zilliztech/deep-searcher

Features

• Private Data Querying - Retrieves relevant information from enterprise internal documents by indexing them into a vector database and querying with natural language.
• Research Engines - An open-source engine that indexes private documents and uses LLMs to answer complex questions with cited reasoning.
• CLI-Based Model Querying - Asks questions in plain language from the command line and receives answers based on indexed private data.
• LLM API Integrations - Integrates multiple large language models such as DeepSeek and OpenAI to answer questions and generate content.
• LLM Orchestration - Coordinates multiple large language model providers to answer queries by routing requests and aggregating responses.
• LLM-Powered Search Interfaces - Provides LLM-powered search over private documents using retrieval-augmented generation.
• Retrieval-Augmented Report Generators - Produces comprehensive written reports by combining retrieved private data with LLM reasoning to answer complex questions.
• LLM Provider Configurations - Selects and authenticates with a large language model service to power question answering and content generation.
• Private Document Search Engines - Enables natural language search and reasoning over private enterprise documents using RAG.
• Document Loading Commands - Loads documents into a vector database by running commands in a terminal.
• Local Document Ingestion - Reads files from local storage, including PDFs and text files, to ingest content into the searchable vector database.
• Vector-Database-Backed Retrievals - Indexes document embeddings in a vector database and retrieves relevant chunks via similarity search for LLM context.
• Vector-Store Augmented Generation - Stores document embeddings in Milvus and queries them with LLMs for grounded, cited answers.
• Document and Web Content Indexers - Ingests documents and crawls web content via CLI commands, then indexes them into the vector database for later querying.
• Vector Databases - Indexes document embeddings in Milvus for similarity search and LLM context retrieval.
• Embedding Model Configurations - Converts text into vector representations using pluggable embedding model services with configurable authentication and model selection.
• Query and Load API Serving - Serves query and data loading functions through a web API for programmatic access.
• Vector Database Configurations - Selects and connects to a supported vector database backend for storing and searching document embeddings.
• Multi-LLM Orchestrators - Coordinates multiple LLMs with indexed private data to produce comprehensive, cited reports.
• Web Content Fetching - Fetches and indexes content from specified URLs using configurable web crawlers for inclusion in the knowledge base.
• Embedding Provider Configurations - Switches the text embedding backend to any supported provider by setting its name and model in the configuration.
• Document Format Loaders - Sets the file loader provider and its options to ingest documents from various formats.
• Plugin-Based Architectures - Loads embedding, LLM, vector database, and file loader implementations as interchangeable plugins through a unified configuration interface.
• RAG Pipeline API Services - Exposes query and data loading functions as HTTP endpoints for programmatic access to the RAG pipeline.
• Research APIs - Exposes deep research capabilities through a CLI and web API for loading data and generating reports.

Star history