interactions:
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '108'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      encoding_format: base64
      input:
      - 'Sales report Q1: Revenue was $100,000.'
      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: 17
        total_tokens: 17
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '108'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      encoding_format: base64
      input:
      - 'Sales report Q2: Revenue was $150,000.'
      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: 17
        total_tokens: 17
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '108'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      encoding_format: base64
      input:
      - 'Sales report Q3: Revenue was $200,000.'
      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: 17
        total_tokens: 17
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '7702'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.

          IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.

          CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
          - search("query")  ✓ CORRECT
          - from haiku.rag import search  ✗ WRONG - will fail

          You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):

          ## Available Functions

          ### search(query, limit=10) -> list[dict]
          Search the knowledge base using hybrid search (vector + full-text).
          Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings

          ### list_documents(limit=10, offset=0) -> list[dict]
          List available documents in the knowledge base.
          Returns list of dicts with keys: id, title, uri, created_at

          ### get_document(id_or_title) -> str | None
          Get the full text content of a document by ID, title, or URI.
          Returns the document content as a string, or None if not found.

          ### get_docling_document(id_or_title) -> DoclingDocument | None
          Get the structured DoclingDocument object for advanced analysis.
          Returns a DoclingDocument object, or None if not found.
          See "DoclingDocument API" section below for how to use it.

          ### ask(question) -> str
          Ask a question using the QA agent with RAG. Returns the answer as a string.
          Use this for semantic analysis that benefits from LLM reasoning.

          ## Standard Library Modules
          You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing

          ## Strategy Guide

          1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
          2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
          3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
          4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
          5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
          6. **Use ask() for Semantic Analysis**: When you need to understand meaning or interpret content, use the ask() function.
          7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.

          ## DoclingDocument API

          When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.

          ### Properties
          - `doc.texts` - List of all text items (paragraphs, headings, etc.)
          - `doc.tables` - List of all tables
          - `doc.pictures` - List of all pictures/figures
          - `doc.name` - Document name

          ### Methods
          - `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
            Returns tuples of (item, level) where level is nesting depth
          - `doc.export_to_markdown()` - Export entire document as markdown string

          ### Text Item Properties
          - `item.text` - The text content
          - `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
          - `item.prov` - Provenance (page numbers, bounding boxes)

          ### Table Access
          - `table.data.num_rows`, `table.data.num_cols` - Dimensions
          - `table.data.table_cells` - List of TableCell objects
          - `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`

          ### Example Usage
          ```python
          doc = get_docling_document("My Document")

          # Get all headings
          headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]

          # Iterate with structure
          for item, level in doc.iterate_items():
              print("  " * level + item.text[:50])

          # Extract table data
          for table in doc.tables:
              for cell in table.data.table_cells:
                  print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
          ```

          ## Example Patterns

          ### Counting documents matching a condition
          ```python
          docs = list_documents(limit=100)
          count = 0
          for doc in docs:
              content = get_document(doc['id'])
              if content and 'keyword' in content.lower():
                  count += 1
                  print(f"Found in: {doc['title']}")
          print(f"Total: {count}")
          ```

          ### Aggregating data across documents
          ```python
          import re
          numbers = []
          results = search("financial data", limit=20)
          for r in results:
              matches = re.findall(r'\$([\d,]+)', r['content'])
              for m in matches:
                  numbers.append(int(m.replace(',', '')))
          print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
          ```

          ### Using ask() for semantic analysis
          ```python
          # First search to find relevant content
          results = search("machine learning approaches")
          # Then use ask() to synthesize an answer
          summary = ask("What are the main machine learning approaches discussed?")
          print(summary)
          ```

          ## Workflow

          1. **ALWAYS start by using execute_code** to explore the knowledge base
          2. Run multiple code blocks as needed to gather information
          3. After collecting data, provide your final answer

          ## Output Format

          After executing code and gathering information, provide:
          1. A clear answer to the user's question
          2. Key findings from your analysis
          3. References to specific documents/chunks that informed your answer

          CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
        role: system
      - content: What is the total revenue across all quarterly reports?
        role: user
      model: gpt-oss
      reasoning_effort: low
      stream: false
      tool_choice: auto
      tools:
      - function:
          description: |-
            <summary>Execute Python code in the sandboxed environment.

            The code has access to haiku.rag functions (search, list_documents,
            get_document, get_docling_document, ask) and safe standard library
            modules (json, re, collections, math, statistics, itertools,
            functools, datetime, typing).

            Use print() to output results. Variables persist between executions.</summary>
            <returns>
            <description>Structured result with success status, stdout, and stderr.</description>
            </returns>
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
          strict: true
        type: function
      - function:
          description: Result from RLM agent execution.
          name: final_result
          parameters:
            $defs:
              CodeExecution:
                additionalProperties: false
                description: Result of executing a code block in the RLM sandbox.
                properties:
                  code:
                    description: The Python code that was executed
                    type: string
                  stderr:
                    description: Standard error captured during execution
                    type: string
                  stdout:
                    description: Standard output captured during execution
                    type: string
                  success:
                    description: Whether execution completed without error
                    type: boolean
                required:
                - code
                - stdout
                - stderr
                - success
                type: object
            additionalProperties: false
            properties:
              answer:
                description: The answer to the user's question
                type: string
              code_executions:
                description: History of code executions during the RLM session
                items:
                  $ref: '#/$defs/CodeExecution'
                type: array
            required:
            - answer
            type: object
        type: function
    uri: http://localhost:11434/v1/chat/completions
  response:
    headers:
      content-length:
      - '695'
      content-type:
      - application/json
    parsed_body:
      choices:
      - finish_reason: tool_calls
        index: 0
        message:
          content: ''
          reasoning: We need revenue from quarterly reports. Search for "quarterly report" and revenue.
          role: assistant
          tool_calls:
          - function:
              arguments: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in
                results[:5]:\n    print(r[''document_title''], r[''score''], r[''page_numbers''])\n"}'
              name: execute_code
            id: call_8cd1wnf6
            index: 0
            type: function
      created: 1769703355
      id: chatcmpl-273
      model: gpt-oss
      object: chat.completion
      system_fingerprint: fp_ollama
      usage:
        completion_tokens: 87
        prompt_tokens: 1601
        total_tokens: 1688
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '94'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      encoding_format: base64
      input:
      - quarterly report revenue
      model: qwen3-embedding:4b
    uri: http://localhost:11434/v1/embeddings
  response:
    headers:
      content-type:
      - application/json
      transfer-encoding:
      - chunked
    parsed_body:
      data:
      - embedding: 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
        index: 0
        object: embedding
      model: qwen3-embedding:4b
      object: list
      usage:
        prompt_tokens: 5
        total_tokens: 5
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '8540'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.

          IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.

          CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
          - search("query")  ✓ CORRECT
          - from haiku.rag import search  ✗ WRONG - will fail

          You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):

          ## Available Functions

          ### search(query, limit=10) -> list[dict]
          Search the knowledge base using hybrid search (vector + full-text).
          Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings

          ### list_documents(limit=10, offset=0) -> list[dict]
          List available documents in the knowledge base.
          Returns list of dicts with keys: id, title, uri, created_at

          ### get_document(id_or_title) -> str | None
          Get the full text content of a document by ID, title, or URI.
          Returns the document content as a string, or None if not found.

          ### get_docling_document(id_or_title) -> DoclingDocument | None
          Get the structured DoclingDocument object for advanced analysis.
          Returns a DoclingDocument object, or None if not found.
          See "DoclingDocument API" section below for how to use it.

          ### ask(question) -> str
          Ask a question using the QA agent with RAG. Returns the answer as a string.
          Use this for semantic analysis that benefits from LLM reasoning.

          ## Standard Library Modules
          You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing

          ## Strategy Guide

          1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
          2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
          3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
          4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
          5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
          6. **Use ask() for Semantic Analysis**: When you need to understand meaning or interpret content, use the ask() function.
          7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.

          ## DoclingDocument API

          When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.

          ### Properties
          - `doc.texts` - List of all text items (paragraphs, headings, etc.)
          - `doc.tables` - List of all tables
          - `doc.pictures` - List of all pictures/figures
          - `doc.name` - Document name

          ### Methods
          - `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
            Returns tuples of (item, level) where level is nesting depth
          - `doc.export_to_markdown()` - Export entire document as markdown string

          ### Text Item Properties
          - `item.text` - The text content
          - `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
          - `item.prov` - Provenance (page numbers, bounding boxes)

          ### Table Access
          - `table.data.num_rows`, `table.data.num_cols` - Dimensions
          - `table.data.table_cells` - List of TableCell objects
          - `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`

          ### Example Usage
          ```python
          doc = get_docling_document("My Document")

          # Get all headings
          headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]

          # Iterate with structure
          for item, level in doc.iterate_items():
              print("  " * level + item.text[:50])

          # Extract table data
          for table in doc.tables:
              for cell in table.data.table_cells:
                  print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
          ```

          ## Example Patterns

          ### Counting documents matching a condition
          ```python
          docs = list_documents(limit=100)
          count = 0
          for doc in docs:
              content = get_document(doc['id'])
              if content and 'keyword' in content.lower():
                  count += 1
                  print(f"Found in: {doc['title']}")
          print(f"Total: {count}")
          ```

          ### Aggregating data across documents
          ```python
          import re
          numbers = []
          results = search("financial data", limit=20)
          for r in results:
              matches = re.findall(r'\$([\d,]+)', r['content'])
              for m in matches:
                  numbers.append(int(m.replace(',', '')))
          print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
          ```

          ### Using ask() for semantic analysis
          ```python
          # First search to find relevant content
          results = search("machine learning approaches")
          # Then use ask() to synthesize an answer
          summary = ask("What are the main machine learning approaches discussed?")
          print(summary)
          ```

          ## Workflow

          1. **ALWAYS start by using execute_code** to explore the knowledge base
          2. Run multiple code blocks as needed to gather information
          3. After collecting data, provide your final answer

          ## Output Format

          After executing code and gathering information, provide:
          1. A clear answer to the user's question
          2. Key findings from your analysis
          3. References to specific documents/chunks that informed your answer

          CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
        role: system
      - content: What is the total revenue across all quarterly reports?
        role: user
      - content: |-
          <think>
          We need revenue from quarterly reports. Search for "quarterly report" and revenue.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
              r[''score''], r[''page_numbers''])\n"}'
            name: execute_code
          id: call_8cd1wnf6
          type: function
      - content: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
          r[''score''], r[''page_numbers''])\n","stdout":"3\nQ1 Report 0.03226645663380623 []\nQ2 Report 0.03226645663380623
          []\nQ3 Report 0.032258063554763794 []\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_8cd1wnf6
      model: gpt-oss
      reasoning_effort: low
      stream: false
      tool_choice: auto
      tools:
      - function:
          description: |-
            <summary>Execute Python code in the sandboxed environment.

            The code has access to haiku.rag functions (search, list_documents,
            get_document, get_docling_document, ask) and safe standard library
            modules (json, re, collections, math, statistics, itertools,
            functools, datetime, typing).

            Use print() to output results. Variables persist between executions.</summary>
            <returns>
            <description>Structured result with success status, stdout, and stderr.</description>
            </returns>
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
          strict: true
        type: function
      - function:
          description: Result from RLM agent execution.
          name: final_result
          parameters:
            $defs:
              CodeExecution:
                additionalProperties: false
                description: Result of executing a code block in the RLM sandbox.
                properties:
                  code:
                    description: The Python code that was executed
                    type: string
                  stderr:
                    description: Standard error captured during execution
                    type: string
                  stdout:
                    description: Standard output captured during execution
                    type: string
                  success:
                    description: Whether execution completed without error
                    type: boolean
                required:
                - code
                - stdout
                - stderr
                - success
                type: object
            additionalProperties: false
            properties:
              answer:
                description: The answer to the user's question
                type: string
              code_executions:
                description: History of code executions during the RLM session
                items:
                  $ref: '#/$defs/CodeExecution'
                type: array
            required:
            - answer
            type: object
        type: function
    uri: http://localhost:11434/v1/chat/completions
  response:
    headers:
      content-length:
      - '635'
      content-type:
      - application/json
    parsed_body:
      choices:
      - finish_reason: tool_calls
        index: 0
        message:
          content: ''
          reasoning: 'We have 3 reports: Q1, Q2, Q3. Likely Q4 also exists. Search again for Q4.'
          role: assistant
          tool_calls:
          - function:
              arguments: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
              name: execute_code
            id: call_mgo4t2ty
            index: 0
            type: function
      created: 1769703357
      id: chatcmpl-775
      model: gpt-oss
      object: chat.completion
      system_fingerprint: fp_ollama
      usage:
        completion_tokens: 85
        prompt_tokens: 1812
        total_tokens: 1897
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '79'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      encoding_format: base64
      input:
      - Q4 Report
      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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/zyum2I8EhM5vF3CDLyCYh2895nNu/lWMTx/WA+8xR7lPJJJFD2mbIc8y3XEu6BJ4TnuAnu7VHUbvRTD5zsCHxw8DIWHuzBEfbybAw48d17QvPCpNLz5zqU8mWz2vExq5zvITSa8qnLrPGEugjzj9vC84cozPBVwvTttS8e8FHy2vFJ2IrrIssW8Jy+quvair7qUH5o7kYt/ui/lSjw2pe08LdrgPOhjJj3ZcA29w0rUu7etc7xC1x+8ypRovFixSL3dx1A8juG5vAndVzzmzRm9Q8k3O69LOzzKIj08nsUHPSPzVLw2Jwc9SftkvA3XujzF2IQ7puSzu/8Hm7tU97U8bKKIu3Xw9bzTK5W8QuB7vClEFT0DfEU8cr0qPTX4jLyGcxu81Nt8vGjU0js7LZi8HvyFO1WN8btQdeu7vhUhPbOqDr3NLqo8+5rSOTFqQ7p9rUc7iimAPKi5jjyrYTy9iTCdu3/GCzzbtlU8lJKXu5NPnbphSTU8GTo2vJ9dTzxmgsQ79/LqO2SQebhq3gQ9ov0xPLmSULyWc4e7Q7dwu3XcirtzQlE7GQOqO0+UjTxZ5Fw8PKvQu9W0yLyXmNg8dbScul2a2zzkSYy8mofHPFmUl7yCniQ8zccBPMXjtDvHsVs8iQfkPFjqgDp6SJy8IUSPvLWwmDzPlBe6wL0GvYLe0btN1Ho8O3UKvSghYDw/ZRm9l3doPIewjrw/re47j6KgO3R+rrrpWOM6jGs8O5KVL73mOji911VHvQcJrryBJZy8yh6SvOhriTzN4xS9yrKHPNNdrzt1riS6lRw6PWjmOjzghTq8vTzPvAHktTxNvyk9oviNOoih57zpcVE8FjVkvBTnijxPjkk80asIPS9Lzzn4V7Y8mjOUvGEsubzwE8u8Kotku/+pL7y1/yY8CKFuuzzI2ztbwW+7G53hPIoZgzx30QC833yDvDnqsLwSppm8Ocr5vMJ/wzx0QiU8gtGUvJouezuNE+S8pKL2O3gnMj2h9zE9naFHu7tiuTznzqk6mfYCPcf/WTyRAiK7F0aQPR9iBj26EhW9ydyyvHVfoDy2RwK9lmT9uyuvNrzRQrI8oEfJO/O/SjwNSiQ9hqUtvRp7XDwUVpw8ohfbvCmOF72tCk08NhC/vA/PmzwN0yE8kGtovJvRkzu67XE8Kv5PO/+1XzyciE28s0x1O+vugLzWpc48aKfFu+JLEj1XvHC8tx2Uu10Fw7x335q8fCbjO74nr7xkrwM8uvrLvJSrcbzVrcc7ue4DPdEBgTYhG5M733QlvMNy6jzQl4C71AGJPKsH97zUSac7QFPVOor67jyAsGw7LB9jPAxgJzyNIsI8MuSNvBdtRbyM01+8iChDPN+Nprxmc3Q8CcsBvW77ar2m8C+9twjNvGIEAbxf/dw36D2bO4FrjLypoBU91oEUPFNAPTzKiZK8JHhjPUTZozwH6wU9sJgUPPxjdLuBRyC9Oo5NPPoErjxYVne7nMq5PAU5kbyNia68npxrPJiuPruPt028uJS2vGRQz7xUsTm8nzDROrD8RTxiNkK82I0WPY2ZizwnQzk99jijPJOOnLzfD7879uo4vLU6mbwM3WE8FvGxPHBHGjx40Zo83kddvPe4oTs8wDg9qTBtPMPaxbzNllQ86IurPBlQobzCkc271ofzu7Ygl7s8nms8Y/JDvBygnjuR+K88+TKfutothrx9iJI8MxcbvXHKJb2+WQS9tUzxvJFI9jw37ta58zmYt7o2gzr62Na8reE6PZMS5zumHhI9TRu9vPMCFj2NWhY7pKTvO18qy7yE1WW7FabPu7L0sDq1lu08lAAyO1PxwroCS7S7Ka5CuuUSrLw5ei69/LxvPYzTkjqKLAm9CSoqPJ9KBzxqENu7h2T4vF9mSjy2cmE77WKWPL1t27zA4KY8nHcpu0L2oLw8gs07ksQkO8E3zrwJEyE6FRcwvbeFMD2krZs8+OygOXjeuDsD6AY8F5O0vCOwvbyNt5g8A9/0O2WO2LnKSHC8s9TTvNTHCjzwBJ67ovYTPJxqELyeg/A8vRMZPSXrjzxQdXA8nhsJvOFAh7vzLv+7AzapPF/EQLxxXqQ8d3aqvLkhyjydqbA7DOGcPEttQzx3wmg8shccPcS3nbxawMW72tVsvBypE715R327G1LjPAdh9DtBXua8bT/YOwmEgbzLMk+8ZSP9O4Ip17wspgI8P/8YPRX/QLy04mA8LwzJPPmfUjuCl9A7zrYCPMh+yTz84Sa9hBbmPNp7KDw1Yg69taqwO6LSqTw3fwm8czVzvKy65Dv6/rQ8qZsSPUeFHbyBZVy8wjcRPdngTrts2gG5O6Cau2Hyu7x6gW+8rge/PHs2V7xXqjI8DznevL0TNT3JUXU8SeXpO1NTxjyiM6k8tRkCPQqm1Lzebys8QA2WvMNdCzy9Q3U8z5Ghu42tBrw6QD+8j6vVOo0/y7x6V988J0OVu0HBwbxkLq87jvlLO3sjHbxx6Ba7+TgGvEUXEr2nAxW8ll+ivLbwKT0D0eC8r+bFvLtVwTu+1/e8O3rXPPqTxryb6nY755UevL9pBT3WmpG8KnMqPHtASLxaAxK8cS/yu7OEwzwIb3+8CJObvAWM2DvQkzi7oi/WvPd4R7wTsWS8yaT8O40h9zvAN4A8QyqMuqxLEj2MWc27bdyGOhe8rjuRX6k84sIbvCCfubzU/xY8M3ftOlmr3LxHiPY8S3cKPM6+T7wunkM8Xrj6PD7zw7z5sVi7JkXXvLE11Dxm5ug7h1iXPGsPD7xMgJ27jPcRPMc0xrxXbII8lCJXPdhFxTuAFtu864Jqu2WZ3LvejS+9TaiIvd4QEj2arDa8xS4QPC3B3rsVLsc82Yt6OmOnT72iJcA8DZbGu/wwTLxkZNs7HvrXOw5GKjwwpD287x/QPMGwqDwSmg28y+acPDmHHjw5qp27VNwmvF2EljzFX5K6zkzOPEXy0zzseQ29XXq6vG5y5rn3x++7iqZpO8ZQjzz5mbg85KmJuzygXTygmOC7qUZOPCpgMrtIyi876dKbu4JhWjwi3bO8GL/gOmK66bkt0VG8/H7nOzVsyjpLejS8hAojPPtoAD2y/Q66/JulvAFYKrx0Vs28XX8RPN+5A70/zLE8drNlPKXeQzz23GQ8/RkNvcSVTrzKHtU8CFVUvKuZDL1FXYk8KdWPvPJ1hDyoJKc8sd2XuZaLfzz1ixO896LRO9HN8Tz/qW88uprfvJg2EjzMr4M8bSYbvDZJ5zriWWQ7n2MrvCFwHzwtuJE8dI0qvA6INjpQkpw8x4Jtuz/BN7u3f9Y7IHf0PED7Dz1yFA47SoGyvBPTurw7GOo5MZFvPIMZ5DzYEg88ICOPPFj9lbyEV7M8++0hPDIFDzx0rae6IISQu8teZLvYxc48NHGJPLI+i7tmU4O8LBCEvJT3T7whzaK8c7sRt51hpLyGbo88MtwvPXlRvLwPjLa8jkLUOgglUTtOn3E8vWysOxY/JbzF1+28ElgZvK8HR7yR6lU9g2oJPCrOrzuDuWi8aeQRPODWjjz/i5q8sbfPu0fo2jtBXXi7Fyjcu033ILteDf48rtVOvL/qwTxlbxU8KHEdPKXqCTvitFc7qkkQPTWtNrzKf7S7XJVsPDe+O7uiEzq8gZb7u48rzTykZ3O8W6GBPNWJAru43TA87uRBvUHlHD0GmXe8m9zNO3QjxrtPqx+9/z3OO7kiM7sD/js9JUmGPNMkULwNbYm8Hf6fvDcemDy4nAe9tiqNvMSmkjuH4bg8rJMLO3WTxjzq5SK9gcXwPLvAWTx3dZK8JzdqPJG0LbwcHko8rZ81PLGcljuld5W8FpzavAaQ3LuKCZe6zhA0Pb/HQDxBhsy8D6WFPATOiruxzxO7SHMmuwcUtrxPa8m7BgnQvO/FRToK2Q89R6W9vGFLXTgmYLY7K+6GPEY02ry/njU8zDBxu2Q8v7z6a0W8MBDAO9fCJLv/QXO8av6uuzeEZTxSKkG8mcmdO9FNjrx8BBw9fRBevL6D+TsWWx+87VWIO7yBWryXvlU7lzcYvMO/qztEfTO9migbOzGUUbkg5Au9VnkMPB4njjnUSjG9ZR53vDug8Lw4UYI73zouPI6qhzzcTLS7gmsFvAKknDxEwHw7xvVLPAO3tzxMSiQ8FWeyPEMRDDtpVTM9fuMLPATOw7s3wzY8hoioOzOxvTzwFhM8xBKZOvGdmTwhqww9O3qRuvXYPD2rP2e9vzO3OyEd1rwK1m46FFCoPF4dkbx3/8i8Q854OpPeRjyfghU7ckzgPDd+/bt0G588EFVDvMlIqzzFfUs8odCFvP4ifztTyoE75fElPAOecLx/Kxy9aCoMPSPgAzwYxZU89MzivE9WmzsB1Ii74pKnvLqrI73UmhK7HrR3vIXC1rw8A3O8yjyoPKJZaLzBXPC8YqqFvJ0WVD1s84c8Tr+UPCAFATyJ2J28cfOkOuQF/Tx18+K7C0jovKhH7DzoVKs8hF74vNooA7xcQwA8El9dPCfBLDx4FQO8ejJ5u0pOQruAYBe8DBQ+PbdSZTpEuy69G1yWPMlvvDyOjZc7TxY3vOyPmju39+A7p19tPOlKJL3LS5c8ZqoAPC0eSTvfyMk8QUWUPBZLqDuUe/A8lGQ4PJTDMD1TLDs97qTjuqFDFjzIcT87mjWgu8CL9rsfXoU6Hhkyu3YhJrw+xsu8esCQvDD677pGR6e8lD3xvEnHubzNGww9JM+uvNvA2ryCeTI6+riUu/Lw5jtTeie9Nrz9vFzlAT0R9Nq8FjW9PMXYxrwK5/A8bdlEOyq7GjwonXQ8SstmvL5xrDxqAMG8JLQQO66+e7yV2iE8Q0UDvScLvzzSnnA8aZu6u/0/PbxK2w47oSC2vA2fYbsT3wa7WZ6EPMarpzxUBUi8DQ2qvKON4Lz54V+8ows6PdQOAz1lgLg7NoeFvIQ16rwhpni8T0PKuQwOAbzv0YW8AXYjvJIfQbzw2vA7kcKHPOFenzyijok7gWvCOy58Q7ySkKk8Dq37PLtYczzDqL88QBq8PGshsrsr9xk8nis7PWyfcbxFfKW8lKL0O2rc6TyxjyQ8WR6PPBBqpTu8lPW7lH6vuN1m1zzPNxm8kc/mOv/dUrx5BQO8TEqMO5wrCjzmh2e8EMhWvBJpzLsEASK7T/GTvGKmc7q5Lgm9uCyCO4ir8jvoHqi6tdgEPCRwczxvgRC8X8ouPF40qLxZr1a8bZtPPFfVqzznXPc8DqLru8Hc8DxX9FQ8jOe0PDUO3ryt5/M6elIDO74/xTwiJgG90InqPN/1ELxFXx68ejUNPObDPDzJGK+7KlbUPJf3HrzzlAC6CBA1PaUktry9vs08snmuOyqXqrww57i8MSyZucHulDkZZi88IO7JvGRTwLqQ5128z7l1O5dA+Lsjxqo6Q1rfO5ru1bxCYOC5DEx3PC9yvDt65jQ83o0IvQg0z7xbo7e77a7gu0TOBj3HLUm8nbLpPPzZMbx6mx08qNoau+FcBbzoqhi8QHCXPI4JejwCF+O7/oZ0PKGXzLt+RZG8uTnPPPWT4zsn1wI7TZmmPCOrAzw4nwM9rZE2PAVyortOz7w8SHsBPQyXK7swhYa8jWXyu62g7jx+DJc8QEdgOrAFrDy2LQA90EKzPJRIKDx/rDY7L5EBPBrXTDyS+ZU729ZovNCtD73zFWu7VRItPCJhLbzkAQU72t6QvJ7nPL3yeta7pYuSvOPjzzr1DdY7vsUIPatg2TyS/Ei8wU+avGpXVzzhMlw85pf0OypQBb2y8Be8j6cevIRIVzyaWjK6o9j0uok84Lwob7M64lMGvDQjTbyz5ZY8RHfOPANnZbzux3m9+K5DvIH95Tzl9Cs6QupYu2L4dTp8B6g8gM36uqDn7rv+VPm8zS5BO65jALxMLvS6fj2OPDA2C7xegGY8J+m+uwi5RbyH+dy8+1gVvYnoBD3cdIa8f8Awu+hHtjsAGJi8OUwkPY+1jTw7rh+9QL3NPKQ2pbwm4y28u+SOPHF66Dy5Sxm6kgWvvC/kDbkKjQS9qNU1u4GPyrwTjKU7G+ODO9R3KD3LYC+9L24mvGFnv7vyaGq7/NGdPIecxDwDA9u7/MuivAMR4LxcahO8bUdIO+nPBr2ZGpa8VgMkPC63LDx91/E6hhTWOzGcczxCBwu81AYaPKjVkTyBvgO8LMpgvMIQmzt6K4I8JE+QPCKHBDzeAJU5uX8HPXJ9NjxGrP67JHu4PDfTwrs6Aci8xqUePFpxcztf1V68P0qXPLZVrTvzpvm8cVnhukmQiDwGcWm7530bOom7Br3836w7x4cjvPGsqryoKis8HG6wu6umDbtY9jC8l+0pPC5JMzzCVJo8W5W6ujwY77ue8Ui8ERBMPE/UAjynJg68PXLhPL+qnDtQMO08yE94OR0mPzySD6G8FCmwu1DoIz0m7no8lup/u1K7OTxG9NC8fOzAOzfdt7vIJpm8+BLjPJsRC71hMyA9w76EPKXSCr0B0LK8C+pWu9xotzsDo3y8RVwrvf8cSDu9/pG8XfekvC+VYbvsptg8xGgju4D3pDyxbwm9TEecOgE/5Dxx3dQ80+LuvLf1lbx0KRm8MlICPHV2AzzRxDi8A/4ivEDKKb3oZDI8VZehvKnumjwY4S883q+6vJGbmzxI1dc8kByYPG/GrzvvWz49Yr+VvLvwCL1SZqW5VoOVPL2uMDyaSwK9R/9uPP5y+rw2QS083kcvvGsmuDyAxw+9J2EHvZxNkztZRQy9s8RKvDnHO7zAMYy8ubaIvIKpPb3iS6881nquvDWcxbvKzgO8M/wIva/CnLyu9ga8cOn0PJC57jzUs/g7VnOjPDyADTzDnRG8cXrGPDFDSzxxZvu6+G7FuoBw4Ltmok088mNiPD4XAL34WkE72zGaPJZg8LvmPIC8+K/Ou5jU1Dy3WnW8ElTEvB7Z7LmxeKi8JaHJvNdqpzo59zi7TsP2vBxsMrzzB767xQB9vY173LxS0Ea8EN+jPMQb6rpjz6K7PuMZu3pAAzzNMFQ803krvAkgMz0M9c07SDjvOSsyZD3F6R48i97MO3ixSjzjbCu9tw25vFjfAr0setG8cLzguyxIiLwg+JW8QwaNPFnLBTsbHrG8W5YovEuTArsmeGu8SjSIu+SEkTxW9rc8DUWYvD8PlLyhfiO9JoarvL8parwA8ao8a7fCPO3U8LvXUfc8l1cfPaYfaTs4LQw80dVEvAz+yzw2BmY7OkvjvJV8Ervjmi08XjNOOgmIg7tg17U7LjZFPJpLtzxQCe+7cOGUPCAlRr3Mlpk8yb38vKiB07tdUCS80NX2vA73krzH81U8Y63IPJJJVzxaDFA8inMfvQu/Hb0VVKc8E2AcubhubzxfEOS77mO1uMKhZTyzKfI7T3BTvLOzX7vZGd47pCp5vFhmP7xWDTu8JwI5vIAtT7tP4qc7kXwYvATxiDy077e70GaFvDcfjLzACPO81CYPPYkOfrylURw8E13tvAyXgzw2jMi7SZaTvOkMiTqJc3G8pK4IPJelDbzqT4+8LSiGvGx5mjw5/js6wspAvEALubw1TcS7STzBPHfhLT2EHqA8LJA+O1YVhLvIv7C8Lk23OpK/LDxCpjG8ATAkvch0j7w82o28xTGwPM7tx7zPVQC8oJe4O1OtMjz/m208SnjqO2I99jyeTJg85jaZOsZ0qTxscxk8TC1vO9KQgjxFzZ486VBKPLS1FzyFOCI8JrZ6uxY84bwxKBM9U+B7PDxat7zEZ8w7m7hjvMTABjsIZj28q28TPfZ847yxQLM8hzQSOvzA/buxjnQ8JbAKumIrL7yhHYW6/9gJvOs7BDxHjuW7j9GqvHrauzz/ZxG893o9OzHw0zxijPY8aRe2vK41Gb3qYta8BqHpu4atH7uDD6I8T/PQvHsQ/ryi5cU7oy6lvDx+3btD5mo7wYgEu6nrqTsbNrk8uVLwvGQT2DzIMcK8bcFIO77vxbwkNya87JTGPPMjdrnX98+8BlhAPIhnJjpqZrY8O+kWPX8O9zvJjPs7CsVWPOUtODxoRLm7jReuPBkiQbwZdLQ7qQeYOzvwWDre54y8CaAvPI1lijwwimc8ld4Wu2gcg7zB2p08x5FjPJlzrDzMsrQ8kOegPLiquLze84+8YfeTPIK5vjzi6Qw8x8GPvOt6Vzsy+UU8ctHwO2e8YD2DPrG8PfGBvPlO2rw1T2o7avbQPPGyKDu3PhU7fD6VO41GcboPLxO7F6uevEkLs7rGTae8hr4xPKVtXrzgzBI9NUi8OxCWSjyDjJG7aJGtu+NW17vPNi68Ci7IPHsJIrxkUE48mKqRu+pZDLx+gJG8XwEtvDWo6buB3XS85B1wuurp5rvAwIK7szp0OzLn5bz13qA848vnvHankTxAZ0+82ms1vPVnLLzrG0a8rKoQvGDT/bzBNdI7aVneu8gNO7hTF4W8lpnjvNt7wbu36eY7BVK3PH7U8rzibKw8c+OnvPEGb7sH5/y7T9t6PG5xhztQPTm8T0mAPHZZizxBbOc8u2FPvLhuGbyXfIm8C19MvF5V5boUwd28pvkQvF2M/ryqNb88w0YkPA98m7xq7SG6WzEUvBq5vzz6Cjs8zTz8O1wyk7zhW407QXznuw==
        index: 0
        object: embedding
      model: qwen3-embedding:4b
      object: list
      usage:
        prompt_tokens: 4
        total_tokens: 4
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '9198'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.

          IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.

          CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
          - search("query")  ✓ CORRECT
          - from haiku.rag import search  ✗ WRONG - will fail

          You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):

          ## Available Functions

          ### search(query, limit=10) -> list[dict]
          Search the knowledge base using hybrid search (vector + full-text).
          Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings

          ### list_documents(limit=10, offset=0) -> list[dict]
          List available documents in the knowledge base.
          Returns list of dicts with keys: id, title, uri, created_at

          ### get_document(id_or_title) -> str | None
          Get the full text content of a document by ID, title, or URI.
          Returns the document content as a string, or None if not found.

          ### get_docling_document(id_or_title) -> DoclingDocument | None
          Get the structured DoclingDocument object for advanced analysis.
          Returns a DoclingDocument object, or None if not found.
          See "DoclingDocument API" section below for how to use it.

          ### ask(question) -> str
          Ask a question using the QA agent with RAG. Returns the answer as a string.
          Use this for semantic analysis that benefits from LLM reasoning.

          ## Standard Library Modules
          You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing

          ## Strategy Guide

          1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
          2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
          3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
          4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
          5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
          6. **Use ask() for Semantic Analysis**: When you need to understand meaning or interpret content, use the ask() function.
          7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.

          ## DoclingDocument API

          When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.

          ### Properties
          - `doc.texts` - List of all text items (paragraphs, headings, etc.)
          - `doc.tables` - List of all tables
          - `doc.pictures` - List of all pictures/figures
          - `doc.name` - Document name

          ### Methods
          - `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
            Returns tuples of (item, level) where level is nesting depth
          - `doc.export_to_markdown()` - Export entire document as markdown string

          ### Text Item Properties
          - `item.text` - The text content
          - `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
          - `item.prov` - Provenance (page numbers, bounding boxes)

          ### Table Access
          - `table.data.num_rows`, `table.data.num_cols` - Dimensions
          - `table.data.table_cells` - List of TableCell objects
          - `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`

          ### Example Usage
          ```python
          doc = get_docling_document("My Document")

          # Get all headings
          headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]

          # Iterate with structure
          for item, level in doc.iterate_items():
              print("  " * level + item.text[:50])

          # Extract table data
          for table in doc.tables:
              for cell in table.data.table_cells:
                  print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
          ```

          ## Example Patterns

          ### Counting documents matching a condition
          ```python
          docs = list_documents(limit=100)
          count = 0
          for doc in docs:
              content = get_document(doc['id'])
              if content and 'keyword' in content.lower():
                  count += 1
                  print(f"Found in: {doc['title']}")
          print(f"Total: {count}")
          ```

          ### Aggregating data across documents
          ```python
          import re
          numbers = []
          results = search("financial data", limit=20)
          for r in results:
              matches = re.findall(r'\$([\d,]+)', r['content'])
              for m in matches:
                  numbers.append(int(m.replace(',', '')))
          print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
          ```

          ### Using ask() for semantic analysis
          ```python
          # First search to find relevant content
          results = search("machine learning approaches")
          # Then use ask() to synthesize an answer
          summary = ask("What are the main machine learning approaches discussed?")
          print(summary)
          ```

          ## Workflow

          1. **ALWAYS start by using execute_code** to explore the knowledge base
          2. Run multiple code blocks as needed to gather information
          3. After collecting data, provide your final answer

          ## Output Format

          After executing code and gathering information, provide:
          1. A clear answer to the user's question
          2. Key findings from your analysis
          3. References to specific documents/chunks that informed your answer

          CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
        role: system
      - content: What is the total revenue across all quarterly reports?
        role: user
      - content: |-
          <think>
          We need revenue from quarterly reports. Search for "quarterly report" and revenue.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
              r[''score''], r[''page_numbers''])\n"}'
            name: execute_code
          id: call_8cd1wnf6
          type: function
      - content: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
          r[''score''], r[''page_numbers''])\n","stdout":"3\nQ1 Report 0.03226645663380623 []\nQ2 Report 0.03226645663380623
          []\nQ3 Report 0.032258063554763794 []\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_8cd1wnf6
      - content: |-
          <think>
          We have 3 reports: Q1, Q2, Q3. Likely Q4 also exists. Search again for Q4.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
            name: execute_code
          id: call_mgo4t2ty
          type: function
      - content: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n","stdout":"3\nQ1
          Report\nQ2 Report\nQ3 Report\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_mgo4t2ty
      model: gpt-oss
      reasoning_effort: low
      stream: false
      tool_choice: auto
      tools:
      - function:
          description: |-
            <summary>Execute Python code in the sandboxed environment.

            The code has access to haiku.rag functions (search, list_documents,
            get_document, get_docling_document, ask) and safe standard library
            modules (json, re, collections, math, statistics, itertools,
            functools, datetime, typing).

            Use print() to output results. Variables persist between executions.</summary>
            <returns>
            <description>Structured result with success status, stdout, and stderr.</description>
            </returns>
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
          strict: true
        type: function
      - function:
          description: Result from RLM agent execution.
          name: final_result
          parameters:
            $defs:
              CodeExecution:
                additionalProperties: false
                description: Result of executing a code block in the RLM sandbox.
                properties:
                  code:
                    description: The Python code that was executed
                    type: string
                  stderr:
                    description: Standard error captured during execution
                    type: string
                  stdout:
                    description: Standard output captured during execution
                    type: string
                  success:
                    description: Whether execution completed without error
                    type: boolean
                required:
                - code
                - stdout
                - stderr
                - success
                type: object
            additionalProperties: false
            properties:
              answer:
                description: The answer to the user's question
                type: string
              code_executions:
                description: History of code executions during the RLM session
                items:
                  $ref: '#/$defs/CodeExecution'
                type: array
            required:
            - answer
            type: object
        type: function
    uri: http://localhost:11434/v1/chat/completions
  response:
    headers:
      content-length:
      - '663'
      content-type:
      - application/json
    parsed_body:
      choices:
      - finish_reason: tool_calls
        index: 0
        message:
          content: ''
          reasoning: All quarterly reports 1-3 only? Maybe Q4 is in separate doc. Search “Quarterly Report 4”.
          role: assistant
          tool_calls:
          - function:
              arguments: '{"code":"results = search(\"Quarterly Report 4\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
              name: execute_code
            id: call_3d3jutqn
            index: 0
            type: function
      created: 1769703359
      id: chatcmpl-718
      model: gpt-oss
      object: chat.completion
      system_fingerprint: fp_ollama
      usage:
        completion_tokens: 84
        prompt_tokens: 1975
        total_tokens: 2059
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '88'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      encoding_format: base64
      input:
      - Quarterly Report 4
      model: qwen3-embedding:4b
    uri: http://localhost:11434/v1/embeddings
  response:
    headers:
      content-type:
      - application/json
      transfer-encoding:
      - chunked
    parsed_body:
      data:
      - embedding: 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
        index: 0
        object: embedding
      model: qwen3-embedding:4b
      object: list
      usage:
        prompt_tokens: 6
        total_tokens: 6
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '9893'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.

          IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.

          CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
          - search("query")  ✓ CORRECT
          - from haiku.rag import search  ✗ WRONG - will fail

          You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):

          ## Available Functions

          ### search(query, limit=10) -> list[dict]
          Search the knowledge base using hybrid search (vector + full-text).
          Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings

          ### list_documents(limit=10, offset=0) -> list[dict]
          List available documents in the knowledge base.
          Returns list of dicts with keys: id, title, uri, created_at

          ### get_document(id_or_title) -> str | None
          Get the full text content of a document by ID, title, or URI.
          Returns the document content as a string, or None if not found.

          ### get_docling_document(id_or_title) -> DoclingDocument | None
          Get the structured DoclingDocument object for advanced analysis.
          Returns a DoclingDocument object, or None if not found.
          See "DoclingDocument API" section below for how to use it.

          ### ask(question) -> str
          Ask a question using the QA agent with RAG. Returns the answer as a string.
          Use this for semantic analysis that benefits from LLM reasoning.

          ## Standard Library Modules
          You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing

          ## Strategy Guide

          1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
          2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
          3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
          4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
          5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
          6. **Use ask() for Semantic Analysis**: When you need to understand meaning or interpret content, use the ask() function.
          7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.

          ## DoclingDocument API

          When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.

          ### Properties
          - `doc.texts` - List of all text items (paragraphs, headings, etc.)
          - `doc.tables` - List of all tables
          - `doc.pictures` - List of all pictures/figures
          - `doc.name` - Document name

          ### Methods
          - `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
            Returns tuples of (item, level) where level is nesting depth
          - `doc.export_to_markdown()` - Export entire document as markdown string

          ### Text Item Properties
          - `item.text` - The text content
          - `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
          - `item.prov` - Provenance (page numbers, bounding boxes)

          ### Table Access
          - `table.data.num_rows`, `table.data.num_cols` - Dimensions
          - `table.data.table_cells` - List of TableCell objects
          - `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`

          ### Example Usage
          ```python
          doc = get_docling_document("My Document")

          # Get all headings
          headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]

          # Iterate with structure
          for item, level in doc.iterate_items():
              print("  " * level + item.text[:50])

          # Extract table data
          for table in doc.tables:
              for cell in table.data.table_cells:
                  print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
          ```

          ## Example Patterns

          ### Counting documents matching a condition
          ```python
          docs = list_documents(limit=100)
          count = 0
          for doc in docs:
              content = get_document(doc['id'])
              if content and 'keyword' in content.lower():
                  count += 1
                  print(f"Found in: {doc['title']}")
          print(f"Total: {count}")
          ```

          ### Aggregating data across documents
          ```python
          import re
          numbers = []
          results = search("financial data", limit=20)
          for r in results:
              matches = re.findall(r'\$([\d,]+)', r['content'])
              for m in matches:
                  numbers.append(int(m.replace(',', '')))
          print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
          ```

          ### Using ask() for semantic analysis
          ```python
          # First search to find relevant content
          results = search("machine learning approaches")
          # Then use ask() to synthesize an answer
          summary = ask("What are the main machine learning approaches discussed?")
          print(summary)
          ```

          ## Workflow

          1. **ALWAYS start by using execute_code** to explore the knowledge base
          2. Run multiple code blocks as needed to gather information
          3. After collecting data, provide your final answer

          ## Output Format

          After executing code and gathering information, provide:
          1. A clear answer to the user's question
          2. Key findings from your analysis
          3. References to specific documents/chunks that informed your answer

          CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
        role: system
      - content: What is the total revenue across all quarterly reports?
        role: user
      - content: |-
          <think>
          We need revenue from quarterly reports. Search for "quarterly report" and revenue.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
              r[''score''], r[''page_numbers''])\n"}'
            name: execute_code
          id: call_8cd1wnf6
          type: function
      - content: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
          r[''score''], r[''page_numbers''])\n","stdout":"3\nQ1 Report 0.03226645663380623 []\nQ2 Report 0.03226645663380623
          []\nQ3 Report 0.032258063554763794 []\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_8cd1wnf6
      - content: |-
          <think>
          We have 3 reports: Q1, Q2, Q3. Likely Q4 also exists. Search again for Q4.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
            name: execute_code
          id: call_mgo4t2ty
          type: function
      - content: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n","stdout":"3\nQ1
          Report\nQ2 Report\nQ3 Report\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_mgo4t2ty
      - content: |-
          <think>
          All quarterly reports 1-3 only? Maybe Q4 is in separate doc. Search “Quarterly Report 4”.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"Quarterly Report 4\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
            name: execute_code
          id: call_3d3jutqn
          type: function
      - content: '{"code":"results = search(\"Quarterly Report 4\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n","stdout":"3\nQ1
          Report\nQ2 Report\nQ3 Report\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_3d3jutqn
      model: gpt-oss
      reasoning_effort: low
      stream: false
      tool_choice: auto
      tools:
      - function:
          description: |-
            <summary>Execute Python code in the sandboxed environment.

            The code has access to haiku.rag functions (search, list_documents,
            get_document, get_docling_document, ask) and safe standard library
            modules (json, re, collections, math, statistics, itertools,
            functools, datetime, typing).

            Use print() to output results. Variables persist between executions.</summary>
            <returns>
            <description>Structured result with success status, stdout, and stderr.</description>
            </returns>
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
          strict: true
        type: function
      - function:
          description: Result from RLM agent execution.
          name: final_result
          parameters:
            $defs:
              CodeExecution:
                additionalProperties: false
                description: Result of executing a code block in the RLM sandbox.
                properties:
                  code:
                    description: The Python code that was executed
                    type: string
                  stderr:
                    description: Standard error captured during execution
                    type: string
                  stdout:
                    description: Standard output captured during execution
                    type: string
                  success:
                    description: Whether execution completed without error
                    type: boolean
                required:
                - code
                - stdout
                - stderr
                - success
                type: object
            additionalProperties: false
            properties:
              answer:
                description: The answer to the user's question
                type: string
              code_executions:
                description: History of code executions during the RLM session
                items:
                  $ref: '#/$defs/CodeExecution'
                type: array
            required:
            - answer
            type: object
        type: function
    uri: http://localhost:11434/v1/chat/completions
  response:
    headers:
      content-length:
      - '1144'
      content-type:
      - application/json
    parsed_body:
      choices:
      - finish_reason: tool_calls
        index: 0
        message:
          content: |-
            <think>
            It appears only Q1‑Q3 reports are present in the index. We need revenue figures from each. We'll retrieve the full text of each report and extract numbers labeled “Revenue”.
          role: assistant
          tool_calls:
          - function:
              arguments: '{"code":"docs = [''Q1 Report'', ''Q2 Report'', ''Q3 Report'']\nrevenues = {}\nimport re\nfor d in
                docs:\n    content = get_document(d)\n    if content:\n        # Find revenue lines\n        matches = re.findall(r''Revenue.*?:\\s*\\$([\\d,\\.]+)'',
                content, re.IGNORECASE)\n        if matches:\n            # take first match\n            rev = float(matches[0].replace('','',''''))\n            revenues[d]
                = rev\n        else:\n            revenues[d] = None\n    else:\n        revenues[d] = None\nprint(revenues)\n"}'
              name: execute_code
            id: call_2rg8jz4m
            index: 0
            type: function
      created: 1769703364
      id: chatcmpl-874
      model: gpt-oss
      object: chat.completion
      system_fingerprint: fp_ollama
      usage:
        completion_tokens: 204
        prompt_tokens: 2139
        total_tokens: 2343
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
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      - gzip, deflate, zstd
      connection:
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      - '11457'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.

          IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.

          CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
          - search("query")  ✓ CORRECT
          - from haiku.rag import search  ✗ WRONG - will fail

          You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):

          ## Available Functions

          ### search(query, limit=10) -> list[dict]
          Search the knowledge base using hybrid search (vector + full-text).
          Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings

          ### list_documents(limit=10, offset=0) -> list[dict]
          List available documents in the knowledge base.
          Returns list of dicts with keys: id, title, uri, created_at

          ### get_document(id_or_title) -> str | None
          Get the full text content of a document by ID, title, or URI.
          Returns the document content as a string, or None if not found.

          ### get_docling_document(id_or_title) -> DoclingDocument | None
          Get the structured DoclingDocument object for advanced analysis.
          Returns a DoclingDocument object, or None if not found.
          See "DoclingDocument API" section below for how to use it.

          ### ask(question) -> str
          Ask a question using the QA agent with RAG. Returns the answer as a string.
          Use this for semantic analysis that benefits from LLM reasoning.

          ## Standard Library Modules
          You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing

          ## Strategy Guide

          1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
          2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
          3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
          4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
          5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
          6. **Use ask() for Semantic Analysis**: When you need to understand meaning or interpret content, use the ask() function.
          7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.

          ## DoclingDocument API

          When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.

          ### Properties
          - `doc.texts` - List of all text items (paragraphs, headings, etc.)
          - `doc.tables` - List of all tables
          - `doc.pictures` - List of all pictures/figures
          - `doc.name` - Document name

          ### Methods
          - `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
            Returns tuples of (item, level) where level is nesting depth
          - `doc.export_to_markdown()` - Export entire document as markdown string

          ### Text Item Properties
          - `item.text` - The text content
          - `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
          - `item.prov` - Provenance (page numbers, bounding boxes)

          ### Table Access
          - `table.data.num_rows`, `table.data.num_cols` - Dimensions
          - `table.data.table_cells` - List of TableCell objects
          - `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`

          ### Example Usage
          ```python
          doc = get_docling_document("My Document")

          # Get all headings
          headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]

          # Iterate with structure
          for item, level in doc.iterate_items():
              print("  " * level + item.text[:50])

          # Extract table data
          for table in doc.tables:
              for cell in table.data.table_cells:
                  print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
          ```

          ## Example Patterns

          ### Counting documents matching a condition
          ```python
          docs = list_documents(limit=100)
          count = 0
          for doc in docs:
              content = get_document(doc['id'])
              if content and 'keyword' in content.lower():
                  count += 1
                  print(f"Found in: {doc['title']}")
          print(f"Total: {count}")
          ```

          ### Aggregating data across documents
          ```python
          import re
          numbers = []
          results = search("financial data", limit=20)
          for r in results:
              matches = re.findall(r'\$([\d,]+)', r['content'])
              for m in matches:
                  numbers.append(int(m.replace(',', '')))
          print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
          ```

          ### Using ask() for semantic analysis
          ```python
          # First search to find relevant content
          results = search("machine learning approaches")
          # Then use ask() to synthesize an answer
          summary = ask("What are the main machine learning approaches discussed?")
          print(summary)
          ```

          ## Workflow

          1. **ALWAYS start by using execute_code** to explore the knowledge base
          2. Run multiple code blocks as needed to gather information
          3. After collecting data, provide your final answer

          ## Output Format

          After executing code and gathering information, provide:
          1. A clear answer to the user's question
          2. Key findings from your analysis
          3. References to specific documents/chunks that informed your answer

          CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
        role: system
      - content: What is the total revenue across all quarterly reports?
        role: user
      - content: |-
          <think>
          We need revenue from quarterly reports. Search for "quarterly report" and revenue.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
              r[''score''], r[''page_numbers''])\n"}'
            name: execute_code
          id: call_8cd1wnf6
          type: function
      - content: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
          r[''score''], r[''page_numbers''])\n","stdout":"3\nQ1 Report 0.03226645663380623 []\nQ2 Report 0.03226645663380623
          []\nQ3 Report 0.032258063554763794 []\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_8cd1wnf6
      - content: |-
          <think>
          We have 3 reports: Q1, Q2, Q3. Likely Q4 also exists. Search again for Q4.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
            name: execute_code
          id: call_mgo4t2ty
          type: function
      - content: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n","stdout":"3\nQ1
          Report\nQ2 Report\nQ3 Report\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_mgo4t2ty
      - content: |-
          <think>
          All quarterly reports 1-3 only? Maybe Q4 is in separate doc. Search “Quarterly Report 4”.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"Quarterly Report 4\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
            name: execute_code
          id: call_3d3jutqn
          type: function
      - content: '{"code":"results = search(\"Quarterly Report 4\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n","stdout":"3\nQ1
          Report\nQ2 Report\nQ3 Report\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_3d3jutqn
      - content: |2-

          It appears only Q1‑Q3 reports are present in the index. We need revenue figures from each. We'll retrieve the full text of each report and extract numbers labeled “Revenue”.
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"docs = [''Q1 Report'', ''Q2 Report'', ''Q3 Report'']\nrevenues = {}\nimport re\nfor d in
              docs:\n    content = get_document(d)\n    if content:\n        # Find revenue lines\n        matches = re.findall(r''Revenue.*?:\\s*\\$([\\d,\\.]+)'',
              content, re.IGNORECASE)\n        if matches:\n            # take first match\n            rev = float(matches[0].replace('','',''''))\n            revenues[d]
              = rev\n        else:\n            revenues[d] = None\n    else:\n        revenues[d] = None\nprint(revenues)\n"}'
            name: execute_code
          id: call_2rg8jz4m
          type: function
      - content: '{"code":"docs = [''Q1 Report'', ''Q2 Report'', ''Q3 Report'']\nrevenues = {}\nimport re\nfor d in docs:\n    content
          = get_document(d)\n    if content:\n        # Find revenue lines\n        matches = re.findall(r''Revenue.*?:\\s*\\$([\\d,\\.]+)'',
          content, re.IGNORECASE)\n        if matches:\n            # take first match\n            rev = float(matches[0].replace('','',''''))\n            revenues[d]
          = rev\n        else:\n            revenues[d] = None\n    else:\n        revenues[d] = None\nprint(revenues)\n","stdout":"{''Q1
          Report'': None, ''Q2 Report'': None, ''Q3 Report'': None}\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_2rg8jz4m
      model: gpt-oss
      reasoning_effort: low
      stream: false
      tool_choice: auto
      tools:
      - function:
          description: |-
            <summary>Execute Python code in the sandboxed environment.

            The code has access to haiku.rag functions (search, list_documents,
            get_document, get_docling_document, ask) and safe standard library
            modules (json, re, collections, math, statistics, itertools,
            functools, datetime, typing).

            Use print() to output results. Variables persist between executions.</summary>
            <returns>
            <description>Structured result with success status, stdout, and stderr.</description>
            </returns>
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
          strict: true
        type: function
      - function:
          description: Result from RLM agent execution.
          name: final_result
          parameters:
            $defs:
              CodeExecution:
                additionalProperties: false
                description: Result of executing a code block in the RLM sandbox.
                properties:
                  code:
                    description: The Python code that was executed
                    type: string
                  stderr:
                    description: Standard error captured during execution
                    type: string
                  stdout:
                    description: Standard output captured during execution
                    type: string
                  success:
                    description: Whether execution completed without error
                    type: boolean
                required:
                - code
                - stdout
                - stderr
                - success
                type: object
            additionalProperties: false
            properties:
              answer:
                description: The answer to the user's question
                type: string
              code_executions:
                description: History of code executions during the RLM session
                items:
                  $ref: '#/$defs/CodeExecution'
                type: array
            required:
            - answer
            type: object
        type: function
    uri: http://localhost:11434/v1/chat/completions
  response:
    headers:
      content-length:
      - '558'
      content-type:
      - application/json
    parsed_body:
      choices:
      - finish_reason: tool_calls
        index: 0
        message:
          content: ''
          reasoning: No revenue found by pattern. Let's inspect content snippet.
          role: assistant
          tool_calls:
          - function:
              arguments: '{"code":"content = get_document(''Q1 Report'')\nprint(content[:500])\n"}'
              name: execute_code
            id: call_oqz8cbe0
            index: 0
            type: function
      created: 1769703365
      id: chatcmpl-151
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    parsed_body:
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      - content: |-
          You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.

          IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.

          CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
          - search("query")  ✓ CORRECT
          - from haiku.rag import search  ✗ WRONG - will fail

          You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):

          ## Available Functions

          ### search(query, limit=10) -> list[dict]
          Search the knowledge base using hybrid search (vector + full-text).
          Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings

          ### list_documents(limit=10, offset=0) -> list[dict]
          List available documents in the knowledge base.
          Returns list of dicts with keys: id, title, uri, created_at

          ### get_document(id_or_title) -> str | None
          Get the full text content of a document by ID, title, or URI.
          Returns the document content as a string, or None if not found.

          ### get_docling_document(id_or_title) -> DoclingDocument | None
          Get the structured DoclingDocument object for advanced analysis.
          Returns a DoclingDocument object, or None if not found.
          See "DoclingDocument API" section below for how to use it.

          ### ask(question) -> str
          Ask a question using the QA agent with RAG. Returns the answer as a string.
          Use this for semantic analysis that benefits from LLM reasoning.

          ## Standard Library Modules
          You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing

          ## Strategy Guide

          1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
          2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
          3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
          4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
          5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
          6. **Use ask() for Semantic Analysis**: When you need to understand meaning or interpret content, use the ask() function.
          7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.

          ## DoclingDocument API

          When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.

          ### Properties
          - `doc.texts` - List of all text items (paragraphs, headings, etc.)
          - `doc.tables` - List of all tables
          - `doc.pictures` - List of all pictures/figures
          - `doc.name` - Document name

          ### Methods
          - `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
            Returns tuples of (item, level) where level is nesting depth
          - `doc.export_to_markdown()` - Export entire document as markdown string

          ### Text Item Properties
          - `item.text` - The text content
          - `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
          - `item.prov` - Provenance (page numbers, bounding boxes)

          ### Table Access
          - `table.data.num_rows`, `table.data.num_cols` - Dimensions
          - `table.data.table_cells` - List of TableCell objects
          - `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`

          ### Example Usage
          ```python
          doc = get_docling_document("My Document")

          # Get all headings
          headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]

          # Iterate with structure
          for item, level in doc.iterate_items():
              print("  " * level + item.text[:50])

          # Extract table data
          for table in doc.tables:
              for cell in table.data.table_cells:
                  print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
          ```

          ## Example Patterns

          ### Counting documents matching a condition
          ```python
          docs = list_documents(limit=100)
          count = 0
          for doc in docs:
              content = get_document(doc['id'])
              if content and 'keyword' in content.lower():
                  count += 1
                  print(f"Found in: {doc['title']}")
          print(f"Total: {count}")
          ```

          ### Aggregating data across documents
          ```python
          import re
          numbers = []
          results = search("financial data", limit=20)
          for r in results:
              matches = re.findall(r'\$([\d,]+)', r['content'])
              for m in matches:
                  numbers.append(int(m.replace(',', '')))
          print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
          ```

          ### Using ask() for semantic analysis
          ```python
          # First search to find relevant content
          results = search("machine learning approaches")
          # Then use ask() to synthesize an answer
          summary = ask("What are the main machine learning approaches discussed?")
          print(summary)
          ```

          ## Workflow

          1. **ALWAYS start by using execute_code** to explore the knowledge base
          2. Run multiple code blocks as needed to gather information
          3. After collecting data, provide your final answer

          ## Output Format

          After executing code and gathering information, provide:
          1. A clear answer to the user's question
          2. Key findings from your analysis
          3. References to specific documents/chunks that informed your answer

          CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
        role: system
      - content: What is the total revenue across all quarterly reports?
        role: user
      - content: |-
          <think>
          We need revenue from quarterly reports. Search for "quarterly report" and revenue.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
              r[''score''], r[''page_numbers''])\n"}'
            name: execute_code
          id: call_8cd1wnf6
          type: function
      - content: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
          r[''score''], r[''page_numbers''])\n","stdout":"3\nQ1 Report 0.03226645663380623 []\nQ2 Report 0.03226645663380623
          []\nQ3 Report 0.032258063554763794 []\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_8cd1wnf6
      - content: |-
          <think>
          We have 3 reports: Q1, Q2, Q3. Likely Q4 also exists. Search again for Q4.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
            name: execute_code
          id: call_mgo4t2ty
          type: function
      - content: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n","stdout":"3\nQ1
          Report\nQ2 Report\nQ3 Report\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_mgo4t2ty
      - content: |-
          <think>
          All quarterly reports 1-3 only? Maybe Q4 is in separate doc. Search “Quarterly Report 4”.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"Quarterly Report 4\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
            name: execute_code
          id: call_3d3jutqn
          type: function
      - content: '{"code":"results = search(\"Quarterly Report 4\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n","stdout":"3\nQ1
          Report\nQ2 Report\nQ3 Report\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_3d3jutqn
      - content: |2-

          It appears only Q1‑Q3 reports are present in the index. We need revenue figures from each. We'll retrieve the full text of each report and extract numbers labeled “Revenue”.
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"docs = [''Q1 Report'', ''Q2 Report'', ''Q3 Report'']\nrevenues = {}\nimport re\nfor d in
              docs:\n    content = get_document(d)\n    if content:\n        # Find revenue lines\n        matches = re.findall(r''Revenue.*?:\\s*\\$([\\d,\\.]+)'',
              content, re.IGNORECASE)\n        if matches:\n            # take first match\n            rev = float(matches[0].replace('','',''''))\n            revenues[d]
              = rev\n        else:\n            revenues[d] = None\n    else:\n        revenues[d] = None\nprint(revenues)\n"}'
            name: execute_code
          id: call_2rg8jz4m
          type: function
      - content: '{"code":"docs = [''Q1 Report'', ''Q2 Report'', ''Q3 Report'']\nrevenues = {}\nimport re\nfor d in docs:\n    content
          = get_document(d)\n    if content:\n        # Find revenue lines\n        matches = re.findall(r''Revenue.*?:\\s*\\$([\\d,\\.]+)'',
          content, re.IGNORECASE)\n        if matches:\n            # take first match\n            rev = float(matches[0].replace('','',''''))\n            revenues[d]
          = rev\n        else:\n            revenues[d] = None\n    else:\n        revenues[d] = None\nprint(revenues)\n","stdout":"{''Q1
          Report'': None, ''Q2 Report'': None, ''Q3 Report'': None}\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_2rg8jz4m
      - content: |-
          <think>
          No revenue found by pattern. Let's inspect content snippet.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"content = get_document(''Q1 Report'')\nprint(content[:500])\n"}'
            name: execute_code
          id: call_oqz8cbe0
          type: function
      - content: '{"code":"content = get_document(''Q1 Report'')\nprint(content[:500])\n","stdout":"Sales report Q1: Revenue
          was $100,000.\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_oqz8cbe0
      model: gpt-oss
      reasoning_effort: low
      stream: false
      tool_choice: auto
      tools:
      - function:
          description: |-
            <summary>Execute Python code in the sandboxed environment.

            The code has access to haiku.rag functions (search, list_documents,
            get_document, get_docling_document, ask) and safe standard library
            modules (json, re, collections, math, statistics, itertools,
            functools, datetime, typing).

            Use print() to output results. Variables persist between executions.</summary>
            <returns>
            <description>Structured result with success status, stdout, and stderr.</description>
            </returns>
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
          strict: true
        type: function
      - function:
          description: Result from RLM agent execution.
          name: final_result
          parameters:
            $defs:
              CodeExecution:
                additionalProperties: false
                description: Result of executing a code block in the RLM sandbox.
                properties:
                  code:
                    description: The Python code that was executed
                    type: string
                  stderr:
                    description: Standard error captured during execution
                    type: string
                  stdout:
                    description: Standard output captured during execution
                    type: string
                  success:
                    description: Whether execution completed without error
                    type: boolean
                required:
                - code
                - stdout
                - stderr
                - success
                type: object
            additionalProperties: false
            properties:
              answer:
                description: The answer to the user's question
                type: string
              code_executions:
                description: History of code executions during the RLM session
                items:
                  $ref: '#/$defs/CodeExecution'
                type: array
            required:
            - answer
            type: object
        type: function
    uri: http://localhost:11434/v1/chat/completions
  response:
    headers:
      content-length:
      - '855'
      content-type:
      - application/json
    parsed_body:
      choices:
      - finish_reason: tool_calls
        index: 0
        message:
          content: ''
          reasoning: Need to handle different phrasing. Search for "$" pattern.
          role: assistant
          tool_calls:
          - function:
              arguments: '{"code":"import re\nrevs={}\nfor d in [''Q1 Report'',''Q2 Report'',''Q3 Report'']:\n    content=get_document(d)\n    if
                content:\n        vals=re.findall(r''\\$([\\d,]+)'',content)\n        if vals:\n            rev=sum(int(v.replace('','',''''))
                for v in vals)\n        else:\n            rev=None\n    else:\n        rev=None\n    revs[d]=rev\nprint(revs)\n"}'
              name: execute_code
            id: call_2j8yqv8s
            index: 0
            type: function
      created: 1769703369
      id: chatcmpl-192
      model: gpt-oss
      object: chat.completion
      system_fingerprint: fp_ollama
      usage:
        completion_tokens: 139
        prompt_tokens: 2641
        total_tokens: 2780
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
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      - keep-alive
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      - '13113'
      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.

          IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.

          CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
          - search("query")  ✓ CORRECT
          - from haiku.rag import search  ✗ WRONG - will fail

          You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):

          ## Available Functions

          ### search(query, limit=10) -> list[dict]
          Search the knowledge base using hybrid search (vector + full-text).
          Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings

          ### list_documents(limit=10, offset=0) -> list[dict]
          List available documents in the knowledge base.
          Returns list of dicts with keys: id, title, uri, created_at

          ### get_document(id_or_title) -> str | None
          Get the full text content of a document by ID, title, or URI.
          Returns the document content as a string, or None if not found.

          ### get_docling_document(id_or_title) -> DoclingDocument | None
          Get the structured DoclingDocument object for advanced analysis.
          Returns a DoclingDocument object, or None if not found.
          See "DoclingDocument API" section below for how to use it.

          ### ask(question) -> str
          Ask a question using the QA agent with RAG. Returns the answer as a string.
          Use this for semantic analysis that benefits from LLM reasoning.

          ## Standard Library Modules
          You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing

          ## Strategy Guide

          1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
          2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
          3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
          4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
          5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
          6. **Use ask() for Semantic Analysis**: When you need to understand meaning or interpret content, use the ask() function.
          7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.

          ## DoclingDocument API

          When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.

          ### Properties
          - `doc.texts` - List of all text items (paragraphs, headings, etc.)
          - `doc.tables` - List of all tables
          - `doc.pictures` - List of all pictures/figures
          - `doc.name` - Document name

          ### Methods
          - `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
            Returns tuples of (item, level) where level is nesting depth
          - `doc.export_to_markdown()` - Export entire document as markdown string

          ### Text Item Properties
          - `item.text` - The text content
          - `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
          - `item.prov` - Provenance (page numbers, bounding boxes)

          ### Table Access
          - `table.data.num_rows`, `table.data.num_cols` - Dimensions
          - `table.data.table_cells` - List of TableCell objects
          - `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`

          ### Example Usage
          ```python
          doc = get_docling_document("My Document")

          # Get all headings
          headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]

          # Iterate with structure
          for item, level in doc.iterate_items():
              print("  " * level + item.text[:50])

          # Extract table data
          for table in doc.tables:
              for cell in table.data.table_cells:
                  print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
          ```

          ## Example Patterns

          ### Counting documents matching a condition
          ```python
          docs = list_documents(limit=100)
          count = 0
          for doc in docs:
              content = get_document(doc['id'])
              if content and 'keyword' in content.lower():
                  count += 1
                  print(f"Found in: {doc['title']}")
          print(f"Total: {count}")
          ```

          ### Aggregating data across documents
          ```python
          import re
          numbers = []
          results = search("financial data", limit=20)
          for r in results:
              matches = re.findall(r'\$([\d,]+)', r['content'])
              for m in matches:
                  numbers.append(int(m.replace(',', '')))
          print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
          ```

          ### Using ask() for semantic analysis
          ```python
          # First search to find relevant content
          results = search("machine learning approaches")
          # Then use ask() to synthesize an answer
          summary = ask("What are the main machine learning approaches discussed?")
          print(summary)
          ```

          ## Workflow

          1. **ALWAYS start by using execute_code** to explore the knowledge base
          2. Run multiple code blocks as needed to gather information
          3. After collecting data, provide your final answer

          ## Output Format

          After executing code and gathering information, provide:
          1. A clear answer to the user's question
          2. Key findings from your analysis
          3. References to specific documents/chunks that informed your answer

          CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
        role: system
      - content: What is the total revenue across all quarterly reports?
        role: user
      - content: |-
          <think>
          We need revenue from quarterly reports. Search for "quarterly report" and revenue.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
              r[''score''], r[''page_numbers''])\n"}'
            name: execute_code
          id: call_8cd1wnf6
          type: function
      - content: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
          r[''score''], r[''page_numbers''])\n","stdout":"3\nQ1 Report 0.03226645663380623 []\nQ2 Report 0.03226645663380623
          []\nQ3 Report 0.032258063554763794 []\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_8cd1wnf6
      - content: |-
          <think>
          We have 3 reports: Q1, Q2, Q3. Likely Q4 also exists. Search again for Q4.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
            name: execute_code
          id: call_mgo4t2ty
          type: function
      - content: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n","stdout":"3\nQ1
          Report\nQ2 Report\nQ3 Report\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_mgo4t2ty
      - content: |-
          <think>
          All quarterly reports 1-3 only? Maybe Q4 is in separate doc. Search “Quarterly Report 4”.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"Quarterly Report 4\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
            name: execute_code
          id: call_3d3jutqn
          type: function
      - content: '{"code":"results = search(\"Quarterly Report 4\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n","stdout":"3\nQ1
          Report\nQ2 Report\nQ3 Report\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_3d3jutqn
      - content: |2-

          It appears only Q1‑Q3 reports are present in the index. We need revenue figures from each. We'll retrieve the full text of each report and extract numbers labeled “Revenue”.
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"docs = [''Q1 Report'', ''Q2 Report'', ''Q3 Report'']\nrevenues = {}\nimport re\nfor d in
              docs:\n    content = get_document(d)\n    if content:\n        # Find revenue lines\n        matches = re.findall(r''Revenue.*?:\\s*\\$([\\d,\\.]+)'',
              content, re.IGNORECASE)\n        if matches:\n            # take first match\n            rev = float(matches[0].replace('','',''''))\n            revenues[d]
              = rev\n        else:\n            revenues[d] = None\n    else:\n        revenues[d] = None\nprint(revenues)\n"}'
            name: execute_code
          id: call_2rg8jz4m
          type: function
      - content: '{"code":"docs = [''Q1 Report'', ''Q2 Report'', ''Q3 Report'']\nrevenues = {}\nimport re\nfor d in docs:\n    content
          = get_document(d)\n    if content:\n        # Find revenue lines\n        matches = re.findall(r''Revenue.*?:\\s*\\$([\\d,\\.]+)'',
          content, re.IGNORECASE)\n        if matches:\n            # take first match\n            rev = float(matches[0].replace('','',''''))\n            revenues[d]
          = rev\n        else:\n            revenues[d] = None\n    else:\n        revenues[d] = None\nprint(revenues)\n","stdout":"{''Q1
          Report'': None, ''Q2 Report'': None, ''Q3 Report'': None}\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_2rg8jz4m
      - content: |-
          <think>
          No revenue found by pattern. Let's inspect content snippet.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"content = get_document(''Q1 Report'')\nprint(content[:500])\n"}'
            name: execute_code
          id: call_oqz8cbe0
          type: function
      - content: '{"code":"content = get_document(''Q1 Report'')\nprint(content[:500])\n","stdout":"Sales report Q1: Revenue
          was $100,000.\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_oqz8cbe0
      - content: |-
          <think>
          Need to handle different phrasing. Search for "$" pattern.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"import re\nrevs={}\nfor d in [''Q1 Report'',''Q2 Report'',''Q3 Report'']:\n    content=get_document(d)\n    if
              content:\n        vals=re.findall(r''\\$([\\d,]+)'',content)\n        if vals:\n            rev=sum(int(v.replace('','',''''))
              for v in vals)\n        else:\n            rev=None\n    else:\n        rev=None\n    revs[d]=rev\nprint(revs)\n"}'
            name: execute_code
          id: call_2j8yqv8s
          type: function
      - content: '{"code":"import re\nrevs={}\nfor d in [''Q1 Report'',''Q2 Report'',''Q3 Report'']:\n    content=get_document(d)\n    if
          content:\n        vals=re.findall(r''\\$([\\d,]+)'',content)\n        if vals:\n            rev=sum(int(v.replace('','',''''))
          for v in vals)\n        else:\n            rev=None\n    else:\n        rev=None\n    revs[d]=rev\nprint(revs)\n","stdout":"{''Q1
          Report'': 100000, ''Q2 Report'': 150000, ''Q3 Report'': 200000}\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_2j8yqv8s
      model: gpt-oss
      reasoning_effort: low
      stream: false
      tool_choice: auto
      tools:
      - function:
          description: |-
            <summary>Execute Python code in the sandboxed environment.

            The code has access to haiku.rag functions (search, list_documents,
            get_document, get_docling_document, ask) and safe standard library
            modules (json, re, collections, math, statistics, itertools,
            functools, datetime, typing).

            Use print() to output results. Variables persist between executions.</summary>
            <returns>
            <description>Structured result with success status, stdout, and stderr.</description>
            </returns>
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
          strict: true
        type: function
      - function:
          description: Result from RLM agent execution.
          name: final_result
          parameters:
            $defs:
              CodeExecution:
                additionalProperties: false
                description: Result of executing a code block in the RLM sandbox.
                properties:
                  code:
                    description: The Python code that was executed
                    type: string
                  stderr:
                    description: Standard error captured during execution
                    type: string
                  stdout:
                    description: Standard output captured during execution
                    type: string
                  success:
                    description: Whether execution completed without error
                    type: boolean
                required:
                - code
                - stdout
                - stderr
                - success
                type: object
            additionalProperties: false
            properties:
              answer:
                description: The answer to the user's question
                type: string
              code_executions:
                description: History of code executions during the RLM session
                items:
                  $ref: '#/$defs/CodeExecution'
                type: array
            required:
            - answer
            type: object
        type: function
    uri: http://localhost:11434/v1/chat/completions
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    parsed_body:
      choices:
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        index: 0
        message:
          content: "**Answer**\n\nThe total revenue reported across the three quarterly reports is:\n\n\\- Q1 Report: **$100,000**
            \ \n\\- Q2 Report: **$150,000**  \n\\- Q3 Report: **$200,000**\n\n**Total Revenue (all quarters combined): $450,000**\n\n**Key
            Findings & Sources**\n\n1. **Q1 Report** – revenue extraction from the text \"Revenue was $100,000.\"\n   - Source
            snippet: *“Sales report Q1: Revenue was $100,000.”*\n2. **Q2 Report** – revenue extracted from \"$150,000\" in
            the document.\n3. **Q3 Report** – revenue extracted from \"$200,000\" in the document.\n\nAll figures were obtained
            by searching each document for dollar amounts and summing them. No Q4 report was found in the available documents."
          role: assistant
      created: 1769703373
      id: chatcmpl-568
      model: gpt-oss
      object: chat.completion
      system_fingerprint: fp_ollama
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    status:
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- request:
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      content-type:
      - application/json
      host:
      - localhost:11434
    method: POST
    parsed_body:
      messages:
      - content: |-
          You are a Recursive Language Model (RLM) agent that solves complex research questions by writing and executing Python code.

          IMPORTANT: You MUST use the `execute_code` tool to run Python code. The functions described below are ONLY available inside the execute_code tool - you cannot access them any other way. Always execute code to answer questions; do not just describe what code would do.

          CRITICAL: Inside execute_code, these functions are ALREADY available in the namespace. Do NOT import them - just use them directly:
          - search("query")  ✓ CORRECT
          - from haiku.rag import search  ✗ WRONG - will fail

          You have access to a sandboxed Python environment with these haiku.rag functions (use them directly, no imports needed):

          ## Available Functions

          ### search(query, limit=10) -> list[dict]
          Search the knowledge base using hybrid search (vector + full-text).
          Returns list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings

          ### list_documents(limit=10, offset=0) -> list[dict]
          List available documents in the knowledge base.
          Returns list of dicts with keys: id, title, uri, created_at

          ### get_document(id_or_title) -> str | None
          Get the full text content of a document by ID, title, or URI.
          Returns the document content as a string, or None if not found.

          ### get_docling_document(id_or_title) -> DoclingDocument | None
          Get the structured DoclingDocument object for advanced analysis.
          Returns a DoclingDocument object, or None if not found.
          See "DoclingDocument API" section below for how to use it.

          ### ask(question) -> str
          Ask a question using the QA agent with RAG. Returns the answer as a string.
          Use this for semantic analysis that benefits from LLM reasoning.

          ## Standard Library Modules
          You can import: json, re, collections, math, statistics, itertools, functools, datetime, typing

          ## Strategy Guide

          1. **Explore First**: Start by listing documents or searching to understand what's available. Document names may differ from filenames (e.g., "tbmed593.pdf" might be stored as "TB MED 593" or similar).
          2. **If get_document returns None**: Use `list_documents()` to see actual document titles, or `search()` to find relevant content.
          3. **Iterative Refinement**: Run code, examine results, adjust your approach based on what you find.
          4. **Use print() Liberally**: The REPL captures stdout - print intermediate results to see what you're working with.
          5. **Aggregate with Code**: For counting, averaging, or comparing across documents, write loops and use collections.
          6. **Use ask() for Semantic Analysis**: When you need to understand meaning or interpret content, use the ask() function.
          7. **Cite Your Sources**: Track which documents/chunks informed your answer for citation.

          ## DoclingDocument API

          When you call `get_docling_document(id_or_title)`, you get a DoclingDocument object for structured document analysis.

          ### Properties
          - `doc.texts` - List of all text items (paragraphs, headings, etc.)
          - `doc.tables` - List of all tables
          - `doc.pictures` - List of all pictures/figures
          - `doc.name` - Document name

          ### Methods
          - `doc.iterate_items(with_groups=False)` - Iterate all items with hierarchy level
            Returns tuples of (item, level) where level is nesting depth
          - `doc.export_to_markdown()` - Export entire document as markdown string

          ### Text Item Properties
          - `item.text` - The text content
          - `item.label` - Type: TITLE, PARAGRAPH, SECTION_HEADER, LIST_ITEM, etc.
          - `item.prov` - Provenance (page numbers, bounding boxes)

          ### Table Access
          - `table.data.num_rows`, `table.data.num_cols` - Dimensions
          - `table.data.table_cells` - List of TableCell objects
          - `cell.text`, `cell.start_row_offset_idx`, `cell.start_col_offset_idx`

          ### Example Usage
          ```python
          doc = get_docling_document("My Document")

          # Get all headings
          headings = [t.text for t in doc.texts if "HEADER" in str(t.label)]

          # Iterate with structure
          for item, level in doc.iterate_items():
              print("  " * level + item.text[:50])

          # Extract table data
          for table in doc.tables:
              for cell in table.data.table_cells:
                  print(f"Row {cell.start_row_offset_idx}, Col {cell.start_col_offset_idx}: {cell.text}")
          ```

          ## Example Patterns

          ### Counting documents matching a condition
          ```python
          docs = list_documents(limit=100)
          count = 0
          for doc in docs:
              content = get_document(doc['id'])
              if content and 'keyword' in content.lower():
                  count += 1
                  print(f"Found in: {doc['title']}")
          print(f"Total: {count}")
          ```

          ### Aggregating data across documents
          ```python
          import re
          numbers = []
          results = search("financial data", limit=20)
          for r in results:
              matches = re.findall(r'\$([\d,]+)', r['content'])
              for m in matches:
                  numbers.append(int(m.replace(',', '')))
          print(f"Average: ${sum(numbers)/len(numbers):,.2f}")
          ```

          ### Using ask() for semantic analysis
          ```python
          # First search to find relevant content
          results = search("machine learning approaches")
          # Then use ask() to synthesize an answer
          summary = ask("What are the main machine learning approaches discussed?")
          print(summary)
          ```

          ## Workflow

          1. **ALWAYS start by using execute_code** to explore the knowledge base
          2. Run multiple code blocks as needed to gather information
          3. After collecting data, provide your final answer

          ## Output Format

          After executing code and gathering information, provide:
          1. A clear answer to the user's question
          2. Key findings from your analysis
          3. References to specific documents/chunks that informed your answer

          CRITICAL: You MUST call execute_code at least once before providing your answer. Never give up without trying to execute code first.
        role: system
      - content: What is the total revenue across all quarterly reports?
        role: user
      - content: |-
          <think>
          We need revenue from quarterly reports. Search for "quarterly report" and revenue.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
              r[''score''], r[''page_numbers''])\n"}'
            name: execute_code
          id: call_8cd1wnf6
          type: function
      - content: '{"code":"results = search(\"quarterly report revenue\", limit=50)\nprint(len(results))\nfor r in results[:5]:\n    print(r[''document_title''],
          r[''score''], r[''page_numbers''])\n","stdout":"3\nQ1 Report 0.03226645663380623 []\nQ2 Report 0.03226645663380623
          []\nQ3 Report 0.032258063554763794 []\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_8cd1wnf6
      - content: |-
          <think>
          We have 3 reports: Q1, Q2, Q3. Likely Q4 also exists. Search again for Q4.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
            name: execute_code
          id: call_mgo4t2ty
          type: function
      - content: '{"code":"results = search(\"Q4 Report\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n","stdout":"3\nQ1
          Report\nQ2 Report\nQ3 Report\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_mgo4t2ty
      - content: |-
          <think>
          All quarterly reports 1-3 only? Maybe Q4 is in separate doc. Search “Quarterly Report 4”.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(\"Quarterly Report 4\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n"}'
            name: execute_code
          id: call_3d3jutqn
          type: function
      - content: '{"code":"results = search(\"Quarterly Report 4\", limit=10)\nprint(len(results))\nfor r in results:\n    print(r[''document_title''])\n","stdout":"3\nQ1
          Report\nQ2 Report\nQ3 Report\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_3d3jutqn
      - content: |2-

          It appears only Q1‑Q3 reports are present in the index. We need revenue figures from each. We'll retrieve the full text of each report and extract numbers labeled “Revenue”.
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"docs = [''Q1 Report'', ''Q2 Report'', ''Q3 Report'']\nrevenues = {}\nimport re\nfor d in
              docs:\n    content = get_document(d)\n    if content:\n        # Find revenue lines\n        matches = re.findall(r''Revenue.*?:\\s*\\$([\\d,\\.]+)'',
              content, re.IGNORECASE)\n        if matches:\n            # take first match\n            rev = float(matches[0].replace('','',''''))\n            revenues[d]
              = rev\n        else:\n            revenues[d] = None\n    else:\n        revenues[d] = None\nprint(revenues)\n"}'
            name: execute_code
          id: call_2rg8jz4m
          type: function
      - content: '{"code":"docs = [''Q1 Report'', ''Q2 Report'', ''Q3 Report'']\nrevenues = {}\nimport re\nfor d in docs:\n    content
          = get_document(d)\n    if content:\n        # Find revenue lines\n        matches = re.findall(r''Revenue.*?:\\s*\\$([\\d,\\.]+)'',
          content, re.IGNORECASE)\n        if matches:\n            # take first match\n            rev = float(matches[0].replace('','',''''))\n            revenues[d]
          = rev\n        else:\n            revenues[d] = None\n    else:\n        revenues[d] = None\nprint(revenues)\n","stdout":"{''Q1
          Report'': None, ''Q2 Report'': None, ''Q3 Report'': None}\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_2rg8jz4m
      - content: |-
          <think>
          No revenue found by pattern. Let's inspect content snippet.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"content = get_document(''Q1 Report'')\nprint(content[:500])\n"}'
            name: execute_code
          id: call_oqz8cbe0
          type: function
      - content: '{"code":"content = get_document(''Q1 Report'')\nprint(content[:500])\n","stdout":"Sales report Q1: Revenue
          was $100,000.\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_oqz8cbe0
      - content: |-
          <think>
          Need to handle different phrasing. Search for "$" pattern.
          </think>
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"import re\nrevs={}\nfor d in [''Q1 Report'',''Q2 Report'',''Q3 Report'']:\n    content=get_document(d)\n    if
              content:\n        vals=re.findall(r''\\$([\\d,]+)'',content)\n        if vals:\n            rev=sum(int(v.replace('','',''''))
              for v in vals)\n        else:\n            rev=None\n    else:\n        rev=None\n    revs[d]=rev\nprint(revs)\n"}'
            name: execute_code
          id: call_2j8yqv8s
          type: function
      - content: '{"code":"import re\nrevs={}\nfor d in [''Q1 Report'',''Q2 Report'',''Q3 Report'']:\n    content=get_document(d)\n    if
          content:\n        vals=re.findall(r''\\$([\\d,]+)'',content)\n        if vals:\n            rev=sum(int(v.replace('','',''''))
          for v in vals)\n        else:\n            rev=None\n    else:\n        rev=None\n    revs[d]=rev\nprint(revs)\n","stdout":"{''Q1
          Report'': 100000, ''Q2 Report'': 150000, ''Q3 Report'': 200000}\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_2j8yqv8s
      - content: "**Answer**\n\nThe total revenue reported across the three quarterly reports is:\n\n\\- Q1 Report: **$100,000**
          \ \n\\- Q2 Report: **$150,000**  \n\\- Q3 Report: **$200,000**\n\n**Total Revenue (all quarters combined): $450,000**\n\n**Key
          Findings & Sources**\n\n1. **Q1 Report** – revenue extraction from the text \"Revenue was $100,000.\"\n   - Source
          snippet: *“Sales report Q1: Revenue was $100,000.”*\n2. **Q2 Report** – revenue extracted from \"$150,000\" in the
          document.\n3. **Q3 Report** – revenue extracted from \"$200,000\" in the document.\n\nAll figures were obtained
          by searching each document for dollar amounts and summing them. No Q4 report was found in the available documents."
        role: assistant
      - content: |-
          1 validation error:
          ```json
          [
            {
              "type": "json_invalid",
              "loc": [],
              "msg": "Invalid JSON: expected value at line 1 column 1",
              "input": "**Answer**\n\nThe total revenue reported across the three quarterly reports is:\n\n\\- Q1 Report: **$100,000**  \n\\- Q2 Report: **$150,000**  \n\\- Q3 Report: **$200,000**\n\n**Total Revenue (all quarters combined): $450,000**\n\n**Key Findings & Sources**\n\n1. **Q1 Report** – revenue extraction from the text \"Revenue was $100,000.\"\n   - Source snippet: *“Sales report Q1: Revenue was $100,000.”*\n2. **Q2 Report** – revenue extracted from \"$150,000\" in the document.\n3. **Q3 Report** – revenue extracted from \"$200,000\" in the document.\n\nAll figures were obtained by searching each document for dollar amounts and summing them. No Q4 report was found in the available documents."
            }
          ]
          ```

          Fix the errors and try again.
        role: user
      model: gpt-oss
      reasoning_effort: low
      stream: false
      tool_choice: auto
      tools:
      - function:
          description: |-
            <summary>Execute Python code in the sandboxed environment.

            The code has access to haiku.rag functions (search, list_documents,
            get_document, get_docling_document, ask) and safe standard library
            modules (json, re, collections, math, statistics, itertools,
            functools, datetime, typing).

            Use print() to output results. Variables persist between executions.</summary>
            <returns>
            <description>Structured result with success status, stdout, and stderr.</description>
            </returns>
          name: execute_code
          parameters:
            additionalProperties: false
            properties:
              code:
                description: Python code to execute.
                type: string
            required:
            - code
            type: object
          strict: true
        type: function
      - function:
          description: Result from RLM agent execution.
          name: final_result
          parameters:
            $defs:
              CodeExecution:
                additionalProperties: false
                description: Result of executing a code block in the RLM sandbox.
                properties:
                  code:
                    description: The Python code that was executed
                    type: string
                  stderr:
                    description: Standard error captured during execution
                    type: string
                  stdout:
                    description: Standard output captured during execution
                    type: string
                  success:
                    description: Whether execution completed without error
                    type: boolean
                required:
                - code
                - stdout
                - stderr
                - success
                type: object
            additionalProperties: false
            properties:
              answer:
                description: The answer to the user's question
                type: string
              code_executions:
                description: History of code executions during the RLM session
                items:
                  $ref: '#/$defs/CodeExecution'
                type: array
            required:
            - answer
            type: object
        type: function
    uri: http://localhost:11434/v1/chat/completions
  response:
    headers:
      content-length:
      - '1126'
      content-type:
      - application/json
    parsed_body:
      choices:
      - finish_reason: tool_calls
        index: 0
        message:
          content: ''
          reasoning: Need to output JSON.
          role: assistant
          tool_calls:
          - function:
              arguments: '{"answer":"The total revenue reported across the three quarterly reports is:\n- Q1 Report: $100,000\n-
                Q2 Report: $150,000\n- Q3 Report: $200,000\n\n**Total Revenue (all quarters combined): $450,000**\n\n**Key
                Findings \u0026 Sources**\n1. Q1 Report – revenue extracted from the text \"Revenue was $100,000.\" (Source:
                Sales report Q1 text)\n2. Q2 Report – revenue extracted from \"$150,000\" in the document.\n3. Q3 Report –
                revenue extracted from \"$200,000\" in the document.\n\nAll figures were obtained by searching each document
                for dollar amounts and summing them. No Q4 report was found in the available documents.","code_executions":[]}'
              name: final_result
            id: call_1ouq42ea
            index: 0
            type: function
      created: 1769703378
      id: chatcmpl-830
      model: gpt-oss
      object: chat.completion
      system_fingerprint: fp_ollama
      usage:
        completion_tokens: 195
        prompt_tokens: 3387
        total_tokens: 3582
    status:
      code: 200
      message: OK
version: 1