diff --git a/haiku_rag_slim/haiku/rag/agents/chat/state.py b/haiku_rag_slim/haiku/rag/agents/chat/state.py index c73a3e9d..3e49bfca 100644 --- a/haiku_rag_slim/haiku/rag/agents/chat/state.py +++ b/haiku_rag_slim/haiku/rag/agents/chat/state.py @@ -1,4 +1,3 @@ -import logging from dataclasses import dataclass, field from typing import TYPE_CHECKING @@ -14,8 +13,6 @@ from haiku.rag.store.models import SearchResult if TYPE_CHECKING: from haiku.rag.embeddings import EmbedderWrapper -logger = logging.getLogger(__name__) - class CitationInfo(BaseModel): """Citation info for frontend display.""" @@ -104,31 +101,25 @@ async def rank_qa_history_by_similarity( if len(qa_history) <= top_k: return qa_history - try: - # Embed current question - question_embedding = np.array(await embedder.embed_query(current_question)) + # Embed current question + question_embedding = np.array(await embedder.embed_query(current_question)) - # Embed Q&A pairs as "Q: {question}\nA: {answer}" - qa_texts = [f"Q: {qa.question}\nA: {qa.answer}" for qa in qa_history] - qa_embeddings = await embedder.embed_documents(qa_texts) + # Embed Q&A pairs as "Q: {question}\nA: {answer}" + qa_texts = [f"Q: {qa.question}\nA: {qa.answer}" for qa in qa_history] + qa_embeddings = await embedder.embed_documents(qa_texts) - # Compute similarities - similarities: list[tuple[int, float]] = [] - for i, qa_emb in enumerate(qa_embeddings): - sim = _cosine_similarity(question_embedding, np.array(qa_emb)) - similarities.append((i, sim)) + # Compute similarities + similarities: list[tuple[int, float]] = [] + for i, qa_emb in enumerate(qa_embeddings): + sim = _cosine_similarity(question_embedding, np.array(qa_emb)) + similarities.append((i, sim)) - # Sort by similarity (descending) and take top-K - similarities.sort(key=lambda x: x[1], reverse=True) - top_indices = sorted([idx for idx, _ in similarities[:top_k]]) + # Sort by similarity (descending) and take top-K + similarities.sort(key=lambda x: x[1], reverse=True) + top_indices = sorted([idx for idx, _ in similarities[:top_k]]) - # Return in original order - return [qa_history[i] for i in top_indices] - - except Exception as e: - logger.warning(f"Failed to rank qa_history by similarity: {e}") - # Fallback: return last top_k entries - return qa_history[-top_k:] + # Return in original order + return [qa_history[i] for i in top_indices] @dataclass diff --git a/tests/agents/chat/test_chat_agent.py b/tests/agents/chat/test_chat_agent.py index c95e9d6a..3f85df1d 100644 --- a/tests/agents/chat/test_chat_agent.py +++ b/tests/agents/chat/test_chat_agent.py @@ -227,3 +227,254 @@ async def test_chat_agent_with_qa_history_ranking(allow_model_requests, temp_db_ # Verify qa_history was updated (new Q&A was added) assert len(session_state.qa_history) == 7 # 6 original + 1 new + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_chat_agent_search_tool(allow_model_requests, temp_db_path): + """Test the chat agent's search tool functionality.""" + async with HaikuRAG(temp_db_path, create=True) as client: + # Add test documents + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + await client.create_document( + content=DOCLAYNET_ANNOTATION, + uri="doclaynet-annotation", + title="DocLayNet Annotation", + ) + + agent = create_chat_agent(Config) + session_state = ChatSessionState(session_id="test-search") + deps = ChatDeps( + client=client, + config=Config, + session_state=session_state, + ) + + # Ask something that should trigger the search tool + result = await agent.run( + "Search for documents about class labels", + deps=deps, + ) + + assert result.output is not None + assert len(result.output) > 0 + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_chat_agent_search_tool_with_filter(allow_model_requests, temp_db_path): + """Test the chat agent's search tool with document filter.""" + async with HaikuRAG(temp_db_path, create=True) as client: + # Add test documents + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + await client.create_document( + content=DOCLAYNET_DATA_SOURCES, + uri="doclaynet-sources", + title="DocLayNet Sources", + ) + + agent = create_chat_agent(Config) + session_state = ChatSessionState(session_id="test-search-filter") + deps = ChatDeps( + client=client, + config=Config, + session_state=session_state, + ) + + # Ask to search within a specific document + result = await agent.run( + "Search for information about class labels in the DocLayNet Class Labels document", + deps=deps, + ) + + assert result.output is not None + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_chat_agent_get_document_tool(allow_model_requests, temp_db_path): + """Test the chat agent's get_document tool.""" + async with HaikuRAG(temp_db_path, create=True) as client: + # Add a test document + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + + agent = create_chat_agent(Config) + deps = ChatDeps( + client=client, + config=Config, + ) + + # Ask to get a specific document + result = await agent.run( + "Get me the DocLayNet Class Labels document", + deps=deps, + ) + + assert result.output is not None + # The response should contain info about the document + assert "DocLayNet" in result.output or "class" in result.output.lower() + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_chat_agent_get_document_not_found(allow_model_requests, temp_db_path): + """Test the chat agent's get_document tool when document is not found.""" + async with HaikuRAG(temp_db_path, create=True) as client: + agent = create_chat_agent(Config) + deps = ChatDeps( + client=client, + config=Config, + ) + + # Ask for a document that doesn't exist + result = await agent.run( + "Get me the nonexistent document", + deps=deps, + ) + + assert result.output is not None + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_search_agent_with_context(allow_model_requests, temp_db_path): + """Test SearchAgent's search method with context.""" + async with HaikuRAG(temp_db_path, create=True) as client: + # Add test documents + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + await client.create_document( + content=DOCLAYNET_ANNOTATION, + uri="doclaynet-annotation", + title="DocLayNet Annotation", + ) + + search_agent = SearchAgent(client, Config) + + # Search with context + results = await search_agent.search( + query="What are the class labels?", + context="We're discussing document layout analysis", + ) + + assert isinstance(results, list) + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_search_agent_with_filter(allow_model_requests, temp_db_path): + """Test SearchAgent's search method with document filter.""" + async with HaikuRAG(temp_db_path, create=True) as client: + # Add test documents + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + await client.create_document( + content=DOCLAYNET_DATA_SOURCES, + uri="doclaynet-sources", + title="DocLayNet Sources", + ) + + search_agent = SearchAgent(client, Config) + + # Search with filter - only the labels document + results = await search_agent.search( + query="What information is available?", + filter="uri LIKE '%labels%'", + ) + + assert isinstance(results, list) + # Results should only come from the labels document + for r in results: + assert "labels" in (r.document_uri or "") + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_search_agent_deduplication(allow_model_requests, temp_db_path): + """Test SearchAgent deduplicates results by chunk_id.""" + async with HaikuRAG(temp_db_path, create=True) as client: + # Add test documents + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + + search_agent = SearchAgent(client, Config) + + # Search - the search agent will likely run multiple queries + # that could return the same chunk, which should be deduplicated + results = await search_agent.search( + query="Tell me about class labels and their counts", + ) + + assert isinstance(results, list) + + # Verify no duplicate chunk_ids + chunk_ids = [r.chunk_id for r in results if r.chunk_id] + assert len(chunk_ids) == len(set(chunk_ids)), "Found duplicate chunk_ids" + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_search_agent_no_results(allow_model_requests, temp_db_path): + """Test SearchAgent handles no results gracefully.""" + async with HaikuRAG(temp_db_path, create=True) as client: + search_agent = SearchAgent(client, Config) + + # Search in empty database + results = await search_agent.search( + query="Find information about nonexistent topic xyz123", + ) + + assert isinstance(results, list) + assert len(results) == 0 + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_chat_agent_ask_adds_citations(allow_model_requests, temp_db_path): + """Test that the ask tool adds citations to the response.""" + async with HaikuRAG(temp_db_path, create=True) as client: + # Add a document with specific content + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + + agent = create_chat_agent(Config) + session_state = ChatSessionState(session_id="test-citations") + deps = ChatDeps( + client=client, + config=Config, + session_state=session_state, + ) + + # Ask a question that should use the ask tool with citations + result = await agent.run( + "What is the highest count class in the DocLayNet dataset?", + deps=deps, + ) + + assert result.output is not None + # The qa_history should have been updated with the new Q&A + assert len(session_state.qa_history) >= 1 diff --git a/tests/agents/chat/test_state.py b/tests/agents/chat/test_state.py index b09cffdf..a75534a8 100644 --- a/tests/agents/chat/test_state.py +++ b/tests/agents/chat/test_state.py @@ -5,6 +5,8 @@ import pytest from haiku.rag.agents.chat.state import ( CitationInfo, QAResponse, + build_document_filter, + format_conversation_context, rank_qa_history_by_similarity, ) from haiku.rag.client import HaikuRAG @@ -166,3 +168,66 @@ async def test_rank_qa_history_preserves_order(temp_db_path, allow_model_request result_questions = [qa.question for qa in result] assert "What is Python?" in result_questions assert "How to use Python for data science?" in result_questions + + +def test_format_conversation_context_empty(): + """Test format_conversation_context with empty history.""" + result = format_conversation_context([]) + assert result == "" + + +def test_format_conversation_context_with_history(): + """Test format_conversation_context formats qa_history as XML.""" + citation = CitationInfo( + index=1, + document_id="doc-123", + chunk_id="chunk-456", + document_uri="test.md", + document_title="Test Document", + content="Test content", + ) + qa_history = [ + QAResponse( + question="What is Python?", + answer="A programming language", + citations=[citation], + ), + QAResponse( + question="What is Java?", + answer="Another programming language", + ), + ] + + result = format_conversation_context(qa_history) + + assert "" in result + assert "previous_qa" in result + assert "What is Python?" in result + assert "A programming language" in result + assert "What is Java?" in result + assert "Another programming language" in result + assert "Test Document" in result # source from first citation + + +def test_build_document_filter_simple(): + """Test build_document_filter with simple name.""" + result = build_document_filter("mytest") + assert "LOWER(uri) LIKE LOWER('%mytest%')" in result + assert "LOWER(title) LIKE LOWER('%mytest%')" in result + + +def test_build_document_filter_with_spaces(): + """Test build_document_filter handles spaces correctly.""" + result = build_document_filter("TB MED 593") + # Should include both the original (with spaces) and without spaces + assert "LOWER(uri) LIKE LOWER('%TB MED 593%')" in result + assert "LOWER(uri) LIKE LOWER('%TBMED593%')" in result + assert "LOWER(title) LIKE LOWER('%TB MED 593%')" in result + assert "LOWER(title) LIKE LOWER('%TBMED593%')" in result + + +def test_build_document_filter_escapes_quotes(): + """Test build_document_filter escapes single quotes.""" + result = build_document_filter("O'Reilly") + # Single quotes should be doubled for SQL escaping + assert "O''Reilly" in result diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_ask_adds_citations.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_ask_adds_citations.yaml new file mode 100644 index 00000000..ff31de66 --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_chat_agent_ask_adds_citations.yaml @@ -0,0 +1,3042 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '730' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Class Labels + DocLayNet defines 11 distinct class labels for document layout analysis: + 1. Caption - Text describing figures or tables + 2. Footnote - Notes at the bottom of pages + 3. Formula - Mathematical expressions + 4. List-item - Items in bulleted or numbered lists + 5. Page-footer - Footer content on pages + 6. Page-header - Header content on pages + 7. Picture - Images and diagrams + 8. Section-header - Headings for document sections + 9. Table - Tabular data + 10. Text - Regular paragraph text (highest count: 510,377 instances) + 11. Title - Document titles + The Text class has the highest count with 510,377 instances in the dataset. + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 166 + total_tokens: 166 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4099' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: What is the highest count class in the DocLayNet dataset? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '529' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need ask. + role: assistant + tool_calls: + - function: + arguments: '{"document_name":"DocLayNet","question":"What is the highest count class in the DocLayNet dataset?"}' + name: ask + id: call_avkrjhyc + index: 0 + type: function + created: 1768225953 + id: chatcmpl-141 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 45 + prompt_tokens: 850 + total_tokens: 895 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2118' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are the research orchestrator for a focused, iterative workflow. + + Responsibilities: + 1. Understand and decompose the main question + 2. Propose a minimal, high-leverage plan + 3. Coordinate specialized agents to gather evidence + 4. Iterate based on gaps and new findings + + Plan requirements: + - Produce at most 3 sub_questions that together cover the main question. + - sub_questions must be a list of plain strings, where each string is a complete + question. Do NOT use objects with nested fields like {question, details}. + - Each sub_question must be a standalone, self-contained query that can run + without extra context. Include concrete entities, scope, timeframe, and any + qualifiers. Avoid ambiguous pronouns (it/they/this/that). + - Prioritize the highest-value aspects first; avoid redundancy and overlap. + - Prefer questions that are likely answerable from the current knowledge base; + if coverage is uncertain, make scopes narrower and specific. + - Order sub_questions by execution priority (most valuable first). + + Use the gather_context tool once on the main question before planning. + + Use the gather_context tool once on the main question before planning. + role: system + - content: |- + Plan a focused approach for the main question. + + Main question: What is the highest count class in the DocLayNet dataset? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: '' + name: gather_context + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: A structured research plan with sub-questions to explore. + name: final_result + parameters: + additionalProperties: false + properties: + sub_questions: + description: Specific questions to research, phrased as complete questions + items: + type: string + type: array + required: + - sub_questions + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '555' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to run gather_context on main question. + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"What is the highest count class in the DocLayNet dataset?"}' + name: gather_context + id: call_0f67xr36 + index: 0 + type: function + created: 1768225954 + id: chatcmpl-304 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 50 + prompt_tokens: 425 + total_tokens: 475 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '127' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - What is the highest count class in the DocLayNet dataset? + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 15 + total_tokens: 15 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2912' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are the research orchestrator for a focused, iterative workflow. + + Responsibilities: + 1. Understand and decompose the main question + 2. Propose a minimal, high-leverage plan + 3. Coordinate specialized agents to gather evidence + 4. Iterate based on gaps and new findings + + Plan requirements: + - Produce at most 3 sub_questions that together cover the main question. + - sub_questions must be a list of plain strings, where each string is a complete + question. Do NOT use objects with nested fields like {question, details}. + - Each sub_question must be a standalone, self-contained query that can run + without extra context. Include concrete entities, scope, timeframe, and any + qualifiers. Avoid ambiguous pronouns (it/they/this/that). + - Prioritize the highest-value aspects first; avoid redundancy and overlap. + - Prefer questions that are likely answerable from the current knowledge base; + if coverage is uncertain, make scopes narrower and specific. + - Order sub_questions by execution priority (most valuable first). + + Use the gather_context tool once on the main question before planning. + + Use the gather_context tool once on the main question before planning. + role: system + - content: |- + Plan a focused approach for the main question. + + Main question: What is the highest count class in the DocLayNet dataset? + role: user + - content: |- + + Need to run gather_context on main question. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"What is the highest count class in the DocLayNet dataset?"}' + name: gather_context + id: call_0f67xr36 + 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_0f67xr36 + 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: + - '1842' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "**Sub‑questions (in priority order)** \n\n1. *What are the DocLayNet class labels, and how many instances + of each class appear in the training split of the dataset?* \n\n2. *Which DocLayNet class has the highest number + of annotated instances in the training split?* \n\n3. *What is the exact numeric count of the class identified + as highest‑count in the training split of DocLayNet?*" + reasoning: |- + We have list of classes, but need highest count class. We don't have counts. Need research. Plan 3 sub-questions. Possibly ask for dataset stats, total counts per class. Probably available in documentation. So sub questions: 1) What are the documented class definitions and counts for DocLayNet? 2) Which class has the highest number of instances in the DocLayNet training set? 3) Provide the numeric count of that class. + + But sub-questions must be single. We can combine: 1) What is the count of each class in DocLayNet? 2) Which class has the highest count? 3) Provide count of highest class. + + Alternatively ask: "According to official DocLayNet documentation, which class has the highest frequency in the training set, and how many instances does it contain?" That's one question. But need 3 independent. + + Let's produce 3 queries: (1) "What are the class labels defined in the DocLayNet dataset and how many instances does each have in the training split?" (2) "Which class in DocLayNet has the maximum number of instances in the training split?" (3) "What is the numeric count for that highest-count class in DocLayNet training data?" + role: assistant + created: 1768225962 + id: chatcmpl-975 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 355 + prompt_tokens: 577 + total_tokens: 932 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5157' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are the research orchestrator for a focused, iterative workflow. + + Responsibilities: + 1. Understand and decompose the main question + 2. Propose a minimal, high-leverage plan + 3. Coordinate specialized agents to gather evidence + 4. Iterate based on gaps and new findings + + Plan requirements: + - Produce at most 3 sub_questions that together cover the main question. + - sub_questions must be a list of plain strings, where each string is a complete + question. Do NOT use objects with nested fields like {question, details}. + - Each sub_question must be a standalone, self-contained query that can run + without extra context. Include concrete entities, scope, timeframe, and any + qualifiers. Avoid ambiguous pronouns (it/they/this/that). + - Prioritize the highest-value aspects first; avoid redundancy and overlap. + - Prefer questions that are likely answerable from the current knowledge base; + if coverage is uncertain, make scopes narrower and specific. + - Order sub_questions by execution priority (most valuable first). + + Use the gather_context tool once on the main question before planning. + + Use the gather_context tool once on the main question before planning. + role: system + - content: |- + Plan a focused approach for the main question. + + Main question: What is the highest count class in the DocLayNet dataset? + role: user + - content: |- + + Need to run gather_context on main question. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"What is the highest count class in the DocLayNet dataset?"}' + name: gather_context + id: call_0f67xr36 + 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_0f67xr36 + - content: "\nWe have list of classes, but need highest count class. We don't have counts. Need research. Plan + 3 sub-questions. Possibly ask for dataset stats, total counts per class. Probably available in documentation. So + sub questions: 1) What are the documented class definitions and counts for DocLayNet? 2) Which class has the highest + number of instances in the DocLayNet training set? 3) Provide the numeric count of that class.\n\nBut sub-questions + must be single. We can combine: 1) What is the count of each class in DocLayNet? 2) Which class has the highest + count? 3) Provide count of highest class.\n\nAlternatively ask: \"According to official DocLayNet documentation, + which class has the highest frequency in the training set, and how many instances does it contain?\" That's one + question. But need 3 independent.\n\nLet's produce 3 queries: (1) \"What are the class labels defined in the DocLayNet + dataset and how many instances does each have in the training split?\" (2) \"Which class in DocLayNet has the maximum + number of instances in the training split?\" (3) \"What is the numeric count for that highest-count class in DocLayNet + training data?\"\n\n\n**Sub‑questions (in priority order)** \n\n1. *What are the DocLayNet class labels, + and how many instances of each class appear in the training split of the dataset?* \n\n2. *Which DocLayNet class + has the highest number of annotated instances in the training split?* \n\n3. *What is the exact numeric count of + the class identified as highest‑count in the training split of DocLayNet?*" + role: assistant + - content: |- + 1 validation error: + ```json + [ + { + "type": "json_invalid", + "loc": [], + "msg": "Invalid JSON: expected value at line 1 column 1", + "input": "**Sub‑questions (in priority order)** \n\n1. *What are the DocLayNet class labels, and how many instances of each class appear in the training split of the dataset?* \n\n2. *Which DocLayNet class has the highest number of annotated instances in the training split?* \n\n3. *What is the exact numeric count of the class identified as highest‑count in the training split of DocLayNet?*" + } + ] + ``` + + 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: + - '804' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need JSON array of strings. + role: assistant + tool_calls: + - function: + arguments: '{"sub_questions":["What are the DocLayNet class labels, and how many instances of each class appear + in the training split of the dataset?","Which DocLayNet class has the highest number of annotated instances + in the training split?","What is the exact numeric count of the class identified as highest‑count in the training + split of DocLayNet?"]}' + name: final_result + id: call_ydun9riz + index: 0 + type: function + created: 1768225965 + id: chatcmpl-329 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 98 + prompt_tokens: 1094 + total_tokens: 1192 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2870' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What are the DocLayNet class labels, and how many instances of each class appear in the training split of + the 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: + - '516' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet class labels instances training split"}' + name: search_and_answer + id: call_zcl4gget + index: 0 + type: function + created: 1768225967 + id: chatcmpl-148 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 40 + prompt_tokens: 627 + total_tokens: 667 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '117' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet class labels instances training split + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 10 + total_tokens: 10 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3742' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What are the DocLayNet class labels, and how many instances of each class appear in the training split of + the dataset? + role: user + - content: |- + + Need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet class labels instances training split"}' + name: search_and_answer + id: call_zcl4gget + type: function + - content: |- + [2b9401ae-5311-4bae-8a6e-188fbb6f6d61] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_zcl4gget + 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: + - '530' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need counts. Search other chunk. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet training split instances per class"}' + name: search_and_answer + id: call_eolvl6wl + index: 0 + type: function + created: 1768225969 + id: chatcmpl-912 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 43 + prompt_tokens: 820 + total_tokens: 863 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '114' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet training split instances per class + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 10 + total_tokens: 10 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4628' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What are the DocLayNet class labels, and how many instances of each class appear in the training split of + the dataset? + role: user + - content: |- + + Need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet class labels instances training split"}' + name: search_and_answer + id: call_zcl4gget + type: function + - content: |- + [2b9401ae-5311-4bae-8a6e-188fbb6f6d61] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_zcl4gget + - content: |- + + Need counts. Search other chunk. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet training split instances per class"}' + name: search_and_answer + id: call_eolvl6wl + type: function + - content: |- + [2b9401ae-5311-4bae-8a6e-188fbb6f6d61] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_eolvl6wl + 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: + - '428' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: I couldn’t find documents that list the instance count for each class in the training split. The search + query returned only the label names. + role: assistant + created: 1768225970 + id: chatcmpl-459 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 31 + prompt_tokens: 1016 + total_tokens: 1047 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4932' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What are the DocLayNet class labels, and how many instances of each class appear in the training split of + the dataset? + role: user + - content: |- + + Need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet class labels instances training split"}' + name: search_and_answer + id: call_zcl4gget + type: function + - content: |- + [2b9401ae-5311-4bae-8a6e-188fbb6f6d61] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_zcl4gget + - content: |- + + Need counts. Search other chunk. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet training split instances per class"}' + name: search_and_answer + id: call_eolvl6wl + type: function + - content: |- + [2b9401ae-5311-4bae-8a6e-188fbb6f6d61] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_eolvl6wl + - content: I couldn’t find documents that list the instance count for each class in the training split. The search query + returned only the label names. + 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: + - '915' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to do final_result. + role: assistant + tool_calls: + - function: + arguments: '{"answer":"DocLayNet defines 11 class labels: Caption, Footnote, Formula, List‑item, Page‑footer, + Page‑header, Picture, Section‑header, and Table. The dataset documentation does not provide per‑class instance + counts for the training split.","cited_chunks":["2b9401ae-5311-4bae-8a6e-188fbb6f6d61"],"confidence":0.4,"query":"What + are the DocLayNet class labels, and how many instances of each class appear in the training split of the dataset?"}' + name: final_result + id: call_mek5tlwb + index: 0 + type: function + created: 1768225973 + id: chatcmpl-454 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 146 + prompt_tokens: 1072 + total_tokens: 1218 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2842' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: Which DocLayNet class has the highest number of annotated instances in the training split? + 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: + - '534' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet highest number of annotated instances training split class"}' + name: search_and_answer + id: call_1ownxxqj + index: 0 + type: function + created: 1768225975 + id: chatcmpl-694 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 42 + prompt_tokens: 619 + total_tokens: 661 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '138' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet highest number of annotated instances training split class + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 13 + total_tokens: 13 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3732' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: Which DocLayNet class has the highest number of annotated instances in the training split? + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet highest number of annotated instances training split class"}' + name: search_and_answer + id: call_1ownxxqj + type: function + - content: |- + [2b9401ae-5311-4bae-8a6e-188fbb6f6d61] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_1ownxxqj + 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: + - '468' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: I couldn’t find reliable information on the specific DocLayNet class that has the most annotated instances + in the training split. + reasoning: Very low data. Probably cannot answer. + role: assistant + created: 1768225976 + id: chatcmpl-820 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 42 + prompt_tokens: 814 + total_tokens: 856 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4086' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: Which DocLayNet class has the highest number of annotated instances in the training split? + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet highest number of annotated instances training split class"}' + name: search_and_answer + id: call_1ownxxqj + type: function + - content: |- + [2b9401ae-5311-4bae-8a6e-188fbb6f6d61] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_1ownxxqj + - content: |- + + Very low data. Probably cannot answer. + + + I couldn’t find reliable information on the specific DocLayNet class that has the most annotated instances in the training split. + role: assistant + - content: |- + Validation feedback: + Please include your response in a tool call. + + Fix the errors and try again. + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '741' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Must use final_result. + role: assistant + tool_calls: + - function: + arguments: '{"answer":"I couldn''t locate a reliable source indicating which DocLayNet class has the highest + number of annotated instances in the training split.","cited_chunks":[],"confidence":0.1,"query":"Which DocLayNet + class has the highest number of annotated instances in the training split?"}' + name: final_result + id: call_j7c19s6a + index: 0 + type: function + created: 1768225978 + id: chatcmpl-870 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 82 + prompt_tokens: 881 + total_tokens: 963 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2862' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What is the exact numeric count of the class identified as highest‑count in the training split of DocLayNet? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '535' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"DocLayNet training split highest count class exactly numeric count"}' + name: search_and_answer + id: call_t7ddi37q + index: 0 + type: function + created: 1768225979 + id: chatcmpl-136 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 44 + prompt_tokens: 625 + total_tokens: 669 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '136' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet training split highest count class exactly numeric count + 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: 13 + total_tokens: 13 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3753' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What is the exact numeric count of the class identified as highest‑count in the training split of DocLayNet? + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"DocLayNet training split highest count class exactly numeric count"}' + name: search_and_answer + id: call_t7ddi37q + type: function + - content: |- + [2b9401ae-5311-4bae-8a6e-188fbb6f6d61] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_t7ddi37q + 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: + - '597' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: |- + **Answer** + + I couldn’t find any source in the supplied knowledge base that provides the exact numeric count of the class with the highest frequency in the training split of DocLayNet. + reasoning: Only low score. Maybe training split stats not in docs. Maybe not in knowledge base. We could say insufficient. + role: assistant + created: 1768225981 + id: chatcmpl-523 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 69 + prompt_tokens: 820 + total_tokens: 889 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4236' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What is the exact numeric count of the class identified as highest‑count in the training split of DocLayNet? + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"query":"DocLayNet training split highest count class exactly numeric count"}' + name: search_and_answer + id: call_t7ddi37q + type: function + - content: |- + [2b9401ae-5311-4bae-8a6e-188fbb6f6d61] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_t7ddi37q + - content: |- + + Only low score. Maybe training split stats not in docs. Maybe not in knowledge base. We could say insufficient. + + + **Answer** + + I couldn’t find any source in the supplied knowledge base that provides the exact numeric count of the class with the highest frequency in the training split of DocLayNet. + 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: + - '816' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: We must use function call final_result. + role: assistant + tool_calls: + - function: + arguments: '{"answer":"I couldn’t find any source in the supplied knowledge base that provides the exact numeric + count of the class with the highest frequency in the training split of DocLayNet.","cited_chunks":[],"confidence":0.2,"query":"What + is the exact numeric count of the class identified as highest‑count in the training split of DocLayNet?"}' + name: final_result + id: call_14vxm1dd + index: 0 + type: function + created: 1768225984 + id: chatcmpl-910 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 100 + prompt_tokens: 914 + total_tokens: 1014 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3485' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + Generate a direct, conversational answer + to the question based on the gathered evidence. + + Output: + - answer: Direct, comprehensive answer with a natural, helpful tone. + Write the actual answer, not a description of what you found. + Use as many sentences as needed to fully address the question. + - confidence: Score from 0.0 to 1.0 indicating answer quality. + + Guidelines: + - Base your answer solely on the collected evidence in qa_responses. + - Be thorough - include all relevant information from the evidence. + - Use formatting (bullet points, numbered lists) when it improves clarity. + - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..." + Instead, directly state the information. + - If the evidence is incomplete, acknowledge limitations briefly. + role: system + - content: |- + Answer the question based on the gathered evidence. + + + What is the highest count class in the DocLayNet dataset? + + + What are the DocLayNet class labels, and how many instances of each class appear in the training split of the dataset? + DocLayNet defines 11 class labels: Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, and Table. The dataset documentation does not provide per‑class instance counts for the training split. + + DocLayNet Class Labels + + + + Which DocLayNet class has the highest number of annotated instances in the training split? + I couldn't locate a reliable source indicating which DocLayNet class has the highest number of annotated instances in the training split. + + + + What is the exact numeric count of the class identified as highest‑count in the training split of DocLayNet? + I couldn’t find any source in the supplied knowledge base that provides the exact numeric count of the class with the highest frequency in the training split of DocLayNet. + + + + + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Conversational answer for chat context. + name: final_result + parameters: + $defs: + Citation: + additionalProperties: false + description: Resolved citation with full metadata for display/visual grounding. + properties: + chunk_id: + type: string + content: + type: string + document_id: + type: string + document_title: + anyOf: + - type: string + - type: 'null' + default: null + document_uri: + type: string + headings: + anyOf: + - items: + type: string + type: array + - type: 'null' + default: null + page_numbers: + items: + type: integer + type: array + required: + - document_id + - chunk_id + - document_uri + - content + type: object + additionalProperties: false + properties: + answer: + description: Direct answer to the question + type: string + citations: + description: Citations supporting the answer + items: + $ref: '#/$defs/Citation' + type: array + confidence: + default: 1.0 + description: Confidence score (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + required: + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '737' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: 'Need answer: highest count class in DocLayNet. Evidence says unknown. So answer: cannot determine.' + role: assistant + tool_calls: + - function: + arguments: '{"answer":"I’m sorry, but I don’t have any information that tells us which DocLayNet class is the + most frequent in the training split, nor the numeric count for that class.","citations":[],"confidence":0.4}' + name: final_result + id: call_slgy0nwa + index: 0 + type: function + created: 1768225986 + id: chatcmpl-610 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 89 + prompt_tokens: 624 + total_tokens: 713 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4607' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: What is the highest count class in the DocLayNet dataset? + role: user + - content: |- + + Need ask. + + role: assistant + tool_calls: + - function: + arguments: '{"document_name":"DocLayNet","question":"What is the highest count class in the DocLayNet dataset?"}' + name: ask + id: call_avkrjhyc + type: function + - content: |- + I’m sorry, but I don’t have any information that tells us which DocLayNet class is the most frequent in the training split, nor the numeric count for that class. + + Sources: [1] + role: tool + tool_call_id: call_avkrjhyc + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '387' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: I’m not able to find that specific information in the DocLayNet data available in the knowledge base. + role: assistant + created: 1768225988 + id: chatcmpl-722 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 25 + prompt_tokens: 952 + total_tokens: 977 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_get_document_not_found.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_get_document_not_found.yaml new file mode 100644 index 00000000..7fdf4bf0 --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_chat_agent_get_document_not_found.yaml @@ -0,0 +1,154 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4073' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Get me the nonexistent document + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '585' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: I’m sorry—I couldn’t find a document matching that name in the knowledge base. If you have any other request + or need help locating a different resource, just let me know! + reasoning: User asks for nonexistent document. Use get_document? but tool should not fabricate. We can explain not + found. + role: assistant + created: 1768225927 + id: chatcmpl-215 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 68 + prompt_tokens: 842 + total_tokens: 910 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_get_document_tool.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_get_document_tool.yaml new file mode 100644 index 00000000..09c3e93d --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_chat_agent_get_document_tool.yaml @@ -0,0 +1,406 @@ +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: + - '4084' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Get me the DocLayNet Class Labels document + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '477' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need get_document. + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet Class Labels"}' + name: get_document + id: call_geowshoi + index: 0 + type: function + created: 1768225921 + id: chatcmpl-162 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 32 + prompt_tokens: 846 + total_tokens: 878 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5169' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Get me the DocLayNet Class Labels document + role: user + - content: |- + + Need get_document. + + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet Class Labels"}' + name: get_document + id: call_geowshoi + type: function + - content: |- + **DocLayNet Class Labels** + + - ID: 50eeb7f5-49be-47d4-b1bd-19d221da9794 + - URI: doclaynet-labels + - Created: 2026-01-12 15:52 + + **Content:** + DocLayNet Dataset - Class Labels + + DocLayNet defines 11 distinct class labels for document layout analysis: + + 1. Caption - Text describing figures or tables + 2. Footnote - Notes at the bottom of pages + 3. Formula - Mathematical expressions + 4. List-item - Items in bulleted or numbered lists + 5. Page-footer - Footer content on pages + 6. Page-header - Header content on pages + 7. Picture - Images and diagrams + 8. Section-header - Headings for document sections + 9. Table - Tabular data + 10. Text - Regular paragraph text (highest count: 510,377 instances) + 11. Title - Document titles + + The Text class has the highest count with 510,377 instances in the dataset. + role: tool + tool_call_id: call_geowshoi + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1105' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "Here’s the document you requested:\n\n**DocLayNet Class Labels**\n\nDocLayNet defines 11 distinct class + labels for document layout analysis:\n\n1. **Caption** – Text describing figures or tables \n2. **Footnote** + – Notes at the bottom of pages \n3. **Formula** – Mathematical expressions \n4. **List‑item** – Items in bulleted + or numbered lists \n5. **Page‑footer** – Footer content on pages \n6. **Page‑header** – Header content on pages + \ \n7. **Picture** – Images and diagrams \n8. **Section‑header** – Headings for document sections \n9. **Table** + – Tabular data \n10. **Text** – Regular paragraph text (highest count: 510,377 instances) \n11. **Title** – + Document titles \n\nThat’s the full list of labels used in DocLayNet. Let me know if you’d like any more details!" + role: assistant + created: 1768225925 + id: chatcmpl-313 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 196 + prompt_tokens: 1110 + total_tokens: 1306 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_search_tool.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_search_tool.yaml new file mode 100644 index 00000000..1d49786e --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_chat_agent_search_tool.yaml @@ -0,0 +1,822 @@ +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: + - '481' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Annotation Process + The annotation process was organized into 4 phases: + - Phase 1: Data selection and preparation by a small team of experts + - Phase 2: Label selection and guideline definition + - Phase 3: Annotation by 40 dedicated annotators + - Phase 4: Quality control and continuous supervision + The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface. + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 90 + total_tokens: 90 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4081' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Search for documents about class labels + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '506' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to use search tool once. + role: assistant + tool_calls: + - function: + arguments: '{"document_name":null,"limit":5,"query":"class labels"}' + name: search + id: call_tnm30qhv + index: 0 + type: function + created: 1768225898 + id: chatcmpl-19 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 49 + prompt_tokens: 843 + total_tokens: 892 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1059' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: class labels + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '575' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: 'Need to do two searches: original query "class labels" and alternative maybe "class label" or "label + classes".' + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels"}' + name: run_search + id: call_zwclglsp + index: 0 + type: function + created: 1768225899 + id: chatcmpl-948 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 53 + prompt_tokens: 263 + total_tokens: 316 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '82' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - class labels + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 3 + total_tokens: 3 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1465' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: class labels + role: user + - content: |- + + Need to do two searches: original query "class labels" and alternative maybe "class label" or "label classes". + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels"}' + name: run_search + id: call_zwclglsp + type: function + - content: 'Found 2 results for: class labels' + role: tool + tool_call_id: call_zwclglsp + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '484' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Now alternative queries. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"label classes"}' + name: run_search + id: call_6jvkp4s1 + index: 0 + type: function + created: 1768225901 + id: chatcmpl-862 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 33 + prompt_tokens: 341 + total_tokens: 374 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '83' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - label classes + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 3 + total_tokens: 3 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1781' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: class labels + role: user + - content: |- + + Need to do two searches: original query "class labels" and alternative maybe "class label" or "label classes". + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels"}' + name: run_search + id: call_zwclglsp + type: function + - content: 'Found 2 results for: class labels' + role: tool + tool_call_id: call_zwclglsp + - content: |- + + Now alternative queries. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"label classes"}' + name: run_search + id: call_6jvkp4s1 + type: function + - content: 'Found 2 results for: label classes' + role: tool + tool_call_id: call_6jvkp4s1 + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '298' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: Search complete + role: assistant + created: 1768225901 + id: chatcmpl-158 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 6 + prompt_tokens: 399 + total_tokens: 405 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5085' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Search for documents about class labels + role: user + - content: |- + + Need to use search tool once. + + role: assistant + tool_calls: + - function: + arguments: '{"document_name":null,"limit":5,"query":"class labels"}' + name: search + id: call_tnm30qhv + type: function + - content: |- + Found 2 results: + + [1] **DocLayNet Class Labels** + DocLayNet defines 11 distinct class labels for document layout analysis: Caption - Text describing figures or tables Footnote - Notes at the bottom of pages Formula - Mathematical expressions List-item - Items in bulleted or numbered lists Page-footer - Footer content on pages Page-header - He... + + [2] **DocLayNet Annotation** + DocLayNet Dataset - Annotation Process The annotation process was organized into 4 phases: Phase 1: Data selection and preparation by a small team of experts Phase 2: Label selection and guideline definition Phase 3: Annotation by 40 dedicated annotators Phase 4: Quality control and continuous... + role: tool + tool_call_id: call_tnm30qhv + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '802' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: |- + Here are some documents that touch on class labels: + + 1. **DocLayNet Class Labels** – Describes 11 distinct class labels used in the DocLayNet document‑layout‑analysis dataset (e.g., Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, etc.). + + 2. **DocLayNet Annotation** – Details the annotation process for DocLayNet, including how label selection and guidelines were defined during the dataset creation. + + Let me know if you'd like more details or a deeper dive into a specific one! + role: assistant + created: 1768225904 + id: chatcmpl-665 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 116 + prompt_tokens: 1052 + total_tokens: 1168 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_search_tool_with_filter.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_search_tool_with_filter.yaml new file mode 100644 index 00000000..ac4c081c --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_chat_agent_search_tool_with_filter.yaml @@ -0,0 +1,981 @@ +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: + - '412' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Data Sources + The data sources for DocLayNet include: + - Publication repositories such as arXiv + - Government offices and official documents + - Company websites and corporate reports + - Data directory services for financial reports + - Patent documents + Scanned documents were excluded to avoid rotation and skewing issues. + 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: 68 + total_tokens: 68 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4122' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Search for information about class labels in the DocLayNet Class Labels document + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '650' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: We need search tool because user says search for information. So call search with document_name "DocLayNet + Class Labels" and query "class labels". + role: assistant + tool_calls: + - function: + arguments: '{"document_name":"DocLayNet Class Labels","limit":5,"query":"class labels"}' + name: search + id: call_oh61r5nk + index: 0 + type: function + created: 1768225910 + id: chatcmpl-302 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 67 + prompt_tokens: 851 + total_tokens: 918 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1059' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: class labels + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '549' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: 'Need 3 searches: original "class labels", alternative "class label", "labels class".' + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels"}' + name: run_search + id: call_c47evhpl + index: 0 + type: function + created: 1768225911 + id: chatcmpl-740 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 48 + prompt_tokens: 263 + total_tokens: 311 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '82' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - class labels + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 3 + total_tokens: 3 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1439' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: class labels + role: user + - content: |- + + Need 3 searches: original "class labels", alternative "class label", "labels class". + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels"}' + name: run_search + id: call_c47evhpl + type: function + - content: 'Found 1 results for: class labels' + role: tool + tool_call_id: call_c47evhpl + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '442' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class label"}' + name: run_search + id: call_6k4xfcul + index: 0 + type: function + created: 1768225912 + id: chatcmpl-60 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 23 + prompt_tokens: 336 + total_tokens: 359 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '81' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - class label + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 3 + total_tokens: 3 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1710' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: class labels + role: user + - content: |- + + Need 3 searches: original "class labels", alternative "class label", "labels class". + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels"}' + name: run_search + id: call_c47evhpl + type: function + - content: 'Found 1 results for: class labels' + role: tool + tool_call_id: call_c47evhpl + - content: null + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class label"}' + name: run_search + id: call_6k4xfcul + type: function + - content: 'Found 1 results for: class label' + role: tool + tool_call_id: call_6k4xfcul + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '444' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"labels class"}' + name: run_search + id: call_1ttc5li3 + index: 0 + type: function + created: 1768225913 + id: chatcmpl-722 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 23 + prompt_tokens: 378 + total_tokens: 401 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '82' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - labels class + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 3 + total_tokens: 3 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1983' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: class labels + role: user + - content: |- + + Need 3 searches: original "class labels", alternative "class label", "labels class". + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels"}' + name: run_search + id: call_c47evhpl + type: function + - content: 'Found 1 results for: class labels' + role: tool + tool_call_id: call_c47evhpl + - content: null + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class label"}' + name: run_search + id: call_6k4xfcul + type: function + - content: 'Found 1 results for: class label' + role: tool + tool_call_id: call_6k4xfcul + - content: null + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"labels class"}' + name: run_search + id: call_1ttc5li3 + type: function + - content: 'Found 1 results for: labels class' + role: tool + tool_call_id: call_1ttc5li3 + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '298' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: Search complete + role: assistant + created: 1768225913 + id: chatcmpl-642 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 6 + prompt_tokens: 420 + total_tokens: 426 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4929' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Search for information about class labels in the DocLayNet Class Labels document + role: user + - content: |- + + We need search tool because user says search for information. So call search with document_name "DocLayNet Class Labels" and query "class labels". + + role: assistant + tool_calls: + - function: + arguments: '{"document_name":"DocLayNet Class Labels","limit":5,"query":"class labels"}' + name: search + id: call_oh61r5nk + type: function + - content: |- + Found 1 results: + + [1] **DocLayNet Class Labels** + DocLayNet defines 11 distinct class labels for document layout analysis: Caption - Text describing figures or tables Footnote - Notes at the bottom of pages Formula - Mathematical expressions List-item - Items in bulleted or numbered lists Page-footer - Footer content on pages Page-header - He... + role: tool + tool_call_id: call_oh61r5nk + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1181' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "Here are the results I found in the **DocLayNet Class Labels** document:\n\n- **DocLayNet defines 11 distinct + class labels for document layout analysis:**\n 1. **Caption** – Text describing figures or tables \n 2. **Footnote** + – Notes at the bottom of pages \n 3. **Formula** – Mathematical expressions \n 4. **List-item** – Items in + bulleted or numbered lists \n 5. **Page-footer** – Footer content on pages \n 6. **Page-header** – Header + content on pages \n 7. **Paragraph** – Paragraph text \n 8. **Picture** – Images or figures \n 9. **Running-header** + – Header information that repeats across pages \n 10. **Table** – Table structures \n 11. **Title** – Document + titles or headings \n\nThese labels are used to classify segments in PDF pages for layout analysis. If you need + more details on each class or how they’re applied, just let me know!" + role: assistant + created: 1768225919 + id: chatcmpl-425 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 220 + prompt_tokens: 1012 + total_tokens: 1232 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_with_qa_history_ranking.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_with_qa_history_ranking.yaml index 45342402..4ea3296a 100644 --- a/tests/cassettes/test_chat_agent/test_chat_agent_with_qa_history_ranking.yaml +++ b/tests/cassettes/test_chat_agent/test_chat_agent_with_qa_history_ranking.yaml @@ -62,7 +62,7 @@ interactions: connection: - keep-alive content-length: - - '3996' + - '4087' content-type: - application/json host: @@ -92,11 +92,11 @@ interactions: - Extract the TOPIC as `query`/`question` - Extract the DOCUMENT NAME as `document_name` - Examples for search: - - "search for latrines in TB MED 593" → query="latrines", document_name="TB MED 593" - - "find waste disposal in the army manual" → query="waste disposal", document_name="army manual" + - "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 TB MED 593 say about latrines?" → question="what are the guidelines for latrines?", document_name="TB MED 593" - - "answer from the army manual about sanitation" → question="what are the sanitation guidelines?", document_name="army manual" + - "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 @@ -122,7 +122,13 @@ interactions: - type: string - type: 'null' default: null - description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + 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 @@ -174,7 +180,7 @@ interactions: response: headers: content-length: - - '552' + - '508' content-type: - application/json parsed_body: @@ -183,24 +189,24 @@ interactions: index: 0 message: content: '' - reasoning: Need to ask about dataset class labels. Use ask tool. + reasoning: Need ask. role: assistant tool_calls: - function: arguments: '{"document_name":null,"question":"What class labels are defined in the dataset?"}' name: ask - id: call_jfdfe741 + id: call_vpk7k8no index: 0 type: function - created: 1768219723 - id: chatcmpl-372 + created: 1768225863 + id: chatcmpl-516 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 48 - prompt_tokens: 839 - total_tokens: 887 + completion_tokens: 39 + prompt_tokens: 846 + total_tokens: 885 status: code: 200 message: OK @@ -439,7 +445,7 @@ interactions: response: headers: content-length: - - '522' + - '732' content-type: - application/json parsed_body: @@ -448,24 +454,26 @@ interactions: index: 0 message: content: '' - reasoning: Need to gather context. + reasoning: Need to research if other class labels exist beyond 11? Confirm count. Use gather_context. role: assistant tool_calls: - function: - arguments: '{"limit":null,"query":"What class labels are defined in the dataset?"}' + arguments: '{"limit":null,"query":"DocLayNet class labels defined in dataset 11 class labels ''Caption'', ''Footnote'', + ''Formula'', ''List-item'', ''Page-footer'', ''Page-header'', ''Picture'', ''Section-header'', ''Table'', + ''Text'', ''Title''"}' name: gather_context - id: call_9ggb205d + id: call_toc0wxqe index: 0 type: function - created: 1768219725 - id: chatcmpl-484 + created: 1768225868 + id: chatcmpl-219 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 44 + completion_tokens: 99 prompt_tokens: 689 - total_tokens: 733 + total_tokens: 788 status: code: 200 message: OK @@ -478,7 +486,7 @@ interactions: connection: - keep-alive content-length: - - '115' + - '258' content-type: - application/json host: @@ -487,7 +495,8 @@ interactions: parsed_body: encoding_format: base64 input: - - What class labels are defined in the dataset? + - DocLayNet class labels defined in dataset 11 class labels 'Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', + 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title' model: qwen3-embedding:4b uri: http://localhost:11434/v1/embeddings response: @@ -498,14 +507,14 @@ interactions: - chunked parsed_body: data: - - embedding: 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OgGAubO8XbveepM70j/wPN6Fh7ogXXU9pnRmPWyJE7p3ta08kJlZuv3tLjwbUgE9eWv+Os0ZGzxVqCy9KS6IvcHfgjzvFTS8ZICWPDVNqruYwEW8HxC/u/95bDwrXQI9/ozOvNPP2rz5gb28Nh8dvUlbjTxoRCw91lsOPXzRDb1xxwg91BwjvFfAYzsVgnS8/nVYvJsHw7u6fEG6HCVOvXEDpjw3CYa8klzfO8eqEjzji2I7wPaKvKddNDzBHsu7cakdvUWVZ7u7rwo8FfANPOEWAb1a1Ya8o7N3PZGFU7xwlOk8Dh86u8Nt1jpw1yQ9JlNWuxlrDDvfWHy8RZvzvCd+N7wY3rq8ywPDPNKxQLzsDYk8lq/nu59LJb0M0qk8DmiKvGuIGDzkAta64Qz2vCsIMbyIvoc8EFXzO/D09TyqowM84mRIvGvgFjzASAg9FP3WulgIazu9vOc8huu6O3kFwbxhl8k8NP2CO1/7cTovw1q8hEYFPPZC5zkV3xk8NALgvOE8drw93zi8LG+7uolOkbvpLJi85NCyPP7L+TpuKQ88H9q9vBDYy7zzm3e63WNdOTgHmzzDodk74K92u+7bI7w1eDw8ZavBOeDUgLwn25C7uxEHPdKofjxflng8fBL3u7NshTs+t3m8MwF5u76WDjzTuzm9QKaRurxCorzwUp08W1QfOwjWVjx8/4m8QuYCPSWAqLyaH8U7xRKWPGWgVLw5dT08nCc2ujg+pDwIj4+8X3sJO82eyLsCTbc8u1FCvHOqfL2kel68O6D4vFuUezyTYva603RIPGlXlbz2+Uc8zzECu+IZ5jtd0hI9lX6xvFEBBrvk7Wo7qe8ZO2rBcLlTUng8fUHyvOuXzjyGq2E8elaHPBi+MTzwaTi7BM+iu8ZU/rzIqok7ajdYvMgho7ux1pm7LTJRvN+6n7wqA1S8pabSu9QRarwu0bA8yU8VPJ1GLz3FsME8LZQ/PLwqLTzokSy8zeRiOx5bPLxWVIk8ECQLPCQz9jsIBHQ6hVuVvHK5UTvCGR68aQtXvCTeNbyX/Ci8eXjMvL4abTx38ZM7OffrO4r0tjzqmae7Af23O+ylJLz0b9c71kbXvOoxUjyMtIy8V4c5uwwoCbwa26K8slqivAwrmzyC8WQ8N1XBvDfrVLytG9Y8ikNjO5eTBbtQIwm8jrYGvFgmjDmEnKC8lLEbPFKENLulCGq86il+udKti7yBboY81tmQOzKl7rtUge66OUPMO3M8KTzU8/G8LO3mO6HtEzw9si28sdTmPEIVX7yIWoa8ONhSPJPobLyjeFy8rii8uzKOB73Nq6O8Kk+2vNqSwTs85HI7BO/MPFXhuLxJpKy8FOWHvM1gfbxDDL28GEVyvJer3rvInPW7ELeGvAv8N7y8CMS79/OHu4iRlTuC2iA8EBC4vL+HcLv5ULk7dF5DPSm7rrtrAYE7mU2oOzVzrzxv5MS8KQBTPOSg3DtzHx07swbUPDckGrz0aq86T6QOvdZSxTtzMNI7YEwuvC6w4jxkyj28OxXYO6y+mzxTp+U8wC0LvCTvhzzXD2W8PBwJvSTMXruk6448SE01vMrjEbtPpX+8B/ZLvAYlCTwmNUE7h4GhPFzRc7y6TZE8cE5Zu4/WSrzkUyU9j0eOPGAlLDyjzdu7rpbsu+6LiTuIcLQ8kbL8vIFUoTquraY7IGjuvKYB6byD4fK7T583vQgww7ycFci7aL9svIYYkzzwiuw8UrXsPChx+jz2Eqe8w0Euu/AKET3ZRz+947V3vIRLnbqQpJK7km0tvI4lFz1Cm1Y7wKnaOx3Z4rzCY5U7RsVYPArV7bymJfy8/balu4M3Qbr6hSG8kOBUvOJVzjtJeY+8fpqvvA3q17ykWEG8bay1O9+nDzukioG89JiXunWUsDwzc9O8Dt4DvdUDALzm5Zc7BbrCPG+bCL2YLLG8VUnRvKU1zjwhMBA9tiCxu9lC9TvUp1M7JcETPZEeEjwn4zE8NV6YvMBXEbwjshW8QR6EuzE3xbuJWjq6EZSRO2ijG7xP0Mc8a3TzvNAKCrsCZIO8kTqyvDwHkznN8yG8sOouPIUxBz2KKRs8301DvHBrVr3mWCU9IygXOtPy+TwW5us8sOlSvbq5Y7w5DnC87zjqvIdlnrzTV8U8jLUXvWRb57wr5Is8kXQ4u3XZgrpTPou8PK1MvDdlRjwYuZa80/c4vfP84TsEkIM8wtjNvBXB3LoYyMM7ShzFu/fwCb061WQ8GCAAPDGocDoUhZ08ZEcXPOi6tTw2PMa8/tgtvX3FJjtRPw89GVCbPJseUT1cGxq85RTduprFlLwtJ6K6s89Uu9t5Dr1lHdM8QgqwO5Mu57u8oeY8JYY/vTmVWrxtuPY7nKHJPN5CXzyHsoy8mZBzvMU0iLvvRr67YpAEvMSKhrtDPN4793mKu6Kxg7tX+0i9iI1xPI9sp72pe4c8YR0bvIUdZ7zCFMI75kbQvIqeo7zSEfC74YX4vJ6M5zzi0JS69+/BvC//zrstmiI8ypqsPDC2GT1aaAY8/GcOPEaZMLxKFqA7ROGFPMfs1TwvDA49auGSO1nWmzxI5wY937ISPb/jXDx5ISS8fXT8u6YTHj3VHnW8a8W8vAxhXjveTLE8BISzPCCpezx/TEa8K5SvvE+GOTo1tdC8z66BvKWWoDzOcgU7FayyvNn9Gj2vhgo8h8V2O2e3/rtk9m08be5bPO3iqzxh3Ve8sa8jvThn7jycItu8FHwpO+k+Krze62i7ujiZPE/7iLyJIjm8EWspvAqGGrzsd9e7TEQ9OyRq2TxlUvO7A198u/QnTjy3n7Y6zWUpPBf5TDwLRvO6uUe0vEAHQTxiSI47NzGFPMazhjxWBw487snBu4JJWjwGe487zQQYPOxVC7ktTuy8ywTQO+LgIDwWk4m8wN20vJG2rbs7ehq9RHlmPO5eVLzcgZO7CGQmPAYVbLwZF+I8JnCqOxSUcDtaXkK92W4aPBmwAT2PVpM75zykuWKixLvvI6+7F00PvCd4A71RE/Q3+sBDvNLRtTtac8Y7pS8+vNwwMjy3csY8yVPfu7zQBDrozCW997ENOnDzejwbtbk5v0lsvCNTB7wJb8O7c7bguzuzfjxviR87YX3XPBCl3bo/rJc6xg3KOzYWITxm3W871AEKvAqcBLwGP5i8xIw1vW8QJDlvlZ+8M36EPBw4S7y1PV28mL/XPGOWI7wsglC7s82APMPjHbvcoIW8rwq0u/Sakzycl+k7TRWBPI9hqjwxCwc8+wnvu1IqiLyntf+8pY2ivEIhW7wptAe9euLaO55aMrz5MUk8+KPTuwNr9bwuo3O8M5jCvC0zVLs1Woo7XN6IvLDu2btmmTs9K2YXPCR5ybzlLDe85TQzvQ0Prjz12AK8oXFVvLXmLT10vxY97HDgPGhEUrzvxEw9CydbvMszRb34Px69yzCDu+0MqLs1TXu7fIaMPOGKg7wtLH08jDmDvJUt7DsYEew8jq3hOs3IvDvTpz88vi4RvE/PmLzUBdi8h/kOPU9IB7vQ0qk8yjMRvTtN3bpVHF08UX93vCauSrxJhgw9+RmAPD4skLxwXp489ZHBvGgFsTv525W7kzFfPMNilbzk5jI8U8FbPJqd9jueKgM8SXxDOypAB7y3zJg67neKvAicZDw316Q8lhc+vA2olrxB7OG7E1AJPV6kjTrWbwU9C9sMPS0+Tzs/47O8v0QcvCTy7btPQhS6lXuVO+Hzx7xKwvm626gHPP4LPTymkQm8lR8OPeUBXTzmra08eX3mu2a4KL0+3Dc8DtLNvGQK9Ltrsza8W03yOykzAbyfYNu8f3POOwm7vjyaatS8fwieu731obzu1LU8GIW1u0eyFDztchy8ExSdPRCkCbx0w2K79yuZO85zVrvs3sS8RcR2OwxxvDwDZw88FrPnvKUlizuteeA8hobJO7I/ujz0H7E7RMa/uX0LPjxLwTa9vTfRO+4YtzzaHo08Xve2O/LBO7szQXI8r8CNvIlotbv4iIM8A+WOPDymD7ttcY08v2CMPKm56rqnI/S8cDotu4/gRDs8wJe85DjOvLwMHT0RMj08EI2DO4GcbzxV/ru6YO12O3drjDusZe67aEFtPQxFQjuy0Q28n26Tu8KBjjyBogW8RSuzvBl3wrxAc547FAo+vL7URztYrQG8dv9hvFRVYrxRNtO72aCrvJ8J8brjzYy7/m1KvFTstTwdpuk71jBBvDkfYDzncEy7kvYJvfsGsLvDzrQ8U3viuwbq5zsX/jo8o8UMvQXN7zwCKKU882kuPCX0arwT2bQ8Jka7OzLFl7y5a8a75E3aO44LQryoju68IJEuPZeyUby9km251Kf3upu6Vjwg/7M7ZXIQPDDDhDsI7as7+g7hO0E7ED2orOK7Xv3gPELusTwL3gY7WIDdPHu68LsWVS88TeuKPNRsMjxq2sY8l8gIvRSVhLwMlGK8X1Luu+CPBb3hkMw8XsaEvI4mELwJeEQ8VrXKvAZtOzzqB5w7URWQu+IOUjy12pk9n9btPA1JDTu/V2e6w45HvKJoXT0gl3I8z/CrO+HjOryPpDw8EykBvGn6c7zJKj68K9ngOwSVurrKcdY7VK7pvLmpGLwaXZC9lGETPXqqC7rUCqG633gQO5sGYD00G5e8nZyPvD7oHTxQ5Ze7LxrRunboeDy3Ari8Z09FPEuW1DyR1rW8HSfhPJxp1ryiDqw8YeJGPEqliLv8E9G8tnCuPNs0kDqkNP0776I4O2BI1Dz1XbS8+7THvMHp/zwv/M+76krwu495OzwTJh68F33gPNbglDxWyaM6+HuZvM1bHjyS2Gk8NPGbvOL23Lz87RK9lk1fPPDhu7trmli9R6cHPDd6GTwJdhK7Q1NNvdGP+rujIJY7ybhOvJiZtbzkOSA8SlnkPHp4SLtJtaW83LjgvB873Lw9F/K7ISGUvO7wXTwhP6a8OLJlPD8YCz1Tyg+8HKz5O0l0hzy7FKQ8gJZqvLqUhbwxMpg8iAG+PGXfgjyjPxQ88tvMu4Y9YDtZCJW7sLaCPCA0Ab15Brc8EBV6vB7fDLzey7I4gTuPvG+EsLw+TgU7qps5vKZrGTz4FCc7VYMGPDNVNTzvRh+9trzbPH51MTxvktQ7OlFNPF4ambx4a8C7eQySO/35J7vLki+8D3kkvA8KIrw8vM26RLpUOwvh+DsNxAk9Gq+5OzjOJrwgOT+8514EvLrKSDuhkGI7Y4mGuxtHHL0ifoG7+b8jPK8yKTz29tk7twuMu9b5FDlIjaQ7jGohO9obLjwG9V89T+ypO6UdRjyeKoM7Rm8ZPFZHU72x6508fkC1O+Wmn7ygsQe8ykBBvD2EFT0MpdY65PnkPF1FEL2rw2S8qnoZvGa4OjzlO406GXaQvMmr0LzSVYE7VpgePWA1j7y2UZs86n7MOurSzDsFeIk7OCUjPM0IPTwa1WM7ZgibPId+6zxeP6G6KLMYPXPpCTwpdss8XWR0vVLciDztvDM8aqK+OzBpiLzaTQ28hAR/O97Cv7x82gM5+PjuPLM1HTtVdEy8VyxTvFv0XjvLy5G7sd/6u5IBMbyx3hm69lq6PEgkJTwe75+6ynKFO3N227z/rJ28MJPOPEb3ZrtYOGC8WF+TPEAAazvv4QS8HRCVPLH1W72JXx67Y3dcvQOH4zvJRW47SUyoOTkOizyfxBG9nGEUPejXqrxOKoW8zvr6u+yR0Lw8Xoi8V3k3veEqnryLq6y7hXyEvDbtL7wRISq8WcujO0LxfrsTiSg8o6aQO7eCBDxNGZS7NSKsPGMdhjwFo048ltBhPIGziryFcxw9SIWUvOvQxLyuy++7EqWpvBhl+joHTT68+Vv+u3RDlLwXZSi8dVMfvV9OibzjB988PcpbOvYrorsEJYQ8elYZvcgK+zwEqQ28xmCYPHiBHjsFuPu7UEJlvNcGrbyWeoS8HjAFve2swjuU66M8CEWdvNIx9TwblFY8nZMDuw+85zzId2G8S2Lou+EQlzxAMAW9AytCPOaukTy3qjq8lByUPNVQ5zy+YqK8JX0APEXj0Tx/TI68mAKavPj7mrx/nYs86NDFvCty4DzT84Q8saVGu4L6WT10/u87RfcEPLQxy7wYT5g6Ni2IPPF4+zx2bzE8TKhhPKOjUbsceJE8fHqsPIzoYTx1ssu7W7aQOpxFBjvsikO8C5GavN7boTszHAc8J1EivD10XztiQiY8C3CKOzzc97xjZmc78K+aPAQDATswXpu7bjgSPC+ra7xb/Gw7HfFmvBTvDLxmRRa89eUaPT+GXLweyhq9sRCUPNgAlbu1GsI8zcwKPMc19jw4WLs8RKdKPE5fJjwmVjO9Kmx4POiVtrxTTz+7XCrvu0suk71nscu8wUByu2sFMr3/ZVi8ew2tvMDoHbu5gn87HYOnvMEODLs0Rwy88jNMPYDFTjyrCRM78SEEvVzgBTydRM67nCkWuwyjxTz1BBq8m4piOzTt6rxW+dK64cVkvCWbHTwHOWK8/tUQvXS/jbyngao7csUmPaYsCz38T2k88cMHPfK9Dj0edlW53jY4PODeHr0qfEU8Y4SyvOEl+rssDrk88XZeu8M3Arx/ik66eB3gPDnxvrzEPEc90HAbvH57BjthBLk8BP1CvPCWgLrVe429pC25PPAfJr1Qh4e8KhZxO3MHiboXP4c8V86MvCvVYLyS9Fo99RXnvKjbNjxiV4y8mNsEPNKeAz0mSU078ZWuPPiV87sdOWO8hj4nPJ+S2zxEE8O8xdyFPBZ5uTx5nju82oLzvCpNCDwgqXs7zwSsu5Ib77xidCc9qoQuvESAULudJrq7Ze03OyAZOLsbJfW8IRqqPNI6b7wnszK9GQ7LPPEJsjhHy1+9kc2XvAtvFL2DszM8L9ZJu6miBjvnmRS6o78KvKN567wOvCQ8c8KEPPHgkzxOPRA8eySCvPlCpjxC+oU7KciavElDhjs8Nzk7RdEIvTyuW7rAZc+6+H+IPKE0IL0K/Mm8ZCjlvAk4l7yUX2S8Bz4SvJCdljzbxXI7xFZfu7IpjzxPsKy7JaQquvtugTw28Iq8/CcGu3NPiLtqF308ViwRPM5IHD2UuoK8RmWFOzz5fTxHh5Q7rr09PfuZorzctj690x72vMvOBr27bQY8jcqFurqEHTzk4om9AqRfPF0pdbyjnkK9lqacu6RxjTun3UI7yZMJPDZBTDyks588iRfVO0qNOjwVGW27vj4rPFTj+7o6in286xgGPTgqJbyYDSw84+dZuxYeJz3rRH48WhmXvAvVkjzhqzQ8mz+DO7wjmLwYSKG8bTSlvJuatTy9HT687qYTPb7UJL0evJO8rqOePFTTRruUq588XswEPEr+tDx2JUI9R9lKvbQAxTxu4SK7ykfvO4rETrsCZbG8wELYvMODCz1nDRk884Qwvc7fDT2dzf87oVYnu7rFqDjDyv87RKw5ut3VE73RIrA8XEC/On2BKbz2tFw8/+saPALj87z8oq68vIatvJH/FD0xJRK9oJasvNJ0cTyAV6O81rCJPAwbt7wMnJu7lygIvLCtAD0hOiy9BrfgvILXMbxfzIA7JaVLvDtPNjxGKAi8/n1KOzFepbs1CrO7lIEdvHcoQrxEGYy8rJyrO6cYfjz1HHu7UrdHu+RaBT2sZPM7+22uu5GtMLwK25Y8TTmguyfd5Lxo3C+7otoUPKmcmzvys0c8JudqPNpwbbzrinY8zjsEPamXyzz8GqE72RV2PAybuLyMWtS813B1PC3VNryKEka7eUaAPIyTk7xjbgY8zNkUPXvm1Lytt6O8sUkMvXTE1LwWHTG83pHVvD+m9TwUQlI8AEe2ulA51bxwtFQ8HvjfOwe/lrwz/4w8wfW3vENjUzz1pGK8RAgBPVA3nLoZDry8oMioPG4eDDvi+tQ7BSq4PBRcBzwq8Mm76qvfusmKojyZ0OO8ZnnkPAcVobw0vQO9XYsNvW8q5Tu9KDK9BdUaPF6sGLxJxJa8Fj3PuwDgWLzGBzi8eG+0O6VTCjxAYjs8RjhWPPfFmbyIbaq8o2dkPJ05vzz1XAm9vy18vINwg7u9sNQ6HoBYvO1MvTsmow87lkSzvGhRYTvqhsK8T6Y9vEeKBbrjuDs9Ru0FO9o927spP4c8yDHVPHNQn7vsWjO6gImBvFeBlrxQuuk57OVcO5XRyzsoG089I/+yvKemdDxN5Xk8KNPVOU+YCztgmO67CPpsvAvXojwlaEQ8tXimOxnwhzuCKSi7kfrkvAerwTx7feK7BWVVvSwbnTykuwA9utFbOxGlLDz/WVQ8WpqKOofLdj2tOmK9zyHeutjDwby7FWE86S6Lu8DAADxkTkq8aSqNPCHei7zE/KQ7zUpUPOiys7tZSxe6l7+sPCJBlbvlPpS5kONOPLMwuLrxpMU8b9HuujH3kLzggY+72WmAO5uXETzVf+88nEYIPfKmoLx/dJG7SmnYO8Dfjbzk68q65dGqPAgfdDyg3JO8ZXU4vMQGvjvNuvA8NAQUPSNJybzazMC8Lt+GvLVxLD2vQiO9x8LSu/C4VDxrFBW9fbYOvNp8EDytchm8FjgsvOYAOTyF4AQ9FM4BvC/dS7zdvQ07oQBgPE68QD1pads8W7RNPdgnGjyXXkO7ZHtKvLIRQjx64Q09ROPzu/d0jbu1kKs8PO60OkF6Mj1Bypu8Mhh4uxlOnLoWdYu8QWsnOxuK57xEVx49hb02PGs867xrD8w8549xvNe6lzxudyi9xE2cvBlWTb2uUw87YLf7O/OEODz4mB07PbHmPIg5Bzug6QU9E4SmPKo2PjzyV748ouQivF+pGzw8mS68wB6CPMKdKDtj97+6PHMPPSVoOzyqr2i8Z0PRPL7zPDzTdeQ6Jhj6vH7DDjxcSz68tG8AvYhV/DzOk148WAeyPMYNmruMRwG94bX3uiHPaLw9dCa7nf4WuqRVDb3GxQm8H3oYPaZFY7wWnxi9mZvpux9jMrvMoCO84ewqPM7OZjxQX7A8k7sFO+97+TthxsA7W79KPfNT2TzTwy+9Wn4/OztZo7u4cpS8ZrZaPF47+zuyVQe94jFAOzCM1rub/Eu8Y5zkvFwRD7yolt26IIgUvCgKBDxiEAW9G/YOvTDao7v6dSA9rZ40u7qW+jt1GRA84BG0OwiwuTzfBX880YhxvMbf4Dtjg9I7s9IAPATXMj0PO8c76jMGOuuChzqX9nE7hWQCvL6wcjwW9CQ8M13OPBhLqbyjoz68JrxJu+DgOrxqftK8nuOtvJE/7bt9Dym7Sfzdup5MqDyp+2m8zKtcPBGOlzyzxQ89stTAO48Os7jnepc5jI7bOxYYeDytLRa9cWJ5PD1o2ruMn228LVisu/H7zDuWKDG8pVtLvBkl4bxKPby7+NUlu9UuB7w1Zzq9GYJOPOz7bbuuwt+87fhEvGDUCD3QUZu8tTETPYXJ/DuLfz282VSVPKkklbt82pe8bZoIvSNLyrz5DF+8pQ8zvVTosTwq59E8XsGhO5bA0joZEaK8D/aLu5NVFrzYGGm9wZEFPCqkUTzgXQK9ZdKQvGS/GTzEgDS98ZZVPO7KL71JtLA8ZmMpvPpOg7wPlom8/fGeu1qx3DxAuxs9D+YivOURgbwcMCk7E4+luVRqzjxK5Xw75qYwuyy3PTvFid47mIFpPKwZNr0qS/w7SG+7O41QmjsJ+LW8n9tgvMpiJL0boJw6PintvMNKhDuq7Yy8ZPIuu8CVsbtOe9Y6OyizvLyMsbq6t6i8pr8Dve2e+LsN0Ka85s1VPADa9rx2hus8qjVCvBvEjTxaQLm7YjZbvN/vjzxv9by7mToRuQMNvbqYELA7q6UJvVYQNTyjd4Y8cTtqu5exJ7s0RL67T0tpPLd1kbkp4Tq7CIPwPBexdLuSyKq8TvOIvFinJr1vqZ687SJTPNjzezyQxa28zTpSPIR/7Treky+8Fk44vBFC37xkA0I8kN7rO2SYD7209OQ7RtL7PEA1yTzv7Ze6JmrwPN017jtgcn87DsQ3PNMJkrz086I8xuu1PEzgGTx2+LI8YP0LPWoi7zw9OLe8QmOHOgo+H7xTWvI8C5P2OD2X57t1s566pJtEOzYC0DyDUqW8dQSGPAyPJrzdagu975QjvTxAMjzr3767QHskPLkNWDyhVY07dIPgO+5TNzq5wQY93ZHZvL9a7bpnQ0a801kLvNxSpzyOeUc80BdyPD/xxzrdmva8ek+LvCNSNjxzGTI89fzcO6MuXrzEmtW7d4havAtkOrwLuC88tu2ruwNYLjzYBwe9bzwDu6nJyTt5l2G89oswPH1vfbwrOug8dxYAPIwII7zabce8MnUGPTWY1DrTKbg6A/RyusEgpLySaJu8huBkPF82YjxKq8C78nkrvJmeArwDpiO8HH4iPbEbeTyrbTS7W3NUOuzs8Lzh1707YugsPPUdXDyPqLM6ccaQvHc0V70u/wy7ADa/PCYR/zxiL+m8XF7Iu24e07zxHEm8fKimvEdnhjuLxza73ff9PMK9jrkgX1U8epk+uRIsJ7zfrmS9pVR0PPN3rbvwH2U8cD/fuhyAcbtbOjG8BgQPPXH66bzaVzm8PUqSPAZKTTznDqw6DpzJvAxc4LxhBCM8DxGlPC0cMz0pC/k8wm3ku0W2xzo+2hW8EhUcvFEk6jzcZMq7TyNKOiG7LryCGhm8niC1usz9/LxC1gy8jDGjvKbeJr3FqzI7JkMqvK2dEb3Fx6a8mgSJOrAubjyQcHK8D8tvvH0Ad7qAebG83qs5PEwLzzzpDds7NAi/vDYHKjxp0mE7Cux5vIGaIzuf0568qwYMOzMdHb2VAdk8OU3ZPGtmXDzbvyS9YOIaO9Xh0zz/OPe8LkCdPBswKD0Qa8s6xldPPBRfFrujMz68vfJwO8D+Sbyk4oy77u93PJh8krwdO2s8DbrMvFWgOrxS2CW9u62cvB8a+Dy1aAI8j/sJPOnjFjyJKJy8j3SiPIiA5Llf/7281VB0PA6OCL1M7/C7XUqiPAE+Az1qbMI76sKQvG7kfTxd/Z+8Fq4UPLK4WTytwoQ8fYV0vETuEbycIaG6wjEku4EVrbxg4Eq7WVCou8RBFD3tnlC7dfm0u78bSrvleJQ7s7XXu1a9KL1JeVO8z4Q2PF8PBjy5Dgw8ZANJPcVQID0unp27F4zXu9IdaDvNWoe8IqwAO135J7z/5625soapPJJa4LyZUZ67LMzCPNGT6rxIHR657/crPGnS7bxnQXe8KLtsvCyor7v2ODg7bKgWu9flujvAK+288E2NPALkF7uojQg91bYKO+dmIrzXotU8kpc4PBtPdrya2OW77RiBOycRtLzFNOq8YyO0vIEhbrw7Rvu6MGrauxmdsLsBAlC8yZTLPEGKBD3AhjM8LE1ZPGzVsDwJ63K86SEYPero/DuXXie8RZWjPCDmNzoH+5668qYBPavr0Dt1CeY7TlB0Ow4dr7z4f5i8iuuTvERc27zPNVM972YivHC9Nb27g6A8ysfKPKzchjt88Mc7hf26vEO2bjz72me8KWwSvYWKNb2+nPq6b7jXunsTYLvQpeG8kGunuyixVLxlATs82YravA//dzyOyxI8XQKdOxKzBTzwHQu8tXpMO1w637w3IIk85TO/vB9+tbxMxSu8bEL8O1gI+zwNyQA9mu6sPNCiH7y7Zpw8eM05vTZdoDzfMUa6J8+NPGc9Rb1mrli8YhpUvC6wOr3tsZE80WFuvIJzTTzKBSi82Q6VuxDvOD01Po68PRW+PBboijy3hgS9b9iTPJ/eh7xzinY8GKMuvGPlOD3mEJU8f6m4u3gRNbyMVAO6TqLwPGnrwjigtjo8Akt7vDn4ljwRNZQ8FwlVPHPOlDx/JSE8KBIROr2HPzvgOBI6pXXcunQwN7xKXRA9FpvSuw+49bw8HIw7R4ARvCI/pzz0t9e81+WyO+0VQ72Jjcw7tOZKvFxmuLz1oLW8xrmfPFfkdjpfFVA8h+qMvCetVryAUpw7CYidOzfzozwxXcQ4iUyuvHGViTxZ3SC7iyndO1tzpzyNJJi8m/42O4JUMz14UZi85F9VupIKNT38GXy7HFBwOi9iE7t87qG7AdSgOoM2p7wvn/461iftPKP74rphnpi8/ri9PJzsdrxIEdU8uEUFPe4xSrxiNnY8oxHcu0sL3btfFiW991EUvVj3irx+pfQ7VUqnPHB3XbxHy0k9uKUhO69gjjvfZfU7hXqovKkI7zyk+LK8g06fucY8ezx09PE7IzwSvGZZwbzETwO90ajsPIvamTyu3PG8lGm3OnOEaLwHNJU8wF2APPW6kTsjP687q+r9vC7T1bz5vBi8XRFFPH1SqzypaHk80x90PMWUp7oWFYk8sh1RvCat3LucCc+8Hdq2vH0TBbxsqIA8GpasvBra2DtLPA69suJIvL14r7wR/ia8YbgRO48KbDw93pE8Lw60vOY97zxyG7u6hxw4vDZjTjzeMjo728JvO2N8truBKuo7Hcm5vPMqZLwiFRK9b8P2uvgROLzWmiM8BVuTPD16Sjr0JNi8r1B3vBGwTzwEHmO8LS5kPPgGZjrcK868mc5PPF/btzxRHVG85/+5PC18xrxnr5m7VIcnO1dNLbuTu7q75Xs8PH+rjjwYlwW9Yr+tPJ83DbyOWAE9m4QBPfBCMbztGh09wxzLPDVaBDwGcpc8ZOc9PCpgKjzG45M7vkeEOik1Ij2QVZC8fhkBu7PpTbviUO08neQAPFcr7bwSbD48OPU8PdjGZzyatrI8nACjO8YS7Ls61S27lRTqPAZaBDyx0tQ74XemO7CLE7zSc7g7GrOXOyw3jzyehr87RLSEvIolArxziLK8m3QEvRGueLsHeMG8DCB2vIXVHDyl23E89sZSvGzqBDyXBne6WzIlvMrNDTviwys99N2qu2hV5ryT60y8EUkdvALjnLww1LK8s7y+uyrphLwknfg81P4uvQUAPDxj44K8BXAdPCrikLz4TaE8fIYGPQCS0zvPW0w8/KL6uo3PMjwf5eK4lNSuuyJV3bwnNnY8u1LkvA6bnrudVMy7NZQpPctK57tKDF28HivuO1z2grxo9as8sXE8vLEr8zzQJRI8LSG2PEm+nzhHKO08gMqxOylcMryvZbY8Km+4O2cYTrzn9io78k8fO7P9U7t09IY8nOhLvZdTObo409e8CD8XvF0EELvwx9G8L4gkvQCm8jxnAAQ7WFiXPMB8Ijwyj2a8D/t8O3+3rryo2pG8R7OHvOCppDvjFdK8+GeOPBmhuLyhgFs9ATAVvOgbCjxJN3c74r+vvDgXM7y5IXQ7AIkUPXaPnrwWo8I7qeXTujw0LDs1tFK8f01vvCPRWDwMaQC9nOSTPMlp8Txl2Ci8IiLvPLJxejsvqxA8jC0EvaiwDzyFmes6NIGivAEyabycfYK8vTVmOk8byrshTCW8pmIbuk48NDy7v6g8Pmg2vPz0Ezwobb88ICgSPHhPirvvnp88/gwdO4S3PbzN7bq7N3nTvMY8z7tKpqU8lKTavM15n7yCfcy8oMVQvGfzhzohnbc7ZssBvI3c17uz7/W78ZijvAfI1ztWasC8PaRGvHhblDyxLzG7vIyIu/grszvQnSg8LXyKumAxEr0UblK8Y1QrPA== index: 0 object: embedding model: qwen3-embedding:4b object: list usage: - prompt_tokens: 10 - total_tokens: 10 + prompt_tokens: 53 + total_tokens: 53 status: code: 200 message: OK @@ -518,7 +527,7 @@ interactions: connection: - keep-alive content-length: - - '3967' + - '4177' content-type: - application/json host: @@ -592,14 +601,16 @@ interactions: role: user - content: |- - Need to gather context. + Need to research if other class labels exist beyond 11? Confirm count. Use gather_context. role: assistant tool_calls: - function: - arguments: '{"limit":null,"query":"What class labels are defined in the dataset?"}' + arguments: '{"limit":null,"query":"DocLayNet class labels defined in dataset 11 class labels ''Caption'', ''Footnote'', + ''Formula'', ''List-item'', ''Page-footer'', ''Page-header'', ''Picture'', ''Section-header'', ''Table'', ''Text'', + ''Title''"}' name: gather_context - id: call_9ggb205d + id: call_toc0wxqe type: function - content: |- DocLayNet defines 11 distinct class labels for document layout analysis: @@ -622,7 +633,7 @@ interactions: Table - Tabular data role: tool - tool_call_id: call_9ggb205d + tool_call_id: call_toc0wxqe model: gpt-oss reasoning_effort: low stream: false @@ -665,7 +676,7 @@ interactions: response: headers: content-length: - - '521' + - '593' content-type: - application/json parsed_body: @@ -673,19 +684,19 @@ interactions: - finish_reason: stop index: 0 message: - content: "**Sub-question (verification)** \nWhat are the exact 11 class labels defined in the DocLayNet dataset?" - reasoning: 'We already have answer: 11 labels. need minimal verification? maybe confirm? We''ll produce one sub-question - to confirm.' + content: "**Sub‑question (verification)** \n- What are the 11 class labels defined in the DocLayNet dataset?" + reasoning: Existing answer lists 11 labels, seems complete. Maybe verify if any additional labels like "Numbering" + or "Table header"? But dataset known has 11. So produce single verification question. role: assistant - created: 1768219727 - id: chatcmpl-643 + created: 1768225870 + id: chatcmpl-128 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 57 - prompt_tokens: 833 - total_tokens: 890 + completion_tokens: 73 + prompt_tokens: 888 + total_tokens: 961 status: code: 200 message: OK @@ -698,7 +709,7 @@ interactions: connection: - keep-alive content-length: - - '4600' + - '4880' content-type: - application/json host: @@ -772,14 +783,16 @@ interactions: role: user - content: |- - Need to gather context. + Need to research if other class labels exist beyond 11? Confirm count. Use gather_context. role: assistant tool_calls: - function: - arguments: '{"limit":null,"query":"What class labels are defined in the dataset?"}' + arguments: '{"limit":null,"query":"DocLayNet class labels defined in dataset 11 class labels ''Caption'', ''Footnote'', + ''Formula'', ''List-item'', ''Page-footer'', ''Page-header'', ''Picture'', ''Section-header'', ''Table'', ''Text'', + ''Title''"}' name: gather_context - id: call_9ggb205d + id: call_toc0wxqe type: function - content: |- DocLayNet defines 11 distinct class labels for document layout analysis: @@ -802,10 +815,10 @@ interactions: Table - Tabular data role: tool - tool_call_id: call_9ggb205d - - content: "\nWe already have answer: 11 labels. need minimal verification? maybe confirm? We'll produce one - sub-question to confirm.\n\n\n**Sub-question (verification)** \nWhat are the exact 11 class labels defined - in the DocLayNet dataset?" + tool_call_id: call_toc0wxqe + - content: "\nExisting answer lists 11 labels, seems complete. Maybe verify if any additional labels like \"Numbering\" + or \"Table header\"? But dataset known has 11. So produce single verification question.\n\n\n**Sub‑question + (verification)** \n- What are the 11 class labels defined in the DocLayNet dataset?" role: assistant - content: |- 1 validation error: @@ -815,7 +828,7 @@ interactions: "type": "json_invalid", "loc": [], "msg": "Invalid JSON: expected value at line 1 column 1", - "input": "**Sub-question (verification)** \nWhat are the exact 11 class labels defined in the DocLayNet dataset?" + "input": "**Sub‑question (verification)** \n- What are the 11 class labels defined in the DocLayNet dataset?" } ] ``` @@ -864,33 +877,26 @@ interactions: response: headers: content-length: - - '569' + - '434' content-type: - application/json parsed_body: choices: - - finish_reason: tool_calls + - finish_reason: stop index: 0 message: - content: '' - reasoning: Need to output as JSON object with keys final_result. + content: '{"sub_questions":["What are the 11 class labels defined in the DocLayNet dataset?"]}' + reasoning: Need to output JSON with sub_questions array. role: assistant - tool_calls: - - function: - arguments: '{"sub_questions":["What are the exact 11 class labels defined in the DocLayNet dataset?"]}' - name: final_result - id: call_emzkc1bh - index: 0 - type: function - created: 1768219728 - id: chatcmpl-940 + created: 1768225871 + id: chatcmpl-243 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: completion_tokens: 52 - prompt_tokens: 978 - total_tokens: 1030 + prompt_tokens: 1050 + total_tokens: 1102 status: code: 200 message: OK @@ -903,7 +909,7 @@ interactions: connection: - keep-alive content-length: - - '2820' + - '2814' content-type: - application/json host: @@ -956,7 +962,7 @@ interactions: - Be concise and direct; avoid meta commentary about the process. - Higher scores indicate more relevant results. role: system - - content: What are the exact 11 class labels defined in the DocLayNet dataset? + - content: What are the 11 class labels defined in the DocLayNet dataset? role: user model: gpt-oss reasoning_effort: low @@ -1027,18 +1033,18 @@ interactions: - function: arguments: '{"limit":5,"query":"DocLayNet dataset 11 class labels"}' name: search_and_answer - id: call_2ro5z7i0 + id: call_fe5rcriz index: 0 type: function - created: 1768219729 - id: chatcmpl-877 + created: 1768225872 + id: chatcmpl-637 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: completion_tokens: 40 - prompt_tokens: 618 - total_tokens: 658 + prompt_tokens: 617 + total_tokens: 657 status: code: 200 message: OK @@ -1091,7 +1097,7 @@ interactions: connection: - keep-alive content-length: - - '3678' + - '3672' content-type: - application/json host: @@ -1144,7 +1150,7 @@ interactions: - Be concise and direct; avoid meta commentary about the process. - Higher scores indicate more relevant results. role: system - - content: What are the exact 11 class labels defined in the DocLayNet dataset? + - content: What are the 11 class labels defined in the DocLayNet dataset? role: user - content: |- @@ -1155,10 +1161,10 @@ interactions: - function: arguments: '{"limit":5,"query":"DocLayNet dataset 11 class labels"}' name: search_and_answer - id: call_2ro5z7i0 + id: call_fe5rcriz type: function - content: |- - [aa6f5b51-0545-432e-993f-51fe0f16e402] (score: 0.03) + [276a4196-6ee9-4547-89b3-5db44c4a65e6] (score: 0.03) Source: "DocLayNet Class Labels" Type: list_item Content: @@ -1182,7 +1188,7 @@ interactions: Table - Tabular data role: tool - tool_call_id: call_2ro5z7i0 + tool_call_id: call_fe5rcriz model: gpt-oss reasoning_effort: low stream: false @@ -1237,7 +1243,7 @@ interactions: response: headers: content-length: - - '525' + - '536' content-type: - application/json parsed_body: @@ -1246,24 +1252,24 @@ interactions: index: 0 message: content: '' - reasoning: Score low. maybe other doc. search again. + reasoning: Need 11 labels. We have 9 listed. Need find others. role: assistant tool_calls: - function: - arguments: '{"limit":5,"query":"DocLayNet \"class labels\""}' + arguments: '{"limit":5,"query":"DocLayNet layout categories\""}' name: search_and_answer - id: call_gnzq5rly + id: call_dmvg8puw index: 0 type: function - created: 1768219731 - id: chatcmpl-270 + created: 1768225874 + id: chatcmpl-966 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 45 - prompt_tokens: 809 - total_tokens: 854 + completion_tokens: 49 + prompt_tokens: 810 + total_tokens: 859 status: code: 200 message: OK @@ -1276,7 +1282,7 @@ interactions: connection: - keep-alive content-length: - - '96' + - '99' content-type: - application/json host: @@ -1285,7 +1291,7 @@ interactions: parsed_body: encoding_format: base64 input: - - DocLayNet "class labels" + - DocLayNet layout categories" model: qwen3-embedding:4b uri: http://localhost:11434/v1/embeddings response: @@ -1296,7 +1302,269 @@ interactions: - chunked parsed_body: data: - - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 8 + total_tokens: 8 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4564' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results and their relevance scores. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] (score: 0.85) + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] (score: 0.72) + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and relevance score + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Higher scores indicate more relevant results. + role: system + - content: What are the 11 class labels defined in the DocLayNet dataset? + role: user + - content: |- + + Need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet dataset 11 class labels"}' + name: search_and_answer + id: call_fe5rcriz + type: function + - content: |- + [276a4196-6ee9-4547-89b3-5db44c4a65e6] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_fe5rcriz + - content: |- + + Need 11 labels. We have 9 listed. Need find others. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet layout categories\""}' + name: search_and_answer + id: call_dmvg8puw + type: function + - content: |- + [276a4196-6ee9-4547-89b3-5db44c4a65e6] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_dmvg8puw + 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: + - '521' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Seems same result. Might need other source. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet 11 categories"}' + name: search_and_answer + id: call_ukpi3dbw + index: 0 + type: function + created: 1768225875 + id: chatcmpl-19 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 43 + prompt_tokens: 1012 + total_tokens: 1055 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '93' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet 11 categories + 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 @@ -1316,7 +1584,7 @@ interactions: connection: - keep-alive content-length: - - '4559' + - '5440' content-type: - application/json host: @@ -1369,7 +1637,7 @@ interactions: - Be concise and direct; avoid meta commentary about the process. - Higher scores indicate more relevant results. role: system - - content: What are the exact 11 class labels defined in the DocLayNet dataset? + - content: What are the 11 class labels defined in the DocLayNet dataset? role: user - content: |- @@ -1380,10 +1648,10 @@ interactions: - function: arguments: '{"limit":5,"query":"DocLayNet dataset 11 class labels"}' name: search_and_answer - id: call_2ro5z7i0 + id: call_fe5rcriz type: function - content: |- - [aa6f5b51-0545-432e-993f-51fe0f16e402] (score: 0.03) + [276a4196-6ee9-4547-89b3-5db44c4a65e6] (score: 0.03) Source: "DocLayNet Class Labels" Type: list_item Content: @@ -1407,20 +1675,20 @@ interactions: Table - Tabular data role: tool - tool_call_id: call_2ro5z7i0 + tool_call_id: call_fe5rcriz - content: |- - Score low. maybe other doc. search again. + Need 11 labels. We have 9 listed. Need find others. role: assistant tool_calls: - function: - arguments: '{"limit":5,"query":"DocLayNet \"class labels\""}' + arguments: '{"limit":5,"query":"DocLayNet layout categories\""}' name: search_and_answer - id: call_gnzq5rly + id: call_dmvg8puw type: function - content: |- - [aa6f5b51-0545-432e-993f-51fe0f16e402] (score: 0.03) + [276a4196-6ee9-4547-89b3-5db44c4a65e6] (score: 0.03) Source: "DocLayNet Class Labels" Type: list_item Content: @@ -1444,7 +1712,44 @@ interactions: Table - Tabular data role: tool - tool_call_id: call_gnzq5rly + tool_call_id: call_dmvg8puw + - content: |- + + Seems same result. Might need other source. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet 11 categories"}' + name: search_and_answer + id: call_ukpi3dbw + type: function + - content: |- + [276a4196-6ee9-4547-89b3-5db44c4a65e6] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_ukpi3dbw model: gpt-oss reasoning_effort: low stream: false @@ -1499,7 +1804,7 @@ interactions: response: headers: content-length: - - '498' + - '375' content-type: - application/json parsed_body: @@ -1507,18 +1812,17 @@ interactions: - finish_reason: stop index: 0 message: - content: I couldn't find a source that lists all 11 DocLayNet class labels. The only reference encountered lists - nine labels. If you need the complete set, please consult the official DocLayNet documentation or repository. + content: I’m sorry, but I couldn’t find a source that lists all 11 class labels for DocLayNet. role: assistant - created: 1768219732 - id: chatcmpl-50 + created: 1768225877 + id: chatcmpl-284 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 46 - prompt_tokens: 1005 - total_tokens: 1051 + completion_tokens: 27 + prompt_tokens: 1208 + total_tokens: 1235 status: code: 200 message: OK @@ -1531,7 +1835,7 @@ interactions: connection: - keep-alive content-length: - - '4934' + - '5691' content-type: - application/json host: @@ -1584,7 +1888,7 @@ interactions: - Be concise and direct; avoid meta commentary about the process. - Higher scores indicate more relevant results. role: system - - content: What are the exact 11 class labels defined in the DocLayNet dataset? + - content: What are the 11 class labels defined in the DocLayNet dataset? role: user - content: |- @@ -1595,10 +1899,10 @@ interactions: - function: arguments: '{"limit":5,"query":"DocLayNet dataset 11 class labels"}' name: search_and_answer - id: call_2ro5z7i0 + id: call_fe5rcriz type: function - content: |- - [aa6f5b51-0545-432e-993f-51fe0f16e402] (score: 0.03) + [276a4196-6ee9-4547-89b3-5db44c4a65e6] (score: 0.03) Source: "DocLayNet Class Labels" Type: list_item Content: @@ -1622,20 +1926,20 @@ interactions: Table - Tabular data role: tool - tool_call_id: call_2ro5z7i0 + tool_call_id: call_fe5rcriz - content: |- - Score low. maybe other doc. search again. + Need 11 labels. We have 9 listed. Need find others. role: assistant tool_calls: - function: - arguments: '{"limit":5,"query":"DocLayNet \"class labels\""}' + arguments: '{"limit":5,"query":"DocLayNet layout categories\""}' name: search_and_answer - id: call_gnzq5rly + id: call_dmvg8puw type: function - content: |- - [aa6f5b51-0545-432e-993f-51fe0f16e402] (score: 0.03) + [276a4196-6ee9-4547-89b3-5db44c4a65e6] (score: 0.03) Source: "DocLayNet Class Labels" Type: list_item Content: @@ -1659,9 +1963,45 @@ interactions: Table - Tabular data role: tool - tool_call_id: call_gnzq5rly - - content: I couldn't find a source that lists all 11 DocLayNet class labels. The only reference encountered lists nine - labels. If you need the complete set, please consult the official DocLayNet documentation or repository. + tool_call_id: call_dmvg8puw + - content: |- + + Seems same result. Might need other source. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet 11 categories"}' + name: search_and_answer + id: call_ukpi3dbw + type: function + - content: |- + [276a4196-6ee9-4547-89b3-5db44c4a65e6] (score: 0.03) + Source: "DocLayNet Class Labels" + Type: list_item + Content: + DocLayNet defines 11 distinct class labels for document layout analysis: + + Caption - Text describing figures or tables + + Footnote - Notes at the bottom of pages + + Formula - Mathematical expressions + + List-item - Items in bulleted or numbered lists + + Page-footer - Footer content on pages + + Page-header - Header content on pages + + Picture - Images and diagrams + + Section-header - Headings for document sections + + Table - Tabular data + role: tool + tool_call_id: call_ukpi3dbw + - content: I’m sorry, but I couldn’t find a source that lists all 11 class labels for DocLayNet. role: assistant - content: |- Validation feedback: @@ -1723,7 +2063,7 @@ interactions: response: headers: content-length: - - '842' + - '1131' content-type: - application/json parsed_body: @@ -1732,27 +2072,29 @@ interactions: index: 0 message: content: '' - reasoning: Need to use final_result. + reasoning: Need to use function final_result. role: assistant tool_calls: - function: - arguments: '{"answer":"I couldn''t find a source that lists all 11 DocLayNet class labels. The only reference - encountered lists nine labels. If you need the complete set, please consult the official DocLayNet documentation - or repository.","cited_chunks":["aa6f5b51-0545-432e-993f-51fe0f16e402"],"confidence":0.45,"query":"What are - the exact 11 class labels defined in the DocLayNet dataset?"}' + arguments: '{"answer":"The DocLayNet dataset defines 11 distinct class labels for document layout analysis:\n\n1. + Caption – text describing figures or tables\n2. Footnote – notes at the bottom of pages\n3. Formula – mathematical + expressions\n4. List‑item – items in bulleted or numbered lists\n5. Page‑footer – footer content on pages\n6. + Page‑header – header content on pages\n7. Picture – images and diagrams\n8. Section‑header – headings for + document sections\n9. Table – tabular data","cited_chunks":["276a4196-6ee9-4547-89b3-5db44c4a65e6"],"confidence":0.7,"query":"What + are the 11 class labels defined in the DocLayNet dataset?"}' name: final_result - id: call_om49ts0j + id: call_tcdsmc31 index: 0 type: function - created: 1768219735 - id: chatcmpl-980 + created: 1768225881 + id: chatcmpl-798 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 124 - prompt_tokens: 1076 - total_tokens: 1200 + completion_tokens: 194 + prompt_tokens: 1260 + total_tokens: 1454 status: code: 200 message: OK @@ -1765,7 +2107,7 @@ interactions: connection: - keep-alive content-length: - - '4122' + - '3882' content-type: - application/json host: @@ -1792,59 +2134,55 @@ interactions: - If the evidence is incomplete, acknowledge limitations briefly. role: system - content: |- - Answer the following question based on the gathered evidence. + Answer the question based on the gathered evidence. - - What class labels are defined in the dataset? - - + + What class labels are defined in the dataset? + What are the 11 class labels in DocLayNet? The 11 class labels are: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, and Title. - 0.9 How was the annotation process organized? The annotation was organized into 4 phases. - 0.9 What data sources were used? Sources include arXiv and government offices. - 0.9 What is the agreement metric? The mAP metric was used. - 0.9 What is machine learning? A field of AI. - 0.9 - What are the exact 11 class labels defined in the DocLayNet dataset? - I couldn't find a source that lists all 11 DocLayNet class labels. The only reference encountered lists nine labels. If you need the complete set, please consult the official DocLayNet documentation or repository. - 0.45 + What are the 11 class labels defined in the DocLayNet dataset? + The DocLayNet dataset 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 - - doclaynet-labels - DocLayNet Class Labels - - null - + DocLayNet Class Labels - - - - Question: What class labels are defined in the dataset? + + role: user model: gpt-oss reasoning_effort: low @@ -1914,7 +2252,7 @@ interactions: response: headers: content-length: - - '847' + - '1280' content-type: - application/json parsed_body: @@ -1923,27 +2261,30 @@ interactions: index: 0 message: content: '' - reasoning: We need answer. Use evidence. The first item gives class labels. Confidence high. Second conflicting. - We'll state 11 labels. + reasoning: Need to include all 11 labels. earlier list incomplete. Provide answer. role: assistant tool_calls: - function: - arguments: '{"answer":"The DocLayNet dataset defines eleven element classes:\n- **Caption**\n- **Footnote**\n- - **Formula**\n- **List‑item**\n- **Page‑footer**\n- **Page‑header**\n- **Picture**\n- **Section‑header**\n- - **Table**\n- **Text**\n- **Title**","citations":[],"confidence":0.9}' + arguments: '{"answer":"The DocLayNet dataset defines 11 distinct class labels for document layout analysis:\n\n1. + Caption – text describing figures or tables\n2. Footnote – notes at the bottom of pages\n3. Formula – mathematical + expressions\n4. List‑item – items in bulleted or numbered lists\n5. Page‑footer – footer content on pages\n6. + Page‑header – header content on pages\n7. Picture – images, diagrams and other visual content\n8. Section‑header + – headings that introduce sections\n9. Table – tabular data\n10. Text – body text that is not a heading\n11. + Title – main title of the document\n\nThese 11 categories cover the main semantic elements found in academic + and technical documents used in DocLayNet.","citations":[],"confidence":0.99}' name: final_result - id: call_ycfysbf7 + id: call_v7rrm25d index: 0 type: function - created: 1768219738 - id: chatcmpl-379 + created: 1768225886 + id: chatcmpl-984 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 143 - prompt_tokens: 796 - total_tokens: 939 + completion_tokens: 205 + prompt_tokens: 738 + total_tokens: 943 status: code: 200 message: OK @@ -1956,7 +2297,7 @@ interactions: connection: - keep-alive content-length: - - '4599' + - '5128' content-type: - application/json host: @@ -1986,11 +2327,11 @@ interactions: - Extract the TOPIC as `query`/`question` - Extract the DOCUMENT NAME as `document_name` - Examples for search: - - "search for latrines in TB MED 593" → query="latrines", document_name="TB MED 593" - - "find waste disposal in the army manual" → query="waste disposal", document_name="army manual" + - "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 TB MED 593 say about latrines?" → question="what are the guidelines for latrines?", document_name="TB MED 593" - - "answer from the army manual about sanitation" → question="what are the sanitation guidelines?", document_name="army manual" + - "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 @@ -1998,32 +2339,35 @@ interactions: role: user - content: |- - Need to ask about dataset class labels. Use ask tool. + Need ask. role: assistant tool_calls: - function: arguments: '{"document_name":null,"question":"What class labels are defined in the dataset?"}' name: ask - id: call_jfdfe741 + id: call_vpk7k8no type: function - content: |- - The DocLayNet dataset defines eleven element classes: - - **Caption** - - **Footnote** - - **Formula** - - **List‑item** - - **Page‑footer** - - **Page‑header** - - **Picture** - - **Section‑header** - - **Table** - - **Text** - - **Title** + The DocLayNet dataset 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, diagrams and other visual content + 8. Section‑header – headings that introduce sections + 9. Table – tabular data + 10. Text – body text that is not a heading + 11. Title – main title of the document + + These 11 categories cover the main semantic elements found in academic and technical documents used in DocLayNet. Sources: [1] role: tool - tool_call_id: call_jfdfe741 + tool_call_id: call_vpk7k8no model: gpt-oss reasoning_effort: low stream: false @@ -2044,7 +2388,13 @@ interactions: - type: string - type: 'null' default: null - description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + 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 @@ -2096,7 +2446,7 @@ interactions: response: headers: content-length: - - '813' + - '1329' content-type: - application/json parsed_body: @@ -2105,33 +2455,33 @@ interactions: index: 0 message: content: |- - The **DocLayNet** dataset defines **eleven** element classes: + Sure! In DocLayNet the documents are annotated with **11 distinct layout classes**: - | # | 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** | + | # | Class label | Description | + |---|-------------|-------------| + | 1 | **Caption** | Text that explains figures, tables, or other visual elements | + | 2 | **Footnote** | Notes that appear at the bottom of pages | + | 3 | **Formula** | Mathematical expressions (equations, symbols, etc.) | + | 4 | **List‑item** | Individual items in bulleted or numbered lists | + | 5 | **Page‑footer** | Footer content that repeats across pages | + | 6 | **Page‑header** | Header content that repeats across pages | + | 7 | **Picture** | Images, diagrams, or other visual media | + | 8 | **Section‑header** | Headings that introduce sections or subsections | + | 9 | **Table** | Tabular data structures | + |10 | **Text** | Regular body text (non‑header, non‑caption) | + |11 | **Title** | The main title of the document | - These 11 labels are the categories used to annotate and classify the different document elements in the dataset. + These twelve categories allow the model to learn and predict the layout of a wide variety of content in technical and academic documents. role: assistant - created: 1768219743 - id: chatcmpl-287 + created: 1768225892 + id: chatcmpl-397 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 157 - prompt_tokens: 971 - total_tokens: 1128 + completion_tokens: 254 + prompt_tokens: 1061 + total_tokens: 1315 status: code: 200 message: OK diff --git a/tests/cassettes/test_chat_agent/test_search_agent_deduplication.yaml b/tests/cassettes/test_chat_agent/test_search_agent_deduplication.yaml new file mode 100644 index 00000000..db5d604e --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_search_agent_deduplication.yaml @@ -0,0 +1,440 @@ +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: + - '1090' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: Tell me about class labels and their counts + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '543' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to search for class labels counts likely refer to dataset. Let's search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels counts"}' + name: run_search + id: call_buoydkxe + index: 0 + type: function + created: 1768225945 + id: chatcmpl-635 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 45 + prompt_tokens: 269 + total_tokens: 314 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '89' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - class labels counts + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 4 + total_tokens: 4 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1471' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: Tell me about class labels and their counts + role: user + - content: |- + + Need to search for class labels counts likely refer to dataset. Let's search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels counts"}' + name: run_search + id: call_buoydkxe + type: function + - content: 'Found 1 results for: class labels counts' + role: tool + tool_call_id: call_buoydkxe + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '497' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need alternative queries. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class label count dataset"}' + name: run_search + id: call_5q2zlv2m + index: 0 + type: function + created: 1768225946 + id: chatcmpl-203 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 35 + prompt_tokens: 340 + total_tokens: 375 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '95' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - class label count 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: 5 + total_tokens: 5 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1812' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: Tell me about class labels and their counts + role: user + - content: |- + + Need to search for class labels counts likely refer to dataset. Let's search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class labels counts"}' + name: run_search + id: call_buoydkxe + type: function + - content: 'Found 1 results for: class labels counts' + role: tool + tool_call_id: call_buoydkxe + - content: |- + + Need alternative queries. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"class label count dataset"}' + name: run_search + id: call_5q2zlv2m + type: function + - content: 'Found 1 results for: class label count dataset' + role: tool + tool_call_id: call_5q2zlv2m + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '298' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: Search complete + role: assistant + created: 1768225946 + id: chatcmpl-689 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 6 + prompt_tokens: 402 + total_tokens: 408 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_search_agent_no_results.yaml b/tests/cassettes/test_chat_agent/test_search_agent_no_results.yaml new file mode 100644 index 00000000..1da165f5 --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_search_agent_no_results.yaml @@ -0,0 +1,547 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1094' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: Find information about nonexistent topic xyz123 + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '587' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: 'Need 3 searches: original "nonexistent topic xyz123", alternate queries: "xyz123 topic", "xyz123 nonexistent".' + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"nonexistent topic xyz123"}' + name: run_search + id: call_s7mq0lun + index: 0 + type: function + created: 1768225948 + id: chatcmpl-854 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 58 + prompt_tokens: 268 + total_tokens: 326 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '94' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - nonexistent topic xyz123 + 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: 8 + total_tokens: 8 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1519' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: Find information about nonexistent topic xyz123 + role: user + - content: |- + + Need 3 searches: original "nonexistent topic xyz123", alternate queries: "xyz123 topic", "xyz123 nonexistent". + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"nonexistent topic xyz123"}' + name: run_search + id: call_s7mq0lun + type: function + - content: 'No results for: nonexistent topic xyz123' + role: tool + tool_call_id: call_s7mq0lun + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '444' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"xyz123 topic"}' + name: run_search + id: call_56qze9cq + index: 0 + type: function + created: 1768225949 + id: chatcmpl-484 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 24 + prompt_tokens: 351 + total_tokens: 375 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '82' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - xyz123 topic + 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: 6 + total_tokens: 6 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1787' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: Find information about nonexistent topic xyz123 + role: user + - content: |- + + Need 3 searches: original "nonexistent topic xyz123", alternate queries: "xyz123 topic", "xyz123 nonexistent". + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"nonexistent topic xyz123"}' + name: run_search + id: call_s7mq0lun + type: function + - content: 'No results for: nonexistent topic xyz123' + role: tool + tool_call_id: call_s7mq0lun + - content: null + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"xyz123 topic"}' + name: run_search + id: call_56qze9cq + type: function + - content: 'No results for: xyz123 topic' + role: tool + tool_call_id: call_56qze9cq + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '450' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"xyz123 nonexistent"}' + name: run_search + id: call_a0m6goro + index: 0 + type: function + created: 1768225949 + id: chatcmpl-119 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 24 + prompt_tokens: 393 + total_tokens: 417 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '88' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - xyz123 nonexistent + 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: 6 + total_tokens: 6 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2067' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: Find information about nonexistent topic xyz123 + role: user + - content: |- + + Need 3 searches: original "nonexistent topic xyz123", alternate queries: "xyz123 topic", "xyz123 nonexistent". + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"nonexistent topic xyz123"}' + name: run_search + id: call_s7mq0lun + type: function + - content: 'No results for: nonexistent topic xyz123' + role: tool + tool_call_id: call_s7mq0lun + - content: null + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"xyz123 topic"}' + name: run_search + id: call_56qze9cq + type: function + - content: 'No results for: xyz123 topic' + role: tool + tool_call_id: call_56qze9cq + - content: null + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"xyz123 nonexistent"}' + name: run_search + id: call_a0m6goro + type: function + - content: 'No results for: xyz123 nonexistent' + role: tool + tool_call_id: call_a0m6goro + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '298' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: Search complete + role: assistant + created: 1768225950 + id: chatcmpl-251 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 6 + prompt_tokens: 435 + total_tokens: 441 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_search_agent_with_context.yaml b/tests/cassettes/test_chat_agent/test_search_agent_with_context.yaml new file mode 100644 index 00000000..5dba584a --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_search_agent_with_context.yaml @@ -0,0 +1,497 @@ +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: + - '481' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Annotation Process + The annotation process was organized into 4 phases: + - Phase 1: Data selection and preparation by a small team of experts + - Phase 2: Label selection and guideline definition + - Phase 3: Annotation by 40 dedicated annotators + - Phase 4: Quality control and continuous supervision + The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface. + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 90 + total_tokens: 90 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1143' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: |- + Context: We're discussing document layout analysis + + Search request: What are the class labels? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '668' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to search for class labels in document layout analysis. Probably classes like 'Text', 'Title', 'Figure', + 'Table', 'Caption', 'Header', 'Footer', 'Paragraph', 'List'. Let's search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"document layout analysis class labels"}' + name: run_search + id: call_7bjv54g6 + index: 0 + type: function + created: 1768225932 + id: chatcmpl-448 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 83 + prompt_tokens: 278 + total_tokens: 361 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '107' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - document layout analysis 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: 6 + total_tokens: 6 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1667' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: |- + Context: We're discussing document layout analysis + + Search request: What are the class labels? + role: user + - content: |- + + Need to search for class labels in document layout analysis. Probably classes like 'Text', 'Title', 'Figure', 'Table', 'Caption', 'Header', 'Footer', 'Paragraph', 'List'. Let's search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"document layout analysis class labels"}' + name: run_search + id: call_7bjv54g6 + type: function + - content: 'Found 2 results for: document layout analysis class labels' + role: tool + tool_call_id: call_7bjv54g6 + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '547' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: 'Let''s search alternative keyword: "document layout analysis categories"' + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"layout analysis class names"}' + name: run_search + id: call_hq38ahtd + index: 0 + type: function + created: 1768225934 + id: chatcmpl-991 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 42 + prompt_tokens: 382 + total_tokens: 424 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '97' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - layout analysis class names + 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: 5 + total_tokens: 5 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2060' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: |- + Context: We're discussing document layout analysis + + Search request: What are the class labels? + role: user + - content: |- + + Need to search for class labels in document layout analysis. Probably classes like 'Text', 'Title', 'Figure', 'Table', 'Caption', 'Header', 'Footer', 'Paragraph', 'List'. Let's search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"document layout analysis class labels"}' + name: run_search + id: call_7bjv54g6 + type: function + - content: 'Found 2 results for: document layout analysis class labels' + role: tool + tool_call_id: call_7bjv54g6 + - content: |- + + Let's search alternative keyword: "document layout analysis categories" + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"layout analysis class names"}' + name: run_search + id: call_hq38ahtd + type: function + - content: 'Found 2 results for: layout analysis class names' + role: tool + tool_call_id: call_hq38ahtd + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '297' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: Search complete + role: assistant + created: 1768225934 + id: chatcmpl-37 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 6 + prompt_tokens: 451 + total_tokens: 457 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_search_agent_with_filter.yaml b/tests/cassettes/test_chat_agent/test_search_agent_with_filter.yaml new file mode 100644 index 00000000..a652cd1c --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_search_agent_with_filter.yaml @@ -0,0 +1,650 @@ +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: + - '412' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Data Sources + The data sources for DocLayNet include: + - Publication repositories such as arXiv + - Government offices and official documents + - Company websites and corporate reports + - Data directory services for financial reports + - Patent documents + Scanned documents were excluded to avoid rotation and skewing issues. + 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: 68 + total_tokens: 68 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1077' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: What information is available? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '632' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: 'Need to run searches. Use original query "What information is available?" That''s vague. Alternate queries: + "information available", "available info".' + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"What information is available?"}' + name: run_search + id: call_8m556d90 + index: 0 + type: function + created: 1768225940 + id: chatcmpl-647 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 60 + prompt_tokens: 266 + total_tokens: 326 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '100' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - What information is available? + 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: 6 + total_tokens: 6 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1558' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: What information is available? + role: user + - content: |- + + Need to run searches. Use original query "What information is available?" That's vague. Alternate queries: "information available", "available info". + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"What information is available?"}' + name: run_search + id: call_8m556d90 + type: function + - content: 'Found 1 results for: What information is available?' + role: tool + tool_call_id: call_8m556d90 + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '453' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"information available"}' + name: run_search + id: call_p2syjpni + index: 0 + type: function + created: 1768225941 + id: chatcmpl-930 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 23 + prompt_tokens: 354 + total_tokens: 377 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '91' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - information available + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: GqwdueINRDyIypQ7/TjyO1pfeLrCyGU94Q2dPU/mLLwm/aA89Oy3PFqvxTsWkdk8HWS+OMEusLxx6Ag8ebp1vOuM5Txp7xU9lXLqvNiW5bvWPnm8M3w6PVHHND3xld67Dy0CvfSRLL0SPtC836GDvftLJj2A13O8GiSBO0bUoLxMoLw7QXUIvISPFTyjeBq9KBc9OvEUjbwhNZu85SFPPHBaAD1apcS7L+T3PAAjBTxu2g299+n7vPPP67qbziI8Mv2YO2PerrzcAho8jwnFOw3t1bx5j8i8CGMPPeuR1Dwb7fU8k5W+uw4jkLwLOA27l6Smu8Staju0yxG93EScu7xZEbtXF/+8QPKJuiQBAr3CBa46RIhhOlXUnTrPkI47lLU0umuWjzs3mos70V4MvfrIBLzFynI8K/DRO1023jxouLw8SS2fuvFp2jsrOBc94jvIPOlxmzlSFMA8MvCkuMuvYLyXmQ68kCyDPEQjwDre88s8vdqdO8+tF7rB/4E8r+LZvMJ/zbwL5p67cDEjPERrM7s6A7+6AF2VO2o4x7t/Wuq8RfsevLLgl7ynksG5MrGtO1IRyLwYyRi6u95ju+l+qry6dVC9hquXvLAWBzy5u++7A4VdPNMT3Tz9B6w8C84pvInb+zxAOmG7JrORO608Kzx71FC9beyYvPknL7wAMaM7lKLNO8XhBT1fIia8sZfquxYYrLyNpbI7Q4xuPAe9zLtVIYU8KAvIvD61hzu3pIW7F3vIOuwYGTnFTSe791mavGoQBr1X6sc6HTGYvLnhtzw4kji7VannPIQZCL1dh3o5bCVwPEvUg7pj0AU94xWruwH5rjyxHJE7pKC5PMjLIrt4lM87otFFvbbUGj1BlTQ7WcGaO321f7xstc68RgHOvK+nW70J7gk7wU/Bu0HyyzsnPLm8eXevvDNNRjuVZJa8QEuUO8o/srtjMq477c+Ou38YI70MtzU8+6cAvIkoMjzVd5g8d18PvNQl3rsucYY8BTW2PGWnWbyhvgs9c4FduwNWojvq/ny8Bin2vC4Dr7t63Q+8PcXMPAn20jzss6A8H/IAu5kqFD2C3be7ZehZu7U2UbummkI672L6u38hzTtEyqC8JcR0POUS7Lz7Kog6tAn9u/iLNTvRHzc8B3X7vP/BhLy4r708a2FYPDjABrx8SGE88HWDvOz597s2xBW9DX9nPCCUqbsoBo28/p7FO2nQ5LrMEe07CxTrO1RJbrt/VxY8tTIGPLN6+Lu9GHe8iwKkOboYtjyBwmw8+YPdvPHrWLzwtx68mKVcPFPNvrwbTMW8dINKPJfMqbwjfa270ZbHvKz5lbyJWl08UK0XO3d6qrxxpzo8DPpvPOUWury73JS8HMKcu6M0CzzWYE85ObevvC2+n7xJKr86x9MPvNcXejxpc068C7wvvC4JQDwNm8y8Cou5PNA2w7tC+ig8C08nPHGgLLweTOC8Pp6+O8dEAbyggmw8ChHLPEp4MbxOO/I7SwF8vLxLFTuZ0828z/53OpJGKDt7UMI7L4cCvbpKkTvdSv07I8RmvTYbjTw6wI28mUhXvX2g8TzDjxM7sHUJvGDyl7tTqzo7os0KO4qtJLsFY/S7BmCyu8Fl6DyzTNw8HKjrO34ZVzwogbW89W76vN1HU7yVyq28PJCfPLuDsbstjKS7sYhWvYvHgzzdTpC81wv7vJsvLr0UyCc7rwuSvV4bZ7sUq/+7KCPRO7y6eDyECsc8OKskPIYxg7whlEk9Pz/JusuA5Ty5sSW7yx8pvIafVjwgm5e8WEmdPIniID2JqDg8PcKdO7Vhmjy4fom8N2TXOxA/TbzCgmW8mvqPO8ISjTo7a3m7eErfvL1HPL2UJh682L7IvBqOVzyBXES7XdD4vDCjAz3pMNe75UnUPFEmbz38aFi9JnGaOoNNCDxu5WQ7lx9aPCnAY7xI2bK82s6jvLCIjDyKm0E8E22EvMQSmbpcwwa9pnChPEWJcLumqDO883fxOz69I7wv37S7xL9ovAnT1Dut5bY8WNO1PJE/MLyashM8fBmTOSERkjy2Wlo8Ta7gPCH8tjws9LY8q3PIvDHxJz0bZ/Q7FLWkO7P5Lr1VWfU8h0rdOm15rDshP4I8AGOhvK0k3LwEQay7BcilvLgNNL2EAB+7mrg0vIbWrrv0tNU7XzB3vFA+ejsBHRU6cBnKPIwiprsKvoU8KQ4EvCS5Yjzt2488OnymvNzN17zv7X085wNIPF1IibzkFAC5mffSuyAIOTuwyIw7J0C7vM4puDyo0im8cCvrvOC8/btxVQm9CvebvDwaFz1QCfq6tC6BPRloRTsIFry7OQW6PEaphrxLiZ68ys65u4hjpDsCjTw9SyI5vZTuqry/wsC8h01kvBB8ojxsTte8R1oGvFz4A73T2Tu6oaRovDxPhr0h3AE8wJUJPbSnC7z3VBS9wkoAPLYBv71R4JA8WjUTPG4DNb0Vl528MWEXvU1dvLwvcrK8hKICvAcozDy7iwC95v4LvCSi8byeUMC6J6VvOmPxITxTKhg7TfNlvICxgLyt2RA8Jr0gPLjLgTwgWSk9AFCfPNw6ArxWcUw8A202PWSVgDwI90Y8239fvMHeBDxH/h49G/W5u0tQ/7slBsA8HBWovIEhTLva5BW9g/DTPPadODxdV1M7r+DEPEFlnLtAJEK8ou9JO8X3BL3qAHg8o9IJvWQjs7wx+W27DDv2O+0Lmjxitzs8PGT3vJDxojwUD4s8rzyPu5nQITpdJcS7QHE+PH4XfLyP8la8CsTqObhp2bv/SKS8y9DnPB3j/DwTLIk8jv1kvEMvhzw7mwG9ZIltuyQehjsN5uM8A501vQhQy7vj32860InqPKFEL7y6f4G8g3ikvHzhITtMjEs8YBOUvDy5xDzh5Qe95MzBvExPFrzd4wK9kblQO4VtpTvSA5W7zX+1u5BX+Ls0ulI8bN5ePQ5sDrzUMVE8ViqiPCmF5jspBT2843YLPCWFfbuKYgW8dpjDO/VxFDwBPhG8TkGFPBNwybyc8vW4Le6hvAIh/jyR66W7iWUMPMBRLTxQJtS7r/LRuTqCDr3h8ae8DoR+vOeis7mMR7y5LNy8vI6n9LsAV/K65BmDu4lcNrwfWII85UjHPDr+Mjyl1VG9eKbnO5PkFDqElYS8dpKPOp8iC7wd11y8nednPKMs0Lwb9Co8ju4IPWSqA73vJoc8ktS+O5EtebuY5p+5KwYMO0FQojzrbIK8u+X7O1DPG7zldg28yGMLvCxMczwkdYu630l9vOe+kzzj/NK8AZVOPMGevbxEsgS9mH66uQ/0Pby4LG88P2BsPNFENr3GbxC9kTG8vNPzSTz6I+e7qAoKPPDdu7zHyh8999pavF7FyDy3usS8xV1uvaOCDD0rOba7dT0YvTEPbbsNQhK8mrG6PEAlA7zr8BM9AeWZvJR7BL0BJsM5NnV/PMIQpjwQuj08WgsMPdy0ebwEgge9/fAmPU4dUTxSHtA8uEcCvRbuujyjEGi7Rg0tPJEdp7zj6S08o43tO/75tLxRAwS8GXtVvXAQCLxpYXQ9Z1nQu2ajArtqSLi89xMwPX2+rbonFhw9M/QUvVUIZjyCmGQ8rvXvPGBFGLuWxpU8sO8KPFw7UjxREAC9meA8Oymihrxih0+8ew1vu0kNvzwHefk8aLLHPIf0oLwca068jnVDPGPOhjxj2so70SrEu1w8jDzy0+G8nFexvB1iYTuyoVA7utfgPIQ1yzz2jyI711dpPSt6Szt6MHw8SHomvM0CODy3Xyy90o9IvcbANL1GcR68EcX+O4ELEL0kEPq8gaSEO6X7P7x0Wzq8wiwfPTg8dDyAfSy9iAEwvJiFdLzXcAW87VS5u01QirwmLhA82eUJPWJMhTzv7LG8uycFvACwijy6JJ28SPM7O8fIqLyn4pa8HCpjvL0z6jx3Iro5LjkRO/ETHLxi4107VeMAvWDB9Dx2JDq9wNuXu94J/Dul5NO8dPCAOnCiQzzHGtA712ezOwrBr7wFgcQ82JIGPX64YTyg9V898AmXOqxuhbtmlZq7IBdUvCMfEbubZZi8H2ehPKBDDrt6O0C8dg4JvbfQxDtV3Li5S9n/PI/q3zxmAFM80ScGvAcHLTzebBW9sBUXvZo63zzSeI48rt35vCqhZ7zbFgE9GhTzupDkqbziLnG7XrewvILbYLyWE9K8Xc4YvS8K3TyOOcw7iEFcO7Ts1juumRE9Kp51vGaXnbySzls8lPXKuxw2KD2MkV48cIT6vAXI8bt/31k9jZmKvG3gzTy8YlA8UOTHu1IFjTkhUr08KWszPY+lIb0MLAK8qpQYPA72yDv07Nc7wC5lPNboi7zUQJK7fn7aPJkXBD2X9VA8iJBlvNkVeLx6weY8NuYAvMLCMDzmxxs8YkOmOgiYHDw1clw7ck6DPJJa8bsdmSQ84obvvEhQD7zoJqC7DKZTvbydE73fZcu8wDuUOzGsr7kyC509KdMGveWrwDybSdM5K+GiPDQa/7v+Wjm9IIRiPFrJPTxvtCI9DyBsu0gKnTx04gO8z8hhPK6KLDxJhBg7rqX8PON9TLtmxas7S433vGrmvLx9UDc8ofwZvDnzdzyH2ic8MEffvDaZaLwjBRW9U67EPJkJtzx9dgU8yy9YPF+nObtlZ9k7dYeKu995n7x73Ng7bB8VPXq10zxn4iC8ySLFPAc1vTxpuPS73xc+PO35lbsjb5s8Bf4dvCabsbtO/028YvctPTo8WzzvX5o8+g9PvGY1v7vhphO8eh7kvCbuJTzSCiO9TG6yOxQeBLx4H6y8Cx/cPEhrdLos4vq6dlCeu8wgNLz8UFU87uU6vHJPAbkrMY28ubaMOj5qrzuzfDm9w9q8O2mJobsXf747CN5Vuf//k7xk+fE8XF2qO4LRJ71vfew79SNCPXkgvLzoGEi8WAbnOwHXCr29cCk8FRz7vL++Ozz/FAS9nFWvvFH8mzzy2tk77n65uzpJzzzNWNk8VQc/u7Fvjrx+ABe8ZNTFOyVjiz0/PEe8yTj3PM3PuLqWnSw8CcVyvICUCjyrzUM71rYIvL52h7vSN348KnK6O6K6yLy+UsK8Cq+Wu+Ji17ppgy68kmjTO/6aOLovEGU4HC2eOwLVxDzsHNE7ng2mPIltEbxZ55e8JqlyPLfrijxaO1+7eAMsvEdGq7yChn45stp5O/hCVTwF05s8q/j8PDqxBz2U3yq8YhZzPGpSFz0NQ4e7odRqPL7u5Lz8Uyk8ZZnUO3qWvbufZMm83NVsOx+IhzwJwNm7VJuxPBj6UbwbaQ09VTxxO7zibzxyAwM9SngNOx5EzbwpmeI8xdipPKujA73Iu8y7HrU3vR1R/jyvbLq7ekDuPP3XrrobVE26gcWyvPtPlLqjiGm8P2PPvJNmiry2RqQ8GgqZu5ubNbpX5tI7In42vPhIMzz7mZy8+P8uPK0bgjzUGw69WdZwPOe0erx41wS8/5PaPG6Bhzz90Zo847O7PC0Gr7vUj5q7O9UWvNISNjzrmMe7y40vPNN9ubywAAG89EdPO32mhDunn0Y7OdvUvN9AjTvlBIA8QlSXO6ZDF709RDg7g2omudVYhTwu7vs7Wu6iO8co6Ds0uDU7DnynPIsAezyhPik8GWLTPJ8NYzsdKva8e3qUPJ2eHLzL0u8512kJvRnUmzzc0WG8VWOju8Ehxzzj3oW8UJQEPYFwUDz7U1s8JNQOPBUHuzu3jLE63Inhupluxbx6k/280zsgvfPaJjzU5QG8xPDKPGKhzLtd7fu8f7UcPSA5bDuE31o7VmzNPDTRSTwTyNC8WaEZvel8DDsBhxc94JawvOMXRrw8uSC8ki9COhNspTwGkGe7KvMSPNHBg7yo2VA8i2EhuxYhtrvfyta80daEPEN0vTqCZpA81N4JvTiJwbuQn667a2MePC0bhrkgRaS7AmaNvFpIDzxS26u8c7X6vB9o+LpYzmo8NTBdPFmx/blAaIA8AgO8uyg1qTvPa7o7B3+FPI6YWTwoGFy7+OQoPU6hCj1rFyy8l7a8PJo4RjztDqu77Y0JPNAujjqAOI68MODrO+lfdDsp35e8ADQGPAAisLxKnNY8MZ3NvLmjLz38WBc97fxqu86Z+7z4TJu7RUVUOzfNyjtNRJY828z4vEPFyDzi2bo82cbUvH1BVDvLzQq91aUzPJzRbjyGnvm7pznLvGy1ET2APo28KmkKvGPkFzvRIva8I4yVvPpMzLxR8oU8UZCtvEXeRrrYbVu8matCPcoxjTzXRv67g+cFvefYuDyPHsW7/kUWPTwTPLx43I07qa1juy1pOjy9ES+8G+mJvC1wPrwzbnE836fHvJ9XyLswvtw7wV7HPLxpyru1HIA8JwB/vDLn2rx+bSW9GrSXPBGU7rw2z6k7pI0DvbUwJLzURME8AP8OvE/OrjxQsTe7xgJkPTFbpztv8fe6lfV6PNwWBz0naGK7GuiSvNCKJzzdUwi9T1m8O9qRlrqS0Pe7vbNRPAz2/LtaJYy8dVPfvD/vDTzK5AO80zkZPKVLqzwhcjC8+veXPITdcjwycPs8BkoFPZbvzryna8E8TqUWvPgcAb0FaIU8+7lsPCTXtjs16Ho8I/2QPBQiEjs661Q9j5OwPFjN8Lsd2GA7UoB7OwTnEr0/6BG8TOQxvMAvVrxZq6S8UOS7umEo5rvmMYo8aiQRvKiOF7ochxI9kPIUvNWhwDutQZ+7k2YevJW6kbsD50K8bsI0PJdcE7yoFQC8MF11PIdZDz32FFI9GuS5vK57Ej3zsLK7yfoZvLJYuLxsATe995GsPHyR4bmzn0Q8YXXLPN93BzsOvd+7U65bPEgTrbzGG3q8zQPRPFfcITxKZDG9xx9gPIyjEz2WKli8j86fuzCaXzsD3pK8EqRzPNk187xZ7hG8EDxpvHM7crvlLCg8yn2mPPqF/btURyQ8uRGivDe7KT1/iKk8EaMaO5rGrDzxuf08FcQmvNHuXTn+k0M78atSPLMLj7yyLjG8QTkAvfhVU7yy6rO8DR2AvLEIwLrlfSS7NO6MPIXuWDz/pla8GmnSO5PMgryaVdK8SQewPEnKwruDMay7fyvIO/Q0OrzV/qc6cS25O1t7AT3P3gU8gTEXPbgEwry85i284/Xau9Gs9Du6z467TI5tul5gTbpt2Yi8Ta2JuzXvejwS/KS8jyPPPJObjbwRIYI8VO9Zu/r0LzxTGA47Ip2XPPkRS7pWU/87KTvQPHUZlDtyzxu9RBGiPLYrQLzoTlC8ahsNPQOs6TwNz/c7QdE0upvnwTxHFK+8UdP+PED9Abu4bBg8+ZoMPfd4RTreiIa75MXmPOouC70xWBw7EPJrvGmIcryW5bU8UJiAPEESrDwRnWw7I4SwvCfTCD2anT88w1MWPS8E+ztSo0e8YM0bvEjxSzw3rIw6bcC/vDLu6jwY2Ss8gZvlvDr9pTxP16g8hRVxPFpbH7zq3yY8J3kkOv/V/ru/4Ko8tsQ1PCI2YLyc11y8LyVDvHqaOD0n71W80lu+vL637DsADXS8HPIoPRcRS7s5LlE5f9ITvAX7Dj3S2vm8X2k0vH3MpLz2im68PPOau++uMT3OPkA8mN2CvHbqEL0rBxC8NfIEve+jDb3k0gG8DkS8PEzv4Lv2wog7anREPP6kAz2RDY08G0osvfiIjLyF/GU8e4pwvC4lj7ySrLc82lT9Oyeg1LzaDgY8/12OuvmOrrwQjvy7mFUAPbO4qDuUGDM8mmgRPN6dgTxMfoK8WJEEPTmRbLsEfXM7Ez9UPK5hXrzZthQ88MQIPX1CXrwR9xy82umvux6gHjvrNeC8oLIyvbRdgzz68eq7dR/DvNTW2zwNBZk80MebOjtPxbyM6j48ivaSvFhCk7wH2Bk8WUGwPET6grtvXiC90eR7vAe2kLpSTdq8JeYYPDjFl7mH30u8qFrCvPZXu7srBSW8EO3tPKVpobxaiPu8VrcqvPb/bTyJsS68SyjgOz6CUzw+Fwo55GlAOk9FWjtOPzA8OXR7PLMuEDvH6Gg8o0jAPGRgJDw8iOW7TkepPMa/QrribGy8DyTDu/UkUzy/Ihc7kIdUvMmr4TtEbYo7UUCGus9mwLytBIy8TYyEO1LOKLy1Tdo8avO1u6PS8zxSnsQ8Igw1OftiAL1FeqK7G7ayvMWsgLxy+EA8JNeCO/pqNbxe/9Y8IfHhO2NxxLvL35o8ZlWWPPT6ujsbdgU9uBbFO8vwmDyN4r87g6eAu1rz0zw1M228XCUsu44cfDz766g7nMF4vMi7rDyqsK88y1+tPGR6ljuLtzk6b8ZHvHZFEz2Oeme8cWd3OsD1Hzz3mTI8KiU3O5I8Oz2JTSQ8wrWmO1HlWL1SQIU8uTx6PKWkuzx1aqy8pAVfPMX6UDzAldA6lIeEO3CAhTx84Bs88AjIvJJ/q7unniK8SAZgPNfTTDu/cEo5i1HeO8bwWjw/Pqq74ukdvJWg7btrw7a77eSvPOOB37qzdlo7Scy4O0xiCz1paw09YW5svLTNgzwugYa8C72NvLMnBz32FOK8Q/KgPKdS7bwsQ3q8SsPjvJYUALynx0Q81cuoPF/9IbyCt4g8XdWbPHaJeLvzuby8G2JUPYL2bTyv2E87NyaAPClD7zvJZBG9f7rrvONUpTy1iMI8AOqEOx9LA7sdTMG3aCZUvLuM3zycG0g8SbUQu9dJRLnfLQm9OSBUvOL9crzoQSU8UlgjPFHVM7ytvWw8XFA5vBKFHjyial29aOIkvHk9BjwQOgq868BvPFLwgTvLXYc855w5PSsjJ7yK9Aw7Ix8LPAzShryc67E7ye5su3XRj7mefgS7VSj0vIHhDjxctsS8IMvJPP5xxjwuRx+9tTY+PM6dYby2Oy281aqlvFZi+DsBgpW8hqufvEc/0jn3QuA8i/3pulUmVLyRgt+8ISX6PN1+A71Lb9G7CkfYuync37zOX3S8AhQXuzMVtbzaVgG9KT0BPGh0rTy0SXO6nl4gPHOWkLqvr9k8oFMBvJB8rTyWNay8euJcu5yjfztN3Qm9AchMOq74iTwkO4e8Hdd/PCrSujxLjdm8qz6+u8WgiTxKauO8ygH0vAD2mrs6jvo72tNbPG4zLzsDvg48TX4pvM890rvsy7C79hTHvJ6YiTz26Pw8lZKkO+o/Zjxrf6s7fSYBvTAVjLyibtu8w3GGOkW/wzy7Y+48QpVdPDaOyTvBbro8nJPvu4/GBDw7k226pB9mvPPK0Lz3dQm7pZyxvOKpILzlT7a76keKvIiM/TzxVpG8vuQauxcJ9Dnd8tQ7qS4WO8v8Ezyvv3s7h2I8vFT1bDwPBJU6U7MkPDenlry3tUy98BrOPK3VOrtShQE8CzEpvCdKwjtWFoU704T2vMXmxburlBi9moCZOzWFjrwb+6q78h5kuziYnrxtvBM84NKkvBzV2TxqsAs8vVvTPCeZfDw/u4q8aoZjPOO8BT3LKpu8tQwWup6IhLxEJoS7dJS1uymQDzy15jw8r9jNPBrrqLp1Wr08I3BGPGwwRbzrjaG8kB/8PKeydzqmRgA7KeCJvPQ5Bz13m768VZvuPC7FFL2Tk4E82WjGvDg7qry0VV+7inu1vG5d3TyR8tY8qxeMO8Fdibz/8vQ8hzqSOTfv6Dz6hRU9YGCGun1ZkroAuWG7cTV8O/LGA7z8Dkq8ZQRyvAV0nzpS01W8MchaOVZ3Yrzpetk7VsNWvTvNaLzlxus79KcnvJoAUrsJcrs8gW8/vJzeBjvTF8K8c0AevYDvHT0kuNm88qGjPPIhvbwMnvE42hrkuk27V7zPB8o7vTQZvEGTqDvB6s28xzOdvJPkmryvNNw6s9BMvETy+Tz39J48xTQ2vEMJ87zqr2w73T+fvOEENzyDlW88MIkQO3IQijzI8sW8B6qPu49yfbu0WES8HV6aPKOGWTzpL4k8oFYOPQOm6rxaZUa9QzvoOsf+CzwSBi28yx8dvOzLXDtDISu8UwMWPVERnzt9dQ88Eu8FPM4je7yfraQ8GVxePHjAxjvdSlY8UY+OPPHYBrxr2b+8iq+yPCa317wf+Cq9A86gu2DWpDyPlWc8A5Yvu1AwMjwHTk28PHs5PPcxazyhc+C8C1QUPVqOo7u6atW8I9MVvFQfbDze/Ps8Om/8u65zBbzAMmy8WlECvQXsFzwICR27xnM5PFFfqbwquw48CyWsu6WCGT3ZF+s7+1H0O1CeWDzgS6e8vNd8u8uoFT1TeMg8Vib2O5BKCjzLXbI8csohPFfrMDuB4nK7k8NCPBv/SzrztAW5A3YRPfxK0Drxn+a4XzijPKKUeTzjYSC7Z7cQPeVPe7xcuWs7pcfIPKo1mzr0H+s70JhKPM5R+7t1DBo7gd2iu0Tl6zrIDiI8qsFRujOCtrx4GGK88miIPKI5DjxzzQg6XFmCOiE6l7wwwpM7eK+gPJp72TxVW4q8KFH3vBEV57yhAou8MwPkO1C62jwGDwi9RWgOPaU4Yry/JNM8m75QPDSR5ryrQYe7ta+HvK1IXTwfpqq7ek4tPNtBVr3Q5DO9W6IjPJwOz7wfx4O8HyLvO46dFz3dX4m6XEozPAA/Fb0amh08eXS4PJ3czLxWGhq879AKvfmyU7t3uMo8EySOvLzrFLuALgM9oXGoujEBqLtr+a28X/9Cum+Gqjzqkua8anqrvIH8D73hduu8EqgDPHq9Hr0ImmC8jqqFO4sakrxgWWE8cLhCPAPVGb3OxpK8J2jHu8eQ+LzVvYi8EEJ2vNjzQbx32BI7PMVPO3khErxC/7w7JoqLOa5Ek7tLyyo8dNd3vDUkQbzsmIs7OjkMvFfHFbzt9eo8PR0hPW4hWrwIkaS8EX9wvKXWyDwtTpe8ZcYcvSV4ITyMbuU8lq4ePWYWEDw/I7y8NapWPAjxGLx1wAg6Hb6DPOSnsruklI85Y+SVu0u2XzyvN7y8Y7bzvLNkMDw5Y4+8LTI5vYJMJz1vP4q9U/CvPAJWsDwTXqW8WKYavBkDk7yKPwq84caeuxxxcDxL9QA7Q+xaOy6tYrwOWW+8fNSnPMbo0by6Gaq7Wg0QveC84juQsgi9s0JWu5myGDxyY8S8vFQlPCy57TxlYDS8uUqCvICt6byZbtG7wNyBu1lK+bwekpk7+O++PFrkET1LcR08cQWJO1LRhTzF0sk7H3+hu2E4irwyT928aIGlPHGoGzxxEa84wYsMPNYJ6TunNj+8qBLUPDl9PTzOZug6K7++PLUBxrw04wq94lyqvBqMgzzKJDw7wUa7O2U/yDrphkm8P6KPvCIKoTzbCmS8he18Oyy+Fr06PcU8Se85vAeWQLtHGvS7/ptUPMdC+LxhE568L8Svu0+lUzyOm7E55M2LvPj1OTzcxP+87vR8PBuHoroqLiW8JSKiPBbw7rvuF2i8mXjkO32B+jvWo8Y7yM4+vD1Ab7tTC1s7Pyf0PFUBqzwUsoO7mf0HPZbwuLzrFz28iYnXPGrBXL1pSpS5OeLPPJ1ytrwonGm8KAWbu+3O6LuYPvm74pAAvJTVCDt4N0C8hd1NvPM9qrwrCpu8QfgNva6lMLtFfRm8Qh37O2TT3rurKrq8BmzUvA2yerwQB5W82E4/vP//UryWzwm8fRGDPA+YFb17yMM8clpWO6N6CTzyPKW8XWiJO3AfQz2aTiM9Td7ku7LlA7yrDPw8apK/uwVS2jovWCM71KwEPbPnhrxb6Oi8QcLpPGSA6LwEmIQ85rrEvFOJDz1qEZm8v1bSvC4xxzywD+q8GAd1vFOPOzxVlpS8BJeNPJ+y7Ly6BSg8TSK2vGJx9TxAdrQ82w5PvN8pljzp8r28qmPQPGniwDsGX6a8FsZVu3ONKTwOdp27W501PGJymTw94Sq7H5UeuickEbxlrQc8Zl9HPECfhLwtCQg9t/3pPJcoF73/C6K7O+yRvMSUejwPE0O8TDXavBx7Eb3sSwM8JiDdO1glvLvxvOi8jySpu9vCeTzuZsi8voC9vKCvJrssB6m7w61GupmcDjuAO8074pUKOySkpbu+nXU8X+ywu2EGsDw2Xxu8z8V1PJW5uzy0anW7inMkvHWBBjxCc668v84HvQmLz7yiwwa7yLibOwD/qrw3u2k8xojjuw2zvzw4uPa7wxfwu5KULzyPKd67txPoO8Q/hbv37ys6782WvBDilLxqU428LTdWvExLELmgeDc8+kkjPWS/1Dm5GF09pJyCPJmYrztNpsI7mevovNY0jjoh/iM8GIFWvDAhvzzp0eS7cPgAPQyL7zt7uIs83SxTO75/Iz248BC8d7z5vI0Uh7yeg847+I40vGgFEbz07i28H3S8vDaGPb2OvJ08yMy3vCXZ2jsqWZo88oUtupcI47x9XQ09XbFxPKhDYzwPD7a7ydUkvabJ1Ltbmg67SdkEvJ7wgjvL1IG8/I3dvF+HWrztjSy87567vCiQIDyvIqg8fHePOpFuqDwZ1c48H7qBu9b6CTz073m8oHt0vK3EDrz2an48gn8Su2Cga7x2ztC8e0ozvP8jLjxP1D+8AjwPPJABR7wHyZm8oUDJvPofmTw6Q4y8FG4HOyWj5bsv5Li8BdozPNfzsDvjCnw8tAwHPGlqCTrDIpi7P8xZPBomq7yNy0y4isKUPIgNJzwJ6vm8PlLvPCRNQLzDeBk7H3iiuZdc9Lv0rOQ8U8amPDLgVzzSNK07bFP6u+iMkDwNTk68ZYHbu6Lu9jwhw0u81hxcu+FjczyR5RI9ajrKuyzQ5TpjiEw9+P8yPZnNwjo/zAo8xW9su2bJXDyQniE8DIUhPG9xwLxepeQ8sKIQvHoEhrztxeM8Bh4EvcmYCDxRNIc8Ghdfu41UCTx9Gb+8GWH2vL+FTDv80JO80EMgvC1qGT3c4t87yjvSvJGS2bz1X1e6wP0bPIBd8Dp2RIE8i4H1vCuEC72514i6lG6UvGEH+DvrOyy9RvCLu46KhjqKCrs744Y7vLrmxTurnc26VnnXPJsFIrzWZa+77WxUPFP9drwloaC8OMc1PFxamTtp/WK7JMTBPO+ZrLu+PLq7URfHO1Ik5jya9iW7W8DLPKR4ZLtZxRm7P496PL5GFTxSvqw8XekevNw+KD3D2V88pIvKOokI/DsWlOi7Ad8TPWpnxTzzWc08YXOCPGxnpbuYW/O6bjZgOYyYdTyPma674POTu1Hn2jtdmLu8MukiPGU24zxvqAo8baITORJt0Dybyas8s8RWPV9GWTylo0a8lKQGvDst4rw+2yS8rybAvFp9IDsMpYy7ngabPLMZDrzkPzA9CtHxPJtNyrrdvi26a1gTu5PSHDwTzW+8ITW5PKWBIDxD4e88qIZtvF77kTx8U5i82AuGvMKaobzMaw+9N9b4PJkyxrwzwdA6tLTqOqtKS7w9oVg89Y+UutjyED2m+Zi6y/HSu3d5jjoQDJE7GIqAvNDZNjtplnA8gBblu36jjbuWP0O856q1ObbtVTzCG0m8/VPGO08xXLsW+IK6Z/fVu6rZZbsVa5M8oyvWu4r1+jpG9IO8zBBkOQKmGD2QOlQ7gFkKuz5mSbuUB6E7o+AfvKWW1Dw522Y7XNsIO0jD2jrbI548AqcGvOLLgbyKo/o78V0uPJaEnjyTZwE9HyAMvPDH1jtM6nO858m3uw== + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 3 + total_tokens: 3 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1849' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: What information is available? + role: user + - content: |- + + Need to run searches. Use original query "What information is available?" That's vague. Alternate queries: "information available", "available info". + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"What information is available?"}' + name: run_search + id: call_8m556d90 + type: function + - content: 'Found 1 results for: What information is available?' + role: tool + tool_call_id: call_8m556d90 + - content: null + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"information available"}' + name: run_search + id: call_p2syjpni + type: function + - content: 'Found 1 results for: information available' + role: tool + tool_call_id: call_p2syjpni + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '446' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"available info"}' + name: run_search + id: call_5adrr7po + index: 0 + type: function + created: 1768225941 + id: chatcmpl-276 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 23 + prompt_tokens: 396 + total_tokens: 419 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '84' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - available info + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 3 + total_tokens: 3 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2126' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search query optimizer. You MUST use the run_search tool to execute searches. + + For each user request: + 1. Use the run_search tool with the original query + 2. Use run_search again with 1-2 alternative keyword queries + 3. Keep all queries SHORT (2-5 words) + 4. After all tool calls complete, respond "Search complete" + + You can optionally specify a limit parameter (default 5). + + IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. + role: system + - content: What information is available? + role: user + - content: |- + + Need to run searches. Use original query "What information is available?" That's vague. Alternate queries: "information available", "available info". + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"What information is available?"}' + name: run_search + id: call_8m556d90 + type: function + - content: 'Found 1 results for: What information is available?' + role: tool + tool_call_id: call_8m556d90 + - content: null + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"information available"}' + name: run_search + id: call_p2syjpni + type: function + - content: 'Found 1 results for: information available' + role: tool + tool_call_id: call_p2syjpni + - content: null + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"available info"}' + name: run_search + id: call_5adrr7po + type: function + - content: 'Found 1 results for: available info' + role: tool + tool_call_id: call_5adrr7po + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Run a single search query against the knowledge base. + name: run_search + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to fetch (default: 5)' + query: + description: The search query + type: string + required: + - query + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '298' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: Search complete + role: assistant + created: 1768225942 + id: chatcmpl-296 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 6 + prompt_tokens: 438 + total_tokens: 444 + status: + code: 200 + message: OK +version: 1