haiku.rag/tests/cassettes/test_rlm/TestClientRLMIntegration.test_rlm_docling_document_structure.yaml

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        Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.
      - Caption, Count = 22524. Caption, % of Total.Train = 2.04. Caption, % of Total.Test = 1.77. Caption, % of Total.Val
        = 2.32. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).All = 84-89. Caption, triple inter-annotator mAP @ 0.5-0.95
        (%).Fin = 40-61. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 86-92. Caption, triple inter-annotator mAP
        @ 0.5-0.95 (%).Sci = 94-99. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 95-99. Caption, triple inter-annotator
        mAP @ 0.5-0.95 (%).Pat = 69-78. Caption, triple inter-annotator mAP @ 0.5-0.95 (%).Ten =
      - n/a. Footnote, Count = 6318. Footnote, % of Total.Train = 0.60. Footnote, % of Total.Test = 0.31. Footnote, % of Total.Val
        = 0.58. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).All = 83-91. Footnote, triple inter-annotator mAP @ 0.5-0.95
        (%).Fin = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 100. Footnote, triple inter-annotator mAP
        @ 0.5-0.95 (%).Sci = 62-88. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 85-94. Footnote, triple inter-annotator
        mAP @ 0.5-0.95 (%).Pat = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Ten
      - = 82-97. Formula, Count = 25027. Formula, % of Total.Train = 2.25. Formula, % of Total.Test = 1.90. Formula, % of
        Total.Val = 2.96. Formula, triple inter-annotator mAP @ 0.5-0.95 (%).All = 83-85. Formula, triple inter-annotator
        mAP @ 0.5-0.95 (%).Fin = . Formula, triple inter-annotator mAP @ 0.5-0.95 (%).Man = n/a. Formula, triple inter-annotator
        mAP @ 0.5-0.95 (%).Sci = 84-87. Formula, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 86-96. Formula, triple inter-annotator
        mAP @ 0.5-0.95 (%).Pat = . Formula, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = n/a. List-item, Count =
      - 185660. List-item, % of Total.Train = 17.19. List-item, % of Total.Test = 13.34. List-item, % of Total.Val = 15.82.
        List-item, triple inter-annotator mAP @ 0.5-0.95 (%).All = 87-88. List-item, triple inter-annotator mAP @ 0.5-0.95
        (%).Fin = 74-83. List-item, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 90-92. List-item, triple inter-annotator
        mAP @ 0.5-0.95 (%).Sci = 97-97. List-item, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 81-85. List-item, triple
        inter-annotator mAP @ 0.5-0.95 (%).Pat = 75-88. List-item, triple inter-annotator mAP @
      - 0.5-0.95 (%).Ten = 93-95. Page-footer, Count = 70878. Page-footer, % of Total.Train = 6.51. Page-footer, % of Total.Test
        = 5.58. Page-footer, % of Total.Val = 6.00. Page-footer, triple inter-annotator mAP @ 0.5-0.95 (%).All = 93-94. Page-footer,
        triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 88-90. Page-footer, triple inter-annotator mAP @ 0.5-0.95 (%).Man
        = 95-96. Page-footer, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 100. Page-footer, triple inter-annotator mAP
        @ 0.5-0.95 (%).Law = 92-97. Page-footer, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 100.
      - Page-footer, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 96-98. Page-header, Count = 58022. Page-header, % of
        Total.Train = 5.10. Page-header, % of Total.Test = 6.70. Page-header, % of Total.Val = 5.06. Page-header, triple inter-annotator
        mAP @ 0.5-0.95 (%).All = 85-89. Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 66-76. Page-header, triple
        inter-annotator mAP @ 0.5-0.95 (%).Man = 90-94. Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 98-100.
        Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 91-92. Page-header, triple inter-annotator mAP @
      - 0.5-0.95 (%).Pat = 97-99. Page-header, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 81-86. Picture, Count = 45976.
        Picture, % of Total.Train = 4.21. Picture, % of Total.Test = 2.78. Picture, % of Total.Val = 5.31. Picture, triple
        inter-annotator mAP @ 0.5-0.95 (%).All = 69-71. Picture, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 56-59. Picture,
        triple inter-annotator mAP @ 0.5-0.95 (%).Man = 82-86. Picture, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 69-82.
        Picture, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 80-95. Picture, triple
      - inter-annotator mAP @ 0.5-0.95 (%).Pat = 66-71. Picture, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 59-76. Section-header,
        Count = 142884. Section-header, % of Total.Train = 12.60. Section-header, % of Total.Test = 15.77. Section-header,
        % of Total.Val = 12.85. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).All = 83-84. Section-header, triple
        inter-annotator mAP @ 0.5-0.95 (%).Fin = 76-81. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 90-92.
        Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 94-95. Section-header, triple inter-annotator mAP
        @
      - 0.5-0.95 (%).Law = 87-94. Section-header, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 69-73. Section-header, triple
        inter-annotator mAP @ 0.5-0.95 (%).Ten = 78-86. Table, Count = 34733. Table, % of Total.Train = 3.20. Table, % of
        Total.Test = 2.27. Table, % of Total.Val = 3.60. Table, triple inter-annotator mAP @ 0.5-0.95 (%).All = 77-81. Table,
        triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 75-80. Table, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 83-86.
        Table, triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 98-99. Table, triple
      - inter-annotator mAP @ 0.5-0.95 (%).Law = 58-80. Table, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 79-84. Table,
        triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 70-85. Text, Count = 510377. Text, % of Total.Train = 45.82. Text,
        % of Total.Test = 49.28. Text, % of Total.Val = 45.00. Text, triple inter-annotator mAP @ 0.5-0.95 (%).All = 84-86.
        Text, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 81-86. Text, triple inter-annotator mAP @ 0.5-0.95 (%).Man =
        88-93. Text, triple inter-annotator mAP @ 0.5-0.95 (%).Sci =
      - 89-93. Text, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 87-92. Text, triple inter-annotator mAP @ 0.5-0.95 (%).Pat
        = 71-79. Text, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 87-95. Title, Count = 5071. Title, % of Total.Train
        = 0.47. Title, % of Total.Test = 0.30. Title, % of Total.Val = 0.50. Title, triple inter-annotator mAP @ 0.5-0.95
        (%).All = 60-72. Title, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 24-63. Title, triple inter-annotator mAP @
        0.5-0.95 (%).Man = 50-63. Title, triple inter-annotator mAP @ 0.5-0.95
      - (%).Sci = 94-100. Title, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 82-96. Title, triple inter-annotator mAP
        @ 0.5-0.95 (%).Pat = 68-79. Title, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 24-56. Total, Count = 1107470.
        Total, % of Total.Train = 941123. Total, % of Total.Test = 99816. Total, % of Total.Val = 66531. Total, triple inter-annotator
        mAP @ 0.5-0.95 (%).All = 82-83. Total, triple inter-annotator mAP @ 0.5-0.95 (%).Fin = 71-74. Total, triple inter-annotator
        mAP @ 0.5-0.95 (%).Man = 79-81. Total, triple inter-annotator
      - |-
        mAP @ 0.5-0.95 (%).Sci = 89-94. Total, triple inter-annotator mAP @ 0.5-0.95 (%).Law = 86-91. Total, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = 71-76. Total, triple inter-annotator mAP @ 0.5-0.95 (%).Ten = 68-85
        Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
        we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.        
      - 'Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large
        effort went into ensuring that all documents are free to use. The data sources include publication repositories such
        as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and
        patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow
        us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.'
      - 'Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural
        features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of
        11 distinct class labels. These 11 class labels are $_{Caption}$, $_{Footnote}$, $_{Formula}$, $_{List-item}$, Page-$_{footer}$,
        $_{Page-header}$, $_{Picture}$, $_{Section-header}$, $_{Table}$, $_{Text}$, and $_{Title}$. Critical factors that
        were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity
        of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall
        coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all
        meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category,
        such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the
        semantics of the text. Labels such as Author and'
      - |-
        $_{Affiliation}$, as seen in DocBank, are often only distinguishable by discriminating on
        Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.
        $^{3}$https://arxiv.org/        
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
        index: 0
        object: embedding
      - embedding: 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
        index: 1
        object: embedding
      - embedding: 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
        index: 2
        object: embedding
      - embedding: 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
        index: 3
        object: embedding
      - embedding: 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
        index: 4
        object: embedding
      - embedding: 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
        index: 5
        object: embedding
      - embedding: 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
        index: 6
        object: embedding
      - embedding: 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
        index: 7
        object: embedding
      - embedding: 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
        index: 8
        object: embedding
      - embedding: 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
        index: 9
        object: embedding
      - embedding: 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
        index: 10
        object: embedding
      - embedding: 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
        index: 11
        object: embedding
      - embedding: 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
        index: 12
        object: embedding
      - embedding: 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
        index: 13
        object: embedding
      - embedding: 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
        index: 14
        object: embedding
      - embedding: 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
        index: 15
        object: embedding
      - embedding: 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
        index: 16
        object: embedding
      model: qwen3-embedding:4b
      object: list
      usage:
        prompt_tokens: 3883
        total_tokens: 3883
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '7735'
      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
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        role: user
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            The code has access to haiku.rag functions (search, list_documents,
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            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:
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                type: string
            required:
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                type: string
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                items:
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          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: How many tables are in the document? Also tell me how many pictures/figures it contains.
        role: user
      - content: |-
          <think>
          We need a specific document. Which one? User didn't specify. Likely the current context: maybe a particular file in the repo. We should list_documents.
          </think>          
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            modules (json, re, collections, math, statistics, itertools,
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            Use print() to output results. Variables persist between executions.</summary>
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          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: How many tables are in the document? Also tell me how many pictures/figures it contains.
        role: user
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          We need a specific document. Which one? User didn't specify. Likely the current context: maybe a particular file in the repo. We should list_documents.
          </think>          
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        index: 0
        object: embedding
      model: qwen3-embedding:4b
      object: list
      usage:
        prompt_tokens: 4
        total_tokens: 4
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '13000'
      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: How many tables are in the document? Also tell me how many pictures/figures it contains.
        role: user
      - content: |-
          <think>
          We need a specific document. Which one? User didn't specify. Likely the current context: maybe a particular file in the repo. We should list_documents.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"docs = list_documents(limit=20)\nprint(docs)"}'
            name: execute_code
          id: call_iea2g7gy
          type: function
      - content: '{"code":"docs = list_documents(limit=20)\nprint(docs)","stdout":"[{''id'': ''65caca96-a2f7-4b43-bdad-9174d4f51e85'',
          ''title'': None, ''uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'',
          ''created_at'': ''2026-01-29 18:38:51.772641''}]\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_iea2g7gy
      - content: |-
          <think>
          Only one doc, uri doclaynet.pdf. Need to get_docling_document.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"doc = get_docling_document(''doclaynet.pdf'')\nprint(doc.name)\nprint(''tables:'', len(doc.tables))\nprint(''pictures:'',
              len(doc.pictures))"}'
            name: execute_code
          id: call_bjlxiv63
          type: function
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          line 355, in _execute_sync\n    exec(code, exec_globals, self.locals)\n    ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File
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        role: tool
        tool_call_id: call_bjlxiv63
      - content: |-
          <think>
          Doc not found. Maybe title differs. Use search for document title.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(''doclaynet'')\nprint(results[:3])"}'
            name: execute_code
          id: call_mloo01q9
          type: function
      - content: '{"code":"results = search(''doclaynet'')\nprint(results[:3])","stdout":"[{''chunk_id'': ''fb4d75c4-3416-49e2-b1b4-f319073a10cc'',
          ''content'': ''\\nTable 1: DocLayNet dataset overview. Along with the frequency of each class label, we present
          the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement
          is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which
          we obtain accuracy ranges.'', ''document_id'': ''65caca96-a2f7-4b43-bdad-9174d4f51e85'', ''document_title'': None,
          ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'',
          ''score'': 0.032786883413791656, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''dc5ab8c7-2b89-4a5e-a7bc-2c7dc2376d41'',
          ''content'': ''Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most
          common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us
          to the definition of 11 distinct class labels. These 11 class labels are $_{Caption}$, $_{Footnote}$, $_{Formula}$,
          $_{List-item}$, Page-$_{footer}$, $_{Page-header}$, $_{Picture}$, $_{Section-header}$, $_{Table}$, $_{Text}$, and
          $_{Title}$. Critical factors that were considered for the choice of these class labels were (1) the overall occurrence
          of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from
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          ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class
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          ''65caca96-a2f7-4b43-bdad-9174d4f51e85'', ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'',
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          ''content'': ''n/a. Footnote, Count = 6318. Footnote, % of Total.Train = 0.60. Footnote, % of Total.Test = 0.31.
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          triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 62-88. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Law =
          85-94. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95
          (%).Ten'', ''document_id'': ''65caca96-a2f7-4b43-bdad-9174d4f51e85'', ''document_title'': None, ''document_uri'':
          ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'', ''score'': 0.01587301678955555,
          ''page_numbers'': [1], ''headings'': None}]\n","stderr":"","success":true}'
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      model: gpt-oss
      reasoning_effort: low
      stream: false
      tool_choice: auto
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            <summary>Execute Python code in the sandboxed environment.

            The code has access to haiku.rag functions (search, list_documents,
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            Use print() to output results. Variables persist between executions.</summary>
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                description: Result of executing a code block in the RLM sandbox.
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                description: The answer to the user's question
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        type: function
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          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: How many tables are in the document? Also tell me how many pictures/figures it contains.
        role: user
      - content: |-
          <think>
          We need a specific document. Which one? User didn't specify. Likely the current context: maybe a particular file in the repo. We should list_documents.
          </think>          
        role: assistant
        tool_calls:
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            name: execute_code
          id: call_iea2g7gy
          type: function
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        role: tool
        tool_call_id: call_iea2g7gy
      - content: |-
          <think>
          Only one doc, uri doclaynet.pdf. Need to get_docling_document.
          </think>          
        role: assistant
        tool_calls:
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        role: tool
        tool_call_id: call_bjlxiv63
      - content: |-
          <think>
          Doc not found. Maybe title differs. Use search for document title.
          </think>          
        role: assistant
        tool_calls:
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            name: execute_code
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          ''65caca96-a2f7-4b43-bdad-9174d4f51e85'', ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'',
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          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: How many tables are in the document? Also tell me how many pictures/figures it contains.
        role: user
      - content: |-
          <think>
          We need a specific document. Which one? User didn't specify. Likely the current context: maybe a particular file in the repo. We should list_documents.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"docs = list_documents(limit=20)\nprint(docs)"}'
            name: execute_code
          id: call_iea2g7gy
          type: function
      - content: '{"code":"docs = list_documents(limit=20)\nprint(docs)","stdout":"[{''id'': ''65caca96-a2f7-4b43-bdad-9174d4f51e85'',
          ''title'': None, ''uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'',
          ''created_at'': ''2026-01-29 18:38:51.772641''}]\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_iea2g7gy
      - content: |-
          <think>
          Only one doc, uri doclaynet.pdf. Need to get_docling_document.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"doc = get_docling_document(''doclaynet.pdf'')\nprint(doc.name)\nprint(''tables:'', len(doc.tables))\nprint(''pictures:'',
              len(doc.pictures))"}'
            name: execute_code
          id: call_bjlxiv63
          type: function
      - content: '{"code":"doc = get_docling_document(''doclaynet.pdf'')\nprint(doc.name)\nprint(''tables:'', len(doc.tables))\nprint(''pictures:'',
          len(doc.pictures))","stdout":"","stderr":"Traceback (most recent call last):\n  File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
          line 355, in _execute_sync\n    exec(code, exec_globals, self.locals)\n    ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File
          \"<string>\", line 2, in <module>\nAttributeError: ''NoneType'' object has no attribute ''name''\n","success":false}'
        role: tool
        tool_call_id: call_bjlxiv63
      - content: |-
          <think>
          Doc not found. Maybe title differs. Use search for document title.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(''doclaynet'')\nprint(results[:3])"}'
            name: execute_code
          id: call_mloo01q9
          type: function
      - content: '{"code":"results = search(''doclaynet'')\nprint(results[:3])","stdout":"[{''chunk_id'': ''fb4d75c4-3416-49e2-b1b4-f319073a10cc'',
          ''content'': ''\\nTable 1: DocLayNet dataset overview. Along with the frequency of each class label, we present
          the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement
          is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which
          we obtain accuracy ranges.'', ''document_id'': ''65caca96-a2f7-4b43-bdad-9174d4f51e85'', ''document_title'': None,
          ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'',
          ''score'': 0.032786883413791656, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''dc5ab8c7-2b89-4a5e-a7bc-2c7dc2376d41'',
          ''content'': ''Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most
          common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us
          to the definition of 11 distinct class labels. These 11 class labels are $_{Caption}$, $_{Footnote}$, $_{Formula}$,
          $_{List-item}$, Page-$_{footer}$, $_{Page-header}$, $_{Picture}$, $_{Section-header}$, $_{Table}$, $_{Text}$, and
          $_{Title}$. Critical factors that were considered for the choice of these class labels were (1) the overall occurrence
          of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from
          previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not
          ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class
          labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also
          avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and'', ''document_id'':
          ''65caca96-a2f7-4b43-bdad-9174d4f51e85'', ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'',
          ''score'': 0.016129031777381897, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''dc1e02d0-84aa-4d48-91ed-fa3339ad4edd'',
          ''content'': ''n/a. Footnote, Count = 6318. Footnote, % of Total.Train = 0.60. Footnote, % of Total.Test = 0.31.
          Footnote, % of Total.Val = 0.58. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).All = 83-91. Footnote, triple
          inter-annotator mAP @ 0.5-0.95 (%).Fin = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 100. Footnote,
          triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 62-88. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Law =
          85-94. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95
          (%).Ten'', ''document_id'': ''65caca96-a2f7-4b43-bdad-9174d4f51e85'', ''document_title'': None, ''document_uri'':
          ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'', ''score'': 0.01587301678955555,
          ''page_numbers'': [1], ''headings'': None}]\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_mloo01q9
      - content: |-
          <think>
          We can attempt get_docling_document with document id string.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"doc = get_docling_document(''65caca96-a2f7-4b43-bdad-9174d4f51e85'')\nprint(doc.name)\nprint(''tables'',
              len(doc.tables))\nprint(''pictures'', len(doc.pictures))"}'
            name: execute_code
          id: call_e1yvi1e7
          type: function
      - content: '{"code":"doc = get_docling_document(''65caca96-a2f7-4b43-bdad-9174d4f51e85'')\nprint(doc.name)\nprint(''tables'',
          len(doc.tables))\nprint(''pictures'', len(doc.pictures))","stdout":"doclaynet\ntables 1\npictures 1\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_e1yvi1e7
      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:
      - '576'
      content-type:
      - application/json
    parsed_body:
      choices:
      - finish_reason: stop
        index: 0
        message:
          content: "**Answer**\n\n- **Tables:** 1  \n- **Pictures/figures:** 1  \n\nThese counts come from the structured
            metadata of the PDF “doclaynet.pdf” (document ID `65caca96-a2f7-4b43-bdad-9174d4f51e85`). The `DoclingDocument`
            representation reports one table and one picture in the entire document."
          role: assistant
      created: 1769704745
      id: chatcmpl-263
      model: gpt-oss
      object: chat.completion
      system_fingerprint: fp_ollama
      usage:
        completion_tokens: 86
        prompt_tokens: 3322
        total_tokens: 3408
    status:
      code: 200
      message: OK
- request:
    headers:
      accept:
      - application/json
      accept-encoding:
      - gzip, deflate, zstd
      connection:
      - keep-alive
      content-length:
      - '14579'
      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: How many tables are in the document? Also tell me how many pictures/figures it contains.
        role: user
      - content: |-
          <think>
          We need a specific document. Which one? User didn't specify. Likely the current context: maybe a particular file in the repo. We should list_documents.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"docs = list_documents(limit=20)\nprint(docs)"}'
            name: execute_code
          id: call_iea2g7gy
          type: function
      - content: '{"code":"docs = list_documents(limit=20)\nprint(docs)","stdout":"[{''id'': ''65caca96-a2f7-4b43-bdad-9174d4f51e85'',
          ''title'': None, ''uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'',
          ''created_at'': ''2026-01-29 18:38:51.772641''}]\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_iea2g7gy
      - content: |-
          <think>
          Only one doc, uri doclaynet.pdf. Need to get_docling_document.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"doc = get_docling_document(''doclaynet.pdf'')\nprint(doc.name)\nprint(''tables:'', len(doc.tables))\nprint(''pictures:'',
              len(doc.pictures))"}'
            name: execute_code
          id: call_bjlxiv63
          type: function
      - content: '{"code":"doc = get_docling_document(''doclaynet.pdf'')\nprint(doc.name)\nprint(''tables:'', len(doc.tables))\nprint(''pictures:'',
          len(doc.pictures))","stdout":"","stderr":"Traceback (most recent call last):\n  File \"/Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/haiku_rag_slim/haiku/rag/agents/rlm/sandbox.py\",
          line 355, in _execute_sync\n    exec(code, exec_globals, self.locals)\n    ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File
          \"<string>\", line 2, in <module>\nAttributeError: ''NoneType'' object has no attribute ''name''\n","success":false}'
        role: tool
        tool_call_id: call_bjlxiv63
      - content: |-
          <think>
          Doc not found. Maybe title differs. Use search for document title.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"results = search(''doclaynet'')\nprint(results[:3])"}'
            name: execute_code
          id: call_mloo01q9
          type: function
      - content: '{"code":"results = search(''doclaynet'')\nprint(results[:3])","stdout":"[{''chunk_id'': ''fb4d75c4-3416-49e2-b1b4-f319073a10cc'',
          ''content'': ''\\nTable 1: DocLayNet dataset overview. Along with the frequency of each class label, we present
          the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement
          is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which
          we obtain accuracy ranges.'', ''document_id'': ''65caca96-a2f7-4b43-bdad-9174d4f51e85'', ''document_title'': None,
          ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'',
          ''score'': 0.032786883413791656, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''dc5ab8c7-2b89-4a5e-a7bc-2c7dc2376d41'',
          ''content'': ''Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most
          common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us
          to the definition of 11 distinct class labels. These 11 class labels are $_{Caption}$, $_{Footnote}$, $_{Formula}$,
          $_{List-item}$, Page-$_{footer}$, $_{Page-header}$, $_{Picture}$, $_{Section-header}$, $_{Table}$, $_{Text}$, and
          $_{Title}$. Critical factors that were considered for the choice of these class labels were (1) the overall occurrence
          of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from
          previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not
          ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class
          labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also
          avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and'', ''document_id'':
          ''65caca96-a2f7-4b43-bdad-9174d4f51e85'', ''document_title'': None, ''document_uri'': ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'',
          ''score'': 0.016129031777381897, ''page_numbers'': [1], ''headings'': None}, {''chunk_id'': ''dc1e02d0-84aa-4d48-91ed-fa3339ad4edd'',
          ''content'': ''n/a. Footnote, Count = 6318. Footnote, % of Total.Train = 0.60. Footnote, % of Total.Test = 0.31.
          Footnote, % of Total.Val = 0.58. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).All = 83-91. Footnote, triple
          inter-annotator mAP @ 0.5-0.95 (%).Fin = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Man = 100. Footnote,
          triple inter-annotator mAP @ 0.5-0.95 (%).Sci = 62-88. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Law =
          85-94. Footnote, triple inter-annotator mAP @ 0.5-0.95 (%).Pat = n/a. Footnote, triple inter-annotator mAP @ 0.5-0.95
          (%).Ten'', ''document_id'': ''65caca96-a2f7-4b43-bdad-9174d4f51e85'', ''document_title'': None, ''document_uri'':
          ''file:///Users/ggozad/dev/open-source/haiku.rag-feat-recursive-llm/tests/data/doclaynet.pdf'', ''score'': 0.01587301678955555,
          ''page_numbers'': [1], ''headings'': None}]\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_mloo01q9
      - content: |-
          <think>
          We can attempt get_docling_document with document id string.
          </think>          
        role: assistant
        tool_calls:
        - function:
            arguments: '{"code":"doc = get_docling_document(''65caca96-a2f7-4b43-bdad-9174d4f51e85'')\nprint(doc.name)\nprint(''tables'',
              len(doc.tables))\nprint(''pictures'', len(doc.pictures))"}'
            name: execute_code
          id: call_e1yvi1e7
          type: function
      - content: '{"code":"doc = get_docling_document(''65caca96-a2f7-4b43-bdad-9174d4f51e85'')\nprint(doc.name)\nprint(''tables'',
          len(doc.tables))\nprint(''pictures'', len(doc.pictures))","stdout":"doclaynet\ntables 1\npictures 1\n","stderr":"","success":true}'
        role: tool
        tool_call_id: call_e1yvi1e7
      - content: "**Answer**\n\n- **Tables:** 1  \n- **Pictures/figures:** 1  \n\nThese counts come from the structured metadata
          of the PDF “doclaynet.pdf” (document ID `65caca96-a2f7-4b43-bdad-9174d4f51e85`). The `DoclingDocument` representation
          reports one table and one picture in the entire document."
        role: assistant
      - content: |-
          1 validation error:
          ```json
          [
            {
              "type": "json_invalid",
              "loc": [],
              "msg": "Invalid JSON: expected value at line 1 column 1",
              "input": "**Answer**\n\n- **Tables:** 1  \n- **Pictures/figures:** 1  \n\nThese counts come from the structured metadata of the PDF “doclaynet.pdf” (document ID `65caca96-a2f7-4b43-bdad-9174d4f51e85`). The `DoclingDocument` representation reports one table and one picture in the entire document."
            }
          ]
          ```

          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:
      - '648'
      content-type:
      - application/json
    parsed_body:
      choices:
      - finish_reason: stop
        index: 0
        message:
          content: '{"answer":"**Answer**\n\n- **Tables:** 1\n- **Pictures/figures:** 1\n\nThese counts come from the structured
            metadata of the PDF \"doclaynet.pdf\" (document ID `65caca96-a2f7-4b43-bdad-9174d4f51e85`). The `DoclingDocument`
            representation reports one table and one picture in the entire document."}'
          reasoning: Need to output JSON. Provide fields.
          role: assistant
      created: 1769704748
      id: chatcmpl-555
      model: gpt-oss
      object: chat.completion
      system_fingerprint: fp_ollama
      usage:
        completion_tokens: 108
        prompt_tokens: 3561
        total_tokens: 3669
    status:
      code: 200
      message: OK
version: 1