diff --git a/tests/agents/chat/test_chat_agent.py b/tests/agents/chat/test_chat_agent.py index 656c52b6..a85d044b 100644 --- a/tests/agents/chat/test_chat_agent.py +++ b/tests/agents/chat/test_chat_agent.py @@ -553,6 +553,121 @@ async def test_chat_agent_ask_triggers_background_summarization( assert cached_context.last_updated is not None +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_chat_agent_multi_turn_with_context(allow_model_requests, temp_db_path): + """Test multi-turn conversation with initial context, summarization, and prior recall. + + Exercises the full conversation flow: + 1. Initial context is transferred to session context + 2. First question triggers background summarization + 3. Second related question uses prior answer recall and updated session context + 4. Both qa_history entries are present after two turns + """ + import asyncio + + from haiku.rag.agents.chat.agent import trigger_background_summarization + from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState + + 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", + ) + + context = ToolContext() + agent = create_chat_agent(Config, client, context) + deps = ChatDeps( + config=Config, + tool_context=context, + state_key=AGUI_STATE_KEY, + ) + + # Set initial state with initial_context (mimicking AG-UI client) + deps.state = { + AGUI_STATE_KEY: { + "session_id": "", + "initial_context": "The user is researching the DocLayNet dataset for a paper on document layout analysis.", + "session_context": None, + "qa_history": [], + "citations": [], + "document_filter": [], + "citation_registry": {}, + } + } + + # session_id should be auto-generated + assert deps.session_id != "" + session_id = deps.session_id + + # initial_context should be transferred to QASessionState + qa_session = context.get(QA_SESSION_NAMESPACE, QASessionState) + assert qa_session is not None + assert ( + qa_session.session_context + == "The user is researching the DocLayNet dataset for a paper on document layout analysis." + ) + + # First question about class labels + result1 = await agent.run( + "What are the class labels defined in DocLayNet?", + deps=deps, + ) + trigger_background_summarization(deps) + assert result1.output is not None + + # Wait for background summarization + cached_context = None + for _ in range(50): + cached_context = get_cached_session_context(session_id) + if cached_context is not None: + break + await asyncio.sleep(0.1) + + assert cached_context is not None + assert cached_context.summary != "" + + # qa_history should have one entry + qa_session = context.get(QA_SESSION_NAMESPACE, QASessionState) + assert qa_session is not None + assert len(qa_session.qa_history) >= 1 + + # Second related question - uses prior answers and updated session context + result2 = await agent.run( + "How were the annotations created and how many annotators were involved?", + deps=deps, + message_history=result1.all_messages(), + ) + trigger_background_summarization(deps) + assert result2.output is not None + + # Wait for updated summarization + for _ in range(50): + updated = get_cached_session_context(session_id) + if ( + updated is not None + and updated.last_updated != cached_context.last_updated + ): + break + await asyncio.sleep(0.1) + + # qa_history should have two entries + qa_session = context.get(QA_SESSION_NAMESPACE, QASessionState) + assert qa_session is not None + assert len(qa_session.qa_history) >= 2 + + # Session context should be updated with newer summary + updated = get_cached_session_context(session_id) + assert updated is not None + assert updated.summary != "" + + @pytest.mark.asyncio @pytest.mark.vcr() async def test_chat_agent_ask_with_prior_answer_retrieval( diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_multi_turn_with_context.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_multi_turn_with_context.yaml new file mode 100644 index 00000000..fd76b59a --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_chat_agent_multi_turn_with_context.yaml @@ -0,0 +1,3292 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '730' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Class Labels + DocLayNet defines 11 distinct class labels for document layout analysis: + 1. Caption - Text describing figures or tables + 2. Footnote - Notes at the bottom of pages + 3. Formula - Mathematical expressions + 4. List-item - Items in bulleted or numbered lists + 5. Page-footer - Footer content on pages + 6. Page-header - Header content on pages + 7. Picture - Images and diagrams + 8. Section-header - Headings for document sections + 9. Table - Tabular data + 10. Text - Regular paragraph text (highest count: 510,377 instances) + 11. Title - Document titles + The Text class has the highest count with 510,377 instances in the dataset. + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 166 + total_tokens: 166 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '481' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Annotation Process + The annotation process was organized into 4 phases: + - Phase 1: Data selection and preparation by a small team of experts + - Phase 2: Label selection and guideline definition + - Phase 3: Annotation by 40 dedicated annotators + - Phase 4: Quality control and continuous supervision + The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface. + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 90 + total_tokens: 90 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5329' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: "You are a helpful research assistant powered by haiku.rag, a knowledge base system.\n\nYou have access to + a knowledge base of documents. Use your tools to search and answer questions.\n\nCRITICAL RULES:\n1. For greetings + or casual chat: respond directly WITHOUT using any tools\n2. NEVER call the same tool multiple times for a single + user message\n3. NEVER make up information - always use tools to get facts from the knowledge base\n4. For questions: + Use the \"ask\" tool EXACTLY ONCE - it automatically uses prior conversation context\n5. For searches: Use the \"search\" + tool EXACTLY ONCE - it handles multi-query expansion internally\n\nHow to decide which tool to use:\n- \"list_documents\" + - Use when the user wants to browse or see what documents are available (e.g., \"what documents are available?\", + \"show me the documents\", \"list available docs\").\n- \"summarize_document\" - Use when the user wants an overview + or summary of a specific document (e.g., \"summarize document X\", \"what does Y cover?\", \"give me an overview + of Z\").\n- \"get_document\" - Use when the user wants the FULL content of a specific document (e.g., \"get the + paper about Y\", \"fetch 2412.00566\", \"show me the full document\").\n- \"ask\" - Use for questions about topics + in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across + documents to return answers with citations.\n- \"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.\n\nIMPORTANT - When user mentions a document + in search/ask:\n- If user says \"search in \", \"find in \", \"answer from \", or \" in \":\n + \ - Extract the TOPIC as `query`/`question`\n - Extract the DOCUMENT NAME as `document_name`\n- Examples for search:\n + \ - \"search for embeddings in the ML paper\" → query=\"embeddings\", document_name=\"ML paper\"\n - \"find transformer + architecture in 2412.00566\" → query=\"transformer architecture\", document_name=\"2412.00566\" \n- Examples for + ask:\n - \"what does the ML paper say about embeddings?\" → question=\"what are the embedding methods?\", document_name=\"ML + paper\"\n - \"answer from 2412.00566 about model training\" → question=\"how is the model trained?\", document_name=\"2412.00566\" + \nBe friendly and conversational. When you use the \"ask\" tool, summarize the key findings for the user." + role: system + - content: What are the class labels defined 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. + + Formatted search results with content and metadata. + + name: search + parameters: + additionalProperties: false + properties: + filter: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional SQL WHERE clause to filter documents. + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: from config).' + query: + description: The search query (what to search for). + type: string + required: + - query + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Paginated list of documents with metadata. + + name: list_documents + parameters: + additionalProperties: false + properties: + page: + default: 1 + description: 'Page number (default: 1, 50 documents per page)' + type: integer + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Document content and metadata, or not found message. + + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up. + type: string + required: + - query + type: object + strict: true + type: function + - function: + description: |- + Generate a summary of a specific document. + + Generated summary or not found message. + + name: summarize_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to summarize. + type: string + required: + - query + type: object + strict: true + type: function + - function: + description: |- + Answer a question using the knowledge base. + + Uses a research graph for searching and synthesizing answers. + + QAResult with answer, confidence, and citations. + + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within. + question: + description: The question to answer. + type: string + required: + - question + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '528' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to ask using ask tool. + role: assistant + tool_calls: + - function: + arguments: '{"question":"What are the class labels defined in DocLayNet?","document_name":null}' + name: ask + id: call_bz5ma4zl + index: 0 + type: function + created: 1770727383 + id: chatcmpl-6 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 45 + prompt_tokens: 1055 + total_tokens: 1100 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1871' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are the research orchestrator planning the investigation. + + If a section is provided, use it to understand the conversation context. + + Your task: + 1. Analyze the original question + 2. Propose the first question to investigate + + For simple questions, investigate them directly. For composite or complex questions, + you may decompose into a focused sub-question. For example: + - "What are the benefits and drawbacks of X?" → Start with "What are the benefits of X?" + - Ambiguous references should be resolved using background context if available + + Output requirements: + - Set is_complete=False (you are just starting the investigation) + - Set next_question to the question to investigate + - Provide brief reasoning explaining your choice + + The question must be standalone and self-contained: + - Include concrete entities, scope, and any qualifiers + - Avoid ambiguous pronouns (it/they/this/that) + role: system + - content: |- + Plan the research investigation. + + + The user is researching the DocLayNet dataset for a paper on document layout analysis. + What are the class labels defined in DocLayNet? + + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Output from iterative planning step. + name: final_result + parameters: + additionalProperties: false + properties: + is_complete: + description: Whether research is complete and can be synthesized + type: boolean + next_question: + anyOf: + - type: string + - type: 'null' + default: null + description: Next question to investigate, if not complete + reasoning: + description: Brief explanation of the decision + type: string + required: + - is_complete + - reasoning + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '830' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: 'Need to create first question. It''s simple: wants class labels. So the first question: "What are the + class labels defined in the DocLayNet dataset?" Provide reasoning.' + role: assistant + tool_calls: + - function: + arguments: '{"is_complete":false,"next_question":"What are the class labels defined in the DocLayNet dataset?","reasoning":"The + user asks for the class labels; this is a straightforward information request. No further decomposition needed."}' + name: final_result + id: call_3ssh9qw3 + index: 0 + type: function + created: 1770727385 + id: chatcmpl-926 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 101 + prompt_tokens: 394 + total_tokens: 495 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2849' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results ordered by relevance. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and rank position (rank 1 = most relevant) + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Results are ordered by relevance, with rank 1 being most relevant. + role: system + - content: What are the class labels defined in the DocLayNet dataset? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '499' + 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 labels","limit":5}' + name: search_and_answer + id: call_llkfxclr + index: 0 + type: function + created: 1770727387 + id: chatcmpl-297 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 38 + prompt_tokens: 628 + total_tokens: 666 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '100' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet dataset 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: 8 + total_tokens: 8 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4238' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results ordered by relevance. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and rank position (rank 1 = most relevant) + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Results are ordered by relevance, with rank 1 being most relevant. + role: system + - content: What are the class labels defined in the DocLayNet dataset? + role: user + - content: |- + + Need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet dataset class labels","limit":5}' + name: search_and_answer + id: call_llkfxclr + type: function + - content: |- + [b731edec-a826-4cfe-96c4-b3e3a3226ba1] [rank 1 of 2] + 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 + + [46be2451-1009-4414-9059-771d99a3b0be] [rank 2 of 2] + Source: "DocLayNet Annotation" + Type: list_item + Content: + 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. + role: tool + tool_call_id: call_llkfxclr + 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: + - '1076' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "**query:** What are the class labels defined in the DocLayNet dataset? \n\n**answer:** \nDocLayNet defines + 11 distinct class labels for document layout analysis:\n\n1. Caption – text describing figures or tables \n2. + Footnote – notes at the bottom of pages \n3. Formula – mathematical expressions \n4. List‑item – items in bulleted + or numbered lists \n5. Page-footer – footer content on pages \n6. Page-header – header content on pages \n7. + Picture – images and diagrams \n8. Section‑header – headings for document sections \n9. Table – tabular data + \ \n10. *(The source lists 9 labels; the complete set includes 11 labels as stated in the dataset documentation.)* + \ \n\n**cited_chunks:** \n[\"b731edec-a826-4cfe-96c4-b3e3a3226ba1\"] \n\n**confidence:** 0.95" + role: assistant + created: 1770727391 + id: chatcmpl-877 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 194 + prompt_tokens: 947 + total_tokens: 1141 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5190' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a search and question-answering specialist. + + Process: + 1. Call search_and_answer with relevant keywords from the question. + 2. Review the results ordered by relevance. + 3. If needed, perform follow-up searches with different keywords (max 3 total). + 4. Provide a concise answer based strictly on the retrieved content. + + The search tool returns results like: + [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5] + Source: "Document Title" > Section > Subsection + Type: paragraph + Content: + The actual text content here... + + [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5] + Source: "Another Document" + Type: table + Content: + | Column 1 | Column 2 | + ... + + Each result includes: + - chunk_id in brackets and rank position (rank 1 = most relevant) + - Source: document title and section hierarchy (when available) + - Type: content type like paragraph, table, code, list_item (when available) + - Content: the actual text + + Output format: + - query: Echo the question you are answering + - answer: Your concise answer based on the retrieved content + - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects) + - confidence: A score from 0.0 to 1.0 indicating answer confidence + + IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs. + + Guidelines: + - Base answers strictly on retrieved content - do not use external knowledge. + - Use the Source and Type metadata to understand context. + - If multiple results are relevant, synthesize them coherently. + - If information is insufficient, say so clearly. + - Be concise and direct; avoid meta commentary about the process. + - Results are ordered by relevance, with rank 1 being most relevant. + role: system + - content: What are the class labels defined in the DocLayNet dataset? + role: user + - content: |- + + Need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet dataset class labels","limit":5}' + name: search_and_answer + id: call_llkfxclr + type: function + - content: |- + [b731edec-a826-4cfe-96c4-b3e3a3226ba1] [rank 1 of 2] + 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 + + [46be2451-1009-4414-9059-771d99a3b0be] [rank 2 of 2] + Source: "DocLayNet Annotation" + Type: list_item + Content: + 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. + role: tool + tool_call_id: call_llkfxclr + - content: "**query:** What are the class labels defined in the DocLayNet dataset? \n\n**answer:** \nDocLayNet defines + 11 distinct class labels for document layout analysis:\n\n1. Caption – text describing figures or tables \n2. Footnote + – notes at the bottom of pages \n3. Formula – mathematical expressions \n4. List‑item – items in bulleted or numbered + lists \n5. Page-footer – footer content on pages \n6. Page-header – header content on pages \n7. Picture – images + and diagrams \n8. Section‑header – headings for document sections \n9. Table – tabular data \n10. *(The source + lists 9 labels; the complete set includes 11 labels as stated in the dataset documentation.)* \n\n**cited_chunks:** + \ \n[\"b731edec-a826-4cfe-96c4-b3e3a3226ba1\"] \n\n**confidence:** 0.95" + 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: + - '1145' + 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":"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; and two additional labels specified in the full dataset documentation.","cited_chunks":["b731edec-a826-4cfe-96c4-b3e3a3226ba1"],"confidence":0.95,"query":"What + are the class labels defined in the DocLayNet dataset?"}' + name: final_result + id: call_71qqxhwg + index: 0 + type: function + created: 1770727395 + id: chatcmpl-167 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 179 + prompt_tokens: 1166 + total_tokens: 1345 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3370' + 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. + + + The user is researching the DocLayNet dataset for a paper on document layout analysis. + What are the class labels defined in DocLayNet? + + + What are the class labels defined in the DocLayNet dataset? + 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; and two additional labels specified in the full dataset documentation. + 0.95 + 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. + + Used by both research graph and chat agent. The optional index field + supports UI display ordering in chat contexts. + properties: + chunk_id: + type: string + content: + type: string + document_id: + type: string + document_title: + anyOf: + - type: string + - type: 'null' + default: null + document_uri: + type: string + headings: + anyOf: + - items: + type: string + type: array + - type: 'null' + default: null + index: + anyOf: + - type: integer + - type: 'null' + default: null + page_numbers: + items: + type: integer + type: array + required: + - document_id + - chunk_id + - document_uri + - content + type: object + additionalProperties: false + properties: + answer: + description: Direct answer to the question + type: string + citations: + description: Citations supporting the answer + items: + $ref: '#/$defs/Citation' + type: array + confidence: + default: 1.0 + description: Confidence score (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + required: + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1436' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to answer with citations? We have prior answer with answer and confidence. We can use that. + role: assistant + tool_calls: + - function: + arguments: '{"answer":"DocLayNet defines 11 distinct class labels for document layout analysis:\n\n1. **Caption** + – text that describes figures or tables.\n2. **Footnote** – notes placed at the bottom of pages.\n3. **Formula** + – mathematical expressions.\n4. **List‑item** – items in bulleted or numbered lists.\n5. **Page‑footer** – + footer content on pages.\n6. **Page‑header** – header content on pages.\n7. **Picture** – images, diagrams, + or other visual media.\n8. **Section‑header** – headings that introduce document sections.\n9. **Table** – + tabular data.\n10. **Title** – the main title of the document.\n11. **Text** – regular body text.\n\nThese + 11 categories cover the typical elements found in scholarly and technical documents, allowing accurate layout + analysis for tasks such as OCR, content extraction, and document structuring.","citations":[],"confidence":0.95}' + name: final_result + id: call_w27z520r + index: 0 + type: function + created: 1770727401 + id: chatcmpl-365 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 254 + prompt_tokens: 554 + total_tokens: 808 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2388' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a session summarizer. Given a conversation history of Q&A pairs (and optionally existing context), produce a structured summary that captures key information for future context. + + If a "Current Context" section is provided at the start of the input, incorporate that context into your summary. This might be initial background context from the user or a previous summary - build upon it rather than discard it. + + Your summary should be concise (aim for 500-1500 tokens) and include: + + 1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation + 2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain + 3. **Current Focus** - What topic or question thread the user is currently exploring + + Rules: + - Extract only high-signal information that would help answer follow-up questions + - When building on existing context, merge new information with prior context + - Omit small talk, greetings, or low-confidence answers + - Use bullet points for clarity + - Keep technical details but compress verbose explanations + - Preserve document names/titles when mentioned in sources + + Output the summary directly in markdown format. Do not include meta-commentary about the summary itself. + role: system + - content: | + ## Q1: What are the class labels defined in DocLayNet? + **Answer** (confidence: 95%): + DocLayNet defines 11 distinct class labels for document layout analysis: + + 1. **Caption** – text that describes figures or tables. + 2. **Footnote** – notes placed at the bottom of pages. + 3. **Formula** – mathematical expressions. + 4. **List‑item** – items in bulleted or numbered lists. + 5. **Page‑footer** – footer content on pages. + 6. **Page‑header** – header content on pages. + 7. **Picture** – images, diagrams, or other visual media. + 8. **Section‑header** – headings that introduce document sections. + 9. **Table** – tabular data. + 10. **Title** – the main title of the document. + 11. **Text** – regular body text. + + These 11 categories cover the typical elements found in scholarly and technical documents, allowing accurate layout analysis for tasks such as OCR, content extraction, and document structuring. + **Sources:** DocLayNet Class Labels + role: user + model: gpt-oss + reasoning_effort: low + stream: false + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '947' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: |- + **Key Facts Established** + - DocLayNet specifies **11 class labels** for document layout elements: + 1. Caption + 2. Footnote + 3. Formula + 4. List‑item + 5. Page‑footer + 6. Page‑header + 7. Picture + 8. Section‑header + 9. Table + 10. Title + 11. Text + - These categories cover typical components in scholarly/technical documents, facilitating OCR, content extraction, and structural analysis. + + **Documents Referenced** + - *DocLayNet Class Labels* – source defining the 11 categories. + + **Current Focus** + - User is exploring the specific class labels defined in the DocLayNet dataset. + reasoning: We need summary. No existing context. + role: assistant + created: 1770727405 + id: chatcmpl-632 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 174 + prompt_tokens: 544 + total_tokens: 718 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '6521' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: "You are a helpful research assistant powered by haiku.rag, a knowledge base system.\n\nYou have access to + a knowledge base of documents. Use your tools to search and answer questions.\n\nCRITICAL RULES:\n1. For greetings + or casual chat: respond directly WITHOUT using any tools\n2. NEVER call the same tool multiple times for a single + user message\n3. NEVER make up information - always use tools to get facts from the knowledge base\n4. For questions: + Use the \"ask\" tool EXACTLY ONCE - it automatically uses prior conversation context\n5. For searches: Use the \"search\" + tool EXACTLY ONCE - it handles multi-query expansion internally\n\nHow to decide which tool to use:\n- \"list_documents\" + - Use when the user wants to browse or see what documents are available (e.g., \"what documents are available?\", + \"show me the documents\", \"list available docs\").\n- \"summarize_document\" - Use when the user wants an overview + or summary of a specific document (e.g., \"summarize document X\", \"what does Y cover?\", \"give me an overview + of Z\").\n- \"get_document\" - Use when the user wants the FULL content of a specific document (e.g., \"get the + paper about Y\", \"fetch 2412.00566\", \"show me the full document\").\n- \"ask\" - Use for questions about topics + in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across + documents to return answers with citations.\n- \"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.\n\nIMPORTANT - When user mentions a document + in search/ask:\n- If user says \"search in \", \"find in \", \"answer from \", or \" in \":\n + \ - Extract the TOPIC as `query`/`question`\n - Extract the DOCUMENT NAME as `document_name`\n- Examples for search:\n + \ - \"search for embeddings in the ML paper\" → query=\"embeddings\", document_name=\"ML paper\"\n - \"find transformer + architecture in 2412.00566\" → query=\"transformer architecture\", document_name=\"2412.00566\" \n- Examples for + ask:\n - \"what does the ML paper say about embeddings?\" → question=\"what are the embedding methods?\", document_name=\"ML + paper\"\n - \"answer from 2412.00566 about model training\" → question=\"how is the model trained?\", document_name=\"2412.00566\" + \nBe friendly and conversational. When you use the \"ask\" tool, summarize the key findings for the user." + role: system + - content: What are the class labels defined in DocLayNet? + role: user + - content: |- + + Need to ask using ask tool. + + role: assistant + tool_calls: + - function: + arguments: '{"question":"What are the class labels defined in DocLayNet?","document_name":null}' + name: ask + id: call_bz5ma4zl + type: function + - content: |- + DocLayNet defines 11 distinct class labels for document layout analysis: + + 1. **Caption** – text that describes figures or tables. + 2. **Footnote** – notes placed at the bottom of pages. + 3. **Formula** – mathematical expressions. + 4. **List‑item** – items in bulleted or numbered lists. + 5. **Page‑footer** – footer content on pages. + 6. **Page‑header** – header content on pages. + 7. **Picture** – images, diagrams, or other visual media. + 8. **Section‑header** – headings that introduce document sections. + 9. **Table** – tabular data. + 10. **Title** – the main title of the document. + 11. **Text** – regular body text. + + These 11 categories cover the typical elements found in scholarly and technical documents, allowing accurate layout analysis for tasks such as OCR, content extraction, and document structuring. + + Sources: [1] + role: tool + tool_call_id: call_bz5ma4zl + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Formatted search results with content and metadata. + + name: search + parameters: + additionalProperties: false + properties: + filter: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional SQL WHERE clause to filter documents. + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: from config).' + query: + description: The search query (what to search for). + type: string + required: + - query + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Paginated list of documents with metadata. + + name: list_documents + parameters: + additionalProperties: false + properties: + page: + default: 1 + description: 'Page number (default: 1, 50 documents per page)' + type: integer + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Document content and metadata, or not found message. + + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up. + type: string + required: + - query + type: object + strict: true + type: function + - function: + description: |- + Generate a summary of a specific document. + + Generated summary or not found message. + + name: summarize_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to summarize. + type: string + required: + - query + type: object + strict: true + type: function + - function: + description: |- + Answer a question using the knowledge base. + + Uses a research graph for searching and synthesizing answers. + + QAResult with answer, confidence, and citations. + + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within. + question: + description: The question to answer. + type: string + required: + - question + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1321' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: |- + DocLayNet defines **11 distinct class labels** for document layout analysis: + + | # | Class label | Description | + |---|----------------|-------------| + | 1 | **Caption** | Text that describes figures, tables, or diagrams. | + | 2 | **Footnote** | Notes placed at the bottom of pages. | + | 3 | **Formula** | Mathematical or LaTeX‑style expressions. | + | 4 | **List‑item** | Items in bulleted or numbered lists. | + | 5 | **Page‑footer**| Footer content that appears on each page. | + | 6 | **Page‑header**| Header content that appears on each page. | + | 7 | **Picture** | Images, diagrams, or other visual media. | + | 8 | **Section‑header**| Headings that introduce new sections. | + | 9 | **Table** | Tabular data blocks. | + |10 | **Title** | The main title or heading of the document. | + |11 | **Text** | Regular body text (paragraphs, sentences). | + + These 11 categories comprise the full set of class labels used in the DocLayNet dataset for document layout segmentation and recognition tasks. + role: assistant + created: 1770727413 + id: chatcmpl-686 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 265 + prompt_tokens: 1309 + total_tokens: 1574 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2388' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a session summarizer. Given a conversation history of Q&A pairs (and optionally existing context), produce a structured summary that captures key information for future context. + + If a "Current Context" section is provided at the start of the input, incorporate that context into your summary. This might be initial background context from the user or a previous summary - build upon it rather than discard it. + + Your summary should be concise (aim for 500-1500 tokens) and include: + + 1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation + 2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain + 3. **Current Focus** - What topic or question thread the user is currently exploring + + Rules: + - Extract only high-signal information that would help answer follow-up questions + - When building on existing context, merge new information with prior context + - Omit small talk, greetings, or low-confidence answers + - Use bullet points for clarity + - Keep technical details but compress verbose explanations + - Preserve document names/titles when mentioned in sources + + Output the summary directly in markdown format. Do not include meta-commentary about the summary itself. + role: system + - content: | + ## Q1: What are the class labels defined in DocLayNet? + **Answer** (confidence: 95%): + DocLayNet defines 11 distinct class labels for document layout analysis: + + 1. **Caption** – text that describes figures or tables. + 2. **Footnote** – notes placed at the bottom of pages. + 3. **Formula** – mathematical expressions. + 4. **List‑item** – items in bulleted or numbered lists. + 5. **Page‑footer** – footer content on pages. + 6. **Page‑header** – header content on pages. + 7. **Picture** – images, diagrams, or other visual media. + 8. **Section‑header** – headings that introduce document sections. + 9. **Table** – tabular data. + 10. **Title** – the main title of the document. + 11. **Text** – regular body text. + + These 11 categories cover the typical elements found in scholarly and technical documents, allowing accurate layout analysis for tasks such as OCR, content extraction, and document structuring. + **Sources:** DocLayNet Class Labels + role: user + model: gpt-oss + reasoning_effort: low + stream: false + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1340' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: |- + **Key Facts Established** + - DocLayNet specifies **11 distinct class labels** for document layout analysis: + 1. Caption – descriptions of figures or tables. + 2. Footnote – notes at page bottoms. + 3. Formula – mathematical expressions. + 4. List‑item – items in bulleted or numbered lists. + 5. Page‑footer – footer content on pages. + 6. Page‑header – header content on pages. + 7. Picture – images, diagrams, or other visual media. + 8. Section‑header – headings introducing document sections. + 9. Table – tabular data. + 10. Title – the main document title. + 11. Text – regular body text. + - These categories cover typical elements in scholarly and technical documents, supporting OCR, content extraction, and document structuring tasks. + + **Documents Referenced** + - *DocLayNet Class Labels* – the source that enumerates the 11 class definitions. + + **Current Focus** + - The user is investigating the specific class labels defined by DocLayNet for document layout analysis. + reasoning: Need summarize. + role: assistant + created: 1770727419 + id: chatcmpl-554 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 240 + prompt_tokens: 544 + total_tokens: 784 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '7689' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: "You are a helpful research assistant powered by haiku.rag, a knowledge base system.\n\nYou have access to + a knowledge base of documents. Use your tools to search and answer questions.\n\nCRITICAL RULES:\n1. For greetings + or casual chat: respond directly WITHOUT using any tools\n2. NEVER call the same tool multiple times for a single + user message\n3. NEVER make up information - always use tools to get facts from the knowledge base\n4. For questions: + Use the \"ask\" tool EXACTLY ONCE - it automatically uses prior conversation context\n5. For searches: Use the \"search\" + tool EXACTLY ONCE - it handles multi-query expansion internally\n\nHow to decide which tool to use:\n- \"list_documents\" + - Use when the user wants to browse or see what documents are available (e.g., \"what documents are available?\", + \"show me the documents\", \"list available docs\").\n- \"summarize_document\" - Use when the user wants an overview + or summary of a specific document (e.g., \"summarize document X\", \"what does Y cover?\", \"give me an overview + of Z\").\n- \"get_document\" - Use when the user wants the FULL content of a specific document (e.g., \"get the + paper about Y\", \"fetch 2412.00566\", \"show me the full document\").\n- \"ask\" - Use for questions about topics + in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across + documents to return answers with citations.\n- \"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.\n\nIMPORTANT - When user mentions a document + in search/ask:\n- If user says \"search in \", \"find in \", \"answer from \", or \" in \":\n + \ - Extract the TOPIC as `query`/`question`\n - Extract the DOCUMENT NAME as `document_name`\n- Examples for search:\n + \ - \"search for embeddings in the ML paper\" → query=\"embeddings\", document_name=\"ML paper\"\n - \"find transformer + architecture in 2412.00566\" → query=\"transformer architecture\", document_name=\"2412.00566\" \n- Examples for + ask:\n - \"what does the ML paper say about embeddings?\" → question=\"what are the embedding methods?\", document_name=\"ML + paper\"\n - \"answer from 2412.00566 about model training\" → question=\"how is the model trained?\", document_name=\"2412.00566\" + \nBe friendly and conversational. When you use the \"ask\" tool, summarize the key findings for the user." + role: system + - content: What are the class labels defined in DocLayNet? + role: user + - content: |- + + Need to ask using ask tool. + + role: assistant + tool_calls: + - function: + arguments: '{"question":"What are the class labels defined in DocLayNet?","document_name":null}' + name: ask + id: call_bz5ma4zl + type: function + - content: |- + DocLayNet defines 11 distinct class labels for document layout analysis: + + 1. **Caption** – text that describes figures or tables. + 2. **Footnote** – notes placed at the bottom of pages. + 3. **Formula** – mathematical expressions. + 4. **List‑item** – items in bulleted or numbered lists. + 5. **Page‑footer** – footer content on pages. + 6. **Page‑header** – header content on pages. + 7. **Picture** – images, diagrams, or other visual media. + 8. **Section‑header** – headings that introduce document sections. + 9. **Table** – tabular data. + 10. **Title** – the main title of the document. + 11. **Text** – regular body text. + + These 11 categories cover the typical elements found in scholarly and technical documents, allowing accurate layout analysis for tasks such as OCR, content extraction, and document structuring. + + Sources: [1] + role: tool + tool_call_id: call_bz5ma4zl + - content: |- + DocLayNet defines **11 distinct class labels** for document layout analysis: + + | # | Class label | Description | + |---|----------------|-------------| + | 1 | **Caption** | Text that describes figures, tables, or diagrams. | + | 2 | **Footnote** | Notes placed at the bottom of pages. | + | 3 | **Formula** | Mathematical or LaTeX‑style expressions. | + | 4 | **List‑item** | Items in bulleted or numbered lists. | + | 5 | **Page‑footer**| Footer content that appears on each page. | + | 6 | **Page‑header**| Header content that appears on each page. | + | 7 | **Picture** | Images, diagrams, or other visual media. | + | 8 | **Section‑header**| Headings that introduce new sections. | + | 9 | **Table** | Tabular data blocks. | + |10 | **Title** | The main title or heading of the document. | + |11 | **Text** | Regular body text (paragraphs, sentences). | + + These 11 categories comprise the full set of class labels used in the DocLayNet dataset for document layout segmentation and recognition tasks. + role: assistant + - content: How were the annotations created and how many annotators were involved? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Formatted search results with content and metadata. + + name: search + parameters: + additionalProperties: false + properties: + filter: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional SQL WHERE clause to filter documents. + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: from config).' + query: + description: The search query (what to search for). + type: string + required: + - query + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Paginated list of documents with metadata. + + name: list_documents + parameters: + additionalProperties: false + properties: + page: + default: 1 + description: 'Page number (default: 1, 50 documents per page)' + type: integer + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Document content and metadata, or not found message. + + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up. + type: string + required: + - query + type: object + strict: true + type: function + - function: + description: |- + Generate a summary of a specific document. + + Generated summary or not found message. + + name: summarize_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to summarize. + type: string + required: + - query + type: object + strict: true + type: function + - function: + description: |- + Answer a question using the knowledge base. + + Uses a research graph for searching and synthesizing answers. + + QAResult with answer, confidence, and citations. + + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within. + question: + description: The question to answer. + type: string + required: + - question + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '549' + 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":"How were the annotations created and how many annotators were + involved in DocLayNet?"}' + name: ask + id: call_dz3lh8dm + index: 0 + type: function + created: 1770727422 + id: chatcmpl-168 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 47 + prompt_tokens: 1593 + total_tokens: 1640 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '154' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - How were the annotations created and how many annotators were involved in DocLayNet? + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 19 + total_tokens: 19 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '117' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - What are the class labels defined in DocLayNet? + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 9+04uejvsbyI3027A/gbPfRbKbp98nY9ZGxCPcMfc7z55ZU8ktckvMMqyTpcNJ48Y/66O2j7szuQPZu8uR52vUWhJTy2dxW8adIPPeYqj7tPKRm8PyCCu7MkBz2ZPO48RTIBvQu7Lb0//668tmJnvJeqtjtBNy49mpYLPTd2Eb01Bha7mIweO7erCTuyB/y7wAP0OvlqJrtwjxa8khMOvYYgtDynWL27Oy1JO3Q7AjysQwy86g+ZOpUQMTzNOqa7PPs0vXN6tLv8fgo8cSoiPLmzyrzAJSy8lP9jPbjvULvZYSg9ZxB1u16iojvXOQQ9hyvOuwgkQ7yaCw6939ukvD1fW7woyru8Vi41PLS6r7sLn4g8Y3KLvGtGNL0lpcY8+0+jvJr8KzxBp4s8mj/QvANfVrxlbbs76uSRO1/cwzyr3yU8FZcTvFx5zzpKI+Q8QAB5vPlLALzWfgs9wZdUPJZ6pbsG2Q096DpUO6Nc8bs3CJm8sOWdPGVyibvoOAA8uhrVvIb7T7zKk3C8rTWuux3YK7t38Mi8Ny1jPN0A+bmG2qY8NOfJvELbobxnfQ28z+QzvMI8mzzMdP07861tvAhnkryzTSi8czjsO0WcgLzzJq67Uga8PO7NpjwWaxo7bg9FvNXl4rva2568aVypu1WQQTulfCC9gDS2O9t187x1Fx49Trwhu0KiUDxGcwO8bnsWPbyPm7yylfo7iJujPMmAvbzoZA48gCOvOwVuwzxZ/Xm8XVfCOWkvHLxGt5I8362NvEtJS71r5Ru7ghoYvflXEzwNDeo6t5LEOjZskryLuY88u6BrObe5njw7i5w8Na/SvKrAyTsz4tA66IzRO1PSmTtnqqw8vFC1u+nYGj0REpw8u99hPBi4oDyuhTC87tGPOg0nJr2lu446GvprvDOuT7xTJTe7Ay7dvHgONbzuphU7koVDuozAn7xSl7I86OHUO4oeGT0FIh07JD4WPHjFojxbk4W8tzYNO/stH7z3ptu6yaJHPGVSWTs3Mpa7Vt/MvNbIjTwQX268UwAEvDhvmjphmOm7Y87SvMQ+nzyxinY8dxAqur3oaTzcx4i8tuSMu872ibzn1Ni4oXTEvCIYPjzjZyu862HKu39S+LncJVG82iNtPMNxYjzsdVk87khavIOynby4R9s8svIjPPFclTuu6ji8B8dUvOkm5ztx+qW88HEzPO/nmzvmTa68bYROO+qim7yWfog8ofzTOxCYJrzuKuA4TKehO9JPpTxeGxS9uxGaPGNTcjxLP8W8C2AzPbALlbxqx4e8z94PO0vIfrxA5RG72LT0uqkim7whYZe8b5jCvHngbDqO+X08W3aLPCxDGbzmvMe8vQp3vBu4orx7V0K81FgqvMOYMrxASdG7IKO8ux5hN7xs0Qu8Cv4su6kdQrzBxMI8Gn/XvHZVJruzcD48hU8SPfpU6TusS288vWoVPCT6nTyOoKu8vfZ4PBYhZjxiBrM7a3WFPJq6J7y3v3+7ikXdvKMHajvKw767xxRqvJmn7zwb1Cu8N/ERPDd6sDyWIPc8MBoLvK5elDyGw5u8tzpku7DRFLvp0n88zmQFvBBWI7xXwP+6J0s3vDoQXDyEnYA7CzpvPMICi7zxoWe7ZshBOmEapbydMZI8PYVIPG5ZEjw3MtI6EoUeO5HxvruyS0w9zw8KvfzLOjtVFLQ71w3AvIcPSbzgzJy8z84yvcrv1bxvBIA7kSVivP6GwDwG4cc8EnTFPEiv6DzVq6G7LA5CuyZTED1n3T69j5DSu2sYY7xSd/W5nuUxvBtDDD17aq85MfUIu1Ig0LwPFOs6/L2uu9bc97ymj9e8Ns26uqkJIb0ygOu761KyvMWL0TtZqeK8InwvvMORGr3KI507TTdOPFNjJTxKuqW8vtGUu3lf0jx8mMq8rQiXvJtnjrxX+jE8MbW2PGWI0Lx4UWa8LwEGvLttrDwc8QA9oEF0vGmMrzx1pcE7B8FmPeYHTjwgn6E8FZy6vC0ICLxQAUW8ZGbUu9IvWLvi3wQ86U8buRYfY7utKZE8MVDjvHAROrxXmuC7588TvYb+2zxj8FU5uZ57PP11OD3kYjY8SvnVu6KfIb0TibI8ML8BOo9aEz2uZas88PcyvQfJfrxAHSS8nx+ovAc1h7wPqCc82QQkvfXrtLw4Rzw8OEMlvF6Jqjrheoe8KSmDvGP44TvVZ7K8k2ibvEQ9ZbtMnE08ZMjauy8dEzvhFiY8P8klvLgVHL1mJTo8TMJbPAwRXbsTif88Wsf2O81e0jyFENS8Zi/rvFlFLLsO4EY9mwN9PNs7Rj0t9uC7hWhOuy2VPrzMSVi8/GfKOxheHL3bmBk91MsSPI40DbzLwb08gmovvZt8irxpPsw7qdYoPUdCDruTirG8Q62svE5/Xjuifmq7Ty/YOgxuj7zGIt+69Bxtux+MYbxFTFy9lxWHPNh3T70e1nE8paSlO1zqbbw82W88ZNizvDWDlrwkJJ+8Z8kqvU71vDz5hfK79dEWvOfdY7yNrzW8fJ3nPItRAz0y5h88fmxzPOxmmbwERca7E0wBu47FdDxvBc487/lMPEZwOjznxek8hnMkPVRlNLgizC68kEK4PAg9KT2jFEI4GhfNvLsqJLwmyKo8Z9vxO5G8XTv4W0y84jQFvDo1ujn13PG8CqkyvGlI9Tx5OyS8u46CvHY/MD0JEnE7s1cJPN74Mbw4M8Q8fbC+PGxdjTxUKEU7eXetvAlTiTtJV1O8tIgePD70BLyR2Zm8exS1PMPBU7wvc3K6g9uXvApgRjunGVU7WNqKO7E2LD0Rmwi6BKwGPNHFzDwN/fU73JPNPADNDDzojyK8f4dbvD9yVjyG6Q67d/7TugM/pjx3mVE8iNsMOxtVDDxBU5u6bIt3PNVSeLzGAQC9DCQKPeG707uQhnG8bSPDvA1Mgzu64/O8xxaiux2XU7wkwoC7GQYIvImrzru+1+Q8gs1JPOs6PrvgtUK9gHqDPNbwrTyorjo8Pz8YuqQDErzb/8C77HaHvGkZWryOO9K76ti2vPpkdry0hMU8MFvzu8hrTzzGYA09wGjKO1WfKbzCCwm9tUkVPCAzDDzpJpy84O1QvPBQX7s2Ezm8y4w6vCgbUzz1PJk8fisSPRs+ALqa+ia5O8ANuzlDxDuO3Ra8On7ovFSM1TmYDGC8xU37vBppOzk19qY7SZq1u6KwarwqpM67e5HzPFX7SDvXmps5pDa6O7VDEbzWF8S8V4Tfuz4AkDxPz9M5Sk4zPG+zRzw0u8k7Z3a0vHXXX7ylMcS8zojVvNtKgLvTHy68SVRJPEVAObukMA87whutvLjKCjtu/+u7hBn2vAdJRLq9EZS7bLndvOXqGLrGxzs92QL7POvFzLxkgYi7GAwIveY/GDwH+Io7lEpwPFqH/jwGh+U8od32PHarRrxUUB09k84outhxKL0KU9+8SGScOq2op7yAMoC8fIbdPNX+ArwEWH489c1fvOitp7siVN482SHaOz6gRTyVSTY8s4suPLwDMb0ohfe8oYCoPD9Anry47Sw8Oq/XvFyLC7vCsQC8J9fHvAId1LxLliU8AQ59O5MDbLyxuzu7A+O7vIWWLbzkG5o7IFYqO7e1VLxMnoI8m0OMPCZCCztZTKC76mgTu928NLwEEiS8ahwiu/omIjwYJpU8fKZ9uj/SJL0O72S8l1r7PEEblLs78vc8a9wKPYlWlLzn+tm8xn17vMW3Yzj/Vpa8xCtvPJlSAb1atwq8iigiO61cpjldTRe8r5wVPFWfjDyy7f88RSFkvB6mE73xrG880o2ivBn/lLoKzSY8M5qLPBD0pLtdZwC99CUrPFM9ET0hPJW8fn33O9D2krwoSZU84zZSPGeapDw1/3G8SESOPVECiry9UQ28mwbLu2krUbykNgO9oU/yO3fwDz16lC+82znevFXnuTzzP6s86HxNPKj+qjtuwx884UqVOcfvvzpHtva8+nyZPD8PIrucHZw8u+TiO0IlY7wdqcS78QxsvK3sS7rk+TQ8ZatePPd+jbvVnvw7dJ/LPOHWgDxMdO68H3wQPEeLZDrpY/u7pOrYvIrQxTzwfIE8oXynPChhET2g/KI7r0RXvG99hzzPztK6qZ1TPSKHGjtb8AY87CqMu03i/zy5vDY79hEZvUs2uLy/gH88sg3KvIy2zTrVP1i8fD08vEJpnbzolW47ISbyvKNQb7x5pU28ir8yvPrVjzxHeYu7O+GevA53QrwPlJO8mwR0vQ7PX7kT8/Q8WV6tOz4ogbspnhI8Uj0IvatEyjx/sxg94PTEPIQ8jbxXw6g89SobO/Et+rvXip67O9xEPHbcXrzqvGi8kC8qPaNph7xjEvI7uy/9urfDpzy7wS67OnEyPKgVdLvt/BU87CJWPDrE2TwaXja7f9D1PPvfqzzUgGc8GSoWPWbcjrxHN2Q8vg7XPDnwkDxiB4Y8+Z3KvCBOsLorbrO8rO8svBGZA71vfMQ8KoKgvBy5IbpnBSY8CLKkvCc8/zvsTds7AvA9vBZZfLqgL5w9j5zTPFTpjDocn8W5CVoEvCNKLT3yfus8xLoYvGSzy7sgNlq7opoeO5xzALyDaMK7+RE8PGqJMbxS3o08s20QvTGDP7zBLVe9MknpPFiKBjsDuyQ8v6qHPG1Qiz0nqVW8YZMFvL3klzya5QU8wCMvvMmz6jzctam8PaYNPDpwFj00AlW8d4yKPKzTubwSxVo8+0UePETieLwxspi81j17PFs77buJ1y08v1aSu0jPyzxI4ay87gryvFVRzzxe6q88H10EvK0ojzy4ggK9ObgIPZAzAT26NJU7rlhBuousnjwVKJg8zslbvMMYAb0mXp68vnBHu/IhTTvwUhi90ujnO52vEbu/egs8qr5DvYiciLu1YzC5EU8SvNkE7LxA0pq8T2zIPCVqizvV+ji8BHgvvDrtUrwCnuq7/aGBvC9C/Dtkddy7Io+DPM1BNz0w20m8clihu1t5mzvRsKE8Kauhuwx4zbwB7OI8XQjjPCngcTzjEg48g88WuxbWODvUopU4n2jMO2SdE70Zll88hRPfunABlbx0r048XerKvAK5NLzQqho7eAjjOkHemTwjz4Y85o4LPHuJtTuKaii9i7TEPNuTQjxHa488QOqEPJfnPrzXyha7E1XWutNHoLmxEGu8BGt0vMb+CrxBVci8tCYHPAfuEztaTQw9xEz1u3TS+DsO/Ju8XuWAvCS0BzvzUYm5yIvSO1qC9rzylHk8ilazOyQ9xzu5PJU8fMVMvKt+WbuzdD88wUU1PAVgAz1gSUQ9wLA0PBz1oLqm05+6Z4gfvHYRN73gE0o8R3ygu2O7ILzWaZ28K8NVvIz5Gz1df4+8mXQNPSaTIL1MXBG9jt2uOwZ3kDuXJCM8eSYhvCpLhrwPUrA8yFgmPY40Kbx37IQ85TvXuif0QLvee/o4CKuVPDU5XzyAVxI8QB4xPO5zDz2Eoq27Brr1PM0FC7phVGk8Q8aCvdZuIjrg1pa8BPWUu+g8rrxC3/o7vIyJu8davLwJ5Ys8LCnIPCKiHTyZ4nq8uyLqvEYySjyv+Ry8LpBeO1Boa7uVvaO6Ls+BPNOAk7scFw68o92dPDQn27xLxUS8W9aLPO5YcjzK4s+82xMEPCKZNjxSUiu8se1muhlMUL00rsq8tlEzvb3qxzsrRNM7KDAtO2HDNTw/bM680NmAPHE5Pbwihcy7u68+vJIG2LxOb3O7UCBMvc/JbLz/QRy4JazfO//IIryXsg+8LKIzO0Wad7x4KqY8JaoEPMXgU7l0VAY6tfbhPCgQ3TxEMD+8NsnEOxZHQryrVfY8qf1uvHieZ7x2H1A8dur0vIMrobuV13+8iNUpvHdFr7xzhkm8xu0ZvT0I0LxxuOk8MiIzO2mbMjz9Gu88L8lkvSuM3jyLkIu8fXqFuy13FzqJtoq8GTFEvBrGIb18VwS8790MvRPPKLtu3dM7bk6/vEP4BT0MubA6ULSXO8qZBTw1xDG8EXHyugiqrTwQPQ69/Ge+u2OvLTxq0cm7Yww0vDd9RzyqYsu87u5MO1ZPvjz+npW8j3oIvKnD8LwfEDc9o0IPvT319jzm5TK7ibdTvPa4QD0ggpm70n2APB63+Lx3Evo7bEBHPG0wxzxTKls7AMQ9O+/9SLzOXYQ8sd2tPH1wPLuneDU8gHIavMFCCDy/sR685/SPvAaZVLxMDSw8ZGynvCv24TtrxB08U0lWO1rVAb1WNIg7xG8rPIqgyDe8BW28/KGRPCy5jLyhMJW7gXvlumHgkLwIQSG8wl4kPRi/r7zV2C29mLjjPLTyETt5EOE8n+eiOlOK5zyqFio9PWEmPO+OKzxlkCW90vtvPCvTQLyJvDq8jSmCPIcjor2Wf2S79lmTPFhBDL2/cRq8E0GuvE9Ym7ldN3c7HpK0vLYniTyEm2i7DWxBPczjiDzY03W5S9sMvTudgLyRW/I6JRApO7O0pjpsU0y8B4QJvNwaobx5aDa8rm6HvKZmLzyuczy5Wu+vvFa817yAp7U8WjBEPRVyEz33SRE8pboPPXORFz2N8DC7UkXGO6SA8Lx50iq6SPpbvPwFuruk5xs859sNOxfK3buYgLc6WGALPUdS6LxwjVE9Ry31vMqSwjp1RMk8eyILvIgLVzzdhFO9MR+qPLm7t7xmQgm80C6+O4rPsLtjtqc8AnihvJTn5bx2VUs9WyqtvHGRPrprH4q8bOOlPDDiAD2SBLM77d0RPQs+n7ymnyW8Bk4RPArwFT2RWhe9uSGEO6dYBT2m8eS7SkcEvXAwhTyXuCc7uYSWvNLWwrzRtAc9hDsJvWGF7rq5A3i7PuFzO/nh/jmlFCK9LokDPQGOfrwG4Dq9XKWRPKQbkbudLCS9BXAgvHQirbwjfGk7VWJOu1kVSLwhyMs8IUhdvLaP4bykVIk7Rx5Nul8T9jzvRk88XJh8vHJ4ObySB1a8G8xKOrvakLv2TNQ7VrpdvF6bnjs5mze84RO3PMSdLr10duO81RwJvVuA7LyEvje8xnNEvO9bczxbgWQ7UCP3vNk2oDw8hES6HanGu/A3pTxqEas7XbNpuHTJ1zvYIpE8Y7lKPIZXXjxM45W8sPV8u6T+mzzrsaS7jbMPPRizxruDTkW9CcKMvHoGCr3q9bs7NqzIvJni5rtnHHa9a2U/PF0OmLzXPQC9o6HIOyT7oruhwty7xe4yPHtppDz9Jb08Tq2UPNdpkjybfD24/LLMu1j+ATzN4a67dy98PEWOm7pE1uI83b9wO0w8Cz0Q5p08+s/VvNsRK7qccAI9MJQmuyJpljpR68i8qk+PvG30hzw/CoS8ouH4PFYQSr2CNJG8pEHuO/iADjoPixk8PBZxu+utIT21UhM9rn4nvVhevzxxIom7xSq4OU5pN7zwKJi8eTquvJL6ED2ypDE7PBTivJLOgzygw0o8r2JaO5ikaTtqEnW7gtHFu6SGGb0JKMc8GGC2ujsmeLz5WII89UjGPABB3by3PnO8TtsZvb1fBj1LtRy95B4gvEHtgTsoewu9RmMEPfQ4v7ySGoA6qtvSu8nwwjwNiv28TcoPvZWtjbvFgyE74sB7uhMoADw2LRS83fivuzFwMrwPfqm71PW6vG5s+7qbrwC8ODB2PEV9qTx9WEm7XsXyO5cmvTwofLW51VVIuBg+1btG0488uJMPuyqwwbwI34G85qk3u8+UWDs4fiM8rULYO+RaV7xj59A8ydkRPc6DhjxXfbC8h4a8PCsEhrxN4uC8LZNUPOQxr7ypwqq7rWkHPDnsmbutoca7Gt6TPMp3BDzfm/S8nnP9vKcwyryTaAK8yZbCvImRET1e/JE8SJLhu92fkLwfo5E8GSdMPGGsfry7F7Y8NsWNvMUqITzJEx68qbjcPGOpvzrzIRm8J73VPCCByDt/+qc8W7uxPEOysTvOgR28Sl+UPDEYhDzvEne8J8rcPEUgzLzygmC8gPjAvEihLbyOKgq9/1GLPPcPNrsb1AW9/+Nnu9HUAbxpcxG8aAANuyfq3Ds1OsM8PImdO8oP2rzvXGs49VrFPIM/7DyqGYC8SF6avPQQ0LviZNE7lIxfvIhfjjtjook7oPLJvMnvCjyGUbu8gtaNvCJHubuabSw9us0Xu2vAmLyzfBI8Q020PApYxrtWyO67jx+nvC+A4Ly0rm27WjAMPJXZCDzegSQ97ucDva/oKzzYSpY8Tv6PPHEtGbyPZbq5MtXmu+nUAj13L3Y7QCubO9a2NTv2jyG66VDsvC9llzx0EL672C43vWarxjq7H/k8ilEjvHqYWjx0BTI80XFjOiCVaj01Ez+9oOksO05+bLxWZgE8wF/Xu33WWDwXXHy72neTPMK9ZLzZc6c89Pr8ut+N1bp4CHe7mzpDPO9YirxIK1O7Yc6sPGThJDrd1+88mkpKPOO8urxMunW7I272uuKVSjsbLtI8HogBPbqKabyGsbo7S/FCuwS8a7w8kJW6kUSZPEceujyrfsO8TRnrvAuYqroRhAg90yECPVBooLy4V8O8tyVZvAQrJT3NICK9xzKFOzYNszprOIS8RdXNu4uVBrxvSUu7KXa1u5nayzy3JEA9d+DgOtVZo7xP25M8HXviPFrkMj0o9cM8Rto8PcJVfjwJwey7hqQEPL8vDzz4rvE8GMEgvA/TMDt/t7s8dXiku+xTFD2uDIC8RLMovD2xubn8cRS8RkOrOypQsrzL7gc9h3CTPJHGgbx7Z1g8b+vAOrhfmTzNqi+93zzIu25n87wmk1e8Y1bQPFgHcTzprak8eSZuPCR8aroYgAw9v67IPKKAsjxphAg9uwXwuzajfjyDq+67VPY3PIVX/7s/+b47hA4/PI0snTwCjyq8M8VcPKrcYDwsNzE7X6QwvYyVhjxcnGC8RIkWvWTL+zwAjFg8JfkLPW8bvbxMBJu8oyqQOmYrurzylWy8hCpHuZomDb1uchs6hjH1PG6PhLzFYUS9IZMUvNTo0Drvxym8JW+QO2df1Txkhdw8cTGfu4U1ETy2xcQ7WsAYPakm5Tz2Ah+9QvOhu4Vxpru2WzS8xGfLPBLn0TuKiQa9sy0KPGa8g7w5m8g7dmktvJvbS7yZvI287S7lO7DrJzxvib+8RwJsuzDrUrx/YCU9DLagvHJd6juJkqs7fYZVvGcBcDznCok8Qq3hvBZ3ZLsD/Vc8j4eFPCB6RT2a/bY8geEaPE/7rrzHq0q7UCJYPLa1BLymcGg8P76CPAIeFLyk5ku8YlWGvMz0wruAV/67R0TMvCVtK7yuS327qemuu7Ur4DsXzXK8krHRuhtVDD3+Yg89GyMPPHNnjTzEwgK51t3uO2OnMzwyuBG9vEwGuevFoTuzpk27JQafu/6zcTuWWzG7U8AmvHqqpbzajqK7khyMOwXF87tWAQ+9O10sPJaChzt8cN28zGcSvDheSzyqV9K8Pb0IPVnHpDyiPIo7Iey0O1baYLsNdYG8zbH1vA9kLrz30L68PKJIvep6fjtYrgA986a5uw+qFDy/AZ+8+QNWuzHPELzdOka9F+JdPI7UVjySyRy9rXQavZtyUzzFuBq9oixJOqBNQL1Y+GU8cxlsuxH/CrqhpKq873FTvEII9Dtdlgo9oruivBLw1rwo9aO7qG5MunCyGTzUrdg6Z5udO20ZV7tbPhY8gcNcPKxyMb3qEZ07lHm6PCsimTs3mZ68du+XvIeHD72S3Os8viWtvJzLvTxoI0i8jpErO1n3YLsWsiM7jsnKvOwamrziSo68gz78vGq2ITqFogO94mjDPII8/LwgHxM9kM6CO0qgmrsFsx+8+Z2BOkIrwzs/a8e7En0CvBGM4Tt+0xs8FyAzvVqonDta9s88AIjoN7y5qbsfb3O8S5SqPMzu7jsNoyU8YHSbPDrxObxHKhi9OvKYvEnDMr22Pl68hp3bO8sbFDyXhFG8gq1UPNM/RroAt1i8Llj1vMVAprwK/5079v/mu+uBFL2jvgE7JCwYPc1HuTwwavK7hRPkPLK/9zzz1t66mQLUO6CxprxqDdA8h1ACPOCUUjv4Aww9GKzFPNJ0nDyqQdy8tlWnuZPlXDtpNY08sEBBu/7boLvbiZ28DSO+uyX11jxxbwe899gKPBBinLybqka9aCg/vfg3uzwsfGS8vZqrOyZNfDxdUwW8J7efuwpoEjoRwgE9YoPXvMzXaDslXAY7sOtNPAM9/znj9cQ84siZPO85JbyT2JW85nLFvJXcvTz08eS8l5g1PEUQybzt3ZY73aQNvCRdh7wIuVM8eMY/vD+UHDxP3RW9JsgVvK/LCTqkBre8FF90OY6uprwqByw8VDeJPP6RMrrb+MG8ZNHaPMSlybuyXnq8UX2Uu3IyC7zDfca8JC6IPMnrRjxQLLc7TxGiu+UXrjoC3E681/kGPc1CwzyfsQS8yDiVu7DwIL2GhwM8aBWDPLbSCTwLMYs8cwKCuzmDNL2dw3c7xfTyO49VrzwrJMO8yEDsukfYjbwqaZa84/nEu8uPKDkrZja8laHXPOptXzrpkX088GzsOrhGArxCYwy9a0OdPBRonrtHr9c8FzUAO+GJGLv5qoC8qHL2PDLstrzN3qC8bRvGuiew+Tudb1A7qZzPu2HvnLxr0Ri6sbqdPON73zzmGcg8ZjeJvKTelTuvAWS8cY/Cu5pIJj3X3o+8XSTputDaT7zm5428/Hzou0bNBL10bB+8zDutvJ1ZJr00iRS8gEIsvBWA+7yikp284oDHu7goezxBcxi21/OAvHTbC7wPvUu8np2QO1CCBT334SM8iySLvNil5TrSem872QPUu55kqzuKQPe8TuiJu2PEPr2xNS09LqLpPCBIsjxkjva8mlSOPHqeEj18sJe8ryCfPILWgDzcEVW8kyvRO+dqNDqkMb+7RfzNu6Xnj7yolLm883hrOgw81rzixrs6B6PJvJLO+bsTtSS97hMru07ptDyFMgY8YDXyO4pOGTzmOXm85D8YPa8NOrzYHIO8RkGJu64FWL2eQD87o51nPIlW6jx2P4q8KwMbvLWSuDwi5968CZHKPJWAATxUH588JcmHvA1Z8LukbLE6QHMFvIbokbxsvjM8De5nvIXMKD0PuIA88iZsOy6HkzyGjug7V8cTvIO/5bzo39e80QrwusLVL7yUAwg8HiwWPSoY/Tzs6kC7H5p8uinBDTxMl6a8L0ftu8rV1ry6Qe67OGzOPNBYgLw+fiY8r0b+PIBtjLwcSQA8Jsc5PN26V7w6wtO8dc4oN0PlU7t5e8Q6UVwUPPcumjuC8Hy8bR0vO1LFgLySKhA9u+K5O0Gi8rwHeoQ8+Xz8O4fy9LvG44G7ODVZPKOAzrx0xSu85GebvE71hbwf5wy7Q5RYu+wEpLwhfq28a0mKPPxF+DwEyJM7kP+ePAxpNDzchlC8UTy/PB5OCTw7tcm7d+gmPCbNyLuoaDe6HFD1POk0EDx3fou7PCO9vCC7lrykqqa8pKZbvC6uWbw/TkE9+XD+vEKzGr2ZW+A8V9cnPSAkJTyokIg8aXgWvaWJkTx2pRK8accevcQPB71lGYY8qySlOvnVaTwk6OW87G28uyK3jLxDLPo7nxLxvN+d8DxohAE88h24PCWk3jw6u/S7Ov8PO5czjLxAjZA8wTYAvRHJ6bz/REy8Shl6uUzm+Tw+/SU90n2aPJY2azsP5W48+X0bvWTqqDzCGoG8KPSEO3Of/LwCnK+8qKE8vB6mDr1Vfjw8hdoBvENV7TuIdUG8qCs9vIQGRT0/gby7rzO0PDMByDw50gG96zJmO1YxgLwgBaY8EOu9vEAYOT1+rXM8x27LvIZp3rvkevO5ML6LPLZBFrwwjs84XAAYvHx7mDx3vUc8S0UpPAXwWjxwj5E89rveu4NW4LoQlpM82sNQvN8/4Tpks+Y8bnqcvJsvrry3/+C7GA2GvGbDuTzX0KS8g5ixPJyMLL04/WA8fw7QvMn6H71vOYS8fEP+PAat6ruQP3M8v5ICvJ6iBrxJXBg6Hx9LPCivG7t+tPk7+mWgvO6rozxI+AS8WuSePCbR6Dzrla27VG6TvJ6AID2u7kO8Lyt+O6oVJD1V0/a7Lr6VPNIERrwvckk74kxnvMsAerxVM7K8DNUjPdYB17sukBG9jLZePD+qQLyaexQ9EuAEPTQMnbyNwGg8GtuDughN4btIjP68ubP0vHFF6rw0UMg6hFbZO//2cLw8Sy89f22kOVJTabvPFVA8tF8tvI5h5zwR0u284MGNuqfXKTzItyY85C9YvJvnvbx00sm87j0FPU3lZTzrzxa8itgburZbVbqOej88XBBkO4URzTsanAQ6dm0evQvZZ7wXl4K7Re1tPGcjUjwnHME773wXPUX+gDyk6Y48v0P2u/v9XbwLRpO8Qg4RvYojCrsK+/A8js2avFdzprvtdgO92G8SvJW1LrxVOAy7113AuDccmjzJPAM8oMmxvC5cnDyLPky8XF9jvFN5IDlWDA887DRMOh2psbzHbg09SMvNvDrkAL2xbhC9PO4cOzjmxbvLnaM8HqrNO5BKLTwQLIG8qACUvKIVmTxXWI+8ZV+sPMOFqjqzIuO8MDppunrkRTwglEe8JhS3PBgEhbzgsrq6Mj6iO0en87utSYM7XqCaO/9tcDyEmNq8wuuCPC4N4ztALcI8VxaRPO4c6LuEEwg9EiPMPEQihzxet3E8PbaVOxBDszto7Zk8jucDvO4BHj2dJii8GlNkuyLfMrtTXKA80d0XO8g59rzbfLY8r04OPZXlwDz2CkE8y3rOO1KYIzzaPTe8+Tu/PGmSiTpbpcE7H2uDPAzYMLyN6z26z7gYvHr1uDz4D5k8zit2vPXBortI90G8se8jvbmzXDzhbYO8InhBO/05l7s735E8+RpIvPW+VjsXxwc8sH07vNxzg7uGwxk9wQcfvOD5gbwWn8W7SGp5u6iYjbvzmfK7eHItO3k517x/BQg9nQIkvfXGhDw8hPK8NG5oO6nuIbxtaWq7p7smPXDHaTx9Fzk8FvXcu/fLqTylhOe7SnDWOXDr1byoTys8hTD1vNGlq7ydoKa81CwwPdzla7sdpuW7jBmCPB4IhjtSsVA8xSz1uzBr8jzBFb08ybfIPLxOkzvh3hY9ofdVuiyXt7xQkFo8oD1Ou5EB7reNPxu8KgFQPGzikrsLpjs8pMBNvZmfdjpmh4y8dngavCRMbTvxJeK881QSvcruBjyZlRY8PP6lPHzLLTyoiqG7GA6JO4hUgbzmpSy8Htuqu0gyVzxf2Kq8Q/CNuCxIML2DUB89WJ6svPS/bTxWEW88QmbsvH9al7tqo5O51DURPc8qt7xzKNA7NRlevDADazsYOfM4OnMJOwDFNzz8Ihy9BT1hPFGgxjzkFoS8/iyQPN+8RTxJoCm61JLcvBxFezwvlFy7mdMLvelmmrzKs5W8Lweiuw3aTbyDvB67hdqjOwZasTzas8Y8I/aMu/XMSzwPpFk8VxxwPK/GjDu5TOw8GjlGPCbxl7y5X1y783+svNICBLv5XYA8JVVWvBCK67zPFyy5FWktvFrC8jtTisi6OH+zO0JDjzsAB3+8SG76u4RdFzweXW28epTkugRcrzy/YwQ8FtmBvGN9WjpxqXs8cjVxPBX06bxcUZC8+3EgPA== + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 13 + total_tokens: 13 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4008' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are the research orchestrator evaluating gathered evidence. + + You have access to context that may include: + - : Domain context for the conversation + - : Previous Q&A pairs with confidence scores + + Your task: + 1. Review the provided evidence carefully + 2. Assess whether it sufficiently answers the original question + 3. Decide whether to continue research or synthesize + + Decision criteria: + - Set is_complete=True if the evidence adequately answers the question + - Set is_complete=False with a next_question if important gaps remain + + If not complete, propose exactly ONE high-value follow-up question in next_question: + - Focus on the most critical gap not covered by prior_answers + - The question must be standalone and self-contained + - Avoid repeating questions that have already been answered + - Include concrete entities, scope, and any qualifiers + + Provide brief reasoning explaining your decision. + role: system + - content: |- + Review the gathered evidence and decide whether to continue or synthesize. + + + **Key Facts Established** + - DocLayNet specifies **11 distinct class labels** for document layout analysis: + 1. Caption – descriptions of figures or tables. + 2. Footnote – notes at page bottoms. + 3. Formula – mathematical expressions. + 4. List‑item – items in bulleted or numbered lists. + 5. Page‑footer – footer content on pages. + 6. Page‑header – header content on pages. + 7. Picture – images, diagrams, or other visual media. + 8. Section‑header – headings introducing document sections. + 9. Table – tabular data. + 10. Title – the main document title. + 11. Text – regular body text. + - These categories cover typical elements in scholarly and technical documents, supporting OCR, content extraction, and document structuring tasks. + + **Documents Referenced** + - *DocLayNet Class Labels* – the source that enumerates the 11 class definitions. + + **Current Focus** + - The user is investigating the specific class labels defined by DocLayNet for document layout analysis. + How were the annotations created and how many annotators were involved in DocLayNet? + + + What are the class labels defined in DocLayNet? + DocLayNet defines 11 distinct class labels for document layout analysis: + + 1. **Caption** – text that describes figures or tables. + 2. **Footnote** – notes placed at the bottom of pages. + 3. **Formula** – mathematical expressions. + 4. **List‑item** – items in bulleted or numbered lists. + 5. **Page‑footer** – footer content on pages. + 6. **Page‑header** – header content on pages. + 7. **Picture** – images, diagrams, or other visual media. + 8. **Section‑header** – headings that introduce document sections. + 9. **Table** – tabular data. + 10. **Title** – the main title of the document. + 11. **Text** – regular body text. + + These 11 categories cover the typical elements found in scholarly and technical documents, allowing accurate layout analysis for tasks such as OCR, content extraction, and document structuring. + 0.95 + DocLayNet Class Labels + + + + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Output from iterative planning step. + name: final_result + parameters: + additionalProperties: false + properties: + is_complete: + description: Whether research is complete and can be synthesized + type: boolean + next_question: + anyOf: + - type: string + - type: 'null' + default: null + description: Next question to investigate, if not complete + reasoning: + description: Brief explanation of the decision + type: string + required: + - is_complete + - reasoning + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1001' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: 'Need to decide if enough evidence to answer question: how annotations created and how many annotators. + We have no info. So incomplete. Need follow-up question.' + role: assistant + tool_calls: + - function: + arguments: '{"is_complete":false,"next_question":"What methods and procedures were used to annotate the documents + in DocLayNet, and how many independent human annotators participated in the annotation process?","reasoning":"The + provided evidence only lists class labels; it does not address annotation methodology or annotator count. + A follow-up question focusing on annotation process and number of annotators is needed."}' + name: final_result + id: call_h7n5gof7 + index: 0 + type: function + created: 1770727426 + id: chatcmpl-26 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 128 + prompt_tokens: 865 + total_tokens: 993 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2949' + 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 methods and procedures were used to annotate the documents in DocLayNet, and how many independent human + annotators participated in the annotation process? + 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: + - '553' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: We need to search. + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet annotation methods procedures independent human annotators participated","limit":5}' + name: search_and_answer + id: call_2efy9ga2 + index: 0 + type: function + created: 1770727428 + id: chatcmpl-845 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 44 + prompt_tokens: 643 + total_tokens: 687 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '151' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet annotation methods procedures independent human annotators participated + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 13 + total_tokens: 13 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4392' + 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 methods and procedures were used to annotate the documents in DocLayNet, and how many independent human + annotators participated in the annotation process? + role: user + - content: |- + + We need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet annotation methods procedures independent human annotators participated","limit":5}' + name: search_and_answer + id: call_2efy9ga2 + type: function + - content: |- + [46be2451-1009-4414-9059-771d99a3b0be] [rank 1 of 2] + Source: "DocLayNet Annotation" + Type: list_item + Content: + 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. + + [b731edec-a826-4cfe-96c4-b3e3a3226ba1] [rank 2 of 2] + 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_2efy9ga2 + 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: + - '1198' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "**Question:** What methods and procedures were used to annotate the documents in DocLayNet, and how many + independent human annotators participated in the annotation process?\n\n**Answer:** \nDocLayNet’s annotation + pipeline consisted of four sequential phases:\n\n1. **Data selection & preparation** by a small expert team. \n2. + **Label selection & guideline definition** to formalize the 11 layout classes. \n3. **Annotation by 40 dedicated + annotators** using the Corpus Conversion Service (CCS), a visual interface that enabled bounding‑box labeling. + \ \n4. **Quality control & continuous supervision** to ensure label consistency and accuracy.\n\nThus, **40 independent + human annotators** were involved in the annotation process.\n\n**Cited chunks:** \n- 46be2451-1009-4414-9059-771d99a3b0be + \ \n- b731edec-a826-4cfe-96c4-b3e3a3226ba1 \n\n**Confidence:** 1.0" + reasoning: Provide answer. + role: assistant + created: 1770727434 + id: chatcmpl-248 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 220 + prompt_tokens: 967 + total_tokens: 1187 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5459' + 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 methods and procedures were used to annotate the documents in DocLayNet, and how many independent human + annotators participated in the annotation process? + role: user + - content: |- + + We need to search. + + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet annotation methods procedures independent human annotators participated","limit":5}' + name: search_and_answer + id: call_2efy9ga2 + type: function + - content: |- + [46be2451-1009-4414-9059-771d99a3b0be] [rank 1 of 2] + Source: "DocLayNet Annotation" + Type: list_item + Content: + 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. + + [b731edec-a826-4cfe-96c4-b3e3a3226ba1] [rank 2 of 2] + 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_2efy9ga2 + - content: "\nProvide answer.\n\n\n**Question:** What methods and procedures were used to annotate the + documents in DocLayNet, and how many independent human annotators participated in the annotation process?\n\n**Answer:** + \ \nDocLayNet’s annotation pipeline consisted of four sequential phases:\n\n1. **Data selection & preparation** + by a small expert team. \n2. **Label selection & guideline definition** to formalize the 11 layout classes. \n3. + **Annotation by 40 dedicated annotators** using the Corpus Conversion Service (CCS), a visual interface that enabled + bounding‑box labeling. \n4. **Quality control & continuous supervision** to ensure label consistency and accuracy.\n\nThus, + **40 independent human annotators** were involved in the annotation process.\n\n**Cited chunks:** \n- 46be2451-1009-4414-9059-771d99a3b0be + \ \n- b731edec-a826-4cfe-96c4-b3e3a3226ba1 \n\n**Confidence:** 1.0" + role: assistant + - content: |- + Validation feedback: + Please include your response in a tool call. + + Fix the errors and try again. + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Search the knowledge base for relevant documents. + name: search_and_answer + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + query: + type: string + required: + - query + type: object + type: function + - function: + description: Answer to a search query with chunk references. + name: final_result + parameters: + additionalProperties: false + properties: + answer: + description: The answer to the question + type: string + cited_chunks: + description: IDs of chunks used to form the answer + items: + type: string + type: array + confidence: + default: 1.0 + description: Confidence score for this answer (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + query: + description: The question that was answered + type: string + required: + - query + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1277' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + role: assistant + tool_calls: + - function: + arguments: '{"answer":"DocLayNet’s annotation pipeline consisted of four sequential phases:\n\n1. **Data selection + \u0026 preparation** by a small expert team.\n2. **Label selection \u0026 guideline definition** to formalize + the 11 layout classes.\n3. **Annotation by 40 dedicated annotators** using the Corpus Conversion Service (CCS), + a visual interface that enabled bounding‑box labeling.\n4. **Quality control \u0026 continuous supervision** + to ensure label consistency and accuracy.\n\nThus, 40 independent human annotators participated in the annotation + process.","cited_chunks":["46be2451-1009-4414-9059-771d99a3b0be","b731edec-a826-4cfe-96c4-b3e3a3226ba1"],"confidence":1,"query":"What + methods and procedures were used to annotate the documents in DocLayNet, and how many independent human annotators + participated in the annotation process?"}' + name: final_result + id: call_k17udb5a + index: 0 + type: function + created: 1770727439 + id: chatcmpl-157 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 214 + prompt_tokens: 1212 + total_tokens: 1426 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5529' + 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. + + + **Key Facts Established** + - DocLayNet specifies **11 distinct class labels** for document layout analysis: + 1. Caption – descriptions of figures or tables. + 2. Footnote – notes at page bottoms. + 3. Formula – mathematical expressions. + 4. List‑item – items in bulleted or numbered lists. + 5. Page‑footer – footer content on pages. + 6. Page‑header – header content on pages. + 7. Picture – images, diagrams, or other visual media. + 8. Section‑header – headings introducing document sections. + 9. Table – tabular data. + 10. Title – the main document title. + 11. Text – regular body text. + - These categories cover typical elements in scholarly and technical documents, supporting OCR, content extraction, and document structuring tasks. + + **Documents Referenced** + - *DocLayNet Class Labels* – the source that enumerates the 11 class definitions. + + **Current Focus** + - The user is investigating the specific class labels defined by DocLayNet for document layout analysis. + How were the annotations created and how many annotators were involved in DocLayNet? + + + What are the class labels defined in DocLayNet? + DocLayNet defines 11 distinct class labels for document layout analysis: + + 1. **Caption** – text that describes figures or tables. + 2. **Footnote** – notes placed at the bottom of pages. + 3. **Formula** – mathematical expressions. + 4. **List‑item** – items in bulleted or numbered lists. + 5. **Page‑footer** – footer content on pages. + 6. **Page‑header** – header content on pages. + 7. **Picture** – images, diagrams, or other visual media. + 8. **Section‑header** – headings that introduce document sections. + 9. **Table** – tabular data. + 10. **Title** – the main title of the document. + 11. **Text** – regular body text. + + These 11 categories cover the typical elements found in scholarly and technical documents, allowing accurate layout analysis for tasks such as OCR, content extraction, and document structuring. + 0.95 + DocLayNet Class Labels + + + What methods and procedures were used to annotate the documents in DocLayNet, and how many independent human annotators participated in the annotation process? + DocLayNet’s annotation pipeline consisted of four sequential phases: + + 1. **Data selection & preparation** by a small expert team. + 2. **Label selection & guideline definition** to formalize the 11 layout classes. + 3. **Annotation by 40 dedicated annotators** using the Corpus Conversion Service (CCS), a visual interface that enabled bounding‑box labeling. + 4. **Quality control & continuous supervision** to ensure label consistency and accuracy. + + Thus, 40 independent human annotators participated in the annotation process. + 1.0 + DocLayNet Annotation + + + + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: Conversational answer for chat context. + name: final_result + parameters: + $defs: + Citation: + additionalProperties: false + description: |- + Resolved citation with full metadata for display/visual grounding. + + Used by both research graph and chat agent. The optional index field + supports UI display ordering in chat contexts. + properties: + chunk_id: + type: string + content: + type: string + document_id: + type: string + document_title: + anyOf: + - type: string + - type: 'null' + default: null + document_uri: + type: string + headings: + anyOf: + - items: + type: string + type: array + - type: 'null' + default: null + index: + anyOf: + - type: integer + - type: 'null' + default: null + page_numbers: + items: + type: integer + type: array + required: + - document_id + - chunk_id + - document_uri + - content + type: object + additionalProperties: false + properties: + answer: + description: Direct answer to the question + type: string + citations: + description: Citations supporting the answer + items: + $ref: '#/$defs/Citation' + type: array + confidence: + default: 1.0 + description: Confidence score (0-1) + maximum: 1.0 + minimum: 0.0 + type: number + required: + - answer + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1102' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need answer. + role: assistant + tool_calls: + - function: + arguments: '{"answer":"DocLayNet’s documents were labeled through a structured, four‑step pipeline. First, a + small expert team selected the image and PDF samples and prepared them for annotation. Next, the team defined + the 11 layout classes and wrote detailed labeling guidelines. Then, 40 independent human annotators performed + the bounding‑box labeling using the Corpus Conversion Service (CCS), a visual annotation interface. Finally, + a quality‑control process supervised the work and resolved inconsistencies for a consistent final dataset.\n\nSo, + **40 annotators** were involved in creating the DocLayNet annotations.\n","citations":[],"confidence":0.99}' + name: final_result + id: call_wourewew + index: 0 + type: function + created: 1770727444 + id: chatcmpl-722 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 154 + prompt_tokens: 1037 + total_tokens: 1191 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '3184' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a session summarizer. Given a conversation history of Q&A pairs (and optionally existing context), produce a structured summary that captures key information for future context. + + If a "Current Context" section is provided at the start of the input, incorporate that context into your summary. This might be initial background context from the user or a previous summary - build upon it rather than discard it. + + Your summary should be concise (aim for 500-1500 tokens) and include: + + 1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation + 2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain + 3. **Current Focus** - What topic or question thread the user is currently exploring + + Rules: + - Extract only high-signal information that would help answer follow-up questions + - When building on existing context, merge new information with prior context + - Omit small talk, greetings, or low-confidence answers + - Use bullet points for clarity + - Keep technical details but compress verbose explanations + - Preserve document names/titles when mentioned in sources + + Output the summary directly in markdown format. Do not include meta-commentary about the summary itself. + role: system + - content: | + ## Q1: What are the class labels defined in DocLayNet? + **Answer** (confidence: 95%): + DocLayNet defines 11 distinct class labels for document layout analysis: + + 1. **Caption** – text that describes figures or tables. + 2. **Footnote** – notes placed at the bottom of pages. + 3. **Formula** – mathematical expressions. + 4. **List‑item** – items in bulleted or numbered lists. + 5. **Page‑footer** – footer content on pages. + 6. **Page‑header** – header content on pages. + 7. **Picture** – images, diagrams, or other visual media. + 8. **Section‑header** – headings that introduce document sections. + 9. **Table** – tabular data. + 10. **Title** – the main title of the document. + 11. **Text** – regular body text. + + These 11 categories cover the typical elements found in scholarly and technical documents, allowing accurate layout analysis for tasks such as OCR, content extraction, and document structuring. + **Sources:** DocLayNet Class Labels + + ## Q2: How were the annotations created and how many annotators were involved in DocLayNet? + **Answer** (confidence: 99%): + DocLayNet’s documents were labeled through a structured, four‑step pipeline. First, a small expert team selected the image and PDF samples and prepared them for annotation. Next, the team defined the 11 layout classes and wrote detailed labeling guidelines. Then, 40 independent human annotators performed the bounding‑box labeling using the Corpus Conversion Service (CCS), a visual annotation interface. Finally, a quality‑control process supervised the work and resolved inconsistencies for a consistent final dataset. + + So, **40 annotators** were involved in creating the DocLayNet annotations. + + **Sources:** DocLayNet Class Labels, DocLayNet Annotation + role: user + model: gpt-oss + reasoning_effort: low + stream: false + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1567' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "## Summary\n\n### Key Facts Established\n- **DocLayNet Class Labels** \n - 11 distinct layout classes: + \ \n 1. Caption \n 2. Footnote \n 3. Formula \n 4. List‑item \n 5. Page‑footer \n 6. Page‑header + \ \n 7. Picture \n 8. Section‑header \n 9. Table \n 10. Title \n 11. Text \n - Designed for + scholarly/technical documents to aid OCR, content extraction, and document structuring.\n\n- **Annotation Process + & Human Resources** \n - Structured four‑step pipeline: selection, guideline creation, labeling, quality control. + \ \n - 40 independent human annotators used the Corpus Conversion Service (CCS) interface for bounding‑box labeling. + \ \n - Final dataset underwent consistency checks and issue resolution.\n\n### Documents Referenced\n- **DocLayNet + Class Labels** – source detailing the 11 layout categories. \n- **DocLayNet Annotation** – source explaining + the annotation workflow and annotator count.\n\n### Current Focus\nThe user is exploring fundamental aspects of + the DocLayNet dataset: specifically, the definition of its class labels and the annotation methodology, including + the number of annotators and workflow steps." + reasoning: We need to summarize key facts, documents referenced, current focus. + role: assistant + created: 1770727461 + id: chatcmpl-507 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 289 + prompt_tokens: 704 + total_tokens: 993 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '8663' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: "You are a helpful research assistant powered by haiku.rag, a knowledge base system.\n\nYou have access to + a knowledge base of documents. Use your tools to search and answer questions.\n\nCRITICAL RULES:\n1. For greetings + or casual chat: respond directly WITHOUT using any tools\n2. NEVER call the same tool multiple times for a single + user message\n3. NEVER make up information - always use tools to get facts from the knowledge base\n4. For questions: + Use the \"ask\" tool EXACTLY ONCE - it automatically uses prior conversation context\n5. For searches: Use the \"search\" + tool EXACTLY ONCE - it handles multi-query expansion internally\n\nHow to decide which tool to use:\n- \"list_documents\" + - Use when the user wants to browse or see what documents are available (e.g., \"what documents are available?\", + \"show me the documents\", \"list available docs\").\n- \"summarize_document\" - Use when the user wants an overview + or summary of a specific document (e.g., \"summarize document X\", \"what does Y cover?\", \"give me an overview + of Z\").\n- \"get_document\" - Use when the user wants the FULL content of a specific document (e.g., \"get the + paper about Y\", \"fetch 2412.00566\", \"show me the full document\").\n- \"ask\" - Use for questions about topics + in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across + documents to return answers with citations.\n- \"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.\n\nIMPORTANT - When user mentions a document + in search/ask:\n- If user says \"search in \", \"find in \", \"answer from \", or \" in \":\n + \ - Extract the TOPIC as `query`/`question`\n - Extract the DOCUMENT NAME as `document_name`\n- Examples for search:\n + \ - \"search for embeddings in the ML paper\" → query=\"embeddings\", document_name=\"ML paper\"\n - \"find transformer + architecture in 2412.00566\" → query=\"transformer architecture\", document_name=\"2412.00566\" \n- Examples for + ask:\n - \"what does the ML paper say about embeddings?\" → question=\"what are the embedding methods?\", document_name=\"ML + paper\"\n - \"answer from 2412.00566 about model training\" → question=\"how is the model trained?\", document_name=\"2412.00566\" + \nBe friendly and conversational. When you use the \"ask\" tool, summarize the key findings for the user." + role: system + - content: What are the class labels defined in DocLayNet? + role: user + - content: |- + + Need to ask using ask tool. + + role: assistant + tool_calls: + - function: + arguments: '{"question":"What are the class labels defined in DocLayNet?","document_name":null}' + name: ask + id: call_bz5ma4zl + type: function + - content: |- + DocLayNet defines 11 distinct class labels for document layout analysis: + + 1. **Caption** – text that describes figures or tables. + 2. **Footnote** – notes placed at the bottom of pages. + 3. **Formula** – mathematical expressions. + 4. **List‑item** – items in bulleted or numbered lists. + 5. **Page‑footer** – footer content on pages. + 6. **Page‑header** – header content on pages. + 7. **Picture** – images, diagrams, or other visual media. + 8. **Section‑header** – headings that introduce document sections. + 9. **Table** – tabular data. + 10. **Title** – the main title of the document. + 11. **Text** – regular body text. + + These 11 categories cover the typical elements found in scholarly and technical documents, allowing accurate layout analysis for tasks such as OCR, content extraction, and document structuring. + + Sources: [1] + role: tool + tool_call_id: call_bz5ma4zl + - content: |- + DocLayNet defines **11 distinct class labels** for document layout analysis: + + | # | Class label | Description | + |---|----------------|-------------| + | 1 | **Caption** | Text that describes figures, tables, or diagrams. | + | 2 | **Footnote** | Notes placed at the bottom of pages. | + | 3 | **Formula** | Mathematical or LaTeX‑style expressions. | + | 4 | **List‑item** | Items in bulleted or numbered lists. | + | 5 | **Page‑footer**| Footer content that appears on each page. | + | 6 | **Page‑header**| Header content that appears on each page. | + | 7 | **Picture** | Images, diagrams, or other visual media. | + | 8 | **Section‑header**| Headings that introduce new sections. | + | 9 | **Table** | Tabular data blocks. | + |10 | **Title** | The main title or heading of the document. | + |11 | **Text** | Regular body text (paragraphs, sentences). | + + These 11 categories comprise the full set of class labels used in the DocLayNet dataset for document layout segmentation and recognition tasks. + role: assistant + - content: How were the annotations created and how many annotators were involved? + role: user + - content: |- + + Need ask. + + role: assistant + tool_calls: + - function: + arguments: '{"document_name":null,"question":"How were the annotations created and how many annotators were involved + in DocLayNet?"}' + name: ask + id: call_dz3lh8dm + type: function + - content: |- + DocLayNet’s documents were labeled through a structured, four‑step pipeline. First, a small expert team selected the image and PDF samples and prepared them for annotation. Next, the team defined the 11 layout classes and wrote detailed labeling guidelines. Then, 40 independent human annotators performed the bounding‑box labeling using the Corpus Conversion Service (CCS), a visual annotation interface. Finally, a quality‑control process supervised the work and resolved inconsistencies for a consistent final dataset. + + So, **40 annotators** were involved in creating the DocLayNet annotations. + + + Sources: [1] [2] + role: tool + tool_call_id: call_dz3lh8dm + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Formatted search results with content and metadata. + + name: search + parameters: + additionalProperties: false + properties: + filter: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional SQL WHERE clause to filter documents. + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: from config).' + query: + description: The search query (what to search for). + type: string + required: + - query + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Paginated list of documents with metadata. + + name: list_documents + parameters: + additionalProperties: false + properties: + page: + default: 1 + description: 'Page number (default: 1, 50 documents per page)' + type: integer + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Document content and metadata, or not found message. + + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up. + type: string + required: + - query + type: object + strict: true + type: function + - function: + description: |- + Generate a summary of a specific document. + + Generated summary or not found message. + + name: summarize_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to summarize. + type: string + required: + - query + type: object + strict: true + type: function + - function: + description: |- + Answer a question using the knowledge base. + + Uses a research graph for searching and synthesizing answers. + + QAResult with answer, confidence, and citations. + + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within. + question: + description: The question to answer. + type: string + required: + - question + type: object + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1043' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: |- + DocLayNet’s labels were produced through a **four‑step annotation pipeline**: + + 1. **Sample curation** – A small expert team collected the PDF/text images and prepared them for labeling. + 2. **Guideline creation** – The same group defined the 11 layout classes and drafted detailed labeling rules. + 3. **Bounding‑box annotation** – The bulk of the work was done by **40 independent human annotators** who drew boxes around every instance of the 11 classes in the PDFs, using the Corpus Conversion Service (CCS) interface. + 4. **Quality control** – A final review stage identified and corrected any inconsistencies, ensuring a consistent, high‑quality final dataset. + + So, *forty annotators* were involved in the DocLayNet labeling effort. + role: assistant + created: 1770727471 + id: chatcmpl-148 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 163 + prompt_tokens: 1779 + total_tokens: 1942 + status: + code: 200 + message: OK +version: 1