RagLLM is a library aimed at developing a Retrieval Augmented Generation (RAG) based Large Language Model (LLM) application. This method enhances the capabilities of standard LLMs by integrating external data sources, enabling more accurate and context-specific responses.
- RAG Integration: Enhances LLMs by combining them with external data sources.
- Scalability: Designed for large datasets and compute-intensive workloads.
- Source Referencing: Includes source references in responses for transparency.
- Data Preparation: Process data sources to create a vector database.
- Content Extraction: Extract content from data sources.
- Chunk Creation: Split content into smaller, manageable chunks.
- Embedding: Embed data chunks and queries using pre-trained models.
- Indexing: Store embedded chunks in a vector database.
- Query Processing: Retrieve relevant chunks for incoming queries.
- Response Generation: Generate LLM responses using retrieved context.
- Query Agent: Combine retrieval and generation processes into a single agent.
Experimentation with various LLMs (e.g., OpenAI, Llama) is a part of the development process.
RagLLM facilitates the adoption and utilization of LLMs with specific data sources, improving response accuracy and relevance.