488 lines
46 KiB
YAML
488 lines
46 KiB
YAML
interactions:
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- request:
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headers:
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accept:
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- application/json
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accept-encoding:
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- gzip, deflate, zstd
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connection:
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- keep-alive
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content-length:
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- '730'
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content-type:
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- application/json
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host:
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- localhost:11434
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method: POST
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parsed_body:
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encoding_format: base64
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input:
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- |-
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DocLayNet Dataset - Class Labels
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DocLayNet defines 11 distinct class labels for document layout analysis:
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1. Caption - Text describing figures or tables
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2. Footnote - Notes at the bottom of pages
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3. Formula - Mathematical expressions
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4. List-item - Items in bulleted or numbered lists
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5. Page-footer - Footer content on pages
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6. Page-header - Header content on pages
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7. Picture - Images and diagrams
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8. Section-header - Headings for document sections
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9. Table - Tabular data
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10. Text - Regular paragraph text (highest count: 510,377 instances)
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11. Title - Document titles
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The Text class has the highest count with 510,377 instances in the dataset.
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model: qwen3-embedding:4b
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uri: http://localhost:11434/v1/embeddings
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response:
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headers:
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content-type:
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- application/json
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transfer-encoding:
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- chunked
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parsed_body:
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data:
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- 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:
|
||
- '5372'
|
||
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.\n\nYou have access to
|
||
a knowledge base of documents. Use your tools to search and answer questions.\n\nCRITICAL RULES:\n1. For greetings
|
||
or casual chat: respond directly WITHOUT using any tools\n2. NEVER call the same tool multiple times for a single
|
||
user message\n3. NEVER make up information - always use tools to get facts from the knowledge base\n4. For questions:
|
||
Use the \"ask\" tool EXACTLY ONCE - it automatically uses prior conversation context\n5. For searches: Use the \"search\"
|
||
tool EXACTLY ONCE - it handles multi-query expansion internally\n\nHow to decide which tool to use:\n- \"search\"
|
||
- Use when the user explicitly asks to search, find, or explore documents. Handles multi-query expansion internally
|
||
and returns matching passages with surrounding context.\n- \"list_documents\" - Use when the user wants to browse
|
||
or see what documents are available (e.g., \"what documents are available?\", \"show me the documents\", \"list
|
||
available docs\").\n- \"summarize_document\" - Use when the user wants an overview or summary of a specific document
|
||
(e.g., \"summarize document X\", \"what does Y cover?\", \"give me an overview of Z\").\n- \"get_document\" - Use
|
||
when the user wants the FULL content of a specific document (e.g., \"get the paper about Y\", \"fetch 2412.00566\",
|
||
\"show me the full document\").\n- \"ask\" - Use for questions about topics in the knowledge base. Searches across
|
||
documents and returns answers with citations. Prior answers are recalled to avoid redundant work.\n\nIMPORTANT -
|
||
When user mentions a document in search/ask:\n- If user says \"search in <doc>\", \"find in <doc>\", \"answer from
|
||
<doc>\", or \"<topic> in <doc>\":\n - Extract the TOPIC as `query`/`question`\n - Extract the DOCUMENT NAME as
|
||
`document_name`\n- Examples for search:\n - \"search for embeddings in the ML paper\" → query=\"embeddings\", document_name=\"ML
|
||
paper\"\n - \"find transformer architecture in 2412.00566\" → query=\"transformer architecture\", document_name=\"2412.00566\"
|
||
\n- Examples for ask:\n - \"what does the ML paper say about embeddings?\" → question=\"what are the embedding
|
||
methods?\", document_name=\"ML paper\"\n - \"answer from 2412.00566 about model training\" → question=\"how is
|
||
the model trained?\", document_name=\"2412.00566\" \nAfter 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.\nBe friendly
|
||
and conversational. When you use the \"ask\" tool, summarize the key findings for the user."
|
||
role: system
|
||
- content: Search for class labels
|
||
role: user
|
||
model: gpt-oss
|
||
reasoning_effort: low
|
||
stream: false
|
||
tool_choice: auto
|
||
tools:
|
||
- function:
|
||
description: |-
|
||
<summary>Search the knowledge base for relevant documents.</summary>
|
||
<returns>
|
||
<description>Formatted search results with content and metadata.</description>
|
||
</returns>
|
||
name: search
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
filter:
|
||
anyOf:
|
||
- type: string
|
||
- type: 'null'
|
||
default: null
|
||
description: Optional SQL WHERE clause to filter documents.
|
||
limit:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: 'Number of results to return (default: from config).'
|
||
query:
|
||
description: The search query (what to search for).
|
||
type: string
|
||
required:
|
||
- query
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
<summary>List available documents in the knowledge base.</summary>
|
||
<returns>
|
||
<description>Paginated list of documents with metadata.</description>
|
||
</returns>
|
||
name: list_documents
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
page:
|
||
default: 1
|
||
description: 'Page number (default: 1, 50 documents per page)'
|
||
type: integer
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
<summary>Retrieve a specific document by title or URI.</summary>
|
||
<returns>
|
||
<description>Document content and metadata, or not found message.</description>
|
||
</returns>
|
||
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
|
||
- function:
|
||
description: |-
|
||
<summary>Generate a summary of a specific document.</summary>
|
||
<returns>
|
||
<description>Generated summary or not found message.</description>
|
||
</returns>
|
||
name: summarize_document
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
query:
|
||
description: The document title or URI to summarize.
|
||
type: string
|
||
required:
|
||
- query
|
||
type: object
|
||
strict: true
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
<summary>Answer a question using the knowledge base.
|
||
|
||
Uses a research graph for searching and synthesizing answers.</summary>
|
||
<returns>
|
||
<description>QAResult with answer, confidence, and citations.</description>
|
||
</returns>
|
||
name: ask
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
document_name:
|
||
anyOf:
|
||
- type: string
|
||
- type: 'null'
|
||
default: null
|
||
description: Optional document name/title to search within.
|
||
question:
|
||
description: The question to answer.
|
||
type: string
|
||
required:
|
||
- question
|
||
type: object
|
||
type: function
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
headers:
|
||
content-length:
|
||
- '505'
|
||
content-type:
|
||
- application/json
|
||
parsed_body:
|
||
choices:
|
||
- finish_reason: tool_calls
|
||
index: 0
|
||
message:
|
||
content: ''
|
||
reasoning: Need to use search tool once.
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"query":"class labels","filter":null,"limit":null}'
|
||
name: search
|
||
id: call_5ipxdc53
|
||
index: 0
|
||
type: function
|
||
created: 1770983496
|
||
id: chatcmpl-160
|
||
model: gpt-oss
|
||
object: chat.completion
|
||
system_fingerprint: fp_ollama
|
||
usage:
|
||
completion_tokens: 39
|
||
prompt_tokens: 1057
|
||
total_tokens: 1096
|
||
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:
|
||
- '6032'
|
||
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.\n\nYou have access to
|
||
a knowledge base of documents. Use your tools to search and answer questions.\n\nCRITICAL RULES:\n1. For greetings
|
||
or casual chat: respond directly WITHOUT using any tools\n2. NEVER call the same tool multiple times for a single
|
||
user message\n3. NEVER make up information - always use tools to get facts from the knowledge base\n4. For questions:
|
||
Use the \"ask\" tool EXACTLY ONCE - it automatically uses prior conversation context\n5. For searches: Use the \"search\"
|
||
tool EXACTLY ONCE - it handles multi-query expansion internally\n\nHow to decide which tool to use:\n- \"search\"
|
||
- Use when the user explicitly asks to search, find, or explore documents. Handles multi-query expansion internally
|
||
and returns matching passages with surrounding context.\n- \"list_documents\" - Use when the user wants to browse
|
||
or see what documents are available (e.g., \"what documents are available?\", \"show me the documents\", \"list
|
||
available docs\").\n- \"summarize_document\" - Use when the user wants an overview or summary of a specific document
|
||
(e.g., \"summarize document X\", \"what does Y cover?\", \"give me an overview of Z\").\n- \"get_document\" - Use
|
||
when the user wants the FULL content of a specific document (e.g., \"get the paper about Y\", \"fetch 2412.00566\",
|
||
\"show me the full document\").\n- \"ask\" - Use for questions about topics in the knowledge base. Searches across
|
||
documents and returns answers with citations. Prior answers are recalled to avoid redundant work.\n\nIMPORTANT -
|
||
When user mentions a document in search/ask:\n- If user says \"search in <doc>\", \"find in <doc>\", \"answer from
|
||
<doc>\", or \"<topic> in <doc>\":\n - Extract the TOPIC as `query`/`question`\n - Extract the DOCUMENT NAME as
|
||
`document_name`\n- Examples for search:\n - \"search for embeddings in the ML paper\" → query=\"embeddings\", document_name=\"ML
|
||
paper\"\n - \"find transformer architecture in 2412.00566\" → query=\"transformer architecture\", document_name=\"2412.00566\"
|
||
\n- Examples for ask:\n - \"what does the ML paper say about embeddings?\" → question=\"what are the embedding
|
||
methods?\", document_name=\"ML paper\"\n - \"answer from 2412.00566 about model training\" → question=\"how is
|
||
the model trained?\", document_name=\"2412.00566\" \nAfter 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.\nBe friendly
|
||
and conversational. When you use the \"ask\" tool, summarize the key findings for the user."
|
||
role: system
|
||
- content: Search for class labels
|
||
role: user
|
||
- content: |-
|
||
<think>
|
||
Need to use search tool once.
|
||
</think>
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"query":"class labels","filter":null,"limit":null}'
|
||
name: search
|
||
id: call_5ipxdc53
|
||
type: function
|
||
- content: |-
|
||
Found 1 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...
|
||
role: tool
|
||
tool_call_id: call_5ipxdc53
|
||
model: gpt-oss
|
||
reasoning_effort: low
|
||
stream: false
|
||
tool_choice: auto
|
||
tools:
|
||
- function:
|
||
description: |-
|
||
<summary>Search the knowledge base for relevant documents.</summary>
|
||
<returns>
|
||
<description>Formatted search results with content and metadata.</description>
|
||
</returns>
|
||
name: search
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
filter:
|
||
anyOf:
|
||
- type: string
|
||
- type: 'null'
|
||
default: null
|
||
description: Optional SQL WHERE clause to filter documents.
|
||
limit:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: 'Number of results to return (default: from config).'
|
||
query:
|
||
description: The search query (what to search for).
|
||
type: string
|
||
required:
|
||
- query
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
<summary>List available documents in the knowledge base.</summary>
|
||
<returns>
|
||
<description>Paginated list of documents with metadata.</description>
|
||
</returns>
|
||
name: list_documents
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
page:
|
||
default: 1
|
||
description: 'Page number (default: 1, 50 documents per page)'
|
||
type: integer
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
<summary>Retrieve a specific document by title or URI.</summary>
|
||
<returns>
|
||
<description>Document content and metadata, or not found message.</description>
|
||
</returns>
|
||
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
|
||
- function:
|
||
description: |-
|
||
<summary>Generate a summary of a specific document.</summary>
|
||
<returns>
|
||
<description>Generated summary or not found message.</description>
|
||
</returns>
|
||
name: summarize_document
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
query:
|
||
description: The document title or URI to summarize.
|
||
type: string
|
||
required:
|
||
- query
|
||
type: object
|
||
strict: true
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
<summary>Answer a question using the knowledge base.
|
||
|
||
Uses a research graph for searching and synthesizing answers.</summary>
|
||
<returns>
|
||
<description>QAResult with answer, confidence, and citations.</description>
|
||
</returns>
|
||
name: ask
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
document_name:
|
||
anyOf:
|
||
- type: string
|
||
- type: 'null'
|
||
default: null
|
||
description: Optional document name/title to search within.
|
||
question:
|
||
description: The question to answer.
|
||
type: string
|
||
required:
|
||
- question
|
||
type: object
|
||
type: function
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
headers:
|
||
content-length:
|
||
- '1054'
|
||
content-type:
|
||
- application/json
|
||
parsed_body:
|
||
choices:
|
||
- finish_reason: stop
|
||
index: 0
|
||
message:
|
||
content: "Here’s what the knowledge base says about **class labels**:\n\n**DocLayNet Class Labels**\n\n> DocLayNet
|
||
defines 11 distinct class labels for document layout analysis:\n> - **Caption** – Text describing figures or tables
|
||
\ \n> - **Footnote** – Notes at the bottom of pages \n> - **Formula** – Mathematical expressions \n> - **List-item**
|
||
– Items in bulleted or numbered lists \n> - **Page-footer** – Footer content on pages \n> - **Page-header**
|
||
– Header content on pages \n> - *(the list continues with the remaining labels, but the excerpt ends here)* \n\nThat’s
|
||
all the relevant information I have on class labels in the current documents. Let me know if you’d like more details
|
||
or have another query!"
|
||
role: assistant
|
||
created: 1770983500
|
||
id: chatcmpl-782
|
||
model: gpt-oss
|
||
object: chat.completion
|
||
system_fingerprint: fp_ollama
|
||
usage:
|
||
completion_tokens: 161
|
||
prompt_tokens: 1190
|
||
total_tokens: 1351
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
version: 1
|