563 lines
35 KiB
YAML
563 lines
35 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:
|
||
- '5379'
|
||
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 automatically uses prior conversation context
|
||
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:
|
||
- "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").
|
||
- "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").
|
||
- "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").
|
||
- "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return 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 <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
|
||
- 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: Summarize the DocLayNet Class Labels document
|
||
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: |-
|
||
List available documents in the knowledge base.
|
||
|
||
Use this when the user wants to browse or see what documents are available.
|
||
name: list_documents
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
limit:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: Maximum number of documents to return
|
||
offset:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: Number of documents to skip (for pagination)
|
||
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
|
||
- function:
|
||
description: |-
|
||
Generate a summary of a specific document.
|
||
|
||
Use this when the user wants an overview or summary of a document's content.
|
||
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
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
headers:
|
||
content-length:
|
||
- '491'
|
||
content-type:
|
||
- application/json
|
||
parsed_body:
|
||
choices:
|
||
- finish_reason: tool_calls
|
||
index: 0
|
||
message:
|
||
content: ''
|
||
reasoning: Need summarize_document.
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"query":"DocLayNet Class Labels"}'
|
||
name: summarize_document
|
||
id: call_080b2wi5
|
||
index: 0
|
||
type: function
|
||
created: 1769523888
|
||
id: chatcmpl-174
|
||
model: gpt-oss
|
||
object: chat.completion
|
||
system_fingerprint: fp_ollama
|
||
usage:
|
||
completion_tokens: 34
|
||
prompt_tokens: 1043
|
||
total_tokens: 1077
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '1250'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
messages:
|
||
- content: |-
|
||
Generate a concise summary of the document content provided below.
|
||
|
||
Start with a one-sentence overview, then list the main topics covered, and highlight any key findings or conclusions.
|
||
|
||
Guidelines:
|
||
- Be 3-5 sentences for short documents, 5-8 for longer ones
|
||
- Focus on factual content and key information
|
||
- Do not include meta-commentary like "This document discusses..." or "The document covers..."
|
||
- Do not speculate beyond what's in the content
|
||
|
||
Document content:
|
||
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.
|
||
role: user
|
||
model: gpt-oss
|
||
reasoning_effort: low
|
||
stream: false
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
headers:
|
||
content-length:
|
||
- '759'
|
||
content-type:
|
||
- application/json
|
||
parsed_body:
|
||
choices:
|
||
- finish_reason: stop
|
||
index: 0
|
||
message:
|
||
content: "DocLayNet lists 11 distinct layout classes for documents, including captions, footnotes, formulas, list
|
||
items, page headers and footers, pictures, section headers, tables, titles, and regular paragraph text. \nThe
|
||
classes are: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table,
|
||
Text, and Title. \nThe Text class is the most common, comprising 510,377 instances in the dataset."
|
||
reasoning: Need concise summary 3-5 sentences.
|
||
role: assistant
|
||
created: 1769523890
|
||
id: chatcmpl-181
|
||
model: gpt-oss
|
||
object: chat.completion
|
||
system_fingerprint: fp_ollama
|
||
usage:
|
||
completion_tokens: 114
|
||
prompt_tokens: 316
|
||
total_tokens: 430
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '6132'
|
||
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 automatically uses prior conversation context
|
||
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:
|
||
- "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").
|
||
- "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").
|
||
- "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").
|
||
- "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return 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 <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
|
||
- 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: Summarize the DocLayNet Class Labels document
|
||
role: user
|
||
- content: |-
|
||
<think>
|
||
Need summarize_document.
|
||
</think>
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"query":"DocLayNet Class Labels"}'
|
||
name: summarize_document
|
||
id: call_080b2wi5
|
||
type: function
|
||
- content: "**Summary of DocLayNet Class Labels:**\n\nDocLayNet lists 11 distinct layout classes for documents, including
|
||
captions, footnotes, formulas, list items, page headers and footers, pictures, section headers, tables, titles,
|
||
and regular paragraph text. \nThe classes are: Caption, Footnote, Formula, List-item, Page-footer, Page-header,
|
||
Picture, Section-header, Table, Text, and Title. \nThe Text class is the most common, comprising 510,377 instances
|
||
in the dataset."
|
||
role: tool
|
||
tool_call_id: call_080b2wi5
|
||
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: |-
|
||
List available documents in the knowledge base.
|
||
|
||
Use this when the user wants to browse or see what documents are available.
|
||
name: list_documents
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
limit:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: Maximum number of documents to return
|
||
offset:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: Number of documents to skip (for pagination)
|
||
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
|
||
- function:
|
||
description: |-
|
||
Generate a summary of a specific document.
|
||
|
||
Use this when the user wants an overview or summary of a document's content.
|
||
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
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
headers:
|
||
content-length:
|
||
- '1334'
|
||
content-type:
|
||
- application/json
|
||
parsed_body:
|
||
choices:
|
||
- finish_reason: stop
|
||
index: 0
|
||
message:
|
||
content: |-
|
||
**DocLayNet Class Labels**
|
||
|
||
DocLayNet categorizes document elements into **11 layout classes**:
|
||
|
||
| Class | Brief Description |
|
||
|-------|-------------------|
|
||
| **Caption** | Labeling of captions (e.g., of figures or tables). |
|
||
| **Footnote** | Footnote text appearing at the bottom of a page. |
|
||
| **Formula** | Mathematical or chemical formulas. |
|
||
| **List‑item** | Items within bulleted or numbered lists. |
|
||
| **Page‑footer** | Footer content that appears on every page. |
|
||
| **Page‑header** | Header content that appears on every page. |
|
||
| **Picture** | Images embedded in the document. |
|
||
| **Section‑header** | Headers that introduce document sections. |
|
||
| **Table** | Tabular data structures. |
|
||
| **Text** | Standard, continuous paragraph text. |
|
||
| **Title** | The main title or subtitle of the document. |
|
||
|
||
These labels are used to annotate the layout of documents in the DocLayNet dataset, enabling researchers to train models for document understanding tasks such as layout analysis, OCR, and information extraction.
|
||
role: assistant
|
||
created: 1769523896
|
||
id: chatcmpl-959
|
||
model: gpt-oss
|
||
object: chat.completion
|
||
system_fingerprint: fp_ollama
|
||
usage:
|
||
completion_tokens: 234
|
||
prompt_tokens: 1201
|
||
total_tokens: 1435
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
version: 1
|