diff --git a/haiku_rag_slim/haiku/rag/agents/chat/agent.py b/haiku_rag_slim/haiku/rag/agents/chat/agent.py index 63247dc2..ad072383 100644 --- a/haiku_rag_slim/haiku/rag/agents/chat/agent.py +++ b/haiku_rag_slim/haiku/rag/agents/chat/agent.py @@ -1,4 +1,5 @@ import asyncio +import math from ag_ui.core import EventType, StateSnapshotEvent from pydantic_ai import Agent, RunContext, ToolReturn @@ -23,8 +24,23 @@ from haiku.rag.agents.research.graph import build_conversational_graph from haiku.rag.agents.research.models import Citation from haiku.rag.agents.research.state import ResearchDeps, ResearchState from haiku.rag.config.models import AppConfig +from haiku.rag.embeddings import get_embedder from haiku.rag.utils import get_model +# Similarity threshold for recall matching +RECALL_SIMILARITY_THRESHOLD = 0.8 + + +def _cosine_similarity(vec1: list[float], vec2: list[float]) -> float: + """Compute cosine similarity between two vectors.""" + dot_product = sum(a * b for a, b in zip(vec1, vec2)) + norm1 = math.sqrt(sum(a * a for a in vec1)) + norm2 = math.sqrt(sum(b * b for b in vec2)) + if norm1 == 0 or norm2 == 0: + return 0.0 + return dot_product / (norm1 * norm2) + + # Track summarization tasks per session to allow cancellation _summarization_tasks: dict[str, asyncio.Task[None]] = {} @@ -365,4 +381,58 @@ def create_chat_agent(config: AppConfig) -> Agent[ChatDeps, str]: f"**Content:**\n{doc.content}" ) + @agent.tool + async def recall( + ctx: RunContext[ChatDeps], + topic: str, + ) -> str: + """Search conversation history for a previous answer on this topic. + + Use this FIRST when the user asks about something that may have been + discussed before. Returns the previous answer with citations if found, + or indicates no match exists. + + Args: + topic: The topic or question to search for in conversation history + """ + if ctx.deps.session_state is None: + return "No conversation history available." + + qa_history = ctx.deps.session_state.qa_history + if not qa_history: + return "No previous answers found." + + # Get embedder and embed the topic + embedder = get_embedder(ctx.deps.config) + topic_embedding = await embedder.embed_query(topic) + + # Embed all previous questions + questions = [qa.question for qa in qa_history] + question_embeddings = await embedder.embed_documents(questions) + + # Find best match by cosine similarity + best_match_idx = -1 + best_similarity = 0.0 + for i, q_embedding in enumerate(question_embeddings): + similarity = _cosine_similarity(topic_embedding, q_embedding) + if similarity > best_similarity: + best_similarity = similarity + best_match_idx = i + + # Check if similarity exceeds threshold + if best_similarity < RECALL_SIMILARITY_THRESHOLD: + return "No previous answer found on this topic." + + # Return the matching answer with citations + matched_qa = qa_history[best_match_idx] + result = f"**Previous answer found** (similarity: {best_similarity:.2f}):\n\n" + result += f"**Question:** {matched_qa.question}\n\n" + result += f"**Answer:** {matched_qa.answer}\n\n" + + if matched_qa.citations: + citation_refs = " ".join(f"[{c.index}]" for c in matched_qa.citations) + result += f"Sources: {citation_refs}" + + return result + return agent diff --git a/haiku_rag_slim/haiku/rag/agents/chat/prompts.py b/haiku_rag_slim/haiku/rag/agents/chat/prompts.py index 7ede72de..7b46919e 100644 --- a/haiku_rag_slim/haiku/rag/agents/chat/prompts.py +++ b/haiku_rag_slim/haiku/rag/agents/chat/prompts.py @@ -4,14 +4,16 @@ You have access to a knowledge base of documents. Use your tools to search and a 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 +2. For follow-up questions about topics already discussed: Use "recall" FIRST to check conversation history +3. For new questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally +4. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally +5. NEVER call the same tool multiple times for a single user message +6. NEVER make up information - always use tools to get facts from the knowledge base How to decide which tool to use: +- "recall" - Use FIRST when the user asks about a topic that may have been discussed before (e.g., "remind me about X", "what did you say about Y", "tell me again about Z"). If recall finds a previous answer, use it directly. If recall returns "no match", proceed with "ask". - "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. +- "ask" - Use for NEW 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: diff --git a/tests/agents/chat/test_chat_agent.py b/tests/agents/chat/test_chat_agent.py index ea3a0057..6727cff4 100644 --- a/tests/agents/chat/test_chat_agent.py +++ b/tests/agents/chat/test_chat_agent.py @@ -774,3 +774,91 @@ def test_search_tool_citation_registry_logic(): "chunk-c": 3, "chunk-d": 4, } + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_recall_tool_finds_previous_answer(allow_model_requests, temp_db_path): + """Test recall tool finds a previous answer on similar topic.""" + async with HaikuRAG(temp_db_path, create=True) as client: + agent = create_chat_agent(Config) + + # Pre-populate qa_history with a previous answer + session_state = ChatSessionState( + session_id="test-recall", + qa_history=[ + QAResponse( + question="What are the class labels in DocLayNet?", + answer="DocLayNet defines 11 class labels including Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, and Title.", + confidence=0.9, + citations=[ + Citation( + index=1, + document_id="doc-1", + chunk_id="chunk-1", + document_uri="doclaynet.md", + document_title="DocLayNet", + content="DocLayNet class labels...", + ) + ], + ), + ], + ) + deps = ChatDeps( + client=client, + config=Config, + session_state=session_state, + ) + + # Ask about a similar topic - should find the previous answer + result = await agent.run( + "Remind me about the DocLayNet class labels", + deps=deps, + ) + + # The agent should use recall and find the previous answer + assert result.output is not None + # Should mention the class labels from the cached answer + assert "11" in result.output or "class" in result.output.lower() + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_recall_tool_no_match(allow_model_requests, temp_db_path): + """Test recall tool returns no match for unrelated topic.""" + async with HaikuRAG(temp_db_path, create=True) as client: + agent = create_chat_agent(Config) + + # Pre-populate qa_history with an unrelated answer + session_state = ChatSessionState( + session_id="test-recall-nomatch", + qa_history=[ + QAResponse( + question="What is the capital of France?", + answer="The capital of France is Paris.", + confidence=0.9, + citations=[], + ), + ], + ) + deps = ChatDeps( + client=client, + config=Config, + session_state=session_state, + ) + + # Add a document so ask tool can find something + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + + # Ask about an unrelated topic - recall should not match + result = await agent.run( + "What are the class labels in DocLayNet?", + deps=deps, + ) + + # The agent should use ask (not recall) since no match + assert result.output is not None diff --git a/tests/cassettes/test_chat_agent/test_recall_tool_finds_previous_answer.yaml b/tests/cassettes/test_chat_agent/test_recall_tool_finds_previous_answer.yaml new file mode 100644 index 00000000..278471f7 --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_recall_tool_finds_previous_answer.yaml @@ -0,0 +1,468 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5001' + 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 follow-up questions about topics already discussed: Use "recall" FIRST to check conversation history + 3. For new questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 4. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 5. NEVER call the same tool multiple times for a single user message + 6. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "recall" - Use FIRST when the user asks about a topic that may have been discussed before (e.g., "remind me about X", "what did you say about Y", "tell me again about Z"). If recall finds a previous answer, use it directly. If recall returns "no match", proceed with "ask". + - "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 NEW 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: Remind me about the DocLayNet 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 + - function: + description: |- + Search conversation history for a previous answer on this topic. + + Use this FIRST when the user asks about something that may have been + discussed before. Returns the previous answer with citations if found, + or indicates no match exists. + name: recall + parameters: + additionalProperties: false + properties: + topic: + description: The topic or question to search for in conversation history + type: string + required: + - topic + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '467' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need recall. + role: assistant + tool_calls: + - function: + arguments: '{"topic":"DocLayNet class labels"}' + name: recall + id: call_oijissv6 + index: 0 + type: function + created: 1769422802 + id: chatcmpl-381 + 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: + - '92' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet 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: 7 + total_tokens: 7 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '109' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - What are the class labels in DocLayNet? + 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: + - '5555' + 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 follow-up questions about topics already discussed: Use "recall" FIRST to check conversation history + 3. For new questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 4. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 5. NEVER call the same tool multiple times for a single user message + 6. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "recall" - Use FIRST when the user asks about a topic that may have been discussed before (e.g., "remind me about X", "what did you say about Y", "tell me again about Z"). If recall finds a previous answer, use it directly. If recall returns "no match", proceed with "ask". + - "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 NEW 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: Remind me about the DocLayNet class labels + role: user + - content: |- + + Need recall. + + role: assistant + tool_calls: + - function: + arguments: '{"topic":"DocLayNet class labels"}' + name: recall + id: call_oijissv6 + type: function + - content: |- + **Previous answer found** (similarity: 0.89): + + **Question:** What are the class labels in DocLayNet? + + **Answer:** DocLayNet defines 11 class labels including Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, and Title. + + Sources: [1] + role: tool + tool_call_id: call_oijissv6 + 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 + - function: + description: |- + Search conversation history for a previous answer on this topic. + + Use this FIRST when the user asks about something that may have been + discussed before. Returns the previous answer with citations if found, + or indicates no match exists. + name: recall + parameters: + additionalProperties: false + properties: + topic: + description: The topic or question to search for in conversation history + type: string + required: + - topic + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '749' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: |- + DocLayNet defines **11** class labels for document layout elements: + + | # | Class label | + |---|-------------| + | 1 | **Caption** | + | 2 | **Footnote** | + | 3 | **Formula** | + | 4 | **List‑item** | + | 5 | **Page‑footer** | + | 6 | **Page‑header** | + | 7 | **Picture** | + | 8 | **Section‑header** | + | 9 | **Table** | + |10 | **Text** | + |11 | **Title** | + + These labels cover the common visual components you’ll find in academic and technical documents. + role: assistant + created: 1769422805 + id: chatcmpl-20 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 140 + prompt_tokens: 1133 + total_tokens: 1273 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_recall_tool_no_match.yaml b/tests/cassettes/test_chat_agent/test_recall_tool_no_match.yaml new file mode 100644 index 00000000..b25a91df --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_recall_tool_no_match.yaml @@ -0,0 +1,2948 @@ +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: + - '4998' + 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 follow-up questions about topics already discussed: Use "recall" FIRST to check conversation history + 3. For new questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 4. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 5. NEVER call the same tool multiple times for a single user message + 6. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "recall" - Use FIRST when the user asks about a topic that may have been discussed before (e.g., "remind me about X", "what did you say about Y", "tell me again about Z"). If recall finds a previous answer, use it directly. If recall returns "no match", proceed with "ask". + - "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 NEW 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 are the class labels 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. + + 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 + - function: + description: |- + Search conversation history for a previous answer on this topic. + + Use this FIRST when the user asks about something that may have been + discussed before. Returns the previous answer with citations if found, + or indicates no match exists. + name: recall + parameters: + additionalProperties: false + properties: + topic: + description: The topic or question to search for in conversation history + type: string + required: + - topic + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '519' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: We need to ask. + role: assistant + tool_calls: + - function: + arguments: '{"document_name":"DocLayNet","question":"What are the class labels in DocLayNet?"}' + name: ask + id: call_j8gqexb9 + index: 0 + type: function + created: 1769422810 + id: chatcmpl-887 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 44 + prompt_tokens: 1013 + total_tokens: 1057 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2129' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are the research orchestrator for a focused workflow. + + If a section is provided, use it to understand the domain context. + + Responsibilities: + 1. Understand and decompose the main question + 2. Propose a minimal, high-leverage plan + 3. Coordinate specialized agents to gather evidence + + 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 are the class labels in DocLayNet? + 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: + - '538' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to call gather_context on main question. + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"What are the class labels in DocLayNet?"}' + name: gather_context + id: call_u2ld39o7 + index: 0 + type: function + created: 1769422811 + id: chatcmpl-257 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 47 + prompt_tokens: 427 + total_tokens: 474 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '109' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - What are the class labels in DocLayNet? + 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: + - '2906' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are the research orchestrator for a focused workflow. + + If a section is provided, use it to understand the domain context. + + Responsibilities: + 1. Understand and decompose the main question + 2. Propose a minimal, high-leverage plan + 3. Coordinate specialized agents to gather evidence + + 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 are the class labels in DocLayNet? + role: user + - content: |- + + Need to call gather_context on main question. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"What are the class labels in DocLayNet?"}' + name: gather_context + id: call_u2ld39o7 + 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_u2ld39o7 + 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: + - '673' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "**Proposed sub‑questions for final investigation**\n\n1. What are the 11 distinct class labels defined + in the DocLayNet dataset? \n2. For each class label in DocLayNet, what typical visual content (e.g., type of + text, image, etc.) is represented? \n3. How does DocLayNet differentiate and annotate “Caption” versus “Table” + or “Picture” labels in its ground‑truth dataset?" + role: assistant + created: 1769422813 + id: chatcmpl-353 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 93 + prompt_tokens: 576 + total_tokens: 669 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3972' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are the research orchestrator for a focused workflow. + + If a section is provided, use it to understand the domain context. + + Responsibilities: + 1. Understand and decompose the main question + 2. Propose a minimal, high-leverage plan + 3. Coordinate specialized agents to gather evidence + + 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 are the class labels in DocLayNet? + role: user + - content: |- + + Need to call gather_context on main question. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"What are the class labels in DocLayNet?"}' + name: gather_context + id: call_u2ld39o7 + 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_u2ld39o7 + - content: "**Proposed sub‑questions for final investigation**\n\n1. What are the 11 distinct class labels defined in + the DocLayNet dataset? \n2. For each class label in DocLayNet, what typical visual content (e.g., type of text, + image, etc.) is represented? \n3. How does DocLayNet differentiate and annotate “Caption” versus “Table” or “Picture” + labels in its ground‑truth dataset?" + role: assistant + - content: |- + 1 validation error: + ```json + [ + { + "type": "json_invalid", + "loc": [], + "msg": "Invalid JSON: expected value at line 1 column 1", + "input": "**Proposed sub‑questions for final investigation**\n\n1. What are the 11 distinct class labels defined in the DocLayNet dataset? \n2. For each class label in DocLayNet, what typical visual content (e.g., type of text, image, etc.) is represented? \n3. How does DocLayNet differentiate and annotate “Caption” versus “Table” or “Picture” labels in its ground‑truth dataset?" + } + ] + ``` + + 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: + - '810' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to output JSON list of strings. + role: assistant + tool_calls: + - function: + arguments: '{"sub_questions":["What are the 11 distinct class labels defined in the DocLayNet dataset?","For + each class label in DocLayNet, what typical visual content (e.g., type of text, image, etc.) is represented?","How + does DocLayNet differentiate and annotate “Caption” versus “Table” or “Picture” labels in its ground‑truth + dataset?"]}' + name: final_result + id: call_di85xa19 + index: 0 + type: function + created: 1769422816 + id: chatcmpl-566 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 106 + prompt_tokens: 828 + total_tokens: 934 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2861' + 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 ordered by relevance. + 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] [rank 1 of 5] + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and rank position (rank 1 = most relevant) + - 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. + - Results are ordered by relevance, with rank 1 being most relevant. + role: system + - content: What are the 11 distinct class labels defined 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: + - '506' + 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":null,"query":"DocLayNet 11 distinct class labels"}' + name: search_and_answer + id: call_rmei09jt + index: 0 + type: function + created: 1769422817 + id: chatcmpl-742 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 40 + prompt_tokens: 631 + total_tokens: 671 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '104' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet 11 distinct 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: 11 + total_tokens: 11 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3723' + 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 ordered by relevance. + 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] [rank 1 of 5] + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and rank position (rank 1 = most relevant) + - 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. + - Results are ordered by relevance, with rank 1 being most relevant. + role: system + - content: What are the 11 distinct class labels defined in the DocLayNet dataset? + role: user + - content: |- + + Need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"DocLayNet 11 distinct class labels"}' + name: search_and_answer + id: call_rmei09jt + type: function + - content: |- + [6e202674-0d3f-4dc8-8b63-2e50781e7f73] [rank 1 of 1] + 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_rmei09jt + 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: + - '692' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "**Query:** What are the 11 distinct class labels defined in the DocLayNet dataset?\n\n**Answer:** DocLayNet + defines these 11 class labels for document layout analysis:\n\n1. Caption \n2. Footnote \n3. Formula \n4. List‑item + \ \n5. Page‑footer \n6. Page‑header \n7. Picture \n8. Section‑header \n9. Table \n\n**Cited chunks:** \n- + [6e202674-0d3f-4dc8-8b63-2e50781e7f73]\n\n**Confidence:** 1.0" + role: assistant + created: 1769422821 + id: chatcmpl-357 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 127 + prompt_tokens: 825 + total_tokens: 952 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4292' + 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 ordered by relevance. + 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] [rank 1 of 5] + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and rank position (rank 1 = most relevant) + - 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. + - Results are ordered by relevance, with rank 1 being most relevant. + role: system + - content: What are the 11 distinct class labels defined in the DocLayNet dataset? + role: user + - content: |- + + Need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"DocLayNet 11 distinct class labels"}' + name: search_and_answer + id: call_rmei09jt + type: function + - content: |- + [6e202674-0d3f-4dc8-8b63-2e50781e7f73] [rank 1 of 1] + 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_rmei09jt + - content: "**Query:** What are the 11 distinct class labels defined in the DocLayNet dataset?\n\n**Answer:** DocLayNet + defines these 11 class labels for document layout analysis:\n\n1. Caption \n2. Footnote \n3. Formula \n4. List‑item + \ \n5. Page‑footer \n6. Page‑header \n7. Picture \n8. Section‑header \n9. Table \n\n**Cited chunks:** \n- + [6e202674-0d3f-4dc8-8b63-2e50781e7f73]\n\n**Confidence:** 1.0" + 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: + - '830' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to use final_result. + role: assistant + tool_calls: + - function: + arguments: '{"answer":"DocLayNet defines these 11 class labels for document layout analysis:\n1. Caption\n2. + Footnote\n3. Formula\n4. List-item\n5. Page-footer\n6. Page-header\n7. Picture\n8. Section-header\n9. Table","cited_chunks":["6e202674-0d3f-4dc8-8b63-2e50781e7f73"],"confidence":1,"query":"What + are the 11 distinct class labels defined in the DocLayNet dataset?"}' + name: final_result + id: call_p9k2u3k5 + index: 0 + type: function + created: 1769422824 + id: chatcmpl-634 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 141 + prompt_tokens: 977 + total_tokens: 1118 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2902' + 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 ordered by relevance. + 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] [rank 1 of 5] + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and rank position (rank 1 = most relevant) + - 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. + - Results are ordered by relevance, with rank 1 being most relevant. + role: system + - content: For each class label in DocLayNet, what typical visual content (e.g., type of text, image, etc.) is represented? + 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: + - '544' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need search. + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet class label typical visual content type of text image representation","limit":5}' + name: search_and_answer + id: call_a0mt4nuz + index: 0 + type: function + created: 1769422825 + id: chatcmpl-524 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 44 + prompt_tokens: 643 + total_tokens: 687 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '148' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet class label typical visual content type of text image representation + 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: + - '3802' + 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 ordered by relevance. + 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] [rank 1 of 5] + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and rank position (rank 1 = most relevant) + - 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. + - Results are ordered by relevance, with rank 1 being most relevant. + role: system + - content: For each class label in DocLayNet, what typical visual content (e.g., type of text, image, etc.) is represented? + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet class label typical visual content type of text image representation","limit":5}' + name: search_and_answer + id: call_a0mt4nuz + type: function + - content: |- + [6e202674-0d3f-4dc8-8b63-2e50781e7f73] [rank 1 of 1] + 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_a0mt4nuz + 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: + - '1449' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "**Query**: For each class label in DocLayNet, what typical visual content (e.g., type of text, image, + etc.) is represented?\n\n**Answer**: \nDocLayNet defines 11 distinct class labels, each corresponding to a common + visual element found in documents:\n\n| Class label | Typical visual content |\n|------------------|------------------------|\n| + Caption | Text that describes figures or tables |\n| Footnote | Notes placed at the bottom of + pages |\n| Formula | Mathematical expressions |\n| List‑item | Bulleted or numbered list items + |\n| Page‑footer | Footer content on pages |\n| Page‑header | Header content on pages |\n| Picture | + Images, photographs, or diagrams |\n| Section‑header | Headings that introduce document sections |\n| Table + \ | Tabular data with rows and columns |\n| … (remaining two labels not shown in the retrieved snippet) + |\n\n(Only nine labels were returned in the search result; the remaining two labels exist in DocLayNet but were + not listed in the retrieved data.)\n\n**Cited_chunks**: \n[\"6e202674-0d3f-4dc8-8b63-2e50781e7f73\"]\n\n**Confidence**: + 0.88" + role: assistant + created: 1769422831 + id: chatcmpl-352 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 266 + prompt_tokens: 841 + total_tokens: 1107 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5127' + 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 ordered by relevance. + 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] [rank 1 of 5] + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and rank position (rank 1 = most relevant) + - 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. + - Results are ordered by relevance, with rank 1 being most relevant. + role: system + - content: For each class label in DocLayNet, what typical visual content (e.g., type of text, image, etc.) is represented? + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet class label typical visual content type of text image representation","limit":5}' + name: search_and_answer + id: call_a0mt4nuz + type: function + - content: |- + [6e202674-0d3f-4dc8-8b63-2e50781e7f73] [rank 1 of 1] + 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_a0mt4nuz + - content: "**Query**: For each class label in DocLayNet, what typical visual content (e.g., type of text, image, etc.) + is represented?\n\n**Answer**: \nDocLayNet defines 11 distinct class labels, each corresponding to a common visual + element found in documents:\n\n| Class label | Typical visual content |\n|------------------|------------------------|\n| + Caption | Text that describes figures or tables |\n| Footnote | Notes placed at the bottom of pages + |\n| Formula | Mathematical expressions |\n| List‑item | Bulleted or numbered list items |\n| Page‑footer + \ | Footer content on pages |\n| Page‑header | Header content on pages |\n| Picture | Images, + photographs, or diagrams |\n| Section‑header | Headings that introduce document sections |\n| Table | + Tabular data with rows and columns |\n| … (remaining two labels not shown in the retrieved snippet) |\n\n(Only nine + labels were returned in the search result; the remaining two labels exist in DocLayNet but were not listed in the + retrieved data.)\n\n**Cited_chunks**: \n[\"6e202674-0d3f-4dc8-8b63-2e50781e7f73\"]\n\n**Confidence**: 0.88" + 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: + - '1471' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to wrap in final_result tool. + role: assistant + tool_calls: + - function: + arguments: '{"answer":"DocLayNet defines 11 distinct class labels, each corresponding to a common visual element + in documents:\n- **Caption** – Text that describes figures or tables\n- **Footnote** – Notes placed at the + bottom of pages\n- **Formula** – Mathematical expressions\n- **List-item** – Bulleted or numbered list items\n- + **Page-footer** – Footer content on pages\n- **Page-header** – Header content on pages\n- **Picture** – Images, + photographs, or diagrams\n- **Section-header** – Headings that introduce document sections\n- **Table** – + Tabular data with rows and columns\n- **…** – Two additional labels exist in DocLayNet but were not retrieved + in this search.\n\nThe above captures the typical visual content each label represents as per the source documentation.","cited_chunks":["6e202674-0d3f-4dc8-8b63-2e50781e7f73"],"confidence":0.88,"query":"For + each class label in DocLayNet, what typical visual content (e.g., type of text, image, etc.) is represented?"}' + name: final_result + id: call_l8fov2i4 + index: 0 + type: function + created: 1769422838 + id: chatcmpl-86 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 266 + prompt_tokens: 1132 + total_tokens: 1398 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2923' + 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 ordered by relevance. + 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] [rank 1 of 5] + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and rank position (rank 1 = most relevant) + - 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. + - Results are ordered by relevance, with rank 1 being most relevant. + role: system + - content: How does DocLayNet differentiate and annotate “Caption” versus “Table” or “Picture” labels in its ground‑truth + 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: + - '560' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: We need search. + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet differentiate annotate Caption versus Table or Picture labels ground truth dataset","limit":5}' + name: search_and_answer + id: call_rkcclpqh + index: 0 + type: function + created: 1769422839 + id: chatcmpl-80 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 46 + prompt_tokens: 642 + total_tokens: 688 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '162' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet differentiate annotate Caption versus Table or Picture labels ground truth 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: 16 + total_tokens: 16 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3840' + 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 ordered by relevance. + 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] [rank 1 of 5] + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and rank position (rank 1 = most relevant) + - 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. + - Results are ordered by relevance, with rank 1 being most relevant. + role: system + - content: How does DocLayNet differentiate and annotate “Caption” versus “Table” or “Picture” labels in its ground‑truth + dataset? + role: user + - content: |- + + We need search. + + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet differentiate annotate Caption versus Table or Picture labels ground truth dataset","limit":5}' + name: search_and_answer + id: call_rkcclpqh + type: function + - content: |- + [6e202674-0d3f-4dc8-8b63-2e50781e7f73] [rank 1 of 1] + 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_rkcclpqh + 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: + - '1217' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "**query**: How does DocLayNet differentiate and annotate “Caption” versus “Table” or “Picture” labels + in its ground‑truth dataset? \n\n**answer**: \nDocLayNet distinguishes these labels by the text or visual content + they describe:\n\n* **Caption** – Refers to the textual description that accompanies a figure or a table. In the + dataset, a Caption is annotated wherever explanatory text that directly refers to a figure or table is present. + \ \n* **Table** – Marks the actual tabular structure on the page (rows, columns, grid lines). \n* **Picture** + – Covers any image or diagram that is not part of a table, including photos, drawings, or other graphic elements.\n\nThe + dataset therefore assigns “Caption” to the explanatory text, while “Table” and “Picture” are used for the visual + elements themselves. \n\n**cited_chunks**: [\"6e202674-0d3f-4dc8-8b63-2e50781e7f73\"] \n\n**confidence**: 0.92" + role: assistant + created: 1769422844 + id: chatcmpl-397 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 221 + prompt_tokens: 842 + total_tokens: 1063 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4933' + 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 ordered by relevance. + 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] [rank 1 of 5] + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and rank position (rank 1 = most relevant) + - 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. + - Results are ordered by relevance, with rank 1 being most relevant. + role: system + - content: How does DocLayNet differentiate and annotate “Caption” versus “Table” or “Picture” labels in its ground‑truth + dataset? + role: user + - content: |- + + We need search. + + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet differentiate annotate Caption versus Table or Picture labels ground truth dataset","limit":5}' + name: search_and_answer + id: call_rkcclpqh + type: function + - content: |- + [6e202674-0d3f-4dc8-8b63-2e50781e7f73] [rank 1 of 1] + 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_rkcclpqh + - content: "**query**: How does DocLayNet differentiate and annotate “Caption” versus “Table” or “Picture” labels in + its ground‑truth dataset? \n\n**answer**: \nDocLayNet distinguishes these labels by the text or visual content + they describe:\n\n* **Caption** – Refers to the textual description that accompanies a figure or a table. In the + dataset, a Caption is annotated wherever explanatory text that directly refers to a figure or table is present. + \ \n* **Table** – Marks the actual tabular structure on the page (rows, columns, grid lines). \n* **Picture** – + Covers any image or diagram that is not part of a table, including photos, drawings, or other graphic elements.\n\nThe + dataset therefore assigns “Caption” to the explanatory text, while “Table” and “Picture” are used for the visual + elements themselves. \n\n**cited_chunks**: [\"6e202674-0d3f-4dc8-8b63-2e50781e7f73\"] \n\n**confidence**: 0.92" + 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: + - '1471' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to output as tool call? It expects to use final_result function. + role: assistant + tool_calls: + - function: + arguments: '{"answer":"DocLayNet differentiates and annotates “Caption” versus “Table” or “Picture” labels by + assigning each to distinct document elements: \n• **Caption** is annotated for the textual description that + accompanies a figure or table. It is marked wherever explanatory text that directly refers to a figure or + table is present.\n• **Table** is used for the actual tabular structure on the page (rows, columns, grid lines).\n• + **Picture** covers any image or diagram that is not part of a table, such as photos, drawings, or other graphic + elements.\nThus, in the ground‑truth dataset, “Caption” annotates the text beside the visual element, while + “Table” and “Picture” annotate the visual element itself.\n","cited_chunks":["6e202674-0d3f-4dc8-8b63-2e50781e7f73"],"confidence":0.92,"query":"How + does DocLayNet differentiate and annotate “Caption” versus “Table” or “Picture” labels in its ground‑truth + dataset?"}' + name: final_result + id: call_od35ylpg + index: 0 + type: function + created: 1769422851 + id: chatcmpl-472 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 260 + prompt_tokens: 1088 + total_tokens: 1348 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5008' + 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 evidence provided in the context. + - If a section is provided, use it to frame your answer appropriately. + - 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.\n\n\n What are the class labels + in DocLayNet?\n \n \n What are the 11 distinct class labels defined + in the DocLayNet dataset?\n DocLayNet defines these 11 class labels for document layout + analysis:\n1. Caption\n2. Footnote\n3. Formula\n4. List-item\n5. Page-footer\n6. Page-header\n7. Picture\n8. Section-header\n9. + Table\n 1.0\n DocLayNet Class Labels\n \n + \ \n For each class label in DocLayNet, what typical visual content (e.g., type of text, + image, etc.) is represented?\n DocLayNet defines 11 distinct class labels, each corresponding + to a common visual element in documents:\n- **Caption** – Text that describes figures or tables\n- **Footnote** + – Notes placed at the bottom of pages\n- **Formula** – Mathematical expressions\n- **List-item** – Bulleted or numbered + list items\n- **Page-footer** – Footer content on pages\n- **Page-header** – Header content on pages\n- **Picture** + – Images, photographs, or diagrams\n- **Section-header** – Headings that introduce document sections\n- **Table** + – Tabular data with rows and columns\n- **…** – Two additional labels exist in DocLayNet but were not retrieved + in this search.\n\nThe above captures the typical visual content each label represents as per the source documentation.\n + \ 0.88\n DocLayNet Class Labels\n \n \n How + does DocLayNet differentiate and annotate “Caption” versus “Table” or “Picture” labels in its ground‑truth dataset?\n + \ DocLayNet differentiates and annotates “Caption” versus “Table” or “Picture” labels by assigning each + to distinct document elements: \n• **Caption** is annotated for the textual description that accompanies a figure + or table. It is marked wherever explanatory text that directly refers to a figure or table is present.\n• **Table** + is used for the actual tabular structure on the page (rows, columns, grid lines).\n• **Picture** covers any image + or diagram that is not part of a table, such as photos, drawings, or other graphic elements.\nThus, in the ground‑truth + dataset, “Caption” annotates the text beside the visual element, while “Table” and “Picture” annotate the visual + element itself.\n\n 0.92\n DocLayNet Class Labels\n + \ \n \n" + 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. + + Used by both research graph and chat agent. The optional index field + supports UI display ordering in chat contexts. + 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 + index: + anyOf: + - type: integer + - 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: + - '1622' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: 'Need answer the question: What are class labels in DocLayNet? Provide list, maybe 11. Use prior.' + role: assistant + tool_calls: + - function: + arguments: '{"answer":"DocLayNet’s document‑layout dataset defines a total of 11 distinct class labels:\n\n1. + **Caption** – text that describes a figure or table\n2. **Footnote** – notes placed at the bottom of a page\n3. + **Formula** – mathematical expressions or equations\n4. **List‑item** – bulleted or numbered list entries\n5. + **Page‑footer** – footer content that appears on every page\n6. **Page‑header** – header content that appears + on every page\n7. **Picture** – photographs, diagrams, or other images that are not part of a table\n8. **Section‑header** + – headings that introduce a new document section\n9. **Table** – the tabular structure itself\n10. **Title** + – main title or cover heading (not always listed in some summaries)\n11. **Text‑block** – generic blocks of + running text\n\nThese labels cover the typical visual elements that appear across a wide variety of PDFs and + scanned documents, enabling automated layout analysis to recognize and separate text, graphics, tables, and + structural metadata.\n","citations":[],"confidence":0.96}' + name: final_result + id: call_99phoffr + index: 0 + type: function + created: 1769422858 + id: chatcmpl-478 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 287 + prompt_tokens: 943 + total_tokens: 1230 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2669' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a session summarizer. Given a conversation history of Q&A pairs (and optionally existing context), produce a structured summary that captures key information for future context. + + If a "Current Context" section is provided at the start of the input, incorporate that context into your summary. This might be initial background context from the user or a previous summary - build upon it rather than discard it. + + Your summary should be concise (aim for 500-1500 tokens) and include: + + 1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation + 2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain + 3. **Current Focus** - What topic or question thread the user is currently exploring + + Rules: + - Extract only high-signal information that would help answer follow-up questions + - When building on existing context, merge new information with prior context + - Omit small talk, greetings, or low-confidence answers + - Use bullet points for clarity + - Keep technical details but compress verbose explanations + - Preserve document names/titles when mentioned in sources + + Output the summary directly in markdown format. Do not include meta-commentary about the summary itself. + role: system + - content: | + ## Q1: What is the capital of France? + **Answer** (confidence: 90%): + The capital of France is Paris. + + ## Q2: What are the class labels in DocLayNet? + **Answer** (confidence: 96%): + DocLayNet’s document‑layout dataset defines a total of 11 distinct class labels: + + 1. **Caption** – text that describes a figure or table + 2. **Footnote** – notes placed at the bottom of a page + 3. **Formula** – mathematical expressions or equations + 4. **List‑item** – bulleted or numbered list entries + 5. **Page‑footer** – footer content that appears on every page + 6. **Page‑header** – header content that appears on every page + 7. **Picture** – photographs, diagrams, or other images that are not part of a table + 8. **Section‑header** – headings that introduce a new document section + 9. **Table** – the tabular structure itself + 10. **Title** – main title or cover heading (not always listed in some summaries) + 11. **Text‑block** – generic blocks of running text + + These labels cover the typical visual elements that appear across a wide variety of PDFs and scanned documents, enabling automated layout analysis to recognize and separate text, graphics, tables, and structural metadata. + + **Sources:** DocLayNet Class Labels + role: user + model: gpt-oss + reasoning_effort: low + stream: false + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1389' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "**Key Facts Established**\n- France’s capital is **Paris**. \n- *DocLayNet* is a document‑layout dataset + that defines **11 class labels** used to annotate visual elements in PDFs and scanned documents:\n 1. Caption + – description of figures/tables \n 2. Footnote – bottom‑of‑page notes \n 3. Formula – mathematical expressions + \ \n 4. List‑item – bulleted/numbered list entries \n 5. Page‑footer – footer content common to all pages \n + \ 6. Page‑header – header content common to all pages \n 7. Picture – non‑table images (photos, diagrams, etc.) + \ \n 8. Section‑header – headings that introduce new sections \n 9. Table – the tabular structure itself \n + 10. Title – main or cover heading (sometimes omitted) \n 11. Text‑block – generic running text\n\n**Documents + Referenced**\n- *DocLayNet Class Labels* (source that lists the 11 categories).\n\n**Current Focus**\n- The user + is exploring basic factual information (capital city of France) and details about the classes used in the DocLayNet + document‑layout dataset." + reasoning: Need summary. + role: assistant + created: 1769422865 + id: chatcmpl-963 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 264 + prompt_tokens: 608 + total_tokens: 872 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '6363' + 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 follow-up questions about topics already discussed: Use "recall" FIRST to check conversation history + 3. For new questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 4. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 5. NEVER call the same tool multiple times for a single user message + 6. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "recall" - Use FIRST when the user asks about a topic that may have been discussed before (e.g., "remind me about X", "what did you say about Y", "tell me again about Z"). If recall finds a previous answer, use it directly. If recall returns "no match", proceed with "ask". + - "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 NEW 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 are the class labels in DocLayNet? + role: user + - content: |- + + We need to ask. + + role: assistant + tool_calls: + - function: + arguments: '{"document_name":"DocLayNet","question":"What are the class labels in DocLayNet?"}' + name: ask + id: call_j8gqexb9 + type: function + - content: |- + DocLayNet’s document‑layout dataset defines a total of 11 distinct class labels: + + 1. **Caption** – text that describes a figure or table + 2. **Footnote** – notes placed at the bottom of a page + 3. **Formula** – mathematical expressions or equations + 4. **List‑item** – bulleted or numbered list entries + 5. **Page‑footer** – footer content that appears on every page + 6. **Page‑header** – header content that appears on every page + 7. **Picture** – photographs, diagrams, or other images that are not part of a table + 8. **Section‑header** – headings that introduce a new document section + 9. **Table** – the tabular structure itself + 10. **Title** – main title or cover heading (not always listed in some summaries) + 11. **Text‑block** – generic blocks of running text + + These labels cover the typical visual elements that appear across a wide variety of PDFs and scanned documents, enabling automated layout analysis to recognize and separate text, graphics, tables, and structural metadata. + + + Sources: [1] + role: tool + tool_call_id: call_j8gqexb9 + 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 + - function: + description: |- + Search conversation history for a previous answer on this topic. + + Use this FIRST when the user asks about something that may have been + discussed before. Returns the previous answer with citations if found, + or indicates no match exists. + name: recall + parameters: + additionalProperties: false + properties: + topic: + description: The topic or question to search for in conversation history + type: string + required: + - topic + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1343' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: |- + DocLayNet’s dataset defines **11** distinct class labels that capture the main visual elements found in documents: + + | # | Class label | Typical content | + |---|-------------|-----------------| + | 1 | **Caption** | Text that describes a figure or table | + | 2 | **Footnote** | Notes placed at the bottom of a page | + | 3 | **Formula** | Mathematical expressions or equations | + | 4 | **List‑item** | Bulleted or numbered list entries | + | 5 | **Page‑footer** | Footer content that appears on every page | + | 6 | **Page‑header** | Header content that appears on every page | + | 7 | **Picture** | Photographs, diagrams, or other images not in a table | + | 8 | **Section‑header** | Headings that introduce a new section | + | 9 | **Table** | The tabular structure itself | + |10 | **Title** | Main title or cover heading (sometimes used interchangeably with section header) | + |11 | **Text‑block** | Generic blocks of running text | + + These labels allow models trained on DocLayNet to classify and segment the main layout components of a document. + role: assistant + created: 1769422873 + id: chatcmpl-742 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 254 + prompt_tokens: 1303 + total_tokens: 1557 + status: + code: 200 + message: OK +version: 1