From 5976cebe8cf8630261c9e9d3b97c1da693845318 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Mon, 26 Jan 2026 15:41:29 +0200 Subject: [PATCH] Concolidate tests --- tests/agents/chat/test_chat_agent.py | 246 +- tests/agents/chat/test_context.py | 3 +- tests/agents/chat/test_state.py | 79 +- tests/agents/research/test_research_graph.py | 122 + .../test_chat_agent_ask_with_state_key.yaml | 3355 ----------------- ...test_chat_agent_search_with_state_key.yaml | 817 ---- 6 files changed, 338 insertions(+), 4284 deletions(-) delete mode 100644 tests/cassettes/test_chat_agent/test_chat_agent_ask_with_state_key.yaml delete mode 100644 tests/cassettes/test_chat_agent/test_chat_agent_search_with_state_key.yaml diff --git a/tests/agents/chat/test_chat_agent.py b/tests/agents/chat/test_chat_agent.py index 2e692f10..2057f6eb 100644 --- a/tests/agents/chat/test_chat_agent.py +++ b/tests/agents/chat/test_chat_agent.py @@ -251,40 +251,6 @@ async def test_chat_agent_search_tool(allow_model_requests, temp_db_path): assert len(result.output) > 0 -@pytest.mark.asyncio -@pytest.mark.vcr() -async def test_chat_agent_search_with_state_key(allow_model_requests, temp_db_path): - """Test search tool emits keyed state when state_key is set.""" - async with HaikuRAG(temp_db_path, create=True) as client: - await client.create_document( - content=DOCLAYNET_CLASS_LABELS, - uri="doclaynet-labels", - title="DocLayNet Class Labels", - ) - await client.create_document( - content=DOCLAYNET_ANNOTATION, - uri="doclaynet-annotation", - title="DocLayNet Annotation", - ) - - agent = create_chat_agent(Config) - session_state = ChatSessionState(session_id="test-search") - deps = ChatDeps( - client=client, - config=Config, - session_state=session_state, - state_key=AGUI_STATE_KEY, - ) - - 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): @@ -501,35 +467,6 @@ async def test_chat_agent_ask_adds_citations(allow_model_requests, temp_db_path) assert len(session_state.qa_history) >= 1 -@pytest.mark.asyncio -@pytest.mark.vcr() -async def test_chat_agent_ask_with_state_key(allow_model_requests, temp_db_path): - """Test ask tool emits keyed state when state_key is set.""" - async with HaikuRAG(temp_db_path, create=True) as client: - await client.create_document( - content=DOCLAYNET_CLASS_LABELS, - uri="doclaynet-labels", - title="DocLayNet Class Labels", - ) - - agent = create_chat_agent(Config) - session_state = ChatSessionState(session_id="test-ask-keyed") - deps = ChatDeps( - client=client, - config=Config, - session_state=session_state, - state_key=AGUI_STATE_KEY, - ) - - result = await agent.run( - "What is the highest count class in the DocLayNet dataset?", - deps=deps, - ) - - assert result.output is not None - assert len(session_state.qa_history) >= 1 - - @pytest.mark.asyncio @pytest.mark.vcr() async def test_chat_agent_ask_triggers_background_summarization( @@ -801,3 +738,186 @@ def test_search_tool_citation_registry_logic(): "chunk-c": 3, "chunk-d": 4, } + + +# ============================================================================= +# Prior Answer Recall Tests +# ============================================================================= + + +def test_cosine_similarity_identical_vectors(): + """Test cosine similarity returns 1.0 for identical vectors.""" + from haiku.rag.agents.chat.agent import _cosine_similarity + + vec = [1.0, 2.0, 3.0] + assert _cosine_similarity(vec, vec) == pytest.approx(1.0) + + +def test_cosine_similarity_orthogonal_vectors(): + """Test cosine similarity returns 0.0 for orthogonal vectors.""" + from haiku.rag.agents.chat.agent import _cosine_similarity + + vec1 = [1.0, 0.0, 0.0] + vec2 = [0.0, 1.0, 0.0] + assert _cosine_similarity(vec1, vec2) == pytest.approx(0.0) + + +def test_cosine_similarity_opposite_vectors(): + """Test cosine similarity returns -1.0 for opposite vectors.""" + from haiku.rag.agents.chat.agent import _cosine_similarity + + vec1 = [1.0, 2.0, 3.0] + vec2 = [-1.0, -2.0, -3.0] + assert _cosine_similarity(vec1, vec2) == pytest.approx(-1.0) + + +def test_cosine_similarity_zero_vector(): + """Test cosine similarity handles zero vectors gracefully.""" + from haiku.rag.agents.chat.agent import _cosine_similarity + + vec = [1.0, 2.0, 3.0] + zero = [0.0, 0.0, 0.0] + assert _cosine_similarity(vec, zero) == 0.0 + assert _cosine_similarity(zero, vec) == 0.0 + assert _cosine_similarity(zero, zero) == 0.0 + + +def test_prior_answer_relevance_threshold_constant(): + """Test PRIOR_ANSWER_RELEVANCE_THRESHOLD is set to expected value.""" + from haiku.rag.agents.chat.agent import PRIOR_ANSWER_RELEVANCE_THRESHOLD + + assert PRIOR_ANSWER_RELEVANCE_THRESHOLD == 0.7 + + +def test_prior_answer_matching_above_threshold(): + """Test that similar questions (above threshold) are matched.""" + from haiku.rag.agents.chat.agent import ( + PRIOR_ANSWER_RELEVANCE_THRESHOLD, + _cosine_similarity, + ) + + # Simulate two nearly identical question embeddings + question_embedding = [0.5, 0.5, 0.5, 0.5] + prior_embedding = [0.51, 0.49, 0.5, 0.5] # Very similar + + similarity = _cosine_similarity(question_embedding, prior_embedding) + assert similarity >= PRIOR_ANSWER_RELEVANCE_THRESHOLD + + +def test_prior_answer_matching_below_threshold(): + """Test that dissimilar questions (below threshold) are not matched.""" + from haiku.rag.agents.chat.agent import ( + PRIOR_ANSWER_RELEVANCE_THRESHOLD, + _cosine_similarity, + ) + + # Simulate two different question embeddings + question_embedding = [1.0, 0.0, 0.0, 0.0] + prior_embedding = [0.0, 1.0, 0.0, 0.0] # Orthogonal = very different + + similarity = _cosine_similarity(question_embedding, prior_embedding) + assert similarity < PRIOR_ANSWER_RELEVANCE_THRESHOLD + + +def test_qa_response_embedding_cache(): + """Test that QAResponse stores and retrieves question_embedding correctly.""" + embedding = [0.1, 0.2, 0.3, 0.4] + qa = QAResponse( + question="What is X?", + answer="X is Y.", + confidence=0.9, + question_embedding=embedding, + ) + + assert qa.question_embedding == embedding + # Embedding should be excluded from serialization (AG-UI state) + serialized = qa.model_dump() + assert "question_embedding" not in serialized + + +def test_qa_response_embedding_default_none(): + """Test that QAResponse.question_embedding defaults to None.""" + qa = QAResponse( + question="What is X?", + answer="X is Y.", + confidence=0.9, + ) + + assert qa.question_embedding is None + + +# ============================================================================= +# Background Task Cancellation Tests +# ============================================================================= + + +@pytest.mark.asyncio +async def test_summarization_task_cancellation(): + """Test that new summarization tasks cancel previous ones for same session.""" + import asyncio + + from haiku.rag.agents.chat.agent import _summarization_tasks + + # Clear any existing tasks + _summarization_tasks.clear() + + session_id = "test-cancel-session" + + # Create a slow task that simulates summarization + async def slow_task(): + await asyncio.sleep(10) # Would take 10 seconds + + # Start first task + task1 = asyncio.create_task(slow_task()) + _summarization_tasks[session_id] = task1 + + # Simulate what happens when second ask comes in - cancel first task + if session_id in _summarization_tasks: + _summarization_tasks[session_id].cancel() + + # Yield to let cancellation propagate + await asyncio.sleep(0) + + # Start second task + task2 = asyncio.create_task(slow_task()) + _summarization_tasks[session_id] = task2 + + # First task should be cancelled + assert task1.cancelled() or task1.done() + + # Second task should be running + assert not task2.done() + + # Cleanup + task2.cancel() + try: + await task2 + except asyncio.CancelledError: + pass + _summarization_tasks.clear() + + +@pytest.mark.asyncio +async def test_summarization_task_cleanup_on_completion(): + """Test that completed tasks are cleaned up from _summarization_tasks.""" + import asyncio + + from haiku.rag.agents.chat.agent import _summarization_tasks + + _summarization_tasks.clear() + + session_id = "test-cleanup-session" + + # Create a fast task + async def fast_task(): + await asyncio.sleep(0.01) + + task = asyncio.create_task(fast_task()) + _summarization_tasks[session_id] = task + task.add_done_callback(lambda t: _summarization_tasks.pop(session_id, None)) + + # Wait for completion + await task + + # Task should be cleaned up + assert session_id not in _summarization_tasks diff --git a/tests/agents/chat/test_context.py b/tests/agents/chat/test_context.py index 4819957c..03d3e894 100644 --- a/tests/agents/chat/test_context.py +++ b/tests/agents/chat/test_context.py @@ -66,8 +66,7 @@ class TestSummarizeSession: """Tests for summarize_session function.""" @pytest.mark.asyncio - @pytest.mark.vcr() - async def test_summarize_session_empty_history(self, allow_model_requests): + async def test_summarize_session_empty_history(self): """Test summarize_session with empty qa_history returns empty string.""" from haiku.rag.agents.chat.context import summarize_session diff --git a/tests/agents/chat/test_state.py b/tests/agents/chat/test_state.py index 15613479..c62a943b 100644 --- a/tests/agents/chat/test_state.py +++ b/tests/agents/chat/test_state.py @@ -376,48 +376,43 @@ def test_chat_deps_state_setter_ignores_session_context(): assert deps.session_state.session_context.summary == "Server-side context" -def test_citation_registry_get_or_assign_index_first_chunk(): - """Test get_or_assign_index assigns index 1 to first chunk.""" +def test_citation_registry_index_assignment(): + """Test get_or_assign_index basic index assignment behavior. + + Verifies: + - First chunk gets index 1 + - Second unique chunk gets index 2 + - Same chunk_id always returns same index + """ from haiku.rag.agents.chat.state import ChatSessionState session_state = ChatSessionState(session_id="test") - index = session_state.get_or_assign_index("chunk-abc") - assert index == 1 - -def test_citation_registry_get_or_assign_index_second_chunk(): - """Test get_or_assign_index assigns incremental indices to new chunks.""" - from haiku.rag.agents.chat.state import ChatSessionState - - session_state = ChatSessionState(session_id="test") + # First chunk gets index 1 index1 = session_state.get_or_assign_index("chunk-abc") - index2 = session_state.get_or_assign_index("chunk-def") assert index1 == 1 + + # Second unique chunk gets index 2 + index2 = session_state.get_or_assign_index("chunk-def") assert index2 == 2 - -def test_citation_registry_get_or_assign_index_same_chunk(): - """Test get_or_assign_index returns same index for same chunk_id.""" - from haiku.rag.agents.chat.state import ChatSessionState - - session_state = ChatSessionState(session_id="test") - index1 = session_state.get_or_assign_index("chunk-abc") - index2 = session_state.get_or_assign_index("chunk-abc") - assert index1 == index2 == 1 + # Same chunk_id returns same index (not incremented) + index1_again = session_state.get_or_assign_index("chunk-abc") + assert index1_again == 1 -def test_citation_registry_get_or_assign_index_stability(): - """Test citation indices are stable across multiple calls.""" +def test_citation_registry_stability(): + """Test citation indices are stable across multiple calls in any order.""" from haiku.rag.agents.chat.state import ChatSessionState session_state = ChatSessionState(session_id="test") - # First call assigns indices 1, 2, 3 + # First round assigns indices 1, 2, 3 idx_a = session_state.get_or_assign_index("chunk-a") idx_b = session_state.get_or_assign_index("chunk-b") idx_c = session_state.get_or_assign_index("chunk-c") - # Second round - existing chunks keep their indices + # Second round - existing chunks keep their indices regardless of order assert session_state.get_or_assign_index("chunk-b") == idx_b assert session_state.get_or_assign_index("chunk-a") == idx_a assert session_state.get_or_assign_index("chunk-c") == idx_c @@ -427,38 +422,28 @@ def test_citation_registry_get_or_assign_index_stability(): assert idx_d == 4 -def test_citation_registry_serialization(): - """Test citation_registry is included in model_dump for AG-UI state.""" +def test_citation_registry_serialization_roundtrip(): + """Test citation_registry serializes and deserializes correctly for AG-UI state.""" from haiku.rag.agents.chat.state import ChatSessionState - session_state = ChatSessionState(session_id="test") - session_state.get_or_assign_index("chunk-a") - session_state.get_or_assign_index("chunk-b") + # Create state and assign indices + original = ChatSessionState(session_id="test") + original.get_or_assign_index("chunk-a") + original.get_or_assign_index("chunk-b") - state_dict = session_state.model_dump() + # Serialize + state_dict = original.model_dump() assert "citation_registry" in state_dict assert state_dict["citation_registry"] == {"chunk-a": 1, "chunk-b": 2} - -def test_citation_registry_deserialization(): - """Test citation_registry is restored from dict.""" - from haiku.rag.agents.chat.state import ChatSessionState - - # Simulate state from AG-UI using model_validate (proper Pydantic deserialization) - session_state = ChatSessionState.model_validate( - { - "session_id": "test", - "citations": [], - "qa_history": [], - "citation_registry": {"chunk-a": 1, "chunk-b": 2}, - } - ) + # Deserialize (simulating AG-UI state restoration) + restored = ChatSessionState.model_validate(state_dict) # Existing chunks should return their persisted indices - assert session_state.get_or_assign_index("chunk-a") == 1 - assert session_state.get_or_assign_index("chunk-b") == 2 + assert restored.get_or_assign_index("chunk-a") == 1 + assert restored.get_or_assign_index("chunk-b") == 2 # New chunk should get next index - assert session_state.get_or_assign_index("chunk-c") == 3 + assert restored.get_or_assign_index("chunk-c") == 3 def test_chat_deps_state_getter_includes_citation_registry(): diff --git a/tests/agents/research/test_research_graph.py b/tests/agents/research/test_research_graph.py index 74b0ef84..ea75053d 100644 --- a/tests/agents/research/test_research_graph.py +++ b/tests/agents/research/test_research_graph.py @@ -62,3 +62,125 @@ def test_research_plan_rejects_too_many_sub_questions(): with pytest.raises(ValidationError, match="Cannot have more than 12"): ResearchPlan(sub_questions=[f"q{i}" for i in range(13)]) + + +# ============================================================================= +# Conversational Graph Tests +# ============================================================================= + + +def test_build_conversational_graph_returns_graph(): + """Test build_conversational_graph returns a valid Graph instance.""" + from pydantic_graph.beta import Graph + + from haiku.rag.agents.research.graph import build_conversational_graph + + graph = build_conversational_graph() + assert graph is not None + assert isinstance(graph, Graph) + + +def test_conversational_answer_model(): + """Test ConversationalAnswer model can be created with all fields.""" + from haiku.rag.agents.research.models import Citation, ConversationalAnswer + + citation = Citation( + index=1, + document_id="doc-1", + chunk_id="chunk-1", + document_uri="test.md", + document_title="Test Doc", + content="Test content", + ) + + answer = ConversationalAnswer( + answer="The answer is 42.", + citations=[citation], + confidence=0.95, + ) + + assert answer.answer == "The answer is 42." + assert len(answer.citations) == 1 + assert answer.confidence == 0.95 + + +def test_conversational_answer_default_values(): + """Test ConversationalAnswer uses correct default values.""" + from haiku.rag.agents.research.models import ConversationalAnswer + + answer = ConversationalAnswer(answer="Just the answer.") + + assert answer.answer == "Just the answer." + assert answer.citations == [] + assert answer.confidence == 1.0 + + +def test_format_context_for_prompt_basic(): + """Test format_context_for_prompt with basic context.""" + from haiku.rag.agents.research.dependencies import ResearchContext + from haiku.rag.agents.research.graph import format_context_for_prompt + + context = ResearchContext(original_question="What is X?") + result = format_context_for_prompt(context) + + assert "" in result + assert "What is X?" in result + + +def test_format_context_for_prompt_with_session_context(): + """Test format_context_for_prompt includes session_context as background.""" + from haiku.rag.agents.research.dependencies import ResearchContext + from haiku.rag.agents.research.graph import format_context_for_prompt + + context = ResearchContext( + original_question="What is Y?", + session_context="Previous discussion about topic Z.", + ) + result = format_context_for_prompt(context) + + assert "" in result + assert "Previous discussion" in result + assert "What is Y?" in result + + +def test_format_context_for_prompt_excludes_pending_questions(): + """Test format_context_for_prompt can exclude pending questions.""" + from haiku.rag.agents.research.dependencies import ResearchContext + from haiku.rag.agents.research.graph import format_context_for_prompt + + context = ResearchContext( + original_question="Main question?", + sub_questions=["Sub Q1?", "Sub Q2?"], + ) + + # With pending questions (default) + with_pending = format_context_for_prompt(context, include_pending_questions=True) + assert "Sub Q1?" in with_pending + + # Without pending questions (for synthesis) + without_pending = format_context_for_prompt( + context, include_pending_questions=False + ) + assert "Sub Q1?" not in without_pending + + +def test_format_context_for_prompt_with_prior_answers(): + """Test format_context_for_prompt includes prior_answers.""" + from haiku.rag.agents.research.dependencies import ResearchContext + from haiku.rag.agents.research.graph import format_context_for_prompt + from haiku.rag.agents.research.models import SearchAnswer + + context = ResearchContext(original_question="Main question?") + context.add_qa_response( + SearchAnswer( + query="Sub question?", + answer="The answer is here.", + confidence=0.9, + ) + ) + + result = format_context_for_prompt(context) + + assert "" in result + assert "Sub question?" in result + assert "The answer is here." in result diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_ask_with_state_key.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_ask_with_state_key.yaml deleted file mode 100644 index 0b447e0e..00000000 --- a/tests/cassettes/test_chat_agent/test_chat_agent_ask_with_state_key.yaml +++ /dev/null @@ -1,3355 +0,0 @@ -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: - - '519' - 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":null,"question":"What is the highest count class in the DocLayNet dataset?"}' - name: ask - id: call_f61rh3p7 - index: 0 - type: function - created: 1769001195 - id: chatcmpl-92 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 43 - prompt_tokens: 850 - total_tokens: 893 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '2147' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are the research orchestrator for a focused workflow. - - If a section is provided, use it to understand the domain context. - - Responsibilities: - 1. Understand and decompose the main question - 2. Propose a minimal, high-leverage plan - 3. Coordinate specialized agents to gather evidence - - Plan requirements: - - Produce at most 3 sub_questions that together cover the main question. - - sub_questions must be a list of plain strings, where each string is a complete - question. Do NOT use objects with nested fields like {question, details}. - - Each sub_question must be a standalone, self-contained query that can run - without extra context. Include concrete entities, scope, timeframe, and any - qualifiers. Avoid ambiguous pronouns (it/they/this/that). - - Prioritize the highest-value aspects first; avoid redundancy and overlap. - - Prefer questions that are likely answerable from the current knowledge base; - if coverage is uncertain, make scopes narrower and specific. - - Order sub_questions by execution priority (most valuable first). - - Use the gather_context tool once on the main question before planning. - - Use the gather_context tool once on the main question before planning. - role: system - - content: |- - Plan a focused approach for the main question. - - Main question: What 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: - - '530' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: We need to gather context first. - role: assistant - tool_calls: - - function: - arguments: '{"limit":null,"query":"highest count class in the DocLayNet dataset"}' - name: gather_context - id: call_6j83gec4 - index: 0 - type: function - created: 1769001196 - id: chatcmpl-223 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 44 - prompt_tokens: 430 - total_tokens: 474 - 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: - - 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: 11 - total_tokens: 11 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '2916' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are the research orchestrator for a focused workflow. - - If a section is provided, use it to understand the domain context. - - Responsibilities: - 1. Understand and decompose the main question - 2. Propose a minimal, high-leverage plan - 3. Coordinate specialized agents to gather evidence - - Plan requirements: - - Produce at most 3 sub_questions that together cover the main question. - - sub_questions must be a list of plain strings, where each string is a complete - question. Do NOT use objects with nested fields like {question, details}. - - Each sub_question must be a standalone, self-contained query that can run - without extra context. Include concrete entities, scope, timeframe, and any - qualifiers. Avoid ambiguous pronouns (it/they/this/that). - - Prioritize the highest-value aspects first; avoid redundancy and overlap. - - Prefer questions that are likely answerable from the current knowledge base; - if coverage is uncertain, make scopes narrower and specific. - - Order sub_questions by execution priority (most valuable first). - - Use the gather_context tool once on the main question before planning. - - Use the gather_context tool once on the main question before planning. - role: system - - content: |- - Plan a focused approach for the main question. - - Main question: What is the highest count class in the DocLayNet dataset? - role: user - - content: |- - - We need to gather context first. - - role: assistant - tool_calls: - - function: - arguments: '{"limit":null,"query":"highest count class in the DocLayNet dataset"}' - name: gather_context - id: call_6j83gec4 - 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_6j83gec4 - 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: - - '1267' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: stop - index: 0 - message: - content: "**Plan for researching “What is the highest count class in the DocLayNet dataset?”**\n\n1. **Confirm the - official list of class labels and their counts** \n *Query:* “DocLayNet dataset class distribution – number - of instances per class (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, - Table, …).”\n\n2. **Identify the class with the maximum count** \n *Query:* “Which DocLayNet class has the - highest instance count according to the official statistics?”\n\n3. **Validate the answer with a secondary source** - \ \n *Query:* “External reference or research paper confirming the most frequent class in DocLayNet (e.g., citation - of the dataset paper).”\n\nThese three sub‑questions will locate the official class counts, determine the maximum, - and ensure the information is reliable." - reasoning: 'We have doc. Now propose plan: ask about class distribution stats, or which class largest. Need sub_questions.' - role: assistant - created: 1769001201 - id: chatcmpl-248 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 213 - prompt_tokens: 576 - total_tokens: 789 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '5058' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are the research orchestrator for a focused workflow. - - If a section is provided, use it to understand the domain context. - - Responsibilities: - 1. Understand and decompose the main question - 2. Propose a minimal, high-leverage plan - 3. Coordinate specialized agents to gather evidence - - Plan requirements: - - Produce at most 3 sub_questions that together cover the main question. - - sub_questions must be a list of plain strings, where each string is a complete - question. Do NOT use objects with nested fields like {question, details}. - - Each sub_question must be a standalone, self-contained query that can run - without extra context. Include concrete entities, scope, timeframe, and any - qualifiers. Avoid ambiguous pronouns (it/they/this/that). - - Prioritize the highest-value aspects first; avoid redundancy and overlap. - - Prefer questions that are likely answerable from the current knowledge base; - if coverage is uncertain, make scopes narrower and specific. - - Order sub_questions by execution priority (most valuable first). - - Use the gather_context tool once on the main question before planning. - - Use the gather_context tool once on the main question before planning. - role: system - - content: |- - Plan a focused approach for the main question. - - Main question: What is the highest count class in the DocLayNet dataset? - role: user - - content: |- - - We need to gather context first. - - role: assistant - tool_calls: - - function: - arguments: '{"limit":null,"query":"highest count class in the DocLayNet dataset"}' - name: gather_context - id: call_6j83gec4 - 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_6j83gec4 - - content: "\nWe have doc. Now propose plan: ask about class distribution stats, or which class largest. Need - sub_questions.\n\n\n**Plan for researching “What is the highest count class in the DocLayNet dataset?”**\n\n1. - **Confirm the official list of class labels and their counts** \n *Query:* “DocLayNet dataset class distribution - – number of instances per class (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, - Table, …).”\n\n2. **Identify the class with the maximum count** \n *Query:* “Which DocLayNet class has the highest - instance count according to the official statistics?”\n\n3. **Validate the answer with a secondary source** \n - \ *Query:* “External reference or research paper confirming the most frequent class in DocLayNet (e.g., citation - of the dataset paper).”\n\nThese three sub‑questions will locate the official class counts, determine the maximum, - and ensure the information is reliable." - role: assistant - - content: |- - 1 validation error: - ```json - [ - { - "type": "json_invalid", - "loc": [], - "msg": "Invalid JSON: expected value at line 1 column 1", - "input": "**Plan for researching “What is the highest count class in the DocLayNet dataset?”**\n\n1. **Confirm the official list of class labels and their counts** \n *Query:* “DocLayNet dataset class distribution – number of instances per class (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, …).”\n\n2. **Identify the class with the maximum count** \n *Query:* “Which DocLayNet class has the highest instance count according to the official statistics?”\n\n3. **Validate the answer with a secondary source** \n *Query:* “External reference or research paper confirming the most frequent class in DocLayNet (e.g., citation of the dataset paper).”\n\nThese three sub‑questions will locate the official class counts, determine the maximum, and ensure the information is reliable." - } - ] - ``` - - 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: - - '923' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: Need to output JSON with sub_questions array. - role: assistant - tool_calls: - - function: - arguments: '{"sub_questions":["What is the distribution of instances for each class in the official DocLayNet - dataset (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, - etc.)?","Which class in DocLayNet has the highest instance count according to the dataset’s official statistics?","Does - a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet?"]}' - name: final_result - id: call_8wikfwfp - index: 0 - type: function - created: 1769001204 - id: chatcmpl-918 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 125 - prompt_tokens: 1050 - total_tokens: 1175 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '2989' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are a search and question-answering specialist. - - Process: - 1. Call search_and_answer with relevant keywords from the question. - 2. Review the results ordered by relevance. - 3. If needed, perform follow-up searches with different keywords (max 3 total). - 4. Provide a concise answer based strictly on the retrieved content. - - The search tool returns results like: - [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] - Source: "Document Title" > Section > Subsection - Type: paragraph - Content: - The actual text content here... - - [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] - Source: "Another Document" - Type: table - Content: - | Column 1 | Column 2 | - ... - - Each result includes: - - chunk_id in brackets and rank position (rank 1 = most relevant) - - Source: document title and section hierarchy (when available) - - Type: content type like paragraph, table, code, list_item (when available) - - Content: the actual text - - Output format: - - query: Echo the question you are answering - - answer: Your concise answer based on the retrieved content - - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) - - confidence: A score from 0.0 to 1.0 indicating answer confidence - - IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. - - Guidelines: - - Base answers strictly on retrieved content - do not use external knowledge. - - Use the Source and Type metadata to understand context. - - If multiple results are relevant, synthesize them coherently. - - If information is insufficient, say so clearly. - - Be concise and direct; avoid meta commentary about the process. - - Results are ordered by relevance, with rank 1 being most relevant. - role: system - - content: What is the distribution of instances for each class in the official DocLayNet dataset (Caption, Footnote, - Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.)? - 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: - - '614' - 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: '{"query":"DocLayNet dataset class distribution Caption Footnote Formula List‑item Page‑footer Page‑header - Picture Section‑header Table distribution","limit":5}' - name: search_and_answer - id: call_sa4rp6ua - index: 0 - type: function - created: 1769001206 - id: chatcmpl-579 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 58 - prompt_tokens: 662 - total_tokens: 720 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '215' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - encoding_format: base64 - input: - - DocLayNet dataset class distribution Caption Footnote Formula List‑item Page‑footer Page‑header Picture Section‑header - Table distribution - 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: 27 - total_tokens: 27 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '3959' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are a search and question-answering specialist. - - Process: - 1. Call search_and_answer with relevant keywords from the question. - 2. Review the results ordered by relevance. - 3. If needed, perform follow-up searches with different keywords (max 3 total). - 4. Provide a concise answer based strictly on the retrieved content. - - The search tool returns results like: - [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] - Source: "Document Title" > Section > Subsection - Type: paragraph - Content: - The actual text content here... - - [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] - Source: "Another Document" - Type: table - Content: - | Column 1 | Column 2 | - ... - - Each result includes: - - chunk_id in brackets and rank position (rank 1 = most relevant) - - Source: document title and section hierarchy (when available) - - Type: content type like paragraph, table, code, list_item (when available) - - Content: the actual text - - Output format: - - query: Echo the question you are answering - - answer: Your concise answer based on the retrieved content - - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) - - confidence: A score from 0.0 to 1.0 indicating answer confidence - - IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. - - Guidelines: - - Base answers strictly on retrieved content - do not use external knowledge. - - Use the Source and Type metadata to understand context. - - If multiple results are relevant, synthesize them coherently. - - If information is insufficient, say so clearly. - - Be concise and direct; avoid meta commentary about the process. - - Results are ordered by relevance, with rank 1 being most relevant. - role: system - - content: What is the distribution of instances for each class in the official DocLayNet dataset (Caption, Footnote, - Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.)? - role: user - - content: |- - - Need to search. - - role: assistant - tool_calls: - - function: - arguments: '{"query":"DocLayNet dataset class distribution Caption Footnote Formula List‑item Page‑footer Page‑header - Picture Section‑header Table distribution","limit":5}' - name: search_and_answer - id: call_sa4rp6ua - type: function - - content: |- - [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1] - Source: "DocLayNet Class Labels" - Type: list_item - Content: - DocLayNet defines 11 distinct class labels for document layout analysis: - - Caption - Text describing figures or tables - - Footnote - Notes at the bottom of pages - - Formula - Mathematical expressions - - List-item - Items in bulleted or numbered lists - - Page-footer - Footer content on pages - - Page-header - Header content on pages - - Picture - Images and diagrams - - Section-header - Headings for document sections - - Table - Tabular data - role: tool - tool_call_id: call_sa4rp6ua - 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: - - '567' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: Only list labels, no distribution. Need another search. - role: assistant - tool_calls: - - function: - arguments: '{"limit":5,"query":"DocLayNet dataset statistics number of instances per class"}' - name: search_and_answer - id: call_87xsuib1 - index: 0 - type: function - created: 1769001207 - id: chatcmpl-100 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 49 - prompt_tokens: 869 - total_tokens: 918 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '128' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - encoding_format: base64 - input: - - DocLayNet dataset statistics number of 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: 12 - total_tokens: 12 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '4882' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are a search and question-answering specialist. - - Process: - 1. Call search_and_answer with relevant keywords from the question. - 2. Review the results ordered by relevance. - 3. If needed, perform follow-up searches with different keywords (max 3 total). - 4. Provide a concise answer based strictly on the retrieved content. - - The search tool returns results like: - [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] - Source: "Document Title" > Section > Subsection - Type: paragraph - Content: - The actual text content here... - - [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] - Source: "Another Document" - Type: table - Content: - | Column 1 | Column 2 | - ... - - Each result includes: - - chunk_id in brackets and rank position (rank 1 = most relevant) - - Source: document title and section hierarchy (when available) - - Type: content type like paragraph, table, code, list_item (when available) - - Content: the actual text - - Output format: - - query: Echo the question you are answering - - answer: Your concise answer based on the retrieved content - - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) - - confidence: A score from 0.0 to 1.0 indicating answer confidence - - IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. - - Guidelines: - - Base answers strictly on retrieved content - do not use external knowledge. - - Use the Source and Type metadata to understand context. - - If multiple results are relevant, synthesize them coherently. - - If information is insufficient, say so clearly. - - Be concise and direct; avoid meta commentary about the process. - - Results are ordered by relevance, with rank 1 being most relevant. - role: system - - content: What is the distribution of instances for each class in the official DocLayNet dataset (Caption, Footnote, - Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.)? - role: user - - content: |- - - Need to search. - - role: assistant - tool_calls: - - function: - arguments: '{"query":"DocLayNet dataset class distribution Caption Footnote Formula List‑item Page‑footer Page‑header - Picture Section‑header Table distribution","limit":5}' - name: search_and_answer - id: call_sa4rp6ua - type: function - - content: |- - [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1] - Source: "DocLayNet Class Labels" - Type: list_item - Content: - DocLayNet defines 11 distinct class labels for document layout analysis: - - Caption - Text describing figures or tables - - Footnote - Notes at the bottom of pages - - Formula - Mathematical expressions - - List-item - Items in bulleted or numbered lists - - Page-footer - Footer content on pages - - Page-header - Header content on pages - - Picture - Images and diagrams - - Section-header - Headings for document sections - - Table - Tabular data - role: tool - tool_call_id: call_sa4rp6ua - - content: |- - - Only list labels, no distribution. Need another search. - - role: assistant - tool_calls: - - function: - arguments: '{"limit":5,"query":"DocLayNet dataset statistics number of instances per class"}' - name: search_and_answer - id: call_87xsuib1 - type: function - - content: |- - [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1] - Source: "DocLayNet Class Labels" - Type: list_item - Content: - DocLayNet defines 11 distinct class labels for document layout analysis: - - Caption - Text describing figures or tables - - Footnote - Notes at the bottom of pages - - Formula - Mathematical expressions - - List-item - Items in bulleted or numbered lists - - Page-footer - Footer content on pages - - Page-header - Header content on pages - - Picture - Images and diagrams - - Section-header - Headings for document sections - - Table - Tabular data - role: tool - tool_call_id: call_87xsuib1 - 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: - - '484' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: stop - index: 0 - message: - content: I couldn't find the exact instance counts for each class in the official DocLayNet dataset. The available - public sources only list the 11 class labels but do not provide the distribution statistics. - role: assistant - created: 1769001209 - id: chatcmpl-968 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 41 - prompt_tokens: 1068 - total_tokens: 1109 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '5242' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are a search and question-answering specialist. - - Process: - 1. Call search_and_answer with relevant keywords from the question. - 2. Review the results ordered by relevance. - 3. If needed, perform follow-up searches with different keywords (max 3 total). - 4. Provide a concise answer based strictly on the retrieved content. - - The search tool returns results like: - [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] - Source: "Document Title" > Section > Subsection - Type: paragraph - Content: - The actual text content here... - - [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] - Source: "Another Document" - Type: table - Content: - | Column 1 | Column 2 | - ... - - Each result includes: - - chunk_id in brackets and rank position (rank 1 = most relevant) - - Source: document title and section hierarchy (when available) - - Type: content type like paragraph, table, code, list_item (when available) - - Content: the actual text - - Output format: - - query: Echo the question you are answering - - answer: Your concise answer based on the retrieved content - - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) - - confidence: A score from 0.0 to 1.0 indicating answer confidence - - IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. - - Guidelines: - - Base answers strictly on retrieved content - do not use external knowledge. - - Use the Source and Type metadata to understand context. - - If multiple results are relevant, synthesize them coherently. - - If information is insufficient, say so clearly. - - Be concise and direct; avoid meta commentary about the process. - - Results are ordered by relevance, with rank 1 being most relevant. - role: system - - content: What is the distribution of instances for each class in the official DocLayNet dataset (Caption, Footnote, - Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.)? - role: user - - content: |- - - Need to search. - - role: assistant - tool_calls: - - function: - arguments: '{"query":"DocLayNet dataset class distribution Caption Footnote Formula List‑item Page‑footer Page‑header - Picture Section‑header Table distribution","limit":5}' - name: search_and_answer - id: call_sa4rp6ua - type: function - - content: |- - [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1] - Source: "DocLayNet Class Labels" - Type: list_item - Content: - DocLayNet defines 11 distinct class labels for document layout analysis: - - Caption - Text describing figures or tables - - Footnote - Notes at the bottom of pages - - Formula - Mathematical expressions - - List-item - Items in bulleted or numbered lists - - Page-footer - Footer content on pages - - Page-header - Header content on pages - - Picture - Images and diagrams - - Section-header - Headings for document sections - - Table - Tabular data - role: tool - tool_call_id: call_sa4rp6ua - - content: |- - - Only list labels, no distribution. Need another search. - - role: assistant - tool_calls: - - function: - arguments: '{"limit":5,"query":"DocLayNet dataset statistics number of instances per class"}' - name: search_and_answer - id: call_87xsuib1 - type: function - - content: |- - [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1] - Source: "DocLayNet Class Labels" - Type: list_item - Content: - DocLayNet defines 11 distinct class labels for document layout analysis: - - Caption - Text describing figures or tables - - Footnote - Notes at the bottom of pages - - Formula - Mathematical expressions - - List-item - Items in bulleted or numbered lists - - Page-footer - Footer content on pages - - Page-header - Header content on pages - - Picture - Images and diagrams - - Section-header - Headings for document sections - - Table - Tabular data - role: tool - tool_call_id: call_87xsuib1 - - content: I couldn't find the exact instance counts for each class in the official DocLayNet dataset. The available - public sources only list the 11 class labels but do not provide the distribution statistics. - 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: - - '1318' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: We must output via final_result. Need to call final_result with answer. Use cited_chunks. We have only - two chunk IDs. But answer says distribution not found. We'll provide that. - role: assistant - tool_calls: - - function: - arguments: '{"query":"What is the distribution of instances for each class in the official DocLayNet dataset - (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.)?","answer":"The - publicly available information about DocLayNet only lists the 11 class labels (Caption, Footnote, Formula, - List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.). It does **not** provide the number - of instances or distribution statistics for each class in the dataset. Consequently, the exact distribution - of instances per class cannot be reported from the available sources.","cited_chunks":["52cb239c-ba6f-42cb-a8ae-8d06437b2c30"],"confidence":0.9}' - name: final_result - id: call_cijvm31f - index: 0 - type: function - created: 1769001214 - id: chatcmpl-714 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 225 - prompt_tokens: 1134 - total_tokens: 1359 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '2895' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are a search and question-answering specialist. - - Process: - 1. Call search_and_answer with relevant keywords from the question. - 2. Review the results ordered by relevance. - 3. If needed, perform follow-up searches with different keywords (max 3 total). - 4. Provide a concise answer based strictly on the retrieved content. - - The search tool returns results like: - [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] - Source: "Document Title" > Section > Subsection - Type: paragraph - Content: - The actual text content here... - - [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] - Source: "Another Document" - Type: table - Content: - | Column 1 | Column 2 | - ... - - Each result includes: - - chunk_id in brackets and rank position (rank 1 = most relevant) - - Source: document title and section hierarchy (when available) - - Type: content type like paragraph, table, code, list_item (when available) - - Content: the actual text - - Output format: - - query: Echo the question you are answering - - answer: Your concise answer based on the retrieved content - - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) - - confidence: A score from 0.0 to 1.0 indicating answer confidence - - IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. - - Guidelines: - - Base answers strictly on retrieved content - do not use external knowledge. - - Use the Source and Type metadata to understand context. - - If multiple results are relevant, synthesize them coherently. - - If information is insufficient, say so clearly. - - Be concise and direct; avoid meta commentary about the process. - - Results are ordered by relevance, with rank 1 being most relevant. - role: system - - content: Which class in DocLayNet has the highest instance count according to the dataset’s official statistics? - 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: - - '524' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: Need search. - role: assistant - tool_calls: - - function: - arguments: '{"query":"DocLayNet highest instance count class official statistics","limit":5}' - name: search_and_answer - id: call_p9zy6129 - index: 0 - type: function - created: 1769001215 - id: chatcmpl-915 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 40 - prompt_tokens: 634 - total_tokens: 674 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '128' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - encoding_format: base64 - input: - - DocLayNet highest instance count class official statistics - model: qwen3-embedding:4b - uri: http://localhost:11434/v1/embeddings - response: - headers: - content-type: - - application/json - transfer-encoding: - - chunked - parsed_body: - data: - - embedding: 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 - index: 0 - object: embedding - model: qwen3-embedding:4b - object: list - usage: - prompt_tokens: 11 - total_tokens: 11 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '3775' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are a search and question-answering specialist. - - Process: - 1. Call search_and_answer with relevant keywords from the question. - 2. Review the results ordered by relevance. - 3. If needed, perform follow-up searches with different keywords (max 3 total). - 4. Provide a concise answer based strictly on the retrieved content. - - The search tool returns results like: - [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] - Source: "Document Title" > Section > Subsection - Type: paragraph - Content: - The actual text content here... - - [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] - Source: "Another Document" - Type: table - Content: - | Column 1 | Column 2 | - ... - - Each result includes: - - chunk_id in brackets and rank position (rank 1 = most relevant) - - Source: document title and section hierarchy (when available) - - Type: content type like paragraph, table, code, list_item (when available) - - Content: the actual text - - Output format: - - query: Echo the question you are answering - - answer: Your concise answer based on the retrieved content - - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) - - confidence: A score from 0.0 to 1.0 indicating answer confidence - - IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. - - Guidelines: - - Base answers strictly on retrieved content - do not use external knowledge. - - Use the Source and Type metadata to understand context. - - If multiple results are relevant, synthesize them coherently. - - If information is insufficient, say so clearly. - - Be concise and direct; avoid meta commentary about the process. - - Results are ordered by relevance, with rank 1 being most relevant. - role: system - - content: Which class in DocLayNet has the highest instance count according to the dataset’s official statistics? - role: user - - content: |- - - Need search. - - role: assistant - tool_calls: - - function: - arguments: '{"query":"DocLayNet highest instance count class official statistics","limit":5}' - name: search_and_answer - id: call_p9zy6129 - type: function - - content: |- - [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1] - Source: "DocLayNet Class Labels" - Type: list_item - Content: - DocLayNet defines 11 distinct class labels for document layout analysis: - - Caption - Text describing figures or tables - - Footnote - Notes at the bottom of pages - - Formula - Mathematical expressions - - List-item - Items in bulleted or numbered lists - - Page-footer - Footer content on pages - - Page-header - Header content on pages - - Picture - Images and diagrams - - Section-header - Headings for document sections - - Table - Tabular data - role: tool - tool_call_id: call_p9zy6129 - 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: - - '605' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: We need highest instance count per official stats. The snippet doesn't include counts. Need further search. - role: assistant - tool_calls: - - function: - arguments: '{"limit":10,"query":"DocLayNet class distribution instance count"}' - name: search_and_answer - id: call_oc5dleh2 - index: 0 - type: function - created: 1769001216 - id: chatcmpl-761 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 54 - prompt_tokens: 824 - total_tokens: 878 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '113' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - encoding_format: base64 - input: - - DocLayNet class distribution instance 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: 9 - total_tokens: 9 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '4736' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are a search and question-answering specialist. - - Process: - 1. Call search_and_answer with relevant keywords from the question. - 2. Review the results ordered by relevance. - 3. If needed, perform follow-up searches with different keywords (max 3 total). - 4. Provide a concise answer based strictly on the retrieved content. - - The search tool returns results like: - [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] - Source: "Document Title" > Section > Subsection - Type: paragraph - Content: - The actual text content here... - - [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] - Source: "Another Document" - Type: table - Content: - | Column 1 | Column 2 | - ... - - Each result includes: - - chunk_id in brackets and rank position (rank 1 = most relevant) - - Source: document title and section hierarchy (when available) - - Type: content type like paragraph, table, code, list_item (when available) - - Content: the actual text - - Output format: - - query: Echo the question you are answering - - answer: Your concise answer based on the retrieved content - - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) - - confidence: A score from 0.0 to 1.0 indicating answer confidence - - IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. - - Guidelines: - - Base answers strictly on retrieved content - do not use external knowledge. - - Use the Source and Type metadata to understand context. - - If multiple results are relevant, synthesize them coherently. - - If information is insufficient, say so clearly. - - Be concise and direct; avoid meta commentary about the process. - - Results are ordered by relevance, with rank 1 being most relevant. - role: system - - content: Which class in DocLayNet has the highest instance count according to the dataset’s official statistics? - role: user - - content: |- - - Need search. - - role: assistant - tool_calls: - - function: - arguments: '{"query":"DocLayNet highest instance count class official statistics","limit":5}' - name: search_and_answer - id: call_p9zy6129 - type: function - - content: |- - [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1] - Source: "DocLayNet Class Labels" - Type: list_item - Content: - DocLayNet defines 11 distinct class labels for document layout analysis: - - Caption - Text describing figures or tables - - Footnote - Notes at the bottom of pages - - Formula - Mathematical expressions - - List-item - Items in bulleted or numbered lists - - Page-footer - Footer content on pages - - Page-header - Header content on pages - - Picture - Images and diagrams - - Section-header - Headings for document sections - - Table - Tabular data - role: tool - tool_call_id: call_p9zy6129 - - content: |- - - We need highest instance count per official stats. The snippet doesn't include counts. Need further search. - - role: assistant - tool_calls: - - function: - arguments: '{"limit":10,"query":"DocLayNet class distribution instance count"}' - name: search_and_answer - id: call_oc5dleh2 - type: function - - content: |- - [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1] - Source: "DocLayNet Class Labels" - Type: list_item - Content: - DocLayNet defines 11 distinct class labels for document layout analysis: - - Caption - Text describing figures or tables - - Footnote - Notes at the bottom of pages - - Formula - Mathematical expressions - - List-item - Items in bulleted or numbered lists - - Page-footer - Footer content on pages - - Page-header - Header content on pages - - Picture - Images and diagrams - - Section-header - Headings for document sections - - Table - Tabular data - role: tool - tool_call_id: call_oc5dleh2 - 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: - - '379' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: stop - index: 0 - message: - content: I couldn’t find the exact class with the highest instance count in the retrieved documents. - role: assistant - created: 1769001217 - id: chatcmpl-591 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 21 - prompt_tokens: 1028 - total_tokens: 1049 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '4991' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are a search and question-answering specialist. - - Process: - 1. Call search_and_answer with relevant keywords from the question. - 2. Review the results ordered by relevance. - 3. If needed, perform follow-up searches with different keywords (max 3 total). - 4. Provide a concise answer based strictly on the retrieved content. - - The search tool returns results like: - [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] - Source: "Document Title" > Section > Subsection - Type: paragraph - Content: - The actual text content here... - - [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] - Source: "Another Document" - Type: table - Content: - | Column 1 | Column 2 | - ... - - Each result includes: - - chunk_id in brackets and rank position (rank 1 = most relevant) - - Source: document title and section hierarchy (when available) - - Type: content type like paragraph, table, code, list_item (when available) - - Content: the actual text - - Output format: - - query: Echo the question you are answering - - answer: Your concise answer based on the retrieved content - - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) - - confidence: A score from 0.0 to 1.0 indicating answer confidence - - IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. - - Guidelines: - - Base answers strictly on retrieved content - do not use external knowledge. - - Use the Source and Type metadata to understand context. - - If multiple results are relevant, synthesize them coherently. - - If information is insufficient, say so clearly. - - Be concise and direct; avoid meta commentary about the process. - - Results are ordered by relevance, with rank 1 being most relevant. - role: system - - content: Which class in DocLayNet has the highest instance count according to the dataset’s official statistics? - role: user - - content: |- - - Need search. - - role: assistant - tool_calls: - - function: - arguments: '{"query":"DocLayNet highest instance count class official statistics","limit":5}' - name: search_and_answer - id: call_p9zy6129 - type: function - - content: |- - [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1] - Source: "DocLayNet Class Labels" - Type: list_item - Content: - DocLayNet defines 11 distinct class labels for document layout analysis: - - Caption - Text describing figures or tables - - Footnote - Notes at the bottom of pages - - Formula - Mathematical expressions - - List-item - Items in bulleted or numbered lists - - Page-footer - Footer content on pages - - Page-header - Header content on pages - - Picture - Images and diagrams - - Section-header - Headings for document sections - - Table - Tabular data - role: tool - tool_call_id: call_p9zy6129 - - content: |- - - We need highest instance count per official stats. The snippet doesn't include counts. Need further search. - - role: assistant - tool_calls: - - function: - arguments: '{"limit":10,"query":"DocLayNet class distribution instance count"}' - name: search_and_answer - id: call_oc5dleh2 - type: function - - content: |- - [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1] - Source: "DocLayNet Class Labels" - Type: list_item - Content: - DocLayNet defines 11 distinct class labels for document layout analysis: - - Caption - Text describing figures or tables - - Footnote - Notes at the bottom of pages - - Formula - Mathematical expressions - - List-item - Items in bulleted or numbered lists - - Page-footer - Footer content on pages - - Page-header - Header content on pages - - Picture - Images and diagrams - - Section-header - Headings for document sections - - Table - Tabular data - role: tool - tool_call_id: call_oc5dleh2 - - content: I couldn’t find the exact class with the highest instance count in the retrieved documents. - role: assistant - - content: |- - Validation feedback: - Please include your response in a tool call. - - Fix the errors and try again. - role: user - model: gpt-oss - reasoning_effort: low - stream: false - tool_choice: auto - tools: - - function: - description: Search the knowledge base for relevant documents. - name: search_and_answer - parameters: - additionalProperties: false - properties: - limit: - anyOf: - - type: integer - - type: 'null' - default: null - query: - type: string - required: - - query - type: object - type: function - - function: - description: Answer to a search query with chunk references. - name: final_result - parameters: - additionalProperties: false - properties: - answer: - description: The answer to the question - type: string - cited_chunks: - description: IDs of chunks used to form the answer - items: - type: string - type: array - confidence: - default: 1.0 - description: Confidence score for this answer (0-1) - maximum: 1.0 - minimum: 0.0 - type: number - query: - description: The question that was answered - type: string - required: - - query - - answer - type: object - type: function - uri: http://localhost:11434/v1/chat/completions - response: - headers: - content-length: - - '726' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: Need to use final_result tool. - role: assistant - tool_calls: - - function: - arguments: '{"answer":"I could not locate the specific class with the highest instance count in the documents - retrieved.","cited_chunks":[],"confidence":0.3,"query":"Which class in DocLayNet has the highest instance - count according to the dataset’s official statistics?"}' - name: final_result - id: call_pxj5e4dt - index: 0 - type: function - created: 1769001219 - id: chatcmpl-834 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 80 - prompt_tokens: 1074 - total_tokens: 1154 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '2904' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are a search and question-answering specialist. - - Process: - 1. Call search_and_answer with relevant keywords from the question. - 2. Review the results ordered by relevance. - 3. If needed, perform follow-up searches with different keywords (max 3 total). - 4. Provide a concise answer based strictly on the retrieved content. - - The search tool returns results like: - [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] - Source: "Document Title" > Section > Subsection - Type: paragraph - Content: - The actual text content here... - - [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] - Source: "Another Document" - Type: table - Content: - | Column 1 | Column 2 | - ... - - Each result includes: - - chunk_id in brackets and rank position (rank 1 = most relevant) - - Source: document title and section hierarchy (when available) - - Type: content type like paragraph, table, code, list_item (when available) - - Content: the actual text - - Output format: - - query: Echo the question you are answering - - answer: Your concise answer based on the retrieved content - - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) - - confidence: A score from 0.0 to 1.0 indicating answer confidence - - IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. - - Guidelines: - - Base answers strictly on retrieved content - do not use external knowledge. - - Use the Source and Type metadata to understand context. - - If multiple results are relevant, synthesize them coherently. - - If information is insufficient, say so clearly. - - Be concise and direct; avoid meta commentary about the process. - - Results are ordered by relevance, with rank 1 being most relevant. - role: system - - content: Does a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet? - role: user - model: gpt-oss - reasoning_effort: low - stream: false - tool_choice: auto - tools: - - function: - description: Search the knowledge base for relevant documents. - name: search_and_answer - parameters: - additionalProperties: false - properties: - limit: - anyOf: - - type: integer - - type: 'null' - default: null - query: - type: string - required: - - query - type: object - type: function - - function: - description: Answer to a search query with chunk references. - name: final_result - parameters: - additionalProperties: false - properties: - answer: - description: The answer to the question - type: string - cited_chunks: - description: IDs of chunks used to form the answer - items: - type: string - type: array - confidence: - default: 1.0 - description: Confidence score for this answer (0-1) - maximum: 1.0 - minimum: 0.0 - type: number - query: - description: The question that was answered - type: string - required: - - query - - answer - type: object - type: function - uri: http://localhost:11434/v1/chat/completions - response: - headers: - content-length: - - '572' - 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":3,"query":"DocLayNet most frequent class class most frequent DocLayNet peer-reviewed article - dataset documentation"}' - name: search_and_answer - id: call_9yxa0j5u - index: 0 - type: function - created: 1769001221 - id: chatcmpl-492 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 52 - prompt_tokens: 638 - total_tokens: 690 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '173' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - encoding_format: base64 - input: - - DocLayNet most frequent class class most frequent DocLayNet peer-reviewed article dataset documentation - 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: 20 - total_tokens: 20 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '3832' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are a search and question-answering specialist. - - Process: - 1. Call search_and_answer with relevant keywords from the question. - 2. Review the results ordered by relevance. - 3. If needed, perform follow-up searches with different keywords (max 3 total). - 4. Provide a concise answer based strictly on the retrieved content. - - The search tool returns results like: - [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] - Source: "Document Title" > Section > Subsection - Type: paragraph - Content: - The actual text content here... - - [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] - Source: "Another Document" - Type: table - Content: - | Column 1 | Column 2 | - ... - - Each result includes: - - chunk_id in brackets and rank position (rank 1 = most relevant) - - Source: document title and section hierarchy (when available) - - Type: content type like paragraph, table, code, list_item (when available) - - Content: the actual text - - Output format: - - query: Echo the question you are answering - - answer: Your concise answer based on the retrieved content - - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) - - confidence: A score from 0.0 to 1.0 indicating answer confidence - - IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. - - Guidelines: - - Base answers strictly on retrieved content - do not use external knowledge. - - Use the Source and Type metadata to understand context. - - If multiple results are relevant, synthesize them coherently. - - If information is insufficient, say so clearly. - - Be concise and direct; avoid meta commentary about the process. - - Results are ordered by relevance, with rank 1 being most relevant. - role: system - - content: Does a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet? - role: user - - content: |- - - Need to search. - - role: assistant - tool_calls: - - function: - arguments: '{"limit":3,"query":"DocLayNet most frequent class class most frequent DocLayNet peer-reviewed article - dataset documentation"}' - name: search_and_answer - id: call_9yxa0j5u - type: function - - content: |- - [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1] - Source: "DocLayNet Class Labels" - Type: list_item - Content: - DocLayNet defines 11 distinct class labels for document layout analysis: - - Caption - Text describing figures or tables - - Footnote - Notes at the bottom of pages - - Formula - Mathematical expressions - - List-item - Items in bulleted or numbered lists - - Page-footer - Footer content on pages - - Page-header - Header content on pages - - Picture - Images and diagrams - - Section-header - Headings for document sections - - Table - Tabular data - role: tool - tool_call_id: call_9yxa0j5u - 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: - - '866' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: stop - index: 0 - message: - content: |- - **query:** Does a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet? - - **answer:** No retrieved source provides evidence that any particular class is the most frequent in DocLayNet. The only document found lists the 11 class labels but does not report class frequencies. Therefore, a confirmation of a most‑frequent class cannot be given based on the available materials. - - **cited_chunks:** ["52cb239c-ba6f-42cb-a8ae-8d06437b2c30"] - - **confidence:** 0.1 - reasoning: No info about frequency. Probably not confirmed. - role: assistant - created: 1769001224 - id: chatcmpl-119 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 139 - prompt_tokens: 837 - total_tokens: 976 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '4583' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are a search and question-answering specialist. - - Process: - 1. Call search_and_answer with relevant keywords from the question. - 2. Review the results ordered by relevance. - 3. If needed, perform follow-up searches with different keywords (max 3 total). - 4. Provide a concise answer based strictly on the retrieved content. - - The search tool returns results like: - [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] - Source: "Document Title" > Section > Subsection - Type: paragraph - Content: - The actual text content here... - - [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] - Source: "Another Document" - Type: table - Content: - | Column 1 | Column 2 | - ... - - Each result includes: - - chunk_id in brackets and rank position (rank 1 = most relevant) - - Source: document title and section hierarchy (when available) - - Type: content type like paragraph, table, code, list_item (when available) - - Content: the actual text - - Output format: - - query: Echo the question you are answering - - answer: Your concise answer based on the retrieved content - - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) - - confidence: A score from 0.0 to 1.0 indicating answer confidence - - IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. - - Guidelines: - - Base answers strictly on retrieved content - do not use external knowledge. - - Use the Source and Type metadata to understand context. - - If multiple results are relevant, synthesize them coherently. - - If information is insufficient, say so clearly. - - Be concise and direct; avoid meta commentary about the process. - - Results are ordered by relevance, with rank 1 being most relevant. - role: system - - content: Does a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet? - role: user - - content: |- - - Need to search. - - role: assistant - tool_calls: - - function: - arguments: '{"limit":3,"query":"DocLayNet most frequent class class most frequent DocLayNet peer-reviewed article - dataset documentation"}' - name: search_and_answer - id: call_9yxa0j5u - type: function - - content: |- - [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1] - Source: "DocLayNet Class Labels" - Type: list_item - Content: - DocLayNet defines 11 distinct class labels for document layout analysis: - - Caption - Text describing figures or tables - - Footnote - Notes at the bottom of pages - - Formula - Mathematical expressions - - List-item - Items in bulleted or numbered lists - - Page-footer - Footer content on pages - - Page-header - Header content on pages - - Picture - Images and diagrams - - Section-header - Headings for document sections - - Table - Tabular data - role: tool - tool_call_id: call_9yxa0j5u - - content: |- - - No info about frequency. Probably not confirmed. - - - **query:** Does a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet? - - **answer:** No retrieved source provides evidence that any particular class is the most frequent in DocLayNet. The only document found lists the 11 class labels but does not report class frequencies. Therefore, a confirmation of a most‑frequent class cannot be given based on the available materials. - - **cited_chunks:** ["52cb239c-ba6f-42cb-a8ae-8d06437b2c30"] - - **confidence:** 0.1 - 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: - - '828' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: We need to output a tool call with final_result. - role: assistant - tool_calls: - - function: - arguments: '{"answer":"No source confirms a most‑frequent class in DocLayNet; the only available document lists - class labels but gives no frequency data.","cited_chunks":["52cb239c-ba6f-42cb-a8ae-8d06437b2c30"],"confidence":0.1,"query":"Does - a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet?"}' - name: final_result - id: call_tfxiz00q - index: 0 - type: function - created: 1769001227 - id: chatcmpl-26 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 120 - prompt_tokens: 1001 - total_tokens: 1121 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '3855' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - Generate a direct, conversational answer - to the question based on the gathered evidence. - - Output: - - answer: Direct, comprehensive answer with a natural, helpful tone. - Write the actual answer, not a description of what you found. - Use as many sentences as needed to fully address the question. - - confidence: Score from 0.0 to 1.0 indicating answer quality. - - Guidelines: - - Base your answer solely on the evidence provided in the context. - - If a section is provided, use it to frame your answer appropriately. - - Be thorough - include all relevant information from the evidence. - - Use formatting (bullet points, numbered lists) when it improves clarity. - - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..." - Instead, directly state the information. - - If the evidence is incomplete, acknowledge limitations briefly. - role: system - - content: |- - Answer the question based on the gathered evidence. - - - What is the highest count class in the DocLayNet dataset? - - - What is the distribution of instances for each class in the official DocLayNet dataset (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.)? - The publicly available information about DocLayNet only lists the 11 class labels (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.). It does **not** provide the number of instances or distribution statistics for each class in the dataset. Consequently, the exact distribution of instances per class cannot be reported from the available sources. - 0.9 - DocLayNet Class Labels - - - Which class in DocLayNet has the highest instance count according to the dataset’s official statistics? - I could not locate the specific class with the highest instance count in the documents retrieved. - 0.3 - null - - - Does a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet? - No source confirms a most‑frequent class in DocLayNet; the only available document lists class labels but gives no frequency data. - 0.1 - DocLayNet Class Labels - - - - 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: - - '980' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: 'Need answer: highest count class in DocLayNet dataset. Evidence shows unknown, no source. So answer: - not determinable.' - role: assistant - tool_calls: - - function: - arguments: '{"answer":"Based on the available public documentation, the DocLayNet dataset does not provide explicit - counts or a distribution for each of its 11 classes. Therefore, there is no published information that identifies - which class has the highest instance count. Without official statistics or a peer‑reviewed source detailing - the class frequencies, the most‑frequent class cannot be determined.","citations":[],"confidence":0.2}' - name: final_result - id: call_5zz8t62w - index: 0 - type: function - created: 1769001230 - id: chatcmpl-712 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 128 - prompt_tokens: 713 - total_tokens: 841 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '4820' - 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":null,"question":"What is the highest count class in the DocLayNet dataset?"}' - name: ask - id: call_f61rh3p7 - type: function - - content: |- - Based on the available public documentation, the DocLayNet dataset does not provide explicit counts or a distribution for each of its 11 classes. Therefore, there is no published information that identifies which class has the highest instance count. Without official statistics or a peer‑reviewed source detailing the class frequencies, the most‑frequent class cannot be determined. - - Sources: [1] - role: tool - tool_call_id: call_f61rh3p7 - 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: - - '553' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: stop - index: 0 - message: - content: I couldn’t find an official ranking of class frequencies for the DocLayNet dataset. The publicly available - documentation lists the 11 classes but does not provide instance‑count statistics, so it’s not possible to say - which one is the most frequent at this time. - role: assistant - created: 1769001233 - id: chatcmpl-964 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 56 - prompt_tokens: 984 - total_tokens: 1040 - status: - code: 200 - message: OK -version: 1 diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_search_with_state_key.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_search_with_state_key.yaml deleted file mode 100644 index 56293451..00000000 --- a/tests/cassettes/test_chat_agent/test_chat_agent_search_with_state_key.yaml +++ /dev/null @@ -1,817 +0,0 @@ -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: qY2fuQjLtrsJ+4e8mk4CPS45KLrdsDU9KsTSOwnCwTphJTM707mGPLojqjxcAgw9M2DmO4RHTTy2AUi80PdSvRwlQzyU/LA8ewWAvOJwcrpjEWq7q1JJPHqfcz0PWEI8tHAMvTlXFb3r+oK8yJiTvPrR8bwrhFs95/fNPGqcR713ex88hI2yO9fM/blNuIu7dA/2Opf1yTpDkcK8WrZSPLzPsDwLs4q8cjd3u0YVXjyeFNI7yhsZPbqPVzxT9D+83JEMve/APbyba3E7jeQ1PB1Clr19SH28ww7nPDvpw7uQaPY8SG4cOw6YT7s849Y86b+HOyYQLryONxu8leEmvNhsaryO60G8bh36OfHD7TyvH/o7D9tUvMoDtroHAME8D8VQu7mfXjznCqw7tsORvHpiDrybTYM8xVzMO+uAnTuY+G08yE4nuwbjlzqg9Qk9yHBhOxLYhzp48Ks8/jepO1TDwLzZ7Iw8A6avOwVYobweOSm86qSWO1MYEbvWo6M8VxVqvC2rpbxnBAq88rDpO7fUbLylQJ68PRfHPNUk/7rAeAA9fn2gvP1uAby95Dm8zWb6O8D7ZzoznBa8QwqOvJp1g7yIHnI8G41pPDly2Lw70wQ8rMNiuybEmzvEHVI8FSHQu48MgzyOrkm8gLqRPMvGijv2SQ69HUqju6ecb7yy6vY8F2wgPGdmdzxLtA294wnJO0wexrygwRa8dHz1u7SFj7zoihw8R3CVPEwlizzRxZG8qhhxOtf6CbvbGec8HyM5Owc7G72g7pM7Zd+AvBGZkTqtqAi8ZRUxPNDSQDzzZhQ85rX9uzIr6DvLmLs84j2gvDfBJDom6qa7ZezIO1L4g7veEqY8JXXfPKWa9DuNmJU7Ru9rPBIhpDxfeLK7cuP0umfB3bxvFTA8bV3jvK/gLznZOay8Bx7ZvL8QNzsuRBe8dFcKPC8X97uCeW48JW/COlcdLT0CLtg52+Gxu+ByjDw5evy7WIQIu/aJhzzVIse7L7q4OqzehLsmu2G8AIh5vNrCyDykcsa6zjtVu0SyoLo5EiY9vLooPDHGbjxyYCk7xKSOO6pRxjwxn0W8YWJDvHj5xzkNlCk8Iy1AvLJVfDyYRuK7VFiSvGUEybv9q6W7DPrEvH02WjzQPx48tRNtvP37HrxwdbY8AWdNPHEWeTwInMo7pApvu8HjrjwXOje7HNaRO0zr8juynRy5R691vNLIUbz1zKA8VlLTuwJk0bpG7i27BMQ8PHKFljz0Z5I7MIabO6cQvTxrZ7C8oZukPKFJZTvQHjO7RQqxO0BO2LtaIxK89fuqPIjW67zUg4G8POOjvCWzcDv/VJY7MbBIPKdBiLw/Uhq9vwa8uvfg/7tTDYS7qhYyPIu5Ibx1iOa7TmyCPL+Sj7yZrTi8u+nYOcUMm7pWUow7Z8FavGedSjsuDfk7RtqMvPcaszwWXVQ8uWudu0rx7zzU0Li8fBpbu3uzxDw/UQ+5ofCyPMnyurwY16K7JC/UvKSof7xMTzS8rIS6PKhiQz03Rnu7cGrIvCxvgjvyeKs8bFojO8Zx8jvmEYm8SMckvIRAy7rJElA8wFOWO74+Wrz4Nzm7sreAvKZF3rv/O1U828xXO6Hgl7wE99s8TduGPEjP+rpITTY9CSfKPA5HJbyOOoQ8KK2HO7NFvzy9tU48lFspvFjJhju0Ido7ZGYSvLaejrtq93i8pqWQvQufGb1dUSc8OZNQPGg6Gj2eXzo8k3NMPdJlijxJXmm8hwikO07t1DxO3YK7kP4hPCW/FLzXwCY5I47lu2uUnjygLHa8G2TDu7wEgbxhKbA8hw3GPDsXCL0Jevu8sbcSvHVyZb0S02g8KFlgPMSDBDyMSIm8kemqu/CHj7vue4A7Gln8PDLYhbucTtq8gzG+u5i9yjzLGQq8SbLBvLg3H7ywJpc8JjOduxS4Cby0J1O80SAuvC+q1jzOZCq85dvmvGk/yTwCY9M8oUkLPRjZqrs+NRq8eH++vHQ0hbzld248nUYVPR9mmTtvqry7r/lzPBs70zoWaSQ7cVXWvHjR1rvubEW8GOcAvaUjFD1sbRW8H5eqPM3hpbtIexk868xmu/uzKbxMnGA9CwOcO6El+Dx2Pqc8d5gOvYOy0ryuFZW7jrRqu8mA07z2nwk8rvYZvUgMm7zKv9W7OFaLvEwCOzzYEWO8MoiXvLGtDz1snC+8SH/evM+jjDwWrxC8dBLAuljSTLvl/Ag7xldzvMj9WbxEVZY83QqROjZ4YrvvhPs6YIcTPYgZubt+7IO8QSvLvAo4BL05AgA9P0YcO4szUD2o0aW7E+dLvHg6/bvet9a6tOP9OzDYc7w9+Yg7YOVfvMu+pbyg6ga7WSDzvFW6XLoa6/m7YThDPLds9bqKHsG8/A+cvDU3PLzlEI28iqSMvOttojuaov87keyvO34ZHjzwj0O9GIaqPAwqgL1EzN483WqkvLpuMLyIZzo7bbhyuzCshDx9sOy8tJJAOxkSZLw9vjq8FNm+Ozi33Ls3Xhi6fAusPJfterr5VBY8IwGqu+bEejzPgzu8qPAIOPCwubztzwQ9jTBRvDPknDzQi2886v4MPEFyRLsSpg+9VdbXOinBOjyeEj29JqZQvMHcg7zYuQK7sLYIPejnWbpKuWO8Ak7fO4pmXbvXBvu8LryovCYDA71U98G7YusBvEqGJbzT2+081n9iPB+Sc7xUkfI862rPPPjezTznhJG8MN2SOjfecDzIjJu6NYEyujzvm7y22Z+8tt7KPDK8E7zFa/m5WZxRvJiZjjtDKUg9HZkcPPBTUzwPb+c6xk31uqhTITzS/Ds8V4EQOwrDoDxhQKs7FXIAvARsV7uy37u8GNSfO6ht2zybJxM9fpftO6H6DDx+Dua7au0NPNT/ijt0LJE88Z7RPCVQXrxK4qi856DCvLq6fDxuUJG84ax1u62FFL0arjY9YsqYvEcBEL0XxQw9o3iDvF9Ij7juPcG8JharO1aNlzyxAvY7CQfPO3LhmDysozw70fQSO1nsr7yMbJm8E92NuyIDkLxmeww8XxXuvH1XGzx9B+k8lNbUu1FupjuLf9a7wQRMOusMILn+mZ68h1MqO5eD0DwsNkM8bnk4O3sBDTtyXQc9S9qoPHJn0bzVGmK8u/mnPPLwdryapCY8vtaUvFaY8Ds1vc08NF9CvXXh3jp7f7k7eoaMPEu7uLwD7HG8xYlFO6K+gzsTaVA8flGlPF9pXLw7c146F5DNPKYpGbxJqYw8lZXmO6WK3TyDMhc9C+YfvWwFybxGOzi8hlqAvIejPT1971e7P6WhO9xzy7ltoRM7OQkKvF7AiLwd2oS8JiUSvbaWeztGx4875HkyvZjHSDklDMM8skytPDJz0Lwv5pY7Kn1QvY6WvzxWh8+6xz3yOibxtzz+Tj48Ylk0O0uRNDyJSXG8LqMnujM7TDsOJwe99M4OO8AZ7LtoWES7KoACPQoeKzssG5s8/qYkvV+NXrucIJQ8JlJyPNIEqLw73Z88n0kjPM+hITw6ieu7IdKHOpk1bDzgbjk8f9hFvJC5GLzpp0s7fI8tvTW9ibxBreQ81dyHvDdznjqGKr88ks5wOjwxHLzJssM7AmuiPN45ojwEO+g8dItwPJvDQ7zAP/o7l6g6PHSeS7vL+p08QaGwO78AuLvqhbM8XN83PIrpFb1Gn8m8Iq91PMKOSjwRaac8v5eWPLmJnbvbnvi8MBkdvaWeF7wRyUe8TJHSvMLNK70EGau8lb2YvEUMAz02oxy8GSqtPPSEtDvLNIY7KAQlvIpxkLzZdha6rzrNvFsfK7wQaUw6EY4GPdo1PLxGtB67fpmmvN2WjbzU1NW762OYurWtCTxlR6a8Dp8BPKIuNjwqa/67AjSJPePqgrwqWtu76pFuvP63hbyObzm9PXPXO8OFAT0d4ri8u9pBvMIJujw3Eig97rsnvG1QfjthUwg8iW5kPMrUQjzXunO98ocjPCGYmjzGKj28pG6oPLUvHzsh+Jm88b2Nu6SiEjyseJI8y2D2PFIOlrwwuHU8U82mu15O7Lx7BgW8Mi/VO2AVpbyl2687tCOKuVRHGTzIwwM6ez/OvIG/QTwsIIS8q1n8OiHqmDyWdf48bo2UPTH4CLxIaR88Z14HvFnU4zwTURm9KH+ZvEQBg7vVdFu8q6i0O85Cqjt3pyE8KTKdOyOnpbzzm8A6stWEPOQBbbxgDwS8uFq1vLO0Bj1vDHw8st2UvGkjOryINHK8mbsPva5BIrwx2b07+rdUvPSqCT3dIw689DowvUq6hzzE+Fs9DxyJPM0yizyWFR+8VQOCvGNdozzujMk7I2AGPZWZFLz7UmS8A3AnPb9aD7z9Ly28enKKO2sugDz6I6e8EZvgusrN/TuMxvm8xvLUPIC+Zzyuc6e8+0EnPd38UjzyQy07He4/PPWV6bwkaoM804XBPE1SDDwSKxW8EfnjvNoJDLz6xl29PBAuvME6xbw/wC87OsivOzg8ljuTo5g6M6HdvKN92zxYSZa56ObyO/Qmyzu+eaA92R0fupkgL7yFfFW8xQ6TPGlemDwJgq+74AV9OyqjirwVWdK6OtYlPVIncry01ey7X7nZvL7g17u9akw82CX3unzaFryCsO+8l71MuwqnILoWuxy7QdQ4PJLdaj22DfI80eADvTJlNTyIMBu8YYnJu1Y9Dz1ja+s7MFRhPB5LhT3nqfu6a41ePK4ISDvgmES8crPKuz88arsd6aw7JCovvKMJvrygNjG87iWkvBiPorvSr8W8kjbEvCa8rDsMnvK7u4n1vK8QwjznIdc7fLrNPGoG7zzKXzW8VgGUuuqVQ7wUbM88AFoTvSiAGb2EGwe8ROkTvPwGxDw0oGq7wncIvI0TCDy9dHi8WMYxvbobiLwr0LO8s5CbOfKA/LwPRKM7PB8FvIUumzwsgDU8gZ4ruq9aqbw4pSk7TC+xO7wo6LuqJcY7ZWcePB2uCzxtO788EHJlvIRA0TspDu48lMCXPIrLi7lCugM9Xo9HO7+bB7ybGAA7o2mEvBmviztf3vS8r7ZXPISKJr0Qzbw8iMCPvDk+DTnmGs08oEevuwDRQrt6gro8l623OxlHgbhEduY88lKwu+FS8TsuUs47z3+EvEJljzo4LWE7HmoxPWf8ELz2RDG9zkWdvERgELztZnE6lk8VvUw5urx1+by8vOZPOowlkTxEydQ8AfRIPKPPz7o0j0A8PmdkvHNfBTwRibE8K5twPAbynryXE+U8uK6lPLJDGjx76sA7rhzKvHkJTDuhAFc82xGVvBQ+GDysnU08EjZOu4KCFT28TTI9kHM5uhIrMrxZO68786S4u+/JZryyug48WgqLvGTvQrzOkF0994pqPCFySLxlmBu8VNYGvL/LeLxVnZS8vWmfvE9J8bx9rb676twtPO+JojtsHDA9h3CnvDlI9rzsT6e8GwIovPYilTkz3Vi7E3zQurFeRbtwEDw8A5qYvNBqOrvGZQE8W5uYvWwQIrkdhE08Rco9u67K+btq/Cw6u1E5vBlNrzpXFSI8/pkpPTkCgrw9OHi86hDeuiWLizwH0IO8YM2wvBO5CbwI1Ac8w2U8PHYFDrw8G0E8z+kPO1i9Jr3QF9g8orgavFQ76DvWxDq83tHOOy1pX7wRHES7iWCZvJbiCb3CrJC7IjklvWphhbxVdaI5lLwkO4EcjbzLEXm7bKyvO9UbHrxNdj+7TGg/PAwa8LxrJBa98I7QvHiJSLzcieS7FVuyvDFkKrw+pso6WLWqO56Z9LyAccQ8r3M+PcraGj0Vrf+8TkULPTljFT1IGvU7rsomPHOufjwMF/e4QoW9vIF7urw+eOA8lZ0BvarWXrs/wza8vvwavLVDFb1wE6k7He3ivB5jOrwq2da71mGRumTwUrsVyOg8PqZXvSHxEbxmwLe7RTrTOgt4c7xaWjI7f4JBvaIDOL3I/je8pUuJvGIDFT0o0aI8maHLu8VrCz2TLYw85oYxvLxhezyuj0I8zpQOPERqbDyBHxi9D3/kvEEKFjx/bEe8ueGxPP0m7TwO9BO91g0XPCYRNTwGTw+6EoDqO3nmaLykgQs97DblvEaHOD1PoSs6bDanvGjCHT3kzsO82+opPY6SRzwOHlq7n2hGPKrVkjx+3cE8tQcBPMtOnzzG8QQ9kgtxPKvWrjsnGCc8v+bgPH/mULwlxZy8kEopvHImq7vRsoK74BXtvGd22Dv0+js8ThhgO36ivrxoiR08fpKeutg2J7zN02q7xuRqPFpSh7yQtkq8kzrquw5QEbxU4wU8rAETPZNVwLxPSPy8B/POO70PAT26Evw7KwyUvBJlojzySY88OPVbPOEia7s8mw29XpcfPIskZzuOERA9ro9VPHRbFb2/U7G8USoNPP1YS7w9VJw8BkPDvOFuDL1uhqg8L6UcO4KwRjzjDWs7FUT0POzbhDsS+5k8shc/vEdCrTz3f+47L0COPAg88DypI5u7h20zPI69CLl7AS69451pvK8CtDxPC6y7cMJuPEm2dL31AxI8qLIFPd6SgDxaVT48N5rQPGEhvDyGNwA7/U+TvHHLWzxkDAI9ksDPvG8lqrxBIeC7j/ySu70vazyGP247XCgEO8JumLvuMmw90AzmO193pbywTC09qEaCu6h+nDzu3fi7/SNuPbMapryRXQg940youg8/kbvIjiO77LT5u6nW0bwbfYw9h7rovLJxfjuPdiK9ZhadPJuI9jzcqEW8MxwJPfioQjtx/L28LZOEvJP7grya/2C8GWBkPCQPSTwrrRA7jMDRu9bzT7xxJT88vbamPFQwp7y/FxM9EHuAvADfiLq91xk8/lE7PAohFLx5UY67bAkpPZSgzbyS0H29Jl7UO36/JbwCZSO9fI/jvEdc0bwVW4G8il+oO4ncq7v0jtI7C7dOPMg6dr21xKI8QM8Vu7UntDy5hVM81gvgvGj4LD1fN3O8+eb6PHkTljx5E+87uxqxvEuGbzw6lu06Kf3BurIICL1BOge7/FUZvFEK1bxZL5g8f5Pcu+zfwTx408S7C4LYuxJfZLvTRBi8Bw33vB6iJTvusQu8Y+uvvCSn3jsypvQ8enWFvOe+kjwWT9W7l64QPMjmgzusB2I8vk7/PM8KqTxdCBu8wy3LvOxBpLv/vVG8DD4/vXtfjLzrS4u8ye63u6uAtTt+rTo7EVkGPb48XTv21UE77I3wPAolOT3R4wU9ASeOPIboAjquEQi7wHhyvJj5fj0V5vy7WpdHPZ8Nvrus4cE7vnqpOzpZBD0OoIM80PpAvBfhrbtXhRU9XnM1PB54Db1z7Vu8jPq3u82CkrxUjMy7CBY9OyAQA72H0IY8b3WvPMCmnjytyHk8QVO+O80HMT1mkKg8jU+PvLSmojyPeem8E3x7O61bF70S2z08QTm9vCz0uzz3q8k8tTifvAyDFDwK+Rq76gKVPK9YJ7yhng69ad+Pu+TS+rzjsRK9kVmiuwy7sLxN1X08HfCvPPFjxTs4Rpm8GgYQvf498DwT4cS89pWru8xBsbw/8/+8i9lSPDtO2ryFQ9U7sYOevDoesDlxypG7XC8KvUhi1zsEIiY77uNkOSFX7TyS+dW7+/UevAk1RLuE76y8PNfovHFGqruYd407H+K0uSfkjDyyQQK8CBGzO2ckozw0XJa7wm9GuzcWKrtRyRk8av2muxnw5rsFwZW8JY5XvHm187vb3AG6sd6kvK/4nzz1sbI8bGOjPKiyUbxpUQi801tMPFYb9jr8ova7cgQ5PaW9N7yCsCk7NpbZPHAlhLyCO8w76MD9O59keruieC69tn6JO+0ZtDxRGoQ8Y89WPGXo2zwkAKS70UXCO9i7Brxvfm+6gs/2vNpTQb2+XSE80xzJvB1bvTsNrIo8rJsAPFO4kbwX/pw5JFOQPOQPbTwu68s66bytPJBsk7wq4fs7ARAePHgOHbzIbkE8gCYHPQ6rejxYYIE8NKakvB2PTLxjeAS9QXFkvJ10v7y+9CC972bYOxBcGTyCJ6G8F4ECPfeUXLvf9LE7LqgDvGGFZrsBymo8Zvq1PM/zMzzY4Eq83EYvu10NY7wbdgw5BpSAvKZq9zvVrpA8c27fvJBOJDxwWiG9X/PQPOf0L7xhtN881xyLvDjHD7t4Zwc8s9nqPNhdNTzY4QO8zR3FOyp73byutEs8uBvTue+VUrtIdQQ9mKWau3sMSDy8Ria7M74IPc3ZnrweYWW8KYnzuzDeNz2Arzu7Qk9qvMqHPzwugJu7pyegvB945jydVF28891wvAmTgrzjhxQ8a4XyPBoGljxZayS8DiUPPBJfRT2fmEa9bJzSu+zZFTwETU08S+OYPLOKirzEwgS6vExnvK6+PLzvh4g7IPImPJKBLj0giB+7szI4u3pILDxdMA08eT2JPLvxEbyB9g49QXiEvJIdUbxU9mK8DEYYPLm7TDxzPb48LcD1PIgYhLwy9IG80iI/vKxQ7bx+8NG8nf+1PKhwtLxVVBQ8H/HkvB7HUjwOLNc8AJYbPN7WWryZb+a7BeUovPCaDD1TM9y7B8MYO6iaXDufTHG6RdAUOxmDyLxUN5O8qTBeO6syyTxS9S88MCg6vJ417rz/nlO8fbzyO2ZT3TsdhvQ48a49PMLe6jx0FpI8PinouwjaEjx/ZQs8+eC6PExCLzwmhdQ8wASpO7ONCD0x1ta8zvpzu8u7nzwHliy8u8E8vDNIfLzW2sU8aerIPE71h7yayYw8UznuO+yOvzyeBBa98lIDu1+SsLwl4ss8hUS0PAeDJjyeTBW8bkw5vOw6R7w7a6A8W6yjO5M7oDxReUo8QZepupkuNjvocxA8MMBkvK/QCDytJqG8vvRXO1x1Bj1cSku8cmdCPFmeU7zhLA+8/RrmvAS7njyxAPy7arPhvOIgDjyjRl67gOjxPBhMhrs+F2688dxyvEKJlbsmnoq8vK68vLQgEL0FAfA5vrLVPN3lY7xX4lq9vC8hvHtZH7w9+M67WOGBPCeRqDw/O5U8hQTIuy/cFzx9Ngm7L9WlOWLvJT1cEei85W/wO8uj0LwMfSY8wOwSPGLk/jvdbN+8ZT7gPGwWa7wS3/e8dzcpPMs4pLv861+8zogBPDhZJTwkn8C7h9/2O8yisTzruqo86Yg3vLTnSTxZcJM8LEGVPMUzxTwcqcM8YQQLvAeheDxI+J27icKMPLj2JT0xS348hl8GvRZC/bz2YYI7ESagPNctsrw23RI8DXLkO8v5NTx9Lbk7DAqSvCSU+7pB7/y8UxVdvA4h97tv5Kq8aMONuW44YDujqJ6807y4vM74nzusm1g97n19vJ4SYLwPEhW8o0zeOls8oDpWqai8Skmsu6DJ2Dw2bqg8U9DPuzvMnjtlnIM81oNbvLWJ57tz2GM8dDXfuqeFF70KQyu9wTXgOr7k6DvND8i8sKalvEf4GT20mXo7xqdDPXk9lbnVMis8MnFnvCMrkbtF7o87eDGzvIL4Ab3ja4U8Cnk5vSYUorzys8y7eDhUvCqI1budw827i9tvPAx7jjtkGC+9AZWqPC/okTzCNWS7SpLAvGVvazxZDwW9tCwfPM1iZ7yejk+8+G2ROyql4jt96N06UQjfu6gaJTxu2H07Z0AavRWYFr0SieC8lYjtO9ndoTuaqwy75nsXvBviyLo/eb+7sFCKPIZMs7yTS0y7pJKZPOH8pjuEAVC8oEaaO93Xh7w3O208ckoavIxUJz0wmKc7hTGlPKysUbsMzeE7MjNSvIqNVrqh6UO9A3HOvERFfLztBQS9H7INO0qlrbwzUww9alY+POPKQ7voJEC9+VwkvFzSn7xtXqi8HCzOuo7/LD2OeKW8GVJuvUlQvjwFi8c8Q5YLvBteArux8a67LIyBuxSTDD33xiu8kvuHPIVu+jqLB5q8xS6SvL4zo7yUTQQ8wWcQu0xn0Dya/S+8ZlpWPBpigbuOWk+8EaSQO9+vybxB6xU7cPKlvMArVLyPSEE8HbT5PEmkizyRKfM6AJyEPGg2/jr9+1Y8b+e5PGIwtLwNZ408gecpPL5cvjphZYE7J6IRPQI8wjwat1y8bF/Lu+OBe7xAoqQ8jtmPOQiQlLtqJDu9NuIUvT4pCTu98DU7hUaCu4mNjrqsHe28fbscvR4D2TxRkBa9OTlsPOw/uDwneQQ9snqUvJlC77ruLQc9BTcBvV2jMDy9gmA8EumSu7CVFT2oPfQ7guW4PPCKP70twL68Sm4nu2fc+DyXn9W8n1M4PEV2Fz2PyGG8hAcgOVhlqTwKmro7CHPxvC/79bs9jAa9y46IO6YJz7wIB7G8O2txPDpWhzypqJM8bRDQPPtvA7vPv468h9RZPP48ijx0KAO8JQbAPKKm8rxpYpC8vOm7uw5Z4js6md47M6kXvDmADjxM4Ia8uj7kO7sikbxTa8S8C02jvLlr3bw78X28EPYmPcuUQDwugLQ6CDq2vFEqkTyXFAS9zwXFO9EMqzyozoG8d955uR2Zurws7we7riQeO0ZQBzzG+Qc8WJiSPDcuiLw5JXY8LLL+OhmcFTxfM2S8V2GDPIHG5rzyviA7MgLQvOUEKbyK2Jq8z34yPeTrzry8iSM5lKbhO9YUw7zZcAg9XhnnvNTHw7x7+RE8KDK0PA6bMT2ZrLc8bfU2PMaqC7vbsaO8SWiSvAeA3zwmix+8feUlu7y2qzv6D208gpCZPL9oAb0LSUe73JzdOwFtk7wU0FM8EP6rvOgixLuIEzm8b7VNPO9GljyJkhi8gqgovG8Zc7y7/GW8KHnkPGEKsTzEnIG8FfH4vDzIsDtKCpy8L6QEvW0iAbuVAvS8ZPSpOzLMG70PkFY9WAVxPFkKH7vTYI28g7lNvDwl4zxZrz08vI7XPKInhLujck48t4hpO+odnzoJ9La7qeyeu4X9JLyKVCu8pnz3u4IJjTzztm4865mOvALvjzx9ws2863rPu8lmZ7zCg0A8fFChO9UhjDy2dS29vBFcPK7KlTxYRHw6A2NkPONN3byCuuy8uWkgvKmR1zxQWb48fagCvHWnJzxV1wG7XrM7vHvs37u5dRQ9dqoTPKZgsDueOkQ8G4vFOwYc97yCziG8xNwYPGcm4Ty/iRw9R9PMu63Mrbxxuq86xYpSvDy7Ab1CxjY9bWi/u9esSTsj7Qc9RCgWPX+NEDvXkbA7wobSPO9YuTyC47W8z5ubvIIigrxaAAk9uHgyPK5dKL3jyIS7n0NsO4ZHBDxP4ig88LAgPN0PM7y48/y82ESGPLVP1bxiSYw5Ky2vOwEgiruIEFe8Zs5DPN8NU7rf+ag67qM+OszPJDyPuWQ8CGETPcWlhruuICK8CknausPZG7zRKJe8YHjtvDa/Lr3ThV88t1hDvKll5btbSnS84jD1PFue+zx7L7K8d2R4OxnuLzrBKeg6COucuqV9FTtBJ/i6bjgVvNX+5zt3vio9cQbaPDb3iDsMDTo8+Iz6vBX5K7w/hdC7tZphvAL9kLw9vuc8aTjLu0ogqbxVZgk8CbYDPZTDSzzpCV87+nQ3vClDCDymYRW7Si+gvMP0yrzvvYm8d86YOv9e07uG2B68fHwvPMQ2szqgmyU7mhnbvGGZszyBp5m8PiazPBZL9TwahFk7j2+dNkKyKrprlJ08c/ocvUzO1LziBqC7piTrvCvLiLwytrw8WQwYPCPodDxg/W45fG8/vb9F8Twhzse7rI5DvJj44byFFAu9gPSOvJZbrLzVYRO8Qb1YO8piBTpQnie7LK4gvPOtwDxXTvA74pUUPa0UvDyj1bC77wIoPJgQ+bwpzQ88kvEYux7sITzvmNk8aIKqO4Pt7rwnhYM8myeBPD3IfDyrW7a5YdozvGIQYzwg8Ck7Zmf5OyOIqjy+b/Q7NsWWvHetVjxnxMu7BwIKPacxGjxmnOg8NGSNvISJKL1z2XU7LPwvu/Fcjzz0jfW89NZTPckz9bza1+27kaFyuky7Vzy9fyC8NsWuOia4yjrqTQW8WuuUuxE1Dr2Anzu83heyvJVBHTynmDI8uw50PNSkdTyXv5s8P7JAO9ul2DyU95W7qh2gvMjcxzzyhZA8shchPPsQHT198LW8S5WWujaZJ7s7dme7a+5rPMqCDb3DAyu9vlrnPOAfNLxiIAW9DrlLOxEEEb33GJc7WzoPPbGZpLvjvp48+KgrPbuxbLy55xO9b2/zOBD8/Lt5CnI7uJK2PGBk47zFUjg9rydBO9f0RjwvalQ6kmfDuwPG+zxuJwe9rfD2ug48iDzeEIa6Jmr7vAh/FL3aZZ68VfcCPeWypzxsTHe8N0QgPO1bkLxJbIY8rtEQO+VlAzuBIds889CDuh8lt7wa35G8znTDO8xWITxrFv+8rNjTu0uUE7zdPgQ7/FAFvdrU1zt1x2i8FqzCvDi66LzwsCM94hlSu2hG2bvpvRi9tZsXvD2UFzz35Mu6+Qw3PIJI4ju3EtO8bEB3vMdWjrlXBkY8keI6PdCmhbwXA5u8J/cVvBV2ErzyN8I8u2GYvA51Db1AbpE7BJUcvBWNT7xVro06HI2JuzwuMTxC77y8r5kwvB+AuzwrkII84uOpOjO5j7vSrSG8v6bKPMo43TxydHc8RiGdPGtDa7vp2XU8cKUPPHYa4bs5mFw7QXrDu0KGuzx5Thy9S7K1PHEyKTuM9ue8UawhPCCV7bqy31M9Q8SROzrcZTzna2c8zH2Wu/zNsryZR4c8GuAKvMiKvDy1flE75CCxOiCZkbnPYQY9AlvwOkCB9Ly8lvI6PR3yPEHkm7sp0GQ8Rr6BPN26gbwF0yi8KN8XPBZnhLxPDz88uCkbu0RR2rs5JLk7AIFFPHxwJLoK/w08mFxSvJgPXrwpVzU8JDb/vGt5B7xeuBu8KZCOu4lfujtZjHM8rox8u5zAnrxagWg8TjIVvbnrrjvNjTw9kJ/IvM1ynDyWPrI8L43dPCW7oDwtbxK9N+mbuzidwbvhFrU8rqGFu9CwObyJfj29pyfvO/ATuzvm1nW8YJCLPJ8RWDxw5cE8dqoPu2rsoTzjlte8oZfkPDSfzLx/0b084Q/CvI7DrLw4FbW6jeeqPGeqsjtpqkY868K9PHGPVryc7Yg82MxVOyTMBDxyPhQ9w5m8OoWCGjymfB49Sm+qO7en17vvMYk8l1HSM2eJsjwGIOy7fh0FPHuIiLsXMqC7gMkHveMTtbticF+9S8M2POTPvjyThRe8pD3RvIJD7rvAnws8yjNhPGM/2zugpwy8h/mKvEO9mDzVUUq8dQKnO5dEhzxKqwS9To4RvZ+APb2WieE8OdciPIC5mrte0rc89dSRu5J0jrwmTXs7KdHqPIIqtDt73KY83toAPM6ILb0KnzG7tEPMO+KRcLrM5MC8nYyIO5qRf7p4xWS7u7QivP1GCDxLhxc5E3efvChIHz1vO/08nOHyvOgE67zDR5+7RJ6dvBr7M7w+Ivk7SgH2vB/2uDvnptS7NZ8aOnPf77vaJIc7cu5jPOYq1Dy/PHo8JYTWvJe+cLx2k3k7gfpBO9MWcrubI6s83eycvEWpcbsgQs67UY3BvO3zpzxoEXC6p4cBPOLTCb2AqTC8cxeivJoJ/zxXgyq8snwAPJJnvzzERbE73xmhPJ4LartKgk+8E6sJOp8hgLv8cVi8SvXpPA== - 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: - - '502' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: Need to use search once. - role: assistant - tool_calls: - - function: - arguments: '{"query":"class labels","document_name":null,"limit":5}' - name: search - id: call_m70fxfgn - index: 0 - type: function - created: 1768998240 - id: chatcmpl-236 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 39 - prompt_tokens: 843 - total_tokens: 882 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '1059' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - messages: - - content: |- - You are a search query optimizer. You MUST use the run_search tool to execute searches. - - For each user request: - 1. Use the run_search tool with the original query - 2. Use run_search again with 1-2 alternative keyword queries - 3. Keep all queries SHORT (2-5 words) - 4. After all tool calls complete, respond "Search complete" - - You can optionally specify a limit parameter (default 5). - - IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text. - role: system - - content: class labels - role: user - model: gpt-oss - reasoning_effort: low - stream: false - tool_choice: auto - tools: - - function: - description: Run a single search query against the knowledge base. - name: run_search - parameters: - additionalProperties: false - properties: - limit: - anyOf: - - type: integer - - type: 'null' - default: null - description: 'Number of results to fetch (default: 5)' - query: - description: The search query - type: string - required: - - query - type: object - type: function - uri: http://localhost:11434/v1/chat/completions - response: - headers: - content-length: - - '478' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: Need to run search. - role: assistant - tool_calls: - - function: - arguments: '{"query":"class labels","limit":5}' - name: run_search - id: call_6dvnsbzh - index: 0 - type: function - created: 1768998241 - id: chatcmpl-107 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 34 - prompt_tokens: 263 - total_tokens: 297 - 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: - - '1368' - 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 run search. - - role: assistant - tool_calls: - - function: - arguments: '{"query":"class labels","limit":5}' - name: run_search - id: call_6dvnsbzh - type: function - - content: 'Found 2 results for: class labels' - role: tool - tool_call_id: call_6dvnsbzh - 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: - - '524' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: 'Now an alternative query: maybe "label classification".' - role: assistant - tool_calls: - - function: - arguments: '{"limit":5,"query":"label classification"}' - name: run_search - id: call_eonlxxeh - index: 0 - type: function - created: 1768998242 - id: chatcmpl-552 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 39 - prompt_tokens: 322 - total_tokens: 361 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '90' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - encoding_format: base64 - input: - - label classification - model: qwen3-embedding:4b - uri: http://localhost:11434/v1/embeddings - response: - headers: - content-type: - - application/json - transfer-encoding: - - chunked - parsed_body: - data: - - embedding: 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 - index: 0 - object: embedding - model: qwen3-embedding:4b - object: list - usage: - prompt_tokens: 3 - total_tokens: 3 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '1731' - 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 run search. - - role: assistant - tool_calls: - - function: - arguments: '{"query":"class labels","limit":5}' - name: run_search - id: call_6dvnsbzh - type: function - - content: 'Found 2 results for: class labels' - role: tool - tool_call_id: call_6dvnsbzh - - content: |- - - Now an alternative query: maybe "label classification". - - role: assistant - tool_calls: - - function: - arguments: '{"limit":5,"query":"label classification"}' - name: run_search - id: call_eonlxxeh - type: function - - content: 'Found 2 results for: label classification' - role: tool - tool_call_id: call_eonlxxeh - 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: 1768998242 - id: chatcmpl-801 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 6 - prompt_tokens: 386 - total_tokens: 392 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '5080' - 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 once. - - role: assistant - tool_calls: - - function: - arguments: '{"query":"class labels","document_name":null,"limit":5}' - name: search - id: call_m70fxfgn - 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_m70fxfgn - 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: - - '700' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: stop - index: 0 - message: - content: "Here are two documents that discuss class labels:\n\n1. **DocLayNet Class Labels** \n - Describes the - 11 distinct class labels used for document layout analysis (e.g., Caption, Footnote, Formula, List‑item, Page‑footer, - Page‑header, etc.).\n\n2. **DocLayNet Annotation** \n - Covers the annotation process, including how labels - were selected and guided during the construction of the DocLayNet dataset." - role: assistant - created: 1768998245 - id: chatcmpl-955 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 96 - prompt_tokens: 1051 - total_tokens: 1147 - status: - code: 200 - message: OK -version: 1