Source: summerfang-jolli/code-to-doc-demo  Last Updated: 2/11/2026

RAGFlow Integration Strategy for Function Docstrings

Overview

This document outlines the recommended approach for storing function signatures and docstrings in RAGFlow for optimal retrieval and search capabilities.

Storage Strategy

1. Document Structure

Each function should be stored as a separate document with:

{
  "id": "filename.py:function_name",
  "title": "function_name - filename.py",
  "content": "Combined signature + docstring + metadata",
  "metadata": {
    "file_path": "src/agents/code_analyzer.py",
    "function_name": "analyze_code",
    "signature": "def analyze_code(self, content: str) -> Dict[str, Any]:",
    "element_type": "method",
    "parent_class": "CodeAnalyzer",
    "complexity_score": 3.5,
    "parameters": [...],
    "return_type": "Dict[str, Any]"
  }
}

2. Content Format

Combine signature and docstring for rich context:

Function: analyze_code
Signature: def analyze_code(self, content: str) -> Dict[str, Any]:
File: src/agents/code_analyzer.py
Type: method
Class: CodeAnalyzer
Parameters: content: str
Returns: Dict[str, Any]
Complexity: 3.5

Documentation:
Analyze Python source code and extract structural information.

This method parses the provided code content using AST analysis
to identify functions, classes, methods, and their relationships.

Args:
    content (str): The Python source code to analyze

Returns:
    Dict[str, Any]: Analysis results containing:
        - functions: List of function definitions
        - classes: List of class definitions
        - imports: List of import statements
        - complexity_metrics: Code complexity analysis

3. RAGFlow Configuration

Knowledge Base Setup

1. Collection Name: code_docstrings
2. Chunking Strategy:
   • Method: Manual (one function per document)
   • Chunk Size: Variable (based on function length)
   • Overlap: 0 (functions are discrete units)

Parsing Configuration

{
  "chunk_token_count": 1024,
  "layout_recognize": false,
  "task_page_size": 12,
  "parser_config": {
    "chunk_token_num": 1024,
    "delimiter": "\n\n",
    "layout_recognize": false,
    "task_page_size": 12
  }
}

Embedding Model

• Recommended: text-embedding-3-small or text-embedding-ada-002
• Reason: Good balance of speed and quality for code documentation

4. Search Optimization

Query Types to Support

1. Function Name Search: “find function parse_content”
2. Functionality Search: “how to analyze Python AST”
3. Parameter Search: “functions that take file_path parameter”
4. Return Type Search: “functions returning Dict”
5. Class Method Search: “methods in CodeAnalyzer class”

Metadata Filters

Enable filtering by:

• file_path: Search within specific files
• element_type: Filter by function/method/class
• parent_class: Find methods in specific classes
• complexity_score: Find simple/complex functions
• is_async: Find async functions

5. Upload Process

Using the extraction script:

# Extract from entire src directory
python extract_docstrings.py -i src/ -r --ragflow-output ragflow_docs.json
 
# Upload to RAGFlow via API
curl -X POST "http://ragflow-server/api/v1/datasets/{dataset_id}/documents" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d @ragflow_docs.json

Batch Processing

For large codebases:

1. Process files in batches of 100-500 functions
2. Use parallel processing for multiple files
3. Implement retry logic for failed uploads

6. Query Examples

RAGFlow Chat Interface

User: "How do I parse Python code using AST?"
RAGFlow: [Returns functions related to AST parsing with their signatures and docstrings]

User: "Show me async functions in the codebase"
RAGFlow: [Filters by is_async=true metadata and returns matching functions]

User: "What functions take a file_path parameter?"
RAGFlow: [Searches parameter metadata and content for file_path usage]

API Queries

{
  "query": "extract function information from AST",
  "filters": {
    "element_type": "function",
    "complexity_score": {"$lt": 5.0}
  },
  "limit": 10
}

7. Maintenance Strategy

Regular Updates

1. Incremental Updates: Track file modification times
2. Hash-based Changes: Compare content hashes to detect changes
3. Automated Pipeline: CI/CD integration for documentation updates

Version Control

• Link documents to specific git commits
• Maintain historical versions of function documentation
• Track documentation evolution over time

Implementation Steps

1. Setup RAGFlow instance with appropriate configuration
2. Run extraction script on your codebase
3. Upload initial dataset to RAGFlow
4. Configure search interface with metadata filters
5. Test query patterns for your use cases
6. Setup automated updates for code changes

Benefits

1. Unified Search: Find functions by name, functionality, or signature
2. Context-Aware: Combines signature with documentation for better understanding
3. Metadata-Rich: Enables complex filtering and categorization
4. Scalable: Handles large codebases efficiently
5. Version-Tracked: Maintains documentation history