From cca03dbe616f665b74993129777e649f5eefaad7 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Tue, 27 Jan 2026 16:39:48 +0200 Subject: [PATCH] Give the conversational agent a list documents and a summarize tools --- CHANGELOG.md | 3 + docs/agents.md | 6 +- haiku_rag_slim/haiku/rag/agents/chat/agent.py | 125 +++- .../haiku/rag/agents/chat/prompts.py | 17 +- tests/agents/chat/test_chat_agent.py | 171 ++++++ .../test_list_documents_basic.yaml | 476 +++++++++++++++ .../test_list_documents_pagination.yaml | 524 ++++++++++++++++ ...st_list_documents_with_session_filter.yaml | 480 +++++++++++++++ .../test_summarize_document_found.yaml | 563 ++++++++++++++++++ .../test_summarize_document_not_found.yaml | 196 ++++++ 10 files changed, 2532 insertions(+), 29 deletions(-) create mode 100644 tests/cassettes/test_chat_agent/test_list_documents_basic.yaml create mode 100644 tests/cassettes/test_chat_agent/test_list_documents_pagination.yaml create mode 100644 tests/cassettes/test_chat_agent/test_list_documents_with_session_filter.yaml create mode 100644 tests/cassettes/test_chat_agent/test_summarize_document_found.yaml create mode 100644 tests/cassettes/test_chat_agent/test_summarize_document_not_found.yaml diff --git a/CHANGELOG.md b/CHANGELOG.md index 908ae75c..2977daaa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,9 @@ ### Added +- **Chat Agent Document Awareness Tools**: Two new tools for browsing and understanding the knowledge base + - `list_documents` — Browse available documents with title, URI, and creation date; respects session document filter + - `summarize_document` — Generate LLM-powered summaries of specific documents - **Read-Only Initial Context**: Initial context is now locked after the first message, providing consistent session context - Chat TUI: `--initial-context` CLI option sets background context for the session - Context can be edited via command palette before the first message is sent diff --git a/docs/agents.md b/docs/agents.md index e02aa10b..5ddfe641 100644 --- a/docs/agents.md +++ b/docs/agents.md @@ -61,11 +61,13 @@ Key features: ### Tools -The chat agent uses three tools: +The chat agent uses five tools: +- `list_documents` — Browse available documents in the knowledge base +- `summarize_document` — Generate a summary of a specific document +- `get_document` — Retrieve a specific document by title or URI - `search` — Hybrid search with optional document filter - `ask` — Answer questions using the conversational research graph (automatically recalls prior answers) -- `get_document` — Retrieve a specific document by title or URI The `ask` tool automatically checks conversation history before running research. It uses embedding similarity (0.7 cosine threshold) to find semantically matching prior answers, which are passed to the research planner as context. When prior answers are sufficient, the planner can skip searching entirely. diff --git a/haiku_rag_slim/haiku/rag/agents/chat/agent.py b/haiku_rag_slim/haiku/rag/agents/chat/agent.py index c3cf1ea5..929f0898 100644 --- a/haiku_rag_slim/haiku/rag/agents/chat/agent.py +++ b/haiku_rag_slim/haiku/rag/agents/chat/agent.py @@ -8,7 +8,7 @@ from haiku.rag.agents.chat.context import ( get_cached_session_context, update_session_context, ) -from haiku.rag.agents.chat.prompts import CHAT_SYSTEM_PROMPT +from haiku.rag.agents.chat.prompts import CHAT_SYSTEM_PROMPT, DOCUMENT_SUMMARY_PROMPT from haiku.rag.agents.chat.search import SearchAgent from haiku.rag.agents.chat.state import ( MAX_QA_HISTORY, @@ -23,6 +23,7 @@ from haiku.rag.agents.research.dependencies import ResearchContext from haiku.rag.agents.research.graph import build_conversational_graph from haiku.rag.agents.research.models import Citation from haiku.rag.agents.research.state import ResearchDeps, ResearchState +from haiku.rag.client import HaikuRAG from haiku.rag.config.models import AppConfig from haiku.rag.embeddings import get_embedder from haiku.rag.utils import get_model @@ -369,6 +370,72 @@ def create_chat_agent(config: AppConfig) -> Agent[ChatDeps, str]: ], ) + @agent.tool + async def list_documents( + ctx: RunContext[ChatDeps], + limit: int | None = None, + offset: int | None = None, + ) -> str: + """List available documents in the knowledge base. + + Use this when the user wants to browse or see what documents are available. + + Args: + limit: Maximum number of documents to return + offset: Number of documents to skip (for pagination) + """ + # Build session filter from document_filter + doc_filter = None + if ctx.deps.session_state and ctx.deps.session_state.document_filter: + doc_filter = build_multi_document_filter( + ctx.deps.session_state.document_filter + ) + + docs = await ctx.deps.client.list_documents( + limit=limit, offset=offset, filter=doc_filter + ) + + if not docs: + return "No documents found in the knowledge base." + + lines = [f"Found {len(docs)} document(s):\n"] + for doc in docs: + title = doc.title or "Untitled" + uri = doc.uri or "N/A" + created = doc.created_at.strftime("%Y-%m-%d") + lines.append(f"- **{title}** (URI: {uri}, Created: {created})") + + return "\n".join(lines) + + async def _find_document(client: HaikuRAG, query: str): + """Find a document by exact URI, partial URI, or partial title match.""" + # Try exact URI match first + doc = await client.get_document_by_uri(query) + if doc is not None: + return doc + + escaped_query = query.replace("'", "''") + # Also try without spaces for matching "TB MED 593" to "tbmed593" + no_spaces = escaped_query.replace(" ", "") + + # Try partial URI match (with and without spaces) + docs = await client.list_documents( + limit=1, + filter=f"LOWER(uri) LIKE LOWER('%{escaped_query}%') OR LOWER(uri) LIKE LOWER('%{no_spaces}%')", + ) + if docs: + return docs[0] + + # Try partial title match (with and without spaces) + docs = await client.list_documents( + limit=1, + filter=f"LOWER(title) LIKE LOWER('%{escaped_query}%') OR LOWER(title) LIKE LOWER('%{no_spaces}%')", + ) + if docs: + return docs[0] + + return None + @agent.tool async def get_document( ctx: RunContext[ChatDeps], @@ -381,30 +448,7 @@ def create_chat_agent(config: AppConfig) -> Agent[ChatDeps, str]: Args: query: The document title or URI to look up """ - # Try exact URI match first - doc = await ctx.deps.client.get_document_by_uri(query) - - escaped_query = query.replace("'", "''") - # Also try without spaces for matching "TB MED 593" to "tbmed593" - no_spaces = escaped_query.replace(" ", "") - - # If not found, try partial URI match (with and without spaces) - if doc is None: - docs = await ctx.deps.client.list_documents( - limit=1, - filter=f"LOWER(uri) LIKE LOWER('%{escaped_query}%') OR LOWER(uri) LIKE LOWER('%{no_spaces}%')", - ) - if docs: - doc = docs[0] - - # If still not found, try partial title match (with and without spaces) - if doc is None: - docs = await ctx.deps.client.list_documents( - limit=1, - filter=f"LOWER(title) LIKE LOWER('%{escaped_query}%') OR LOWER(title) LIKE LOWER('%{no_spaces}%')", - ) - if docs: - doc = docs[0] + doc = await _find_document(ctx.deps.client, query) if doc is None: return f"Document not found: {query}" @@ -412,9 +456,38 @@ def create_chat_agent(config: AppConfig) -> Agent[ChatDeps, str]: return ( f"**{doc.title or 'Untitled'}**\n\n" f"- ID: {doc.id}\n" - f"- URI: {doc.uri or 'N/A'}\n" + f"- URI: {doc.uri}\n" f"- Created: {doc.created_at.strftime('%Y-%m-%d %H:%M')}\n\n" f"**Content:**\n{doc.content}" ) + @agent.tool + async def summarize_document( + ctx: RunContext[ChatDeps], + query: str, + ) -> str: + """Generate a summary of a specific document. + + Use this when the user wants an overview or summary of a document's content. + + Args: + query: The document title or URI to summarize + """ + doc = await _find_document(ctx.deps.client, query) + + if doc is None: + return f"Document not found: {query}" + + # Use LLM to generate summary + summary_model = get_model(ctx.deps.config.qa.model, ctx.deps.config) + summary_agent: Agent[None, str] = Agent( + summary_model, + output_type=str, + ) + result = await summary_agent.run( + DOCUMENT_SUMMARY_PROMPT.format(content=doc.content or "") + ) + + return f"**Summary of {doc.title or doc.uri}:**\n\n{result.output}" + return agent diff --git a/haiku_rag_slim/haiku/rag/agents/chat/prompts.py b/haiku_rag_slim/haiku/rag/agents/chat/prompts.py index 4f004fe9..023352d7 100644 --- a/haiku_rag_slim/haiku/rag/agents/chat/prompts.py +++ b/haiku_rag_slim/haiku/rag/agents/chat/prompts.py @@ -10,7 +10,9 @@ CRITICAL RULES: 5. NEVER make up information - always use tools to get facts from the knowledge base How to decide which tool to use: -- "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. +- "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs"). +- "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z"). +- "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document"). - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations. - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. @@ -58,3 +60,16 @@ Rules: - Preserve document names/titles when mentioned in sources Output the summary directly in markdown format. Do not include meta-commentary about the summary itself.""" + +DOCUMENT_SUMMARY_PROMPT = """Generate a summary of the document content provided below. + +Start with a one-paragraph overview, then list the main topics covered, and highlight any key findings or conclusions. + +Guidelines: +- Aim for 1-2 paragraphs for short documents, 3-4 paragraphs for longer ones +- Focus on factual content and key information +- Do not include meta-commentary like "This document discusses..." or "The document covers..." +- Do not speculate beyond what's in the content + +Document content: +{content}""" diff --git a/tests/agents/chat/test_chat_agent.py b/tests/agents/chat/test_chat_agent.py index 46323e52..407ba901 100644 --- a/tests/agents/chat/test_chat_agent.py +++ b/tests/agents/chat/test_chat_agent.py @@ -966,6 +966,177 @@ async def test_summarization_task_cancellation(): _summarization_tasks.clear() +# ============================================================================= +# list_documents Tool Tests +# ============================================================================= + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_list_documents_basic(allow_model_requests, temp_db_path): + """Test that list_documents tool returns available documents.""" + async with HaikuRAG(temp_db_path, create=True) as client: + # Add test documents + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + await client.create_document( + content=DOCLAYNET_ANNOTATION, + uri="doclaynet-annotation", + title="DocLayNet Annotation", + ) + + agent = create_chat_agent(Config) + deps = ChatDeps( + client=client, + config=Config, + ) + + # Ask to list documents + result = await agent.run( + "What documents are available in the knowledge base?", + deps=deps, + ) + + assert result.output is not None + # Should mention both documents + assert "DocLayNet" in result.output + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_list_documents_with_session_filter(allow_model_requests, temp_db_path): + """Test that list_documents respects session document_filter.""" + async with HaikuRAG(temp_db_path, create=True) as client: + # Add test documents + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + await client.create_document( + content=DOCLAYNET_DATA_SOURCES, + uri="doclaynet-sources", + title="DocLayNet Sources", + ) + + agent = create_chat_agent(Config) + # Set session filter to only include the labels document + session_state = ChatSessionState( + session_id="test-list-filter", + document_filter=["DocLayNet Class Labels"], + ) + deps = ChatDeps( + client=client, + config=Config, + session_state=session_state, + ) + + # Ask to list documents - should only show filtered documents + result = await agent.run( + "Show me what documents are available", + deps=deps, + ) + + assert result.output is not None + # Should only mention the Labels document, not Sources + assert "Labels" in result.output or "labels" in result.output + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_list_documents_pagination(allow_model_requests, temp_db_path): + """Test that list_documents supports pagination via limit/offset.""" + async with HaikuRAG(temp_db_path, create=True) as client: + # Add multiple test documents + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + await client.create_document( + content=DOCLAYNET_ANNOTATION, + uri="doclaynet-annotation", + title="DocLayNet Annotation", + ) + await client.create_document( + content=DOCLAYNET_DATA_SOURCES, + uri="doclaynet-sources", + title="DocLayNet Sources", + ) + + agent = create_chat_agent(Config) + deps = ChatDeps( + client=client, + config=Config, + ) + + # Ask to list first 2 documents + result = await agent.run( + "List the first 2 documents available", + deps=deps, + ) + + assert result.output is not None + + +# ============================================================================= +# summarize_document Tool Tests +# ============================================================================= + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_summarize_document_found(allow_model_requests, temp_db_path): + """Test that summarize_document generates a summary for a found document.""" + async with HaikuRAG(temp_db_path, create=True) as client: + # Add a test document + await client.create_document( + content=DOCLAYNET_CLASS_LABELS, + uri="doclaynet-labels", + title="DocLayNet Class Labels", + ) + + agent = create_chat_agent(Config) + deps = ChatDeps( + client=client, + config=Config, + ) + + # Ask to summarize a specific document + result = await agent.run( + "Summarize the DocLayNet Class Labels document", + deps=deps, + ) + + assert result.output is not None + # Should contain summary content about class labels + assert len(result.output) > 50 + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_summarize_document_not_found(allow_model_requests, temp_db_path): + """Test that summarize_document handles not found documents gracefully.""" + async with HaikuRAG(temp_db_path, create=True) as client: + agent = create_chat_agent(Config) + deps = ChatDeps( + client=client, + config=Config, + ) + + # Ask to summarize a document that doesn't exist + result = await agent.run( + "Summarize the nonexistent document", + deps=deps, + ) + + assert result.output is not None + # Should indicate the document wasn't found + + def test_citation_index_fallback_without_session_state(): """Test that citation indices fall back to sequential numbering without session_state. diff --git a/tests/cassettes/test_chat_agent/test_list_documents_basic.yaml b/tests/cassettes/test_chat_agent/test_list_documents_basic.yaml new file mode 100644 index 00000000..ee77e6b5 --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_list_documents_basic.yaml @@ -0,0 +1,476 @@ +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: + - '4848' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs"). + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: What documents are available in the knowledge base? + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Use this when the user wants to browse or see what documents are available. + name: list_documents + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: Maximum number of documents to return + offset: + anyOf: + - type: integer + - type: 'null' + default: null + description: Number of documents to skip (for pagination) + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '470' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need list_documents. + role: assistant + tool_calls: + - function: + arguments: '{"limit":10,"offset":0}' + name: list_documents + id: call_e3klcjfu + index: 0 + type: function + created: 1769523738 + id: chatcmpl-179 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 35 + prompt_tokens: 956 + total_tokens: 991 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5290' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs"). + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: What documents are available in the knowledge base? + role: user + - content: |- + + Need list_documents. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":10,"offset":0}' + name: list_documents + id: call_e3klcjfu + type: function + - content: |- + Found 2 document(s): + + - **DocLayNet Class Labels** (URI: doclaynet-labels, Created: 2026-01-27) + - **DocLayNet Annotation** (URI: doclaynet-annotation, Created: 2026-01-27) + role: tool + tool_call_id: call_e3klcjfu + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Use this when the user wants to browse or see what documents are available. + name: list_documents + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: Maximum number of documents to return + offset: + anyOf: + - type: integer + - type: 'null' + default: null + description: Number of documents to skip (for pagination) + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '509' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "Here are the documents available in the knowledge base:\n\n- **DocLayNet Class Labels** (URI: `doclaynet-labels`, + Created: 2026‑01‑27) \n- **DocLayNet Annotation** (URI: `doclaynet-annotation`, Created: 2026‑01‑27)" + role: assistant + created: 1769523739 + id: chatcmpl-793 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 70 + prompt_tokens: 1064 + total_tokens: 1134 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_list_documents_pagination.yaml b/tests/cassettes/test_chat_agent/test_list_documents_pagination.yaml new file mode 100644 index 00000000..0abdf7dc --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_list_documents_pagination.yaml @@ -0,0 +1,524 @@ +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: + - '412' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Data Sources + The data sources for DocLayNet include: + - Publication repositories such as arXiv + - Government offices and official documents + - Company websites and corporate reports + - Data directory services for financial reports + - Patent documents + Scanned documents were excluded to avoid rotation and skewing issues. + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 68 + total_tokens: 68 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4833' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs"). + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: List the first 2 documents available + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Use this when the user wants to browse or see what documents are available. + name: list_documents + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: Maximum number of documents to return + offset: + anyOf: + - type: integer + - type: 'null' + default: null + description: Number of documents to skip (for pagination) + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '472' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: We need list_documents. + role: assistant + tool_calls: + - function: + arguments: '{"limit":2,"offset":0}' + name: list_documents + id: call_tsw9p0as + index: 0 + type: function + created: 1769523751 + id: chatcmpl-975 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 33 + prompt_tokens: 954 + total_tokens: 987 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5277' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs"). + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: List the first 2 documents available + role: user + - content: |- + + We need list_documents. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":2,"offset":0}' + name: list_documents + id: call_tsw9p0as + type: function + - content: |- + Found 2 document(s): + + - **DocLayNet Class Labels** (URI: doclaynet-labels, Created: 2026-01-27) + - **DocLayNet Annotation** (URI: doclaynet-annotation, Created: 2026-01-27) + role: tool + tool_call_id: call_tsw9p0as + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Use this when the user wants to browse or see what documents are available. + name: list_documents + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: Maximum number of documents to return + offset: + anyOf: + - type: integer + - type: 'null' + default: null + description: Number of documents to skip (for pagination) + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '453' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "Here are the first two documents available:\n\n1. **DocLayNet Class Labels** – URI: `doclaynet-labels` + \ \n2. **DocLayNet Annotation** – URI: `doclaynet-annotation`" + role: assistant + created: 1769523752 + id: chatcmpl-973 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 50 + prompt_tokens: 1063 + total_tokens: 1113 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_list_documents_with_session_filter.yaml b/tests/cassettes/test_chat_agent/test_list_documents_with_session_filter.yaml new file mode 100644 index 00000000..4df19677 --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_list_documents_with_session_filter.yaml @@ -0,0 +1,480 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '730' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Class Labels + DocLayNet defines 11 distinct class labels for document layout analysis: + 1. Caption - Text describing figures or tables + 2. Footnote - Notes at the bottom of pages + 3. Formula - Mathematical expressions + 4. List-item - Items in bulleted or numbered lists + 5. Page-footer - Footer content on pages + 6. Page-header - Header content on pages + 7. Picture - Images and diagrams + 8. Section-header - Headings for document sections + 9. Table - Tabular data + 10. Text - Regular paragraph text (highest count: 510,377 instances) + 11. Title - Document titles + The Text class has the highest count with 510,377 instances in the dataset. + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 166 + total_tokens: 166 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '412' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - |- + DocLayNet Dataset - Data Sources + The data sources for DocLayNet include: + - Publication repositories such as arXiv + - Government offices and official documents + - Company websites and corporate reports + - Data directory services for financial reports + - Patent documents + Scanned documents were excluded to avoid rotation and skewing issues. + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: 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 + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 68 + total_tokens: 68 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4833' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs"). + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Show me what documents are available + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Use this when the user wants to browse or see what documents are available. + name: list_documents + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: Maximum number of documents to return + offset: + anyOf: + - type: integer + - type: 'null' + default: null + description: Number of documents to skip (for pagination) + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '503' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: User wants list of documents. Use list_documents. + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"offset":null}' + name: list_documents + id: call_6004erad + index: 0 + type: function + created: 1769523744 + id: chatcmpl-41 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 38 + prompt_tokens: 953 + total_tokens: 991 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5232' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs"). + - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content. + - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Show me what documents are available + role: user + - content: |- + + User wants list of documents. Use list_documents. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":null,"offset":null}' + name: list_documents + id: call_6004erad + type: function + - content: |- + Found 1 document(s): + + - **DocLayNet Class Labels** (URI: doclaynet-labels, Created: 2026-01-27) + role: tool + tool_call_id: call_6004erad + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Use this when the user wants to browse or see what documents are available. + name: list_documents + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: Maximum number of documents to return + offset: + anyOf: + - type: integer + - type: 'null' + default: null + description: Number of documents to skip (for pagination) + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '513' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: |- + Here’s what’s available in the knowledge base: + + - **DocLayNet Class Labels** (URI: **doclaynet-labels**, Created: 2026-01-27) + + Let me know if you’d like to see the content of this document or have any other questions! + role: assistant + created: 1769523745 + id: chatcmpl-378 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 62 + prompt_tokens: 1041 + total_tokens: 1103 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_summarize_document_found.yaml b/tests/cassettes/test_chat_agent/test_summarize_document_found.yaml new file mode 100644 index 00000000..1ed2a175 --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_summarize_document_found.yaml @@ -0,0 +1,563 @@ +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: + - '5379' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs"). + - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z"). + - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document"). + - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Summarize the DocLayNet Class Labels document + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Use this when the user wants to browse or see what documents are available. + name: list_documents + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: Maximum number of documents to return + offset: + anyOf: + - type: integer + - type: 'null' + default: null + description: Number of documents to skip (for pagination) + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + - function: + description: |- + Generate a summary of a specific document. + + Use this when the user wants an overview or summary of a document's content. + name: summarize_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to summarize + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '491' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need summarize_document. + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet Class Labels"}' + name: summarize_document + id: call_080b2wi5 + index: 0 + type: function + created: 1769523888 + id: chatcmpl-174 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 34 + prompt_tokens: 1043 + total_tokens: 1077 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1250' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + Generate a concise summary of the document content provided below. + + Start with a one-sentence overview, then list the main topics covered, and highlight any key findings or conclusions. + + Guidelines: + - Be 3-5 sentences for short documents, 5-8 for longer ones + - Focus on factual content and key information + - Do not include meta-commentary like "This document discusses..." or "The document covers..." + - Do not speculate beyond what's in the content + + Document content: + DocLayNet Dataset - Class Labels + + DocLayNet defines 11 distinct class labels for document layout analysis: + + 1. Caption - Text describing figures or tables + 2. Footnote - Notes at the bottom of pages + 3. Formula - Mathematical expressions + 4. List-item - Items in bulleted or numbered lists + 5. Page-footer - Footer content on pages + 6. Page-header - Header content on pages + 7. Picture - Images and diagrams + 8. Section-header - Headings for document sections + 9. Table - Tabular data + 10. Text - Regular paragraph text (highest count: 510,377 instances) + 11. Title - Document titles + + The Text class has the highest count with 510,377 instances in the dataset. + role: user + model: gpt-oss + reasoning_effort: low + stream: false + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '759' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: "DocLayNet lists 11 distinct layout classes for documents, including captions, footnotes, formulas, list + items, page headers and footers, pictures, section headers, tables, titles, and regular paragraph text. \nThe + classes are: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, + Text, and Title. \nThe Text class is the most common, comprising 510,377 instances in the dataset." + reasoning: Need concise summary 3-5 sentences. + role: assistant + created: 1769523890 + id: chatcmpl-181 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 114 + prompt_tokens: 316 + total_tokens: 430 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '6132' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs"). + - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z"). + - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document"). + - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Summarize the DocLayNet Class Labels document + role: user + - content: |- + + Need summarize_document. + + role: assistant + tool_calls: + - function: + arguments: '{"query":"DocLayNet Class Labels"}' + name: summarize_document + id: call_080b2wi5 + type: function + - content: "**Summary of DocLayNet Class Labels:**\n\nDocLayNet lists 11 distinct layout classes for documents, including + captions, footnotes, formulas, list items, page headers and footers, pictures, section headers, tables, titles, + and regular paragraph text. \nThe classes are: Caption, Footnote, Formula, List-item, Page-footer, Page-header, + Picture, Section-header, Table, Text, and Title. \nThe Text class is the most common, comprising 510,377 instances + in the dataset." + role: tool + tool_call_id: call_080b2wi5 + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Use this when the user wants to browse or see what documents are available. + name: list_documents + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: Maximum number of documents to return + offset: + anyOf: + - type: integer + - type: 'null' + default: null + description: Number of documents to skip (for pagination) + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + - function: + description: |- + Generate a summary of a specific document. + + Use this when the user wants an overview or summary of a document's content. + name: summarize_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to summarize + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '1334' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: |- + **DocLayNet Class Labels** + + DocLayNet categorizes document elements into **11 layout classes**: + + | Class | Brief Description | + |-------|-------------------| + | **Caption** | Labeling of captions (e.g., of figures or tables). | + | **Footnote** | Footnote text appearing at the bottom of a page. | + | **Formula** | Mathematical or chemical formulas. | + | **List‑item** | Items within bulleted or numbered lists. | + | **Page‑footer** | Footer content that appears on every page. | + | **Page‑header** | Header content that appears on every page. | + | **Picture** | Images embedded in the document. | + | **Section‑header** | Headers that introduce document sections. | + | **Table** | Tabular data structures. | + | **Text** | Standard, continuous paragraph text. | + | **Title** | The main title or subtitle of the document. | + + These labels are used to annotate the layout of documents in the DocLayNet dataset, enabling researchers to train models for document understanding tasks such as layout analysis, OCR, and information extraction. + role: assistant + created: 1769523896 + id: chatcmpl-959 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 234 + prompt_tokens: 1201 + total_tokens: 1435 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/cassettes/test_chat_agent/test_summarize_document_not_found.yaml b/tests/cassettes/test_chat_agent/test_summarize_document_not_found.yaml new file mode 100644 index 00000000..53a0670e --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_summarize_document_not_found.yaml @@ -0,0 +1,196 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5368' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are a helpful research assistant powered by haiku.rag, a knowledge base system. + + You have access to a knowledge base of documents. Use your tools to search and answer questions. + + CRITICAL RULES: + 1. For greetings or casual chat: respond directly WITHOUT using any tools + 2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context + 3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally + 4. NEVER call the same tool multiple times for a single user message + 5. NEVER make up information - always use tools to get facts from the knowledge base + + How to decide which tool to use: + - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs"). + - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z"). + - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document"). + - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations. + - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results. + + IMPORTANT - When user mentions a document in search/ask: + - If user says "search in ", "find in ", "answer from ", or " in ": + - Extract the TOPIC as `query`/`question` + - Extract the DOCUMENT NAME as `document_name` + - Examples for search: + - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper" + - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" + - Examples for ask: + - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper" + - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" + + Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user. + role: system + - content: Summarize the nonexistent document + role: user + model: gpt-oss + reasoning_effort: low + stream: false + tool_choice: auto + tools: + - function: + description: |- + Search the knowledge base for relevant documents. + + Use this when you need to find documents or explore the knowledge base. + Results are displayed to the user - just list the titles found. + name: search + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: 'Number of results to return (default: 5)' + query: + description: The search query (what to search for) + type: string + required: + - query + type: object + type: function + - function: + description: |- + Answer a specific question using the knowledge base. + + Use this for direct questions that need a focused answer with citations. + Uses a research graph for planning, searching, and synthesis. + name: ask + parameters: + additionalProperties: false + properties: + document_name: + anyOf: + - type: string + - type: 'null' + default: null + description: Optional document name/title to search within (e.g., "tbmed593", "army manual") + question: + description: The question to answer + type: string + required: + - question + type: object + type: function + - function: + description: |- + List available documents in the knowledge base. + + Use this when the user wants to browse or see what documents are available. + name: list_documents + parameters: + additionalProperties: false + properties: + limit: + anyOf: + - type: integer + - type: 'null' + default: null + description: Maximum number of documents to return + offset: + anyOf: + - type: integer + - type: 'null' + default: null + description: Number of documents to skip (for pagination) + type: object + type: function + - function: + description: |- + Retrieve a specific document by title or URI. + + Use this when the user wants to fetch/get/retrieve a specific document. + name: get_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to look up + type: string + required: + - query + type: object + strict: true + type: function + - function: + description: |- + Generate a summary of a specific document. + + Use this when the user wants an overview or summary of a document's content. + name: summarize_document + parameters: + additionalProperties: false + properties: + query: + description: The document title or URI to summarize + type: string + required: + - query + type: object + strict: true + type: function + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '655' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: I’m sorry, but I couldn’t find a document with that name. If you have the exact title or a related keyword, + let me know and I’ll try again. + reasoning: User asks to summarize nonexistent document. According to rule, for summary use summarize_document tool, + but if document doesn't exist? We must search? Likely we respond that document not found. No tool needed. + role: assistant + created: 1769523898 + id: chatcmpl-69 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 86 + prompt_tokens: 1039 + total_tokens: 1125 + status: + code: 200 + message: OK +version: 1