llamavector-llamasearch 0.1.0

Vector embedding generation, storage, and similarity search for LlamaAI Ecosystem

LlamaVector

Handles vector embedding generation, storage, and similarity search within the LlamaAI Ecosystem. Provides interfaces to various embedding models and vector databases.

Features

• Embedding Generation: Supports multiple embedding models (e.g., via sentence-transformers).
• Vector Storage Adapters: Interfaces for various vector databases (e.g., FAISS, ChromaDB, Pinecone, Qdrant, Weaviate).
• Similarity Search: Efficiently find vectors similar to a query vector.
• Data Models: Pydantic models for structured vector data.
• Indexing Utilities: Tools for building and managing vector indexes.
• (Optional) API: Can expose functionality via a FastAPI server.

Installation

# Core installation
pip install llamavector

# To install with specific vector database support (e.g., ChromaDB):
pip install llamavector[chromadb]

# To install with API support:
pip install llamavector[api]

Quick Start

# Example (TBD after code migration)
# from llamavector import VectorStore, EmbeddingModel

# model = EmbeddingModel(model_name='all-MiniLM-L6-v2')
# store = VectorStore(adapter='chromadb', collection_name='my_vectors')

# texts = ["This is the first document.", "This document is the second document."]
# embeddings = model.encode(texts)
# ids = ["doc1", "doc2"]

# store.add(ids=ids, embeddings=embeddings)

# query_embedding = model.encode(["A query about the second doc"])
# results = store.search(query_embeddings=query_embedding, k=1)
# print(results)

Contributing

Contributions are welcome! Please see CONTRIBUTING.md.

License

MIT License. See LICENSE file.