Perform comprehensive search on R2R knowledge base with full parameter control.

This tool supports semantic search, hybrid search (semantic + full-text), knowledge graph search, and web search. Use presets for common scenarios or customize all parameters manually.

Args: query: The search query to find relevant documents. Required. preset: Preset configuration for common use cases. Options: - "default": Basic semantic search, 10 results - "development": Hybrid search optimized for code development, 15 results - "refactoring": Hybrid + graph search for code refactoring, 20 results - "debug": Minimal graph search for debugging, 5 results - "research": Comprehensive search with global graph, 30 results - "production": Balanced hybrid search for production, 10 results use_semantic_search: Enable semantic/vector search (default: True) use_hybrid_search: Enable hybrid search combining semantic and full-text search (default: False) use_graph_search: Enable knowledge graph search for entity/relationship discovery (default: False) limit: Maximum number of results to return. Must be between 1 and 100 (default: 10) kg_search_type: Knowledge graph search type. "local" for local context, "global" for broader connections (default: "local") semantic_weight: Weight for semantic search in hybrid mode. Must be between 0.0 and 10.0 (default: 5.0) full_text_weight: Weight for full-text search in hybrid mode. Must be between 0.0 and 10.0 (default: 1.0) full_text_limit: Maximum full-text results to consider in hybrid search. Must be between 1 and 1000 (default: 200) rrf_k: Reciprocal Rank Fusion parameter for hybrid search. Must be between 1 and 100 (default: 50) search_strategy: Advanced search strategy (e.g., "hyde", "rag_fusion"). Optional. include_web_search: Include web search results from the internet (default: False)

Returns: Formatted search results including: - Vector search results (chunks) - Graph search results (entities, relationships, communities) - Web search results (if enabled) - Document search results (local documents with chunks)

Examples: # Simple search with default settings search("What is machine learning?")

# Development preset for code search
search("async function implementation", preset="development")

# Custom hybrid search
search(
    "API documentation",
    use_hybrid_search=True,
    semantic_weight=7.0,
    limit=20
)

# Research with knowledge graph
search("neural network architectures", preset="research")

Perform Retrieval-Augmented Generation (RAG) query with full parameter control.

This tool retrieves relevant context from the knowledge base and generates an answer using a language model. Supports all search modes (semantic, hybrid, graph) and customizable generation parameters.

Args: query: The question to answer using the knowledge base. Required. preset: Preset configuration for common use cases. Options: - "default": Basic RAG with gpt-4o-mini, temperature 0.7, 10 results - "development": Hybrid search with higher temperature for creative answers, 15 results - "refactoring": Hybrid + graph search with gpt-4o for code analysis, 20 results - "debug": Minimal graph search with low temperature for precise answers, 5 results - "research": Comprehensive search with gpt-4o for research questions, 30 results - "production": Balanced hybrid search optimized for production, 10 results model: LLM model to use for generation. Examples: - "vertex_ai/gemini-2.5-flash" (default, fast and cost-effective) - "vertex_ai/gemini-2.5-pro" (more capable, higher cost) - "openai/gpt-4-turbo" (high performance) - "anthropic/claude-3-haiku-20240307" (fast) - "anthropic/claude-3-sonnet-20240229" (balanced) - "anthropic/claude-3-opus-20240229" (most capable) temperature: Generation temperature controlling randomness. Must be between 0.0 and 1.0. Lower values (0.0-0.3) = more deterministic, precise answers Medium values (0.4-0.7) = balanced creativity and accuracy (default: 0.7) Higher values (0.8-1.0) = more creative, diverse answers max_tokens: Maximum number of tokens to generate. Optional, uses model default if not specified. use_semantic_search: Enable semantic/vector search for retrieval (default: True) use_hybrid_search: Enable hybrid search combining semantic and full-text search (default: False) use_graph_search: Enable knowledge graph search for entity/relationship context (default: False) limit: Maximum number of search results to retrieve. Must be between 1 and 100 (default: 10) kg_search_type: Knowledge graph search type. "local" for local context, "global" for broader connections (default: "local") semantic_weight: Weight for semantic search in hybrid mode. Must be between 0.0 and 10.0 (default: 5.0) full_text_weight: Weight for full-text search in hybrid mode. Must be between 0.0 and 10.0 (default: 1.0) full_text_limit: Maximum full-text results to consider in hybrid search. Must be between 1 and 1000 (default: 200) rrf_k: Reciprocal Rank Fusion parameter for hybrid search. Must be between 1 and 100 (default: 50) search_strategy: Advanced search strategy (e.g., "hyde", "rag_fusion"). Optional. include_web_search: Include web search results from the internet (default: False) task_prompt_override: Custom system prompt to override the default RAG task prompt. Useful for specializing AI behavior for specific domains or tasks. Optional.

Returns: Generated answer based on relevant context from the knowledge base.

Examples: # Simple RAG query rag("What is machine learning?")

# Development preset for code questions
rag("How to implement async/await in Python?", preset="development")

# Custom RAG with specific model and temperature
rag(
    "Explain neural networks",
    model="vertex_ai/gemini-2.5-pro",
    temperature=0.5
)

# Research preset with comprehensive search
rag(
    "Latest developments in transformer architectures",
    preset="research"
)

# Debug preset for precise technical answers
rag("What causes this error?", preset="debug")