vision-agents-plugins-turbopuffer 0.3.0

TurboPuffer RAG integration for Vision Agents with hybrid search

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]

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 key
• GOOGLE_API_KEY: Google API key (for Gemini embeddings)

Dependencies

• turbopuffer: TurboPuffer vector database client
• langchain-google-genai: Gemini embeddings
• langchain-text-splitters: Text chunking utilities

References

• TurboPuffer Hybrid Search
• MTEB Embedding Leaderboard