Vector-Database-Open-Source

Yeh hai open-source vector databases ki list, jo embeddings aur vector search ke liye use hoti hain, saath hi inke links aur features:

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Open-Source Vector Databases

1. Weaviate

   • Link: https://weaviate.io/
   • Features:
     • Hybrid search (text + vector).
     • Integrates with Hugging Face, OpenAI.
     • Scalable and production-ready.

2. Milvus

   • Link: https://milvus.io/
   • Features:
     • Optimized for large-scale vector search.
     • Integration with PyTorch, TensorFlow.
     • Billion-scale datasets support.

3. Qdrant

   • Link: https://qdrant.tech/
   • Features:
     • Real-time vector search.
     • LangChain and Hugging Face support.
     • RESTful API.

4. Chroma

   • Link: https://www.trychroma.com/
   • Features:
     • Lightweight vector database.
     • Native integration with LangChain.
     • Ideal for small-to-medium projects.

5. FAISS

   • Link: https://github.com/facebookresearch/faiss
   • Features:
     • High-performance similarity search library.
     • Best for research and local tasks.
     • Requires manual setup for scaling.

6. HNSWlib

   • Link: https://github.com/nmslib/hnswlib
   • Features:
     • Fast approximate nearest neighbor search.
     • Lightweight and efficient.
     • Suitable for small-scale projects.

7. ElasticSearch (with k-NN plugin)

   • Link: https://www.elastic.co/
   • Features:
     • Hybrid search capabilities.
     • Scalable with Elastic Stack.
     • Requires k-NN plugin for vector support.

8. OpenSearch

   • Link: https://opensearch.org/
   • Features:
     • Fork of ElasticSearch with vector support.
     • Scalable and production-ready.
     • Hybrid search capabilities.

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Best Options for Free Use:

• Weaviate and Qdrant: Easy to use and scalable.
• FAISS and HNSWlib: Ideal for research or small-scale projects.
• Milvus: Best for massive datasets.

Agar aapko setup ya implementation ka help chahiye, toh zarur batayein!

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Yeh hai embeddings data aur vectorstore ke liye free aur open-source vector databases ki list, jo LLM workflows aur vector search ke liye suitable hain:

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Free Vector Databases

1. Weaviate

   • Type: Open-source
   • Link: https://weaviate.io/
   • Features:
     • Built-in support for vector search and hybrid search (text + vectors).
     • Integrates with Hugging Face, OpenAI, and custom embeddings.
     • Scalable and production-ready.

2. Milvus

   • Type: Open-source
   • Link: https://milvus.io/
   • Features:
     • Highly optimized for vector similarity search.
     • Integration with PyTorch, TensorFlow, and OpenAI.
     • Supports billions of vectors.

3. Qdrant

   • Type: Open-source
   • Link: https://qdrant.tech/
   • Features:
     • Fast and efficient for real-time vector search.
     • RESTful API and WebSocket support.
     • Tight integration with LangChain and Hugging Face.

4. Chroma

   • Type: Open-source
   • Link: https://www.trychroma.com/
   • Features:
     • Lightweight embedding database for small-to-medium projects.
     • Native integration with LangChain.
     • Easy to use and deploy.

5. FAISS (Facebook AI Similarity Search)

   • Type: Open-source
   • Link: https://github.com/facebookresearch/faiss
   • Features:
     • High-performance similarity search library.
     • Best for local or research purposes.
     • No built-in scaling; needs manual setup for distributed environments.

6. HNSWlib

   • Type: Open-source
   • Link: https://github.com/nmslib/hnswlib
   • Features:
     • Efficient approximate nearest neighbor (ANN) search.
     • Lightweight and fast.
     • Best for projects with fewer scalability needs.

7. ElasticSearch (with k-NN plugin)

   • Type: Open-source
   • Link: https://www.elastic.co/
   • Features:
     • Hybrid search capabilities (text + vector).
     • Scalable and production-ready with Elastic Stack.
     • k-NN plugin needed for vector support.

8. OpenSearch (Vector Search)

   • Type: Open-source
   • Link: https://opensearch.org/
   • Features:
     • Fork of ElasticSearch with vector search capabilities.
     • Suitable for hybrid search and analytics.

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Comparison of Features

Database	Open-Source	Supports LLM Embeddings	Scalability	Ease of Use	Best For
Weaviate	✅	✅	High	Easy	Full-scale LLM applications
Milvus	✅	✅	Very High	Moderate	Billion-scale datasets
Qdrant	✅	✅	High	Easy	Real-time vector search
Chroma	✅	✅	Medium	Very Easy	Lightweight embedding DB
FAISS	✅	✅	Low (manual setup)	Moderate	Research and local tasks
HNSWlib	✅	✅	Low (manual setup)	Easy	Small-scale projects
ElasticSearch	✅	✅ (k-NN plugin)	High	Moderate	Hybrid search scenarios
OpenSearch	✅	✅	High	Moderate	Scalable hybrid search

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Recommendation for Free Use:

• Use Weaviate or Qdrant for production-ready applications.
• Choose FAISS or HNSWlib for research or lightweight local setups.
• Milvus is ideal for large-scale vector datasets.

Kya aapko kisi specific tool ke liye setup guide ya code example chahiye?