langchain-envector 0.1.2

LangChain VectorStore integration for Envector (ES2) encrypted vector search

LangChain Envector Integration

Encrypted vector search for LangChain using Envector (ES2), powered by homomorphic encryption (CKKS). This repo ships a LangChain-compatible VectorStore and retriever utilities built on the high-level es2 Python SDK.

Features

• LangChain VectorStore interface with similarity_search, from_texts, etc.
• Optional VectorStoreRetriever helper for quick RAG integrations.
• Client-side encryption handled transparently by the SDK, including score thresholds and filtering.

Installation

• Python 3.9–3.13 (recommend 3.11)
• Create and activate a virtualenv:
  • python3.11 -m venv .venv && source .venv/bin/activate
• Install runtime dependencies:
  • pip install -U pip setuptools wheel
  • pip install es2==1.1.0 langchain sentence-transformers

Usage Overview

1. Configure Envector using EnvectorConfig, pointing to your ES2 endpoint and keys.
2. Initialize embeddings (or provide pre-computed vectors).
3. Instantiate Envector(config=cfg, embeddings=emb) and call add_texts or as_retriever.
4. Run similarity_search or plug the retriever into your LangChain pipeline.

See notebooks/ for end-to-end walkthroughs and the libs/envector package for implementation details.

Configuration

Key dataclasses live in libs/envector/config.py:

• ConnectionConfig: address or host/port for ES2.
• KeyConfig: key path, key ID, optional preset/eval mode.
• IndexSettings: index name, dimension (16–4096), query encryption mode, optional output fields and fetch parameters.
• EnvectorConfig: wraps the above and enables auto-creation via create_if_missing.

Data Model

• Each vector stores a single metadata string in ES2.
• To align with LangChain’s Document, inserts wrap data as JSON: {"text": ..., "metadata": ...}.
• Retrieval unwraps JSON, returning Document(page_content=text, metadata={...}).
• Client-side filtering requires the JSON envelope to include an object under metadata.

Limitations

• Item-level delete/update is unsupported (drop the index to reset).
• Manual item IDs are not accepted; returned IDs from add_texts are ephemeral.
• Filtering happens client-side; ensure metadata is JSON for structured filters.

Troubleshooting

• Connection issues: verify ES2 address and registered keys.
• Embeddings mismatch: ensure embedding dimension equals index.dim when supplying vectors.
• Unexpected raw strings: confirm inserts used the JSON envelope.

Contributing

See CONTRIBUTE.md for development, testing, and PR guidelines.