From bff613b7dc17bee0f89f6a700a301e6d4059c6ef Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Wed, 14 Jan 2026 17:12:22 +0200 Subject: [PATCH] Better test coverage --- tests/agents/chat/test_chat_agent.py | 63 + .../test_chat_agent_ask_with_state_key.yaml | 3612 +++++++++++++++++ ...test_chat_agent_search_with_state_key.yaml | 815 ++++ 3 files changed, 4490 insertions(+) create mode 100644 tests/cassettes/test_chat_agent/test_chat_agent_ask_with_state_key.yaml create mode 100644 tests/cassettes/test_chat_agent/test_chat_agent_search_with_state_key.yaml diff --git a/tests/agents/chat/test_chat_agent.py b/tests/agents/chat/test_chat_agent.py index e7f58484..323cee6f 100644 --- a/tests/agents/chat/test_chat_agent.py +++ b/tests/agents/chat/test_chat_agent.py @@ -287,6 +287,40 @@ async def test_chat_agent_search_tool(allow_model_requests, temp_db_path): assert len(result.output) > 0 +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_chat_agent_search_with_state_key(allow_model_requests, temp_db_path): + """Test search tool emits keyed state when state_key is set.""" + async with HaikuRAG(temp_db_path, create=True) as client: + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + await client.create_document( + content=DOCLAYNET_ANNOTATION, + uri="doclaynet-annotation", + title="DocLayNet Annotation", + ) + + agent = create_chat_agent(Config) + session_state = ChatSessionState(session_id="test-search") + deps = ChatDeps( + client=client, + config=Config, + session_state=session_state, + state_key="haiku_rag", + ) + + result = await agent.run( + "Search for documents about class labels", + deps=deps, + ) + + assert result.output is not None + assert len(result.output) > 0 + + @pytest.mark.asyncio @pytest.mark.vcr() async def test_chat_agent_search_tool_with_filter(allow_model_requests, temp_db_path): @@ -503,6 +537,35 @@ async def test_chat_agent_ask_adds_citations(allow_model_requests, temp_db_path) assert len(session_state.qa_history) >= 1 +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_chat_agent_ask_with_state_key(allow_model_requests, temp_db_path): + """Test ask tool emits keyed state when state_key is set.""" + async with HaikuRAG(temp_db_path, create=True) as client: + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + + agent = create_chat_agent(Config) + session_state = ChatSessionState(session_id="test-ask-keyed") + deps = ChatDeps( + client=client, + config=Config, + session_state=session_state, + state_key="haiku_rag", + ) + + result = await agent.run( + "What is the highest count class in the DocLayNet dataset?", + deps=deps, + ) + + assert result.output is not None + assert len(session_state.qa_history) >= 1 + + def test_fifo_limit_enforcement(): """Test that FIFO limit enforcement logic works correctly. diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_ask_with_state_key.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_ask_with_state_key.yaml new file mode 100644 index 00000000..db6f265a --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_chat_agent_ask_with_state_key.yaml @@ -0,0 +1,3612 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '730' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Class Labels + DocLayNet defines 11 distinct class labels for document layout analysis: + 1. Caption - Text describing figures or tables + 2. Footnote - Notes at the bottom of pages + 3. Formula - Mathematical expressions + 4. List-item - Items in bulleted or numbered lists + 5. Page-footer - Footer content on pages + 6. Page-header - Header content on pages + 7. Picture - Images and diagrams + 8. Section-header - Headings for document sections + 9. Table - Tabular data + 10. Text - Regular paragraph text (highest count: 510,377 instances) + 11. Title - Document titles + The Text class has the highest count with 510,377 instances in the dataset. + 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: 166 + total_tokens: 166 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4099' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: What is the highest count class in the DocLayNet dataset? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '523' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to ask. + role: assistant + tool_calls: + - function: + arguments: '{"document_name":null,"question":"What is the highest count class in the DocLayNet dataset?"}' + name: ask + id: call_88vgyw16 + index: 0 + type: function + created: 1768403367 + id: chatcmpl-790 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 44 + prompt_tokens: 850 + total_tokens: 894 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2118' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are the research orchestrator for a focused, iterative workflow. + + Responsibilities: + 1. Understand and decompose the main question + 2. Propose a minimal, high-leverage plan + 3. Coordinate specialized agents to gather evidence + 4. Iterate based on gaps and new findings + + Plan requirements: + - Produce at most 3 sub_questions that together cover the main question. + - sub_questions must be a list of plain strings, where each string is a complete + question. Do NOT use objects with nested fields like {question, details}. + - Each sub_question must be a standalone, self-contained query that can run + without extra context. Include concrete entities, scope, timeframe, and any + qualifiers. Avoid ambiguous pronouns (it/they/this/that). + - Prioritize the highest-value aspects first; avoid redundancy and overlap. + - Prefer questions that are likely answerable from the current knowledge base; + if coverage is uncertain, make scopes narrower and specific. + - Order sub_questions by execution priority (most valuable first). + + Use the gather_context tool once on the main question before planning. + + Use the gather_context tool once on the main question before planning. + role: system + - content: |- + Plan a focused approach for the main question. + + Main question: What is the highest count class in the DocLayNet dataset? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: '' + name: gather_context + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: A structured research plan with sub-questions to explore. + name: final_result + parameters: + additionalProperties: false + properties: + sub_questions: + description: Specific questions to research, phrased as complete questions + items: + type: string + type: array + required: + - sub_questions + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '558' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: We need to call gather_context on main question. + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"What is the highest count class in the DocLayNet dataset?"}' + name: gather_context + id: call_oh4krl1y + index: 0 + type: function + created: 1768403368 + id: chatcmpl-53 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 51 + prompt_tokens: 425 + total_tokens: 476 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '127' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - What is the highest count class in the DocLayNet dataset? + 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: 15 + total_tokens: 15 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2916' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are the research orchestrator for a focused, iterative workflow. + + Responsibilities: + 1. Understand and decompose the main question + 2. Propose a minimal, high-leverage plan + 3. Coordinate specialized agents to gather evidence + 4. Iterate based on gaps and new findings + + Plan requirements: + - Produce at most 3 sub_questions that together cover the main question. + - sub_questions must be a list of plain strings, where each string is a complete + question. Do NOT use objects with nested fields like {question, details}. + - Each sub_question must be a standalone, self-contained query that can run + without extra context. Include concrete entities, scope, timeframe, and any + qualifiers. Avoid ambiguous pronouns (it/they/this/that). + - Prioritize the highest-value aspects first; avoid redundancy and overlap. + - Prefer questions that are likely answerable from the current knowledge base; + if coverage is uncertain, make scopes narrower and specific. + - Order sub_questions by execution priority (most valuable first). + + Use the gather_context tool once on the main question before planning. + + Use the gather_context tool once on the main question before planning. + role: system + - content: |- + Plan a focused approach for the main question. + + Main question: What is the highest count class in the DocLayNet dataset? + role: user + - content: |- + + We need to call gather_context on main question. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"What is the highest count class in the DocLayNet dataset?"}' + name: gather_context + id: call_oh4krl1y + type: function + - content: |- + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_oh4krl1y + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: '' + name: gather_context + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: A structured research plan with sub-questions to explore. + name: final_result + parameters: + additionalProperties: false + properties: + sub_questions: + description: Specific questions to research, phrased as complete questions + items: + type: string + type: array + required: + - sub_questions + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1053' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "**Sub‑questions for focused evidence collection**\n\n1. *What is the exact count of instances for each + of the 11 class labels (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, + Table) in the DocLayNet dataset?* \n2. *Based on those counts, which single class label has the maximum number + of instances in DocLayNet?* \n3. *What are the top three class labels by instance count in DocLayNet, and what + are their respective counts?*" + reasoning: 'Now craft 3 sub_questions. Highest count class likely "Text" but not in list. We need confirm counts. + Ask about class distribution. Provide questions: 1. What is frequency count per class in DocLayNet? 2. Which class + has the highest count? 3. Provide top 3 classes. Done.' + role: assistant + created: 1768403372 + id: chatcmpl-690 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 192 + prompt_tokens: 578 + total_tokens: 770 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4460' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are the research orchestrator for a focused, iterative workflow. + + Responsibilities: + 1. Understand and decompose the main question + 2. Propose a minimal, high-leverage plan + 3. Coordinate specialized agents to gather evidence + 4. Iterate based on gaps and new findings + + Plan requirements: + - Produce at most 3 sub_questions that together cover the main question. + - sub_questions must be a list of plain strings, where each string is a complete + question. Do NOT use objects with nested fields like {question, details}. + - Each sub_question must be a standalone, self-contained query that can run + without extra context. Include concrete entities, scope, timeframe, and any + qualifiers. Avoid ambiguous pronouns (it/they/this/that). + - Prioritize the highest-value aspects first; avoid redundancy and overlap. + - Prefer questions that are likely answerable from the current knowledge base; + if coverage is uncertain, make scopes narrower and specific. + - Order sub_questions by execution priority (most valuable first). + + Use the gather_context tool once on the main question before planning. + + Use the gather_context tool once on the main question before planning. + role: system + - content: |- + Plan a focused approach for the main question. + + Main question: What is the highest count class in the DocLayNet dataset? + role: user + - content: |- + + We need to call gather_context on main question. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"What is the highest count class in the DocLayNet dataset?"}' + name: gather_context + id: call_oh4krl1y + type: function + - content: |- + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_oh4krl1y + - content: "\nNow craft 3 sub_questions. Highest count class likely \"Text\" but not in list. We need confirm + counts. Ask about class distribution. Provide questions: 1. What is frequency count per class in DocLayNet? 2. Which + class has the highest count? 3. Provide top 3 classes. Done.\n\n\n**Sub‑questions for focused evidence collection**\n\n1. + *What is the exact count of instances for each of the 11 class labels (Caption, Footnote, Formula, List‑item, Page‑footer, + Page‑header, Picture, Section‑header, Table) in the DocLayNet dataset?* \n2. *Based on those counts, which single + class label has the maximum number of instances in DocLayNet?* \n3. *What are the top three class labels by instance + count in DocLayNet, and what are their respective counts?*" + role: assistant + - content: |- + 1 validation error: + ```json + [ + { + "type": "json_invalid", + "loc": [], + "msg": "Invalid JSON: expected value at line 1 column 1", + "input": "**Sub‑questions for focused evidence collection**\n\n1. *What is the exact count of instances for each of the 11 class labels (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table) in the DocLayNet dataset?* \n2. *Based on those counts, which single class label has the maximum number of instances in DocLayNet?* \n3. *What are the top three class labels by instance count in DocLayNet, and what are their respective counts?*" + } + ] + ``` + + Fix the errors and try again. + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: '' + name: gather_context + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: A structured research plan with sub-questions to explore. + name: final_result + parameters: + additionalProperties: false + properties: + sub_questions: + description: Specific questions to research, phrased as complete questions + items: + type: string + type: array + required: + - sub_questions + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '901' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: We need to output JSON with sub_questions field. + role: assistant + tool_calls: + - function: + arguments: '{"sub_questions":["What is the exact count of instances for each of the 11 class labels (Caption, + Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table) in the DocLayNet dataset?","Based + on those counts, which single class label has the maximum number of instances in DocLayNet?","What are the + top three class labels by instance count in DocLayNet, and what are their respective counts?"]}' + name: final_result + id: call_k5uxrkw7 + index: 0 + type: function + created: 1768403375 + id: chatcmpl-304 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 125 + prompt_tokens: 956 + total_tokens: 1081 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2944' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What is the exact count of instances for each of the 11 class labels (Caption, Footnote, Formula, List-item, + Page-footer, Page-header, Picture, Section-header, Table) in the DocLayNet dataset? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '584' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"DocLayNet class label counts Caption Footnote Formula List-item Page-footer + Page-header Picture Section-header Table"}' + name: search_and_answer + id: call_t0hcvev7 + index: 0 + type: function + created: 1768403377 + id: chatcmpl-18 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 51 + prompt_tokens: 648 + total_tokens: 699 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '186' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet class label counts Caption Footnote Formula List-item Page-footer Page-header Picture Section-header Table + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3885' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What is the exact count of instances for each of the 11 class labels (Caption, Footnote, Formula, List-item, + Page-footer, Page-header, Picture, Section-header, Table) in the DocLayNet dataset? + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"DocLayNet class label counts Caption Footnote Formula List-item Page-footer + Page-header Picture Section-header Table"}' + name: search_and_answer + id: call_t0hcvev7 + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_t0hcvev7 + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '528' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: No counts. Need other doc. + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"DocLayNet dataset instance count class labels"}' + name: search_and_answer + id: call_ch4rrldb + index: 0 + type: function + created: 1768403378 + id: chatcmpl-950 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 43 + prompt_tokens: 852 + total_tokens: 895 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '115' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet dataset instance count class labels + 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: 10 + total_tokens: 10 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4769' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What is the exact count of instances for each of the 11 class labels (Caption, Footnote, Formula, List-item, + Page-footer, Page-header, Picture, Section-header, Table) in the DocLayNet dataset? + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"DocLayNet class label counts Caption Footnote Formula List-item Page-footer + Page-header Picture Section-header Table"}' + name: search_and_answer + id: call_t0hcvev7 + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_t0hcvev7 + - content: |- + + No counts. Need other doc. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"DocLayNet dataset instance count class labels"}' + name: search_and_answer + id: call_ch4rrldb + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_ch4rrldb + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + 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: I couldn’t find the exact per‑label counts in the retrieved documents. + role: assistant + created: 1768403379 + id: chatcmpl-236 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 19 + prompt_tokens: 1048 + total_tokens: 1067 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5005' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What is the exact count of instances for each of the 11 class labels (Caption, Footnote, Formula, List-item, + Page-footer, Page-header, Picture, Section-header, Table) in the DocLayNet dataset? + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"DocLayNet class label counts Caption Footnote Formula List-item Page-footer + Page-header Picture Section-header Table"}' + name: search_and_answer + id: call_t0hcvev7 + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_t0hcvev7 + - content: |- + + No counts. Need other doc. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"DocLayNet dataset instance count class labels"}' + name: search_and_answer + id: call_ch4rrldb + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_ch4rrldb + - content: I couldn’t find the exact per‑label counts in the retrieved documents. + role: assistant + - content: |- + Validation feedback: + Please include your response in a tool call. + + Fix the errors and try again. + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '686' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to call final_result. + role: assistant + tool_calls: + - function: + arguments: '{"answer":"I couldn’t find the exact per‑label counts in the retrieved documents.","cited_chunks":[],"confidence":0,"query":"What + is the exact count of instances for each of the 11 class labels in the DocLayNet dataset?"}' + name: final_result + id: call_arwhnv9h + index: 0 + type: function + created: 1768403381 + id: chatcmpl-436 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 79 + prompt_tokens: 1092 + total_tokens: 1171 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2849' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: Based on those counts, which single class label has the maximum number of instances in DocLayNet? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '542' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to search DocLayNet counts. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet counts class label maximum number of instances"}' + name: search_and_answer + id: call_urvhwy3k + index: 0 + type: function + created: 1768403382 + id: chatcmpl-707 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 46 + prompt_tokens: 622 + total_tokens: 668 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '126' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet counts class label maximum number of instances + 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: 12 + total_tokens: 12 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3747' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: Based on those counts, which single class label has the maximum number of instances in DocLayNet? + role: user + - content: |- + + Need to search DocLayNet counts. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet counts class label maximum number of instances"}' + name: search_and_answer + id: call_urvhwy3k + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_urvhwy3k + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '541' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Only list of labels, no counts. Need another search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet instance counts per class"}' + name: search_and_answer + id: call_j72e1ddm + index: 0 + type: function + created: 1768403384 + id: chatcmpl-412 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 47 + prompt_tokens: 821 + total_tokens: 868 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '105' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet instance counts per class + 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: 9 + total_tokens: 9 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4644' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: Based on those counts, which single class label has the maximum number of instances in DocLayNet? + role: user + - content: |- + + Need to search DocLayNet counts. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet counts class label maximum number of instances"}' + name: search_and_answer + id: call_urvhwy3k + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_urvhwy3k + - content: |- + + Only list of labels, no counts. Need another search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet instance counts per class"}' + name: search_and_answer + id: call_j72e1ddm + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_j72e1ddm + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '405' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: I’m sorry, but I couldn’t find the specific instance counts for each class in DocLayNet in the retrieved + documents. + role: assistant + created: 1768403385 + id: chatcmpl-667 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 29 + prompt_tokens: 1021 + total_tokens: 1050 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4925' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: Based on those counts, which single class label has the maximum number of instances in DocLayNet? + role: user + - content: |- + + Need to search DocLayNet counts. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet counts class label maximum number of instances"}' + name: search_and_answer + id: call_urvhwy3k + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_urvhwy3k + - content: |- + + Only list of labels, no counts. Need another search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet instance counts per class"}' + name: search_and_answer + id: call_j72e1ddm + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_j72e1ddm + - content: I’m sorry, but I couldn’t find the specific instance counts for each class in DocLayNet in the retrieved + documents. + role: assistant + - content: |- + Validation feedback: + Please include your response in a tool call. + + Fix the errors and try again. + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '741' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to output in final_result. + role: assistant + tool_calls: + - function: + arguments: '{"answer":"I’m sorry, but I couldn’t find the specific instance counts for each class in DocLayNet + in the retrieved documents.","cited_chunks":[],"confidence":0.4,"query":"Based on those counts, which single + class label has the maximum number of instances in DocLayNet?"}' + name: final_result + id: call_q59u394d + index: 0 + type: function + created: 1768403387 + id: chatcmpl-886 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 88 + prompt_tokens: 1075 + total_tokens: 1163 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2857' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What are the top three class labels by instance count in DocLayNet, and what are their respective counts? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '516' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet class labels instance count top three"}' + name: search_and_answer + id: call_yf2hct6b + index: 0 + type: function + created: 1768403388 + id: chatcmpl-612 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 42 + prompt_tokens: 624 + total_tokens: 666 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '117' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet class labels instance count top three + 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: 11 + total_tokens: 11 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3729' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What are the top three class labels by instance count in DocLayNet, and what are their respective counts? + role: user + - content: |- + + Need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet class labels instance count top three"}' + name: search_and_answer + id: call_yf2hct6b + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_yf2hct6b + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '532' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need instance counts. Search again. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet dataset class distribution counts"}' + name: search_and_answer + id: call_y87ai4nm + index: 0 + type: function + created: 1768403390 + id: chatcmpl-133 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 42 + prompt_tokens: 818 + total_tokens: 860 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '113' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet dataset class distribution counts + 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: 9 + total_tokens: 9 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4617' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What are the top three class labels by instance count in DocLayNet, and what are their respective counts? + role: user + - content: |- + + Need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet class labels instance count top three"}' + name: search_and_answer + id: call_yf2hct6b + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_yf2hct6b + - content: |- + + Need instance counts. Search again. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet dataset class distribution counts"}' + name: search_and_answer + id: call_y87ai4nm + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_y87ai4nm + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '411' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: I’m sorry, but I couldn’t find the specific instance counts for the class labels in DocLayNet in the material + I searched. + role: assistant + created: 1768403391 + id: chatcmpl-962 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 31 + prompt_tokens: 1013 + total_tokens: 1044 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4904' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What are the top three class labels by instance count in DocLayNet, and what are their respective counts? + role: user + - content: |- + + Need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet class labels instance count top three"}' + name: search_and_answer + id: call_yf2hct6b + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_yf2hct6b + - content: |- + + Need instance counts. Search again. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet dataset class distribution counts"}' + name: search_and_answer + id: call_y87ai4nm + type: function + - content: |- + [4e6737a4-4af5-43f5-9f39-d71449fa4a57] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_y87ai4nm + - content: I’m sorry, but I couldn’t find the specific instance counts for the class labels in DocLayNet in the material + I searched. + role: assistant + - content: |- + Validation feedback: + Please include your response in a tool call. + + Fix the errors and try again. + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '705' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to output using final_result function. + role: assistant + tool_calls: + - function: + arguments: '{"answer":"I’m sorry, but I couldn’t find the specific instance counts for the class labels in DocLayNet + in the material I searched.","cited_chunks":[],"confidence":0.1,"query":"DocLayNet dataset class distribution + counts"}' + name: final_result + id: call_ehty2gqf + index: 0 + type: function + created: 1768403393 + id: chatcmpl-338 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 78 + prompt_tokens: 1069 + total_tokens: 1147 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3110' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + Generate a direct, conversational answer + to the question based on the gathered evidence. + + Output: + - answer: Direct, comprehensive answer with a natural, helpful tone. + Write the actual answer, not a description of what you found. + Use as many sentences as needed to fully address the question. + - confidence: Score from 0.0 to 1.0 indicating answer quality. + + Guidelines: + - Base your answer solely on the collected evidence in qa_responses. + - Be thorough - include all relevant information from the evidence. + - Use formatting (bullet points, numbered lists) when it improves clarity. + - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..." + Instead, directly state the information. + - If the evidence is incomplete, acknowledge limitations briefly. + role: system + - content: |- + Answer the question based on the gathered evidence. + + + What is the highest count class in the DocLayNet dataset? + + + What is the exact count of instances for each of the 11 class labels in the DocLayNet dataset? + I couldn’t find the exact per‑label counts in the retrieved documents. + + + + Based on those counts, which single class label has the maximum number of instances in DocLayNet? + I’m sorry, but I couldn’t find the specific instance counts for each class in DocLayNet in the retrieved documents. + + + + DocLayNet dataset class distribution counts + I’m sorry, but I couldn’t find the specific instance counts for the class labels in DocLayNet in the material I searched. + + + + + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Conversational answer for chat context. + name: final_result + parameters: + $defs: + Citation: + additionalProperties: false + description: Resolved citation with full metadata for display/visual grounding. + properties: + chunk_id: + type: string + content: + type: string + document_id: + type: string + document_title: + anyOf: + - type: string + - type: 'null' + default: null + document_uri: + type: string + headings: + anyOf: + - items: + type: string + type: array + - type: 'null' + default: null + page_numbers: + items: + type: integer + type: array + required: + - document_id + - chunk_id + - document_uri + - content + type: object + additionalProperties: false + properties: + answer: + description: Direct answer to the question + type: string + citations: + description: Citations supporting the answer + items: + $ref: '#/$defs/Citation' + type: array + confidence: + default: 1.0 + description: Confidence score (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + required: + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '696' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: 'Need answer: highest count class. We have no evidence, so acknowledge limitation.' + role: assistant + tool_calls: + - function: + arguments: '{"answer":"I’m sorry, but I couldn’t find information about the class with the highest count in + the DocLayNet dataset in the sources I consulted.\n","citations":[],"confidence":0.2}' + name: final_result + id: call_f8aqek0d + index: 0 + type: function + created: 1768403395 + id: chatcmpl-461 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 79 + prompt_tokens: 549 + total_tokens: 628 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4560' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: What is the highest count class in the DocLayNet dataset? + role: user + - content: |- + + Need to ask. + + role: assistant + tool_calls: + - function: + arguments: '{"document_name":null,"question":"What is the highest count class in the DocLayNet dataset?"}' + name: ask + id: call_88vgyw16 + type: function + - content: | + I’m sorry, but I couldn’t find information about the class with the highest count in the DocLayNet dataset in the sources I consulted. + role: tool + tool_call_id: call_88vgyw16 + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '436' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: I’m sorry, but I couldn’t find that information right now. If you’d like, I can help you search the database + for it or look into related datasets. + role: assistant + created: 1768403397 + id: chatcmpl-141 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 38 + prompt_tokens: 939 + total_tokens: 977 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_search_with_state_key.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_search_with_state_key.yaml new file mode 100644 index 00000000..7cccfa60 --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_chat_agent_search_with_state_key.yaml @@ -0,0 +1,815 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '730' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Class Labels + DocLayNet defines 11 distinct class labels for document layout analysis: + 1. Caption - Text describing figures or tables + 2. Footnote - Notes at the bottom of pages + 3. Formula - Mathematical expressions + 4. List-item - Items in bulleted or numbered lists + 5. Page-footer - Footer content on pages + 6. Page-header - Header content on pages + 7. Picture - Images and diagrams + 8. Section-header - Headings for document sections + 9. Table - Tabular data + 10. Text - Regular paragraph text (highest count: 510,377 instances) + 11. Title - Document titles + The Text class has the highest count with 510,377 instances in the dataset. + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: kXgbucxNPr1AUaw80XgAPSmMDLrPXDc9S1JHPTYNq7pwHD88bqk+vBAwK7w59iM9pPoxO2XbabtXIBy9mQuDvSTQEzy3a7W84SckPTL8Krtohvi75l4KPALzWTwHIs88JO3BvC+hJ71y4KK8Ek/kvKVKSDwyCLQ8GrlnPf0SEr0WJqo7/h9GvC73AbmWnTu86OJ0vHlNJrtq2wm8yr00vcyqAzzSIhw7Iqa6u1/8LjsHaVm6WusduzZHSTzyyhs8v1H0vCeT97sE4ok7k2n3O/qtM72Wreu7o4kZPUS347sEd7Q8NsEFOgaZiDuUOAg90Zylu5kWy7z0zJy8erOXvKTSUrxA74i8WieBPLYWZLxjrIg8GGFmvBuc6ryBVwM8BTOrvP3uwDvPqFY6Sk7fvJG1DLwg1wQ8vAGLOt9zAD0dRp08Iuo1vCmADzyIz4w8w3gzvDEgoryMweU8uYMhPBnAubzhV2c8u7XQO2ObZjyj7ue7ic2pPKgXYTvPniI7xGisvHpE0LoiKnu6HpCdugit3jsjspe89EhxPBLe8Ts05wc9sAGkvLmz4bxefgk8rSgIPHMZyjzwdRA86QA7vM9X7Lyfjxy8JvNzO1DRabzT7iQ8kwR3PFTBfTzwJq07At8puwxSILtTiCy8233ju6Xct7pS+oC96BaAOy+Zw7xb5/g7xThGO3PQhTzBvEa8+Fs8PRcmgbz9y3I7HA7YPPkInbzG7ZQ8cQVIvGgaSTxL2oO85LvAu67UQLywbw492BAcvAPAFL3BA0U7X+gXvfthiTwEp267UGWIO0ORg7x7gVE8GpeNO2IrHjzNX6g8y+/ZvIa5BzvjQzg6pyMsPOb1tLuKYJo8+W+TvCJj9zwCjfA8CE6qO2keDTyWuf26L3qXO0a8Ab1ejc87t/l2vK8MWby/odK7DhGSvBypK7z751+78rc2u5I5abyFIzg8K1wxPL8mWz1y/RI9SH6HPErlLDwD2be8fSm8utR87bu8Dj88GwIhPF4RMjznc1u8n5Y9vPAuDzxdxsW629WkvD3UDbx2DNO6LY7KvGoqXzxZFsE6V5xlPFY4qTz9mhK8cP4ju/THdLzwmiY8Gu63vMrbYDxgbCi8VgmsuleUdjzTRES8FKyePBywfDv9SKU7WA+9vGWxX7wiwaA8v3fqO3HupzviTQS844c8OTIU1jr6Rou8+UJyPLpU5bt9NYm8HXqyu8EIbbxjFoQ8V0bLOrPFG7xndvQ72O0UPE0VoDp7xdy8AeFWPCmSjzzC0w69yepCPdr7Yrwf3sO8u4eaOmhOwLtY9XK6+cnjOsjLxrxT4Sm8HUyxvDJsgzzNvrk7Wg+JPKbOQLyynPW8wW2gvAiGPbtaKoa6C8hDvMCZtLteB8u6BGLXvMtqh7zHGcq7hR+MulKVyToAxLM8/tesvOcaGrzOc1U6YllBPcwAzbjohQ07igf3Ow/PqTz+blu89yCOPPqvWzoaF827CocxPEs/3zva/1w78u6/vLLTk7vKdCa7HzTZu3LHnDxCgAm8Wm0IPO6EmjxGUcY8jgS/uyKZVDywJYy8t+2YvGAjrbymOlA81F02vK4fabxiJg0818VQu+Ns9jsj+VQ7f1lQPL02h7x2s7m6KgqFvFsVPrz3SV89g0xGO4BHSTwgFpA7yymAvGZt+bsvcgU9fAjbvMErgToP+o883JD1vMKJ5by0ZiO8NzP4vP08mrzZSeS7vkabvLWxAT1EJbE8Jb6+PJI9CD0Lu+o7DtEBvKtJAT3rYku9Kn0cu7pYFbz26iy8Pnl2vN1C+zw1Bi08hcOJOq4d1LxDNoc7t5LUuzDgvry8zRG9d+Q6OopWxbxGKNy5R5m/vJKkSDxO30W96tdxvAzfCb06GwS7dBO4PFEJgTtfJ628tn9yvAt7CT31+b28Ii/PvAVqrLvsY4c8aStKPCRlCb3x+5a8T9FavBzG/jzNB/Y8Fu2UvP+aoTsRe7U7KRAhPe6AmzwKzw89QGe9vDdWjLuDlRS8ohnju5lSmDqxcas7l1/au0GiNryahbc8f0aWvHtaxbu9cJa82c1CvU0okzzq/t+68wFnPDGI7zwEX6U8Z6OXu5KkAr1iS+k734RzuTHCvjy27AA9BWlEvdqRTrz+qiW866w2vFkFsbys6aY8VH3bvBEh1bzrEek7qyLJu3ekyruk1TY6ktulvGqiejwVyKq8SeghvdYPLbsXBk48pGPuuQ4EGbzRQww8xdn8ugVNy7y4exE8txH/Oy40ZTslNso8nqf8uzxLhjx00Hy8fmHqvM3GEzt6Rz89lzbGPDJqCT1xYlQ71MYDO/1ifryVdHi7CItNu2QJzLw+URc9XkI0PINVfbxl+Ko8CLXpvAmbC7z5E0U8eTbrPIXr7TqL4uK81BG4vFYZ2zo2D3m8y1d+vPF+67vXcbC65GQIunnn07wS+2+9gtpzPDv5Zr1tdqo72ZArvJ58j7waM/M7JljMvMpgE7v+4E28984bvCqqXzzTQlk8wRhQvIYSejz17Zw7oXitPGzz6DzeiCk86tqlPHjDgry8fVU7lbGyPBS/vzzS1u88+F84u8XbUTx8/e086nVSPRdozbuRQqa7MSYQu5+JQD37tTK8GoW6vC8fC7y4xO88csdePL19OLzf8eE6xKcvvC/9nbwDj+y8a8K/vKsX1DxJwoY6NP/Hu5lMVT1cnXK7MDfXO63Yjry6EHM81p67PEP5oDyj21K7aQ/MvO8Kdzy3Gfq8X6AAPGdKtbw3h1K8FoDcPEwYs7zB4mW78OvEvKtyJLs4NAm8lGPAur4RCT1Igh48oEuauzt3kjxBJyI8s3uAPJ9iRTzpfRm8RqTTvJYJdjxVIxm80wRUPNgX3Dx2mVg8smcAvMhfVDzG6Xo72OpDPHiEhzrlfju9ZD0sPPC1CTwMstS7J0FXvNaiH7waGS29FxmuO0TRwbupaOi77t6EvDVQ17t3iCQ87LFFO1MfVrurj2G9FxXGPCQBAj3WN2U8tfAUPJIEr7wPdhk7kRcFvXZ/trxgfAQ8L4qDvFpjEzwbP/87tv30u2UilzxGGzU9nof2u76JB7251Py8LLe6u68LWzwtZHA4CqxovI1dmDsJLAi920HEO4lhPjyTv9W7yovYPMZMzDvJOOw7QpbruzZItjytx707z7DZvJ6+g7tiZsO7lJQpvRmXALzHoNm8TZGruyjzfLyiF7G592snPcmikjtnoOy76o4rPNKwirxHQ0C7MrLKvLh4wDz4Dvw7FbkbvN+dqDvgzA08mf3VuwS1sbwZlQu9o68IvYcHEb1E7Hm8bGEIPPb2hjwr28A69vqcvHlLKzo8aKE7DqjDu/kRKDkXcy28+J8QvIS2cDvHmHI9aB8KPer4hrxWACC8IWXlvG3zPbuUeBq8P0j1O1WhFD1cvz49cQHvPBq7cbuIWSY94WVOPJFGV70jgvi8p+j+OlbMCLxrtX+8jsLuPFM0i7u20W08gGyGvDMR0Ls2wts8rbfbPLrKNzs963I8Nth1PCKSCb1cPZW8YnOCPFmvgDwIEoE8/YG6u75PXTyVUOk7Hhi6uxWiAbzkwgw9KebsO+2zibxGfDM8wB60vEDqB7wGGbi8nckQPNu8w7sa/EA7i19fPMx0/zv3Ium7FVKPPJ/f0rx5BhC7AgpyvCcihjxi3408kSgYuw5vArwQ9Di8xWuLPPd65bpXSPw8xIQXPazJoby/Dxa90UqxO8MK2bv3LYG8jr6mOv0mCbxB+0m86MlNu3IaXTsZ2qi88LruOxR0bzz0/Js86KF4vAYM17x6lNU8I0wRvXxC+zorDRg8MeutPOEqhbwn5MC8Ai4QPOLpnzyMT5684vPiO1oD1bs4PQQ9xfOgu5b0sTy1igc8pD6dPSXl6buyWVy7XY99OxK+gzuOafK8XnzAOzH1DT3vxGW8Vr4bvcU1DTxiuog8nn4FPLDiyDycAYi7pI8/PJgmBzxOASe9dAmyPO+lBLpeQuc8GE6ePE3Bfby5hKM79f8HvVZCrLt3TSA8WHqbPHwDiru6YV08nX8DPaL6nzzACri80eFMOy3p8jpbN3W7f6aPvMON7DyVc/s7AerEukm3gDtFGrI7a7kpvNnIPzzCFvY6MkpvPW5pybtUpnK5ZZmSuR1zkjwPlVc72MNKvbIa17tIUbo8nrngvFGY17sLHe+7K4bfuipdXruQCAY8rOuFvC1vMTvuZCS86I50vNpnXDw9JD88cL51upw2q7xPE0686fIcvUN4XLxItcM88yOYOGjPrTtLNUg8wGjmvKuBATyd4Z48JWAfPXiTo7wHmkU8CzpVPBUGIbyxuVY8AVvZOXNwirzNe5+8ensDPeuvSryvASi7IlkpO49kEj0puk27j6NUPJgJKLw0N+2757pKO2UIvzwGJ3I7h4zSPK8bFj1v4Oo7AMkHPd6NhrtDayU8qDH9PLKnpjxkCPw8Ak7QvEyqhjvJSgy89pwtvAfAqrx+Ms0809vavIULWLxhZrM8KRqGvNirRTzsHnY8h+VwO3odg7yZO0c9DdzvPDgk4Lr4aZW8yZmnvKhgLT0MFLc8EKoXvLYhQrzxspC7cqqqPBHrNryjHdK7gqWyPKm1zLzrQXw8NPz9vEXMQbyx9Xq9+44CPefNfbtIUAy7cFbEO1crfD1qg6C8gDYdvHh/GDxQD3K7rNj1u9HEHj3N57+86xiNOxr/+DzYTYq8ZNkVOwuM4by+dQo9j11gPF6YxrzxSee8/o9PPLEBNLwx4ZA8mrUXPMMX1jxrG86826XvvMN8Vz0Kuyo8AOFRvLioXjysdcO8pjUTPXw5FTyiMao7sAhqvHF4BD1eRSg8F+SJurxBD720rdK8YS5pu/Xzx7mDXRy9wzsxPMCROrlqPqg7bWNgvVZnBbwfjdm7Fp0HvMAQjLzoJBw8h9i+PCdPWDt2AZq8vhSRvI5AhLwoiSC8zoqMvLh5SjwX8jy9sGFOu9xKVj0N96O7fdUqu6o5iTxh96E8WfiFvK+GVrw8Z8M8yhTTPOcV/Do+SS+7953wu4I34DswIo08MoKLPNpP+rwm3LA804EJvBjOHbyX1dk8AXwQvTIZqrwiRPu6BG+4u7lHnDx4H/Y6jBVtPOMURDzq4iW9a3a2PEqKsDyhrVc7QDXyPJZrrbs9Ly67YWq2OJRuejzp99K8VTCeOgrPhTu8Cxq8cykVPM8vAbwDpDI9j625vO4WubrEff67R5wjvChJL7wMFCe870+UPGAErLzid6G8bRQsPLTisjuqQp881AicvK8++LtvaXg8ZIyCOyRpGz0cWls98SrvutwYnTwnj+q7oAUwO2zuIL3VbxU8B8AHvPpFi7wQ2m26EesKO8GkEz3CiGC6EMAaPUqy17yrvLO8gO+BvLI3jzzveGw8jGISvPHxl7qbf8I86G7FPGk7pru0ecY8oVeSuwIq5TtCXGw8P6DJO4N72ru56Bg82iXVO5mcrjyBEqc8MmkIPc+E1Dt0AKc8ki54vRjg8rvdefa7VrKdOmEW4LyGdCC8yRrAu/NNx7yJ0xw8LILhPDip2joRZ0G8huIsvIQ8V7w9UAu8ftN7OaXgXrwuTAO83wYOPY0SmryruTE6xBxJPNalYbxJKlG7KOuDPGnIzboU6re8IylZPKd2iDyWQwW89q1APAlcSL2Dm0284GAUvVd0FDwT/7g6kOo0PIoOoTzpTD68WJUFPNxjjbxTBtO6o0ZwO7XVvrxbsly8Hjw3vfwzpryv2tg7CwGJPJZGAbxrInK896+/PMWtC7w0pYQ8/Bn3Ohf03jzSzIY8Y3pbPGy8mTwbyPg7ftBvPAs317yYNe88EXjdvF9wLbwKwok7OfsOvYyNVTzY3H86Dsz7OiI1mLxYIPU6v7sqvXGOE7yVIBE9SRsMvGucULvZBr08GQxbvbG9lDwnJxO8Z8nDuSllqTwbnlS8cF2nvM1JtrznhFw8GbvIvG8HZTp8Grw8SKLSvPIjBD0846M8VrMAOwp1cTwB11U5hhSTPELFibu5peC89PeYOr0eIzzotpE5wOYNvHwFiTyWjZu86DG0PFUl8DyW2TS8LvldvNn7Jr2+H7c85bHMvMUkKD1q0yU87J0BPOd2Tj31mSS8gvKZPGY+oLynZIS7KaaHOnr+9zxU9Jo82B6Yu2Bs0rsRnJs8pEyzPNkQ8jsGdhq7bLM+vO3aZDrhpIS8pETMvNG6qrs7uZQ8H5Kquxdtr7sx56Y8hBCzPEj/Db1z9J07kbjfOzc+MDsTROu7OzrbO7uWiLyg9aY7wIZXvBzWXrx5Hiu8bW7bPHikG7yw8g29YAlOPMc5L7swkoA8DX0NPH7XJTzxqA09rFCDPDAAADyVGSu9mYAVurvRbryt0XS8IQQcPPA4nL0oEjG7Qc0fvEKZorwfEd+8ATm1vAJKKDxLrTE7ibr2u826GDyTd9Q7Ue0JPfFodzyG68A79fMovXDoars2hIU6gUuPuzWiFTzWvIK8Yzj5uz/sbrxAmB68WbvSvO/WFzxbdJg7EnihvFax77yqqS07+Rw0PS2cFT3fqZ88b84/PUEHtDxRgaS6/xmYOuTZyLwB0yc8fg+AvEAb57sFEdU8qyFHvMadfLzEFkE8M2sYPbxu8LxrYdE8/9S1vFpWIDzxO4c8ADCBvIhtxzzlo1291YFOPHSAqLwZlnK8Ch5hvPg6dLsFwaI8R6nEvBdN/rvnvkQ9N5pMvKP3cDwhpdq8Jd6iPHDsMD0df4+6Co/lPM18CbxqyZi71WINvLWVrDy1Z7G8Rza+u+Wj9jzozw27a6wOvcKt9TyD2dk7eC6RvImFiryUaUU9yIlpvPJZWLyblAW9iALQO+vwK7z9d828JRbfPGDEirxjVFK9ZgsDPNHxsjrQMPK8MZr2utyhgbznwII7lMELOxQps7s3xD+6Q7mvvIcP3rzBiR88w39OPPW8vTygCP08nXLdu1fXv7sjEku8TU1bu6xGIjmPORs8O/MSvc3fRzwdbeE7YcHhPDFgN737mMi8HEsHu7s0RryxjNm7BKCtuz+XlDxH0UK6IySavM4psjxGfQ26PnmmvK04XzwAbpA7lPAtPIrmhby1CRA88/h4PO5B+jynXzi8ZpiXu/JfmDuE1ww8mDGAPBvoGr14yrq8TiKPvAmBOLzpgwK8muF7u9hqUDyQvIi93ap5PNjrErwThVO90P5PPPuJ4rt52eS8fzgvPM45GzyyuZQ8ekBDO57TJzzIrPu67JHtueUdDLwRXqi8gYuzPHw4X7l57Ik89itzPDvQ7zwihgg81xouvGmlFzy0P1w8qX2jOviwE7yQMBG9USPFvLOdlTwbZga73h7kPDoMWL2GSQS8LvANPWZZu7rqEg489SDPOzTiEz1FUBc9zsHRvAs6STxQCAG8Wuc9vBS23Lw9+C+9pGMJvTfPaz0AfMM7XyMNvccp7zxc/qs8zLCPOpK/gzvITSC7VVcgvFDCoryK+XY8zvELOt4si7wpnbq7qNO5O2XAbLyPT4y88EExvM2O0TzBxGW92CKGvElQyDtV77u8ovEDPfQUCbxy0S26xsc6OqYdrjxOrSi9Rsq4vKtUAryn4kC7cIYnvLpLGjyycCG7HWneOgSOTLwd/Vs7DgOxvA7BpLzLzSu70zqRPM3DEbqAz328arpDvADQjDy7oRs8L5o2Ovmau7vY4qk7CyHruqsI/7yxLxw8cOPTO2ZaCTyDbg88AoaSup/nh7x96fk8jsn4PA+XFjy8WG27/hpAuzpnM7zQQ5q8bQAKPIragbzgGBy896EVO59h7zs4BpA71FtFPE8Shbyb0eK7mi0UvUMsBr1n9zi8zhervB/dAz2cgek7cOllvK2VOrww3DE8qrBIOlGDoLsWrdI8TrU0vMYPpjyZp2K8GGu/PCl8ADud6ea8wVjoOwtX6zszAZ08w5RbPFo5izuS9T28CNtSOxZpvzyeU8y8K7sUPWcf4bxhQfa8QZ0FvX3u97tBQYe8UOgCPPx2jLyVHAu9wEqaOo7tKrwxIye8wt+6u8YnGjxLNmM8Z4vFO7q0Dr2J1xW6qIv+O4xCTjwXUne8P4rBvIcW6Lx5b5c8Q0ikvBGuvDyW3Oc6y4BBvAu2N7upnyW9aFlUvJffnzuDgiI9RG/auzrE3zoZsro8dcEdPMj6OztL0NK72pPou1xr6rz+xt47Ivk4O/HRqLtpVTA9T+cQvRu/tjw5C0Q8bU4xux9VLTxmW/07tMscvAJ4jDzZeyQ8552oujn9Qjt5ODK8uGzfvDVMgDxSYba8jm8hvf8hNDwct/w8CnTjO+Q9RzysVX480biAPHxTKj3uUza9keCmOwaEz7zIt5a725IiO6TQlzw893o8bEfRPNaK1Lq668E8U/OJPF9AArtxupi7Rx+CPJHlIbwYW3O8fXOZPKSKirt0B6w8+KY/OsPyl7xUthC88CElvFLegDyWFhk8+jwqPQ9jI7wmlKs6huWlu1/f4Lz+D9G7U3M6PM7G6TyPwIu8+naUvPtn77vbCvM8+EO1PKuT0Lzit8u8sIIYvdtZYDzJxbm8UwA9uiLFOLs/DOS8xYyGvFc3WDx/4w28jc8oO9GxUDxWoz49672Ku/NiP7yClww80zC8O659dz33ltM8Aq9QPfcqzzsG2hU8eMyGvEW1l7v5DfI8oFY8vNLnR7wUMKI8FtuduqtJ6DwWXKa8V7jouwoNcTu07By8HREYuYfNLr0ACOs8VUmHPFiBt7tnS508RjHZO2QXXTzSTuC81JKAvCyAAL2v1NW7ultQPHFhZzzhP8o83cW4POU+aLwo+PI8v5mUPFCJ7Tuy7QI9e3Pau7SlBrvYbFQ7cJPXPI5Zwrqt+FQ8UoXDPAIWvTxj8oK82hZLPMuFqjwCewA7kQoevR7IDTzJpte7cOELvURwoTxtQCc8y+IwPQz7XLzFjwW9tFR6O3SDXzu5GoC7e8rbu1+W67z6ti08gKUAPSRJY7w2zwS9UrgTvASRMztc6Y+8a6GjO5v52TzjOig9/MspuV0zADqUE1S8lzEnPZwi7jy+HDG92kU7vDHoj7xubeC8+N3WO5d6pzz/M+68+9kyuo9WTLyoI/S70dudvH95/bsgmGK8h/S8Op42HzywMu+8+iuYvA6YJbyKoQ89YIQWvLvImzq8zxk8HvKFu1g6VDw2PoQ8xHCwvMgr7LtNV9o8ocQoPBKXWz2MpaI8MbTKO/V/A722IRC8oPFePI4mILzVZZQ8qX2WPGX6g7yeOMS8lLHIuzpah7z97FC8KU3AvH2yBLwSLVG8rGwOOr4HuDyDI3O7QKehPNsHJT2UvM48zbfLO6Z0PTwIfR+7ZXSRuuaCIDx7U6286I6XPPASLLyG2bi8R2WhOm+qxTzQ1YC8TeAiPKfI77w44gK8ppBVPDsO1jqP2mq92CUVO19DCLywDzS9WUcFu+inYjw0Ldi8BDEhPc+dLzx88xy65Xi8PCdlDrwXfzq8wSlyvNY5mbwaD0K8C1kPvQZu6jyhzh494UoTOmNiOTyP15q8kdHOO/HDVDqtCka9Gt8IPHnxfTs+eK+8h98RvRC7ObxDzTi9yIXIPOAbL71wAsk79Mo4vE1SmLyAdEe8L1VavNI7YDxlPww9i8PgvNCwNbx2rVU7fpM+u2LwBjwbaTg86CeIOy6I0DvgL3K7+IX9O0QsXL3E1EE8Kw5fPL+zoTxQhlK8tXQSu9iezrwgSvs7BE3hvMBClDyDEbe84ZBuuxji5LtBC5I7KcwjvBnNarx/9Je8xgDgvMHrDLwsw3i82Pi/PMUi57w9Ass8r6k1ux+GGTzugye8mGlBvGdyFDzUdZi8qZuyuzuy4zvu+xM8rB0RvUbo/7ssHsM8Sz6Vu+c6BzuS0l68gWVVObnQwLu6eoa6KUG1PExehTuwZO28azskvQi3vrxqXc680d2LO+0ML7ogz368ezgoPNNKOjs3h1m87R1YvPqy0rwNxni7dPgGu+wqOr0AozW793AEPYiYGj0bkrs78oMOPRvrvDw2Dw+85gdBPDYYm7y1Duc8wpeyO3yZU7wIXSQ9+crkPInz87sftda8jD+DPJmSqzogx4E8o2OpunLDS7yiiky8n7KHvBv5sDwPH9S8XmsbPP8ui7wGygC9KznDvJNQWzwgZ/e8WNsuu2fcmzys83q8pg9ru0l1e7uR3PY8D5HzvPtY/DoTTgo7AO6ju4BigDu69Fg8tLUMuhvQULyYfFu837QUvD0mwLqxqQ28tJdAvHYUF710xgA8xbDTvNweDbwyAC67UP26uqxufjy3XJm8IC4POUBxt7sZgLq8KztfOwzXB7wMUYI89AdOPJkRPrsugqu8vA8yPW+xiLyqSuO8p8aFupLUlbz2Ih685NlSPCd9mjv5ayO8kETyu42fdzx8Omm82j/cPJDTJTzbscU7ETecOpqVsLzuKTY8YfuEPFznpDwQCOI8rA0PPFhQC73CjoW7zquYPPyl4TxD1s+8I2bMu3026bt3XqG6UiMJvI1kSrwHkEC8AA0nPbBr87rrXpk8m5jjOsxkCbpDNbK8YJKZPJPvMbvGV8U8eKtGu0LLpDuO+V67WaE9PZypobzM9W07eQ1lPGJHxTtgCeg71C6hvBx6gLymLow8MHnvPJMMGT0Nd9k8cXWHvEZVcrwQVrC8mKwFvPkxCj0cIwC8eUHUuzrpArwEOky6YP+Tu8+DA70NLIG8ufH6u+P1Ib2S+oU7JB8jvI9RDb0GFZ28Tbc6Oix3RjxG1au68/dovH8bLDuq5j+80WwOu0HzjzzCyh47aLxovGVzXzyL/T08vcnkvLfjcTvWMd28N3TNvLYH7bzyruU8dTucPDpfEjxmL8q8rMdJPPnXyTylLi29eznpPM5GsDziFU87ZHMRPDm5CrsxNwq9x7XJuy6qeLxJF4i8oUcxPBJc8Lwtk1M7p++cvMdekLzpp7K8cs3quwQcrjwlip66K3E/PNNeXDzdluG7CyFyPNNQXzyWEIe845UUPP/TXL1v+D68PBXRPKP/yDz2JVo7ZsN6vHAitzxuMVG8UjkTPX5DhDy9DjA8Q9iQvKTmmLy6Zly4508YvHaj1rxtLMy6objRu6qDBT1sSio77MpBu5u8gzptFle7ATyiOxE7lLylwqi8qGirOIg1ejqBSEY8tc0vPThUAD0YSIm85L3+ujrJDDtNSIe8fPqKu6JYyLy5gI+8s1jmPCDzkbwOoDA7UlW4PA3l47zq4xi8ei2lPCl5xbwk9mi8HN6evJhq6rtE91O7u5Wcu/UTHTyLkwG9jfWHPPoxDTswn/s8TOXvu6cyrbx5qVo80puPPEF9ZLwYT/y7+mIRuzJ7bLwUYIk7uFbcvN0kZryT1gm7Ylu7O3lyVrwBO/m89gd7O0XwnDzH9NO6TdO2PMnd6jtTqj+8A+0ZPU7TbDxr/qI5DX9UO3AydLw/X0U6YKUyPVX5ojyKTQc6LCFtvB9LgLycJoO8+SlBuxkYRrxa7wc9IMOIvDTckry7owE95VcwPKD3LrskRB88p+NXvHO/yDx9wIG8NmANvUT7ybuvY0I8EfAOPPZHADz5oO28PXfFu0jIYbwFIAc7ZBXPvC9onDzjNAU8xYtpPC5ljzyPJca6ftgAPLaKsrxBlYw7vaWKvOnz5rzQ6Hm859htu/sdlDxdkCk9DBDKPLTCCzxrkhk8IdYZvRLz9TxKl4K8qQX/O/6hEL17F2G8171Yuk6BB739o/O77000O9lwlTy2zC28k7ieuy2IWz1i8mW8qjEnPN+tnTwCYx29/fXPuig8dbwXSvc7mIxnvPPNaz2S67Q85Ts5vGqs/bvC5iU7/KBhPFeBr7tN+k08BZIqvCJVjjztlG08KRIZPLzv9jwy0AY8Q8RwvCD497ua1rs7mTriO/5dIbxIMh89WR79vMTjcryBQ5u8BnOAvJkqoDzfXAC9BFXlPFLRJr1FEwc9uQ38vFyS47yOLeC8ulBSPLONmrtQU/M8kNyQu8JeAL0x8gO85dszPF+3NzyTH1E71QaFvNimPDwwjZa7niOnPOH34zxqyLS7DAsKvJuMFz0PIQC8OD5NO6FMHD1VUK87+4Oju8MJnrydduY66xVAvCEFsLxpu4a6QQYJPfhfZjwNwQK9i6NnPBpRxLxGFCE9RJIAPXDhNbyyRnO7LhfFu+6KDTz6Iyy9FImmvMYmabwEb/a719yXO/fEybyc0CA9tvtfu41o17x7Lw88Cc1DvMEwrzzENZ68XxMpPMalWrstBzM83JfvvLIRFL3rWze8YfgnPYLazToLD0i9sAm5u5JqGbxD+fI8TDaIO/jCXDnLe3M8wxnSvNyll7s+lwK8QV14PEdJkjykDV08IweCPJSehTxRWq678CZJvId1aLyOLp280Y+TvA6DP7wGH9g8PinWuwFSjjtZs+q8P56wuzoCA71JHYe8nmRVvI4Ihzz9S4A8hJWKvC/sjTwlyrO5qzfKvHJwijt3mJA7QTiguiH8Z7zlFSM8oqYZvP+nnby/ULW8a9sOum06A7tJI4U8UuelPMqPjjw9RgW9eMvevPLmADyMHs27BOG2PGz+vDss6sa8l1wGPBowID3bry28feAdPeeIAb3FNZi7h4fauwkfO7sLTxu829ckPLI5zTwUDSO9/F0WPGPPFjv1q3w8LvZuPDhjObxowCs91v2FPGBugjzrKTM8hXxsPH9M9zxPFBo8jww6PB3mCT3CYfS7KL9rO6i3tLuhgo0809nUOzvbNb3BIcI6fY3CPLqmXDyIy7M82I+fO6gwU7tVWYa87SUePZMKfTsyhwo80nsWvA8JjbxRMIE75LgXPMIfzjyUegE8bwAUu0+fC73SGo+89isMvdedOryKRYy8QR2UvEE2cLzIAlY8w/i5vOLzNryJIeM7bD2Qu7dTozuba9087gKHOyaSBbxTfDI6kDiwvNSWhDuBTz67AqVWuz8eFruqDrM8xkYbvR4MxTuBFsi7zAqVPKO4WbzTDvU65uuzPPo32Dslzg48CLFkvJ1RwjylZym8tPUsu0DS4LwkZw08tsIUvUHSdrwPo7+8mPNbPcaw3jtb0yO7EDA5PPe2rLwBv8o8iSwmu6h7Gj25HNI8NvIfPbFVRTzr5BI9hhmMuv+Vrbz32eY8Z0MTu3bkBLz8OcK77krJPOSaBTvHKR27edT7vCn+DLyWi628lbPLO2LOyDuXXfq8/eouvXzArDxa4Vo7+JwQPD55iTzkGzG8R96cO4B2qryBoGy5eqKIu7CkIDxyF5w7mllSOwAIvrwJ0GY9yC2evLhCvTsFWLE7HO+VvIoMB7w2gYK8sE6iPDEGsryRNdy7ZzEWvB1Wp7us/Ou61pmBuQmbvDxoKfK8TiEkPJNHDT3H2ee7HFWIPDupoTyIpNa6q8g6vKVTqDxkSKo7Bg19vNJZAbzTAY+8cZydOmtItbnr/ya8y18LPLeCmjz/mKc8g2kMvNVaTDyeLc48VDypO815prx14K48qCQxPG9ajLtW8Ca8CeZkvMEwaLqw0Y88fCVNvHIGuLxF4b+8helhOydxYjucVyW8N9NlOyLEwzvrOkm89oLJvNmskTxSPeS8ly+OvOiCgjwdR5e8rLe0uwS01DvciG08wqwSO/mwxbyVF6A7mARGOg== + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 166 + total_tokens: 166 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '481' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Annotation Process + The annotation process was organized into 4 phases: + - Phase 1: Data selection and preparation by a small team of experts + - Phase 2: Label selection and guideline definition + - Phase 3: Annotation by 40 dedicated annotators + - Phase 4: Quality control and continuous supervision + The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface. + 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: 90 + total_tokens: 90 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4081' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Search for documents about class labels + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '493' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need search. + role: assistant + tool_calls: + - function: + arguments: '{"document_name":null,"limit":null,"query":"class labels"}' + name: search + id: call_bji3vk31 + index: 0 + type: function + created: 1768403358 + id: chatcmpl-424 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 39 + prompt_tokens: 843 + total_tokens: 882 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1059' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: class labels + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '565' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to search "class labels". Likely referring to machine learning classification labels. Provide search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels"}' + name: run_search + id: call_gcwrxbl4 + index: 0 + type: function + created: 1768403359 + id: chatcmpl-9 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 48 + prompt_tokens: 263 + total_tokens: 311 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '82' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - class labels + 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: 3 + total_tokens: 3 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1457' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: class labels + role: user + - content: |- + + Need to search "class labels". Likely referring to machine learning classification labels. Provide search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels"}' + name: run_search + id: call_gcwrxbl4 + type: function + - content: 'Found 2 results for: class labels' + role: tool + tool_call_id: call_gcwrxbl4 + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '452' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"label classification"}' + name: run_search + id: call_di042ewg + index: 0 + type: function + created: 1768403360 + id: chatcmpl-890 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 23 + prompt_tokens: 336 + total_tokens: 359 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '90' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - label classification + 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: 3 + total_tokens: 3 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1746' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: class labels + role: user + - content: |- + + Need to search "class labels". Likely referring to machine learning classification labels. Provide search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels"}' + name: run_search + id: call_gcwrxbl4 + type: function + - content: 'Found 2 results for: class labels' + role: tool + tool_call_id: call_gcwrxbl4 + - content: null + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"label classification"}' + name: run_search + id: call_di042ewg + type: function + - content: 'Found 2 results for: label classification' + role: tool + tool_call_id: call_di042ewg + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '297' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: Search complete + role: assistant + created: 1768403361 + id: chatcmpl-58 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 6 + prompt_tokens: 378 + total_tokens: 384 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5071' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Search for documents about class labels + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"document_name":null,"limit":null,"query":"class labels"}' + name: search + id: call_bji3vk31 + type: function + - content: |- + Found 2 results: + + [1] **DocLayNet Class Labels** + DocLayNet defines 11 distinct class labels for document layout analysis: Caption - Text describing figures or tables Footnote - Notes at the bottom of pages Formula - Mathematical expressions List-item - Items in bulleted or numbered lists Page-footer - Footer content on pages Page-header - He... + + [2] **DocLayNet Annotation** + DocLayNet Dataset - Annotation Process The annotation process was organized into 4 phases: Phase 1: Data selection and preparation by a small team of experts Phase 2: Label selection and guideline definition Phase 3: Annotation by 40 dedicated annotators Phase 4: Quality control and continuous... + role: tool + tool_call_id: call_bji3vk31 + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '660' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: |- + Here are the documents that mention **class labels**: + + 1. **DocLayNet Class Labels** – Describes the 11 distinct class labels used for document layout analysis, such as Caption, Footnote, Formula, etc. + + 2. **DocLayNet Annotation** – Discusses the annotation process for the DocLayNet dataset, including how the class labels were selected and applied during labeling. + role: assistant + created: 1768403363 + id: chatcmpl-294 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 83 + prompt_tokens: 1048 + total_tokens: 1131 + status: + code: 200 + message: OK +version: 1