Overview
Canopy is an open-source Retrieval Augmented Generation (RAG) framework and context engine built on top of the Pinecone vector database. It enables rapid experimentation and development of RAG applications.
Important Note: The Canopy team is no longer maintaining this repository. For a managed RAG solution with continued updates, consider Pinecone Assistant.
Architecture Components
1. Knowledge Base
Prepares data for the RAG workflow:
- Automatically chunks text data
- Transforms data into text embeddings
- Upserts embeddings into Pinecone vector database
- Handles data preprocessing and indexing
2. Context Engine
Implements the retrieval part of RAG:
- Finds most relevant documents from Pinecone via the Knowledge Base
- Structures documents as context for LLM prompts
- Optimizes context selection for relevance
- Manages context window limitations
3. Chat Engine
Implements the complete RAG workflow:
- Understands chat history and context
- Identifies multi-part questions
- Generates multiple relevant queries from single prompts
- Transforms queries into embeddings
- Uses generated context for LLM responses
- Presents highly relevant responses to end users
Key Features
- Automatic Chunking: Intelligent text segmentation
- Embedding Management: Handles text-to-vector conversion
- Chat History: Maintains conversation context
- Query Optimization: Enhances retrieval quality
- Prompt Engineering: Built-in optimization for LLM prompts
- Augmented Generation: Complete RAG pipeline implementation
Workflows
Knowledge Base Creation Flow
- Users upload documents
- Documents transformed into vector representations
- Stored in Pinecone's Vector Database
- Indexed for efficient retrieval
Chat Flow
- Incoming queries and chat history captured
- Queries optimized for retrieval
- Knowledge base queried for relevant documents
- Context generated for LLM
- LLM generates contextual answer
Deployment Options
Canopy Server
- Built on FastAPI, Uvicorn, and Gunicorn
- Exposes Canopy Core library as REST API
- Production-ready deployment
- Easy integration with existing systems
Library Usage
Integrate Canopy Core library directly into Python applications for maximum control and customization.
Use Cases
- Question-answering systems over proprietary documents
- Customer support chatbots with knowledge bases
- Document search and retrieval applications
- Enterprise knowledge management systems
- RAG prototyping and experimentation
Availability
Open-source on GitHub: https://github.com/pinecone-io/canopy
Note on Maintenance
While the repository is no longer actively maintained, it remains available as a reference implementation and can be forked for custom development.
