TurboPuffer RAG Plugin
Hybrid search RAG (Retrieval Augmented Generation) implementation using TurboPuffer for vector + BM25 full-text search, with Gemini for embeddings.
Features
- Hybrid Search: Combines vector (semantic) and BM25 (keyword) search for better retrieval quality
- Reciprocal Rank Fusion: Merges results from multiple search strategies
- Gemini Embeddings: Uses Google's Gemini embedding model for high-quality vectors
- Low-latency Queries: Supports cache warming for fast query responses
- Implements RAG Interface: Compatible with Vision Agents RAG base class
Installation
uv add "vision-agents[turbopuffer]"
# or directly
uv add vision-agents-plugins-turbopuffer
Usage
from vision_agents.plugins import turbopuffer
# Initialize RAG
rag = turbopuffer.TurboPufferRAG(namespace="my-knowledge")
await rag.add_directory("./knowledge")
# Hybrid search (default)
results = await rag.search("How does the chat API work?")
# Vector-only search
results = await rag.search("How does the chat API work?", mode="vector")
# BM25-only search
results = await rag.search("chat API pricing", mode="bm25")
# Or use convenience function
rag = await turbopuffer.create_rag(
namespace="product-knowledge",
knowledge_dir="./knowledge"
)
Configuration
| Parameter | Description | Default |
|---|---|---|
namespace |
TurboPuffer namespace for storing vectors | Required |
embedding_model |
Gemini embedding model | models/gemini-embedding-001 |
chunk_size |
Size of text chunks for splitting documents | 10000 |
chunk_overlap |
Overlap between chunks for context continuity | 200 |
region |
TurboPuffer region | gcp-us-central1 |
Environment Variables
TURBO_PUFFER_KEY: TurboPuffer API keyGOOGLE_API_KEY: Google API key (for Gemini embeddings)
Dependencies
turbopuffer: TurboPuffer vector database clientlangchain-google-genai: Gemini embeddingslangchain-text-splitters: Text chunking utilities
References
2 maintainers
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