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Deep Searcher is a local open-source deep research solution that integrates Milvus and LangChain to provide advanced vector search and retrieval capabilities using open-source models.
An open-source library for approximate nearest neighbor search in high-dimensional spaces, often used as a backend for vector databases and search engines.
HNSWLIB is a C++ library with Python bindings for fast approximate nearest neighbor search using Hierarchical Navigable Small World (HNSW) graphs, commonly used in vector database backends.
PostgreSQL supports vector indexing and similarity search via the PGVector extension, allowing relational databases to manage and retrieve vector embeddings efficiently.
RediSearch is a Redis module that provides high-performance vector search and similarity search capabilities on top of Redis, enabling advanced search and retrieval features for AI and data applications.
A library by Google Research for efficient vector similarity search, suitable for large-scale nearest neighbor applications in AI.
Tantivy is a full-text search engine library inspired by Apache Lucene, offering fast and scalable similarity search capabilities. While primarily focused on text, it supports efficient vector-based similarity searches, making it useful for vector search tasks.
Category: SDKs & Libraries
Tags: open-source, milvus, langchain, vector-search
Deep Searcher is a local open-source deep research solution that integrates Milvus (an open-source vector database) and LangChain to provide advanced vector search and retrieval capabilities using open-source models. It is designed as both a Python library and a command-line tool for agentic research workflows.
No pricing information is specified. Deep Searcher is open-source and can be self-hosted. (Inference services such as DeepSeek-R1 may have their own costs.)