|
Additionally, maybe revenue appears in items.jsonl but not in content.txt; our script already tries to parse items if no matches found in text. That may catch some.
|
|
|
|
But we need to ensure we read all lines of items.jsonl and search for "Revenue" inside each item.text. But the regex will apply to that too.
|
|
|
|
Ok.
|
|
|
|
Let's update code with new pattern.
|
|
|
|
Also, we might want to print the revenue value for debugging.
|
|
|
|
Let's run again.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"import json, re\nfrom pathlib import Path\n\n# Get list of documents\ndocs = await list_documents()\nquarterly_docs
|
|
= []\nfor doc in docs:\n title = doc.get(''title'', '''') or ''''\n # Check for ''Quarterly Report'' in
|
|
title or Q1-Q4 pattern\n if ''Quarterly Report'' in title or re.search(r\"\\bQ[1-4]\\b\", title, re.IGNORECASE):\n quarterly_docs.append(doc)\n\nprint(f\"Found
|
|
{len(quarterly_docs)} quarterly report documents.\")\n\n# Function to parse revenue from text\nrevenue_pattern
|
|
= re.compile(r''(?i)\\b(?:Total\\s+|Net\\s+)?Revenue\\b[^0-9]*\\$?([0-9.,]+)([MmBb])?'', re.IGNORECASE)\n\ntotal_revenue
|
|
= 0.0\nfor doc in quarterly_docs:\n try:\n content_path = Path(f\"/documents/{doc[''id'']}/content.txt\")\n text
|
|
= content_path.read_text()\n except Exception as e:\n print(f\"Error reading {doc[''title'']} ({doc[''id'']}):
|
|
{e}\")\n continue\n matches = revenue_pattern.findall(text)\n if not matches:\n # Try searching
|
|
items.jsonl for revenue patterns\n try:\n items_path = Path(f\"/documents/{doc[''id'']}/items.jsonl\")\n lines
|
|
= items_path.read_text().splitlines()\n for line in lines:\n item = json.loads(line)\n if
|
|
''Revenue'' in item.get(''text'', ''''):\n matches = revenue_pattern.findall(item[''text''])\n if
|
|
matches:\n break\n except Exception as e2:\n print(f\"Error reading
|
|
items.jsonl for {doc[''title'']} ({doc[''id'']}): {e2}\")\n if not matches:\n print(f\"No revenue
|
|
found in {doc[''title'']} ({doc[''id'']})\")\n continue\n # Take first match\n num_str, unit =
|
|
matches[0]\n try:\n num_val = float(num_str.replace('','', ''''))\n except ValueError:\n print(f\"Could
|
|
not parse number {num_str} in {doc[''title'']} ({doc[''id'']})\")\n continue\n if unit.lower() ==
|
|
''m'':\n num_val *= 1_000_000\n elif unit.lower() == ''b'':\n num_val *= 1_000_000_000\n total_revenue
|
|
+= num_val\n print(f\"{doc[''title'']} revenue: {num_val}\")\n\nprint(\"Total revenue across all quarterly
|
|
reports:\", total_revenue)\n"}'
|
|
name: analysis_execute_code
|
|
id: call_1kz75zth
|
|
type: function
|
|
- content: "Error: TimeoutError: time limit exceeded: 128.590520458s > 60s\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_1kz75zth
|
|
- content: null
|
|
reasoning: |+
|
|
The script timed out after 60 seconds; maybe there are many documents or reading large files is slow. We need to optimize.
|
|
|
|
We found only 3 quarterly docs, but reading content.txt for each might be fine. But the code may have blocked due to something else. The timeout indicates that the entire execution took >128 seconds (the limit). That suggests that maybe our script attempted to read many documents or did a heavy operation like searching items.jsonl lines which could be large.
|
|
|
|
But we only processed 3 docs, so why would it take long? Maybe reading content.txt for each doc is fine. But perhaps the code didn't finish due to some infinite loop? Let's check: The code loops over quarterly_docs (3). Inside each, it reads content_path.read_text() - that's fine. Then matches = revenue_pattern.findall(text) - fine.
|
|
|
|
If no matches, then try items.jsonl reading lines and iterating. That might be heavy if items.jsonl is huge. But only 3 docs; maybe each has many items? But still not that large.
|
|
|
|
The code may have hung due to some error like recursion or something else. Let's see the output: It says "Error: TimeoutError: time limit exceeded: 128.590520458s > 60s". That means the entire function didn't finish within 60 seconds, but we had no prints before that? Actually we printed "Found X quarterly report documents." at start; maybe that printed but not shown because of timeout? The output shows nothing except error.
|
|
|
|
Wait, the output shows only:
|
|
|
|
```
|
|
Error: TimeoutError: time limit exceeded: 128.590520458s > 60s
|
|
```
|
|
|
|
So it didn't print anything else. That means the code crashed before printing "Found X quarterly report documents." maybe due to an earlier error like reading list_documents? But we printed after retrieving docs.
|
|
|
|
But the output shows nothing else, meaning that the script didn't even reach the first print. So the problem might be with `await list_documents()` or something else. Let's test a minimal code: just call await list_documents() and print length.
|
|
|
|
Let's run a simpler snippet to see if list_documents works.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"code":"docs = await list_documents()\nprint(''Number of docs:'', len(docs))\n"}'
|
|
name: analysis_execute_code
|
|
id: call_yyp7tcdy
|
|
type: function
|
|
- content: "Error: TimeoutError: time limit exceeded: 146.704897417s > 60s\n\nOutput: "
|
|
role: tool
|
|
tool_call_id: call_yyp7tcdy
|
|
- content: null
|
|
reasoning: |+
|
|
It timed out again. That suggests that calling `list_documents()` is slow or blocked. Maybe the corpus is huge and listing all documents takes too long.
|
|
|
|
We need to avoid listing all docs. Instead, we can use search queries to find relevant docs. The search function likely returns results sorted by relevance across the entire corpus but limited in number (limit). We can query for "Quarterly Report" with a limit of maybe 1000 or something. That will return hits from documents containing that phrase.
|
|
|
|
But we need to get unique document IDs from those hits. Then we can process each doc individually.
|
|
|
|
Alternatively, we can use search for "Revenue" and then filter by docs that have quarterly report context. But we want all quarterly reports; maybe the corpus has many but not too many.
|
|
|
|
We cannot list all documents due to performance constraints. So we need to use search.
|
|
|
|
Let's try a search query: `analysis_search` with query "Quarterly Report". That will return results across the corpus, likely limited to 10 by default. But we can set limit=1000 maybe? The tool signature: analysis_search(query: string, limit: number | null). We can call it with limit=200 or something.
|
|
|
|
We need to get chunk IDs from search results; but we also want doc ids. Each result includes document_id and other fields. So we can collect unique document_ids.
|
|
|
|
Then for each doc id, read content.txt etc.
|
|
|
|
But we might not get all quarterly reports if the query is too narrow. But it's likely fine.
|
|
|
|
Alternatively, we could search for "Q1" or "Quarterly Report". But let's start with "Quarterly Report".
|
|
|
|
Let's call analysis_search with limit=200.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"query":"Quarterly Report","limit":200}'
|
|
name: analysis_search
|
|
id: call_lk5136v9
|
|
type: function
|
|
- content: |-
|
|
[a1352ff9-a8ba-4a1d-9ee2-de89e0fc1ccd] [rank 1 of 3]
|
|
Source: "Q1 Report"
|
|
Type: text
|
|
Content:
|
|
Sales report Q1: Revenue was $100,000.
|
|
|
|
---
|
|
|
|
[05fd1668-3a01-4679-a3c5-cb55ac62d0aa] [rank 2 of 3]
|
|
Source: "Q3 Report"
|
|
Type: text
|
|
Content:
|
|
Sales report Q3: Revenue was $200,000.
|
|
|
|
---
|
|
|
|
[513adcea-e401-432d-bcde-cbd024cda5f4] [rank 3 of 3]
|
|
Source: "Q2 Report"
|
|
Type: text
|
|
Content:
|
|
Sales report Q2: Revenue was $150,000.
|
|
role: tool
|
|
tool_call_id: call_lk5136v9
|
|
- content: null
|
|
reasoning: |+
|
|
The search returned 3 results with doc IDs:
|
|
|
|
- a1352ff9-a8ba-4a1d-9ee2-de89e0fc1ccd (Source "Q1 Report")
|
|
- 05fd1668-3a01-4679-a3c5-cb55ac62d0aa (Source "Q3 Report")
|
|
- 513adcea-e401-432d-bcde-cbd024cda5f4 (Source "Q2 Report")
|
|
|
|
The content shows revenue amounts: $100,000; $200,000; $150,000.
|
|
|
|
Thus the total revenue across all quarterly reports is $450,000. That seems straightforward.
|
|
|
|
But we need to confirm that these are indeed all quarterly reports. The search returned only 3 results. There might be more but maybe not. But if there were more, they'd appear in search results. But we set limit=200; but the search returned only 3 hits. So likely those are the only quarterly reports.
|
|
|
|
Thus total revenue = $100k + $150k + $200k = $450k.
|
|
|
|
We can compute that easily.
|
|
|
|
But we need to provide answer with citations. We should cite the chunk IDs from the search results: a1352ff9-a8ba-4a1d-9ee2-de89e0fc1ccd, 05fd1668-3a01-4679-a3c5-cb55ac62d0aa, 513adcea-e401-432d-bcde-cbd024cda5f4.
|
|
|
|
But we need to call analysis_cite with these chunk_ids before writing answer. The chunk IDs are the ones from search results: those three IDs.
|
|
|
|
We should confirm that each ID is a chunk ID. In search results, they appear as `[a1352ff9-a8ba-4a1d-9ee2-de89e0fc1ccd]`. That looks like a chunk ID. So we can cite them.
|
|
|
|
Thus answer: $450,000.
|
|
|
|
We must not fabricate citations; we have these three chunks that support the revenue figures.
|
|
|
|
Now we need to call analysis_cite with chunk_ids list containing those IDs.
|
|
|
|
Then write final answer: "The total revenue across all quarterly reports is $450,000."
|
|
|
|
Let's do that.
|
|
|
|
role: assistant
|
|
tool_calls:
|
|
- function:
|
|
arguments: '{"chunk_ids":["a1352ff9-a8ba-4a1d-9ee2-de89e0fc1ccd","05fd1668-3a01-4679-a3c5-cb55ac62d0aa","513adcea-e401-432d-bcde-cbd024cda5f4"]}'
|
|
name: analysis_cite
|
|
id: call_5xm0fvg7
|
|
type: function
|
|
- content: Registered 3 citation(s).
|
|
role: tool
|
|
tool_call_id: call_5xm0fvg7
|
|
model: gpt-oss
|
|
reasoning_effort: high
|
|
stream: false
|
|
temperature: 0.3
|
|
tool_choice: auto
|
|
tools:
|
|
- function:
|
|
description: Search the knowledge base for evidence to analyze.
|
|
name: analysis_search
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
limit:
|
|
anyOf:
|
|
- type: integer
|
|
- type: 'null'
|
|
default: null
|
|
query:
|
|
type: string
|
|
required:
|
|
- query
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Execute Python against the sandboxed document filesystem.
|
|
name: analysis_execute_code
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
code:
|
|
type: string
|
|
required:
|
|
- code
|
|
type: object
|
|
type: function
|
|
- function:
|
|
description: Register exact retrieved chunk IDs as citations for the answer.
|
|
name: analysis_cite
|
|
parameters:
|
|
additionalProperties: false
|
|
properties:
|
|
chunk_ids:
|
|
items:
|
|
type: string
|
|
type: array
|
|
required:
|
|
- chunk_ids
|
|
type: object
|
|
type: function
|
|
uri: http://localhost:11434/v1/chat/completions
|
|
response:
|
|
headers:
|
|
content-length:
|
|
- '360'
|
|
content-type:
|
|
- application/json
|
|
parsed_body:
|
|
choices:
|
|
- finish_reason: stop
|
|
index: 0
|
|
message:
|
|
content: The total revenue reported across all quarterly reports is **$450,000**.
|
|
role: assistant
|
|
created: 1784300657
|
|
id: chatcmpl-871
|
|
model: gpt-oss
|
|
object: chat.completion
|
|
system_fingerprint: fp_ollama
|
|
usage:
|
|
completion_tokens: 20
|
|
prompt_tokens: 10428
|
|
total_tokens: 10448
|
|
status:
|
|
code: 200
|
|
message: OK
|
|
version: 1
|
|
...
|