nilsherzig/LLocalSearchArchived
LLocalSearch
LLocalSearch is a privacy-focused search engine and agent framework that uses locally hosted large language models to search the internet and aggregate answers. It functions as a retrieval augmented generation interface where all queries and processing remain on the user's own hardware to ensure data privacy and remove dependency on external cloud API providers.
The system employs a chain of autonomous agents that perform recursive internet searches, calling search tools multiple times to gather and synthesize information. It coordinates these models to reason through complex queries, providing a real-time stream of logs and source citations to monitor the agent's reasoning process.
The project includes capabilities for stateful conversation management to support multi-turn dialogues and iterative refinements. It integrates tool-based information retrieval and local model inference to provide a complete pipeline from initial search to final answer generation.
Features
- Recursive Research Agents - Implements autonomous agents that perform recursive internet searches to gather and synthesize comprehensive information.
- Autonomous Research Agents - Automates the process of gathering information through recursive internet searches handled by autonomous local agents.
- Local Model Generation - Provides answer generation using locally hosted models to ensure data privacy and remove cloud dependencies.
- Local LLM Search - Uses a chain of local language models to find answers to questions without requiring external API keys.
- API-Free Local Inference - Executes foundation models on local hardware to eliminate dependency on external cloud API providers.
- Private Information Retrieval - Retrieves data and answers using locally hosted models to ensure all processing remains on the user's hardware.
- Search Query Recursion - Employs autonomous agents that iteratively generate new search queries to gather current external information.
- Local LLM Research Agents - Provides a framework of autonomous agents that recursively call search tools using local model inference.
- Tool-Augmented Retrieval - Equips language models with search tools to fetch external web data into the local prompt context.
- LLM Task Chains - Coordinates a sequence of LLM calls in a pipeline to process queries from initial search to final synthesis.
- Context-Aware Conversation Managers - Combines local language models with retrieved search data to maintain context in multi-turn dialogues.
- Conversation State Persistence - Maintains a persistent record of interactions to provide context for follow-up queries and iterative refinements.
- Conversational Search Engines - Allows users to refine search results and ask follow-up questions within a persistent, LLM-powered conversation.
- Retrieval-Augmented Generation Interfaces - Provides a specialized interface for monitoring the real-time reasoning and source attribution of local RAG.
- AI-Powered Search Aggregators - Coordinates multiple local models to search and synthesize information into comprehensive answers without external APIs.
- Privacy-Focused Search Engines - Functions as a self-hosted search tool that processes all queries locally to ensure complete data privacy.
- Task Progress Streams - Pushes real-time logs and source citations of the agent's reasoning process directly to the user interface.
- Agent Frameworks - Locally running search aggregator using LLM agents.
- Application Development - Local web search implementation using LLM chains.
Star history
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