Agent
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Transforms documentation into intelligent, searchable knowledge bases using advanced vector embeddings and Retrieval-Augmented Generation.
About
Agent is an intelligent system designed to transform any documentation into a dynamic, searchable knowledge base. It achieves this by recursively crawling documentation websites, converting their content into high-dimensional vector embeddings using OpenAI's models, and leveraging advanced Retrieval-Augmented Generation (RAG). This enables users to query complex documentation using natural language, receive detailed, context-aware responses with source citations, and efficiently manage multiple knowledge bases through its robust namespace support.
Key Features
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- Intelligent Web Crawler with recursive crawling, content filtering, and depth control.
- Pinecone-powered Vector Database Integration for 512-dimensional semantic search.
- OpenAI GPT-4o and embedding model integration for intelligent response generation and vectorization.
- Advanced RAG implementation with context-aware retrieval, confidence scoring, and source citation.
- Multi-Namespace Support to organize knowledge bases by project, language, or domain.
Use Cases
- Enabling fast and accurate semantic search across extensive documentation corpora.
- Creating intelligent, natural language-queryable knowledge bases from various documentation websites.
- Providing context-aware answers to complex technical questions by querying processed documentation.
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