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RAG Approach
1. Document Loaders: Document loaders facilitate handling various document types by extracting raw text. Key loader includes:
֍ UnstructuredTextLoader:Ideal for processing unstructured text documents, such as plain text files, by extracting raw text from non-formatted data.
2. Text Splitter :
The Text Splitter divides large documents into smaller, manageable chunks for efficient processing:
֍ Recursive Character Text Splitter:Commonly used to split documents into sections based on character count, ensuring the chunks are small enough for models to handle effectively.
3.Vector Store: A vector store is used to store embeddings of document chunks for retrieval.
֍ Chroma:A widely adopted vector store in LangChain, enabling efficient storage, indexing, and retrieval of document embeddings.
4. Hybrid Search Integration:Hybrid search enhances document retrieval by combining dense vector searches (semantic similarity) with keyword searches (exact matches).
֍ Hybrid Search:Leverages both semantic and keyword relevance, ensuring comprehensive retrieval. Tools like FAISS or ElasticSearch can be used alongside Chroma for optimal results. Combined search results are ranked based on adjustable weights.
5. Language Model :
The language model performs tasks such as question answering, summarization, and content generation.
֍ OpenAI GPT Models:Integrated models like GPT-4 generate responses based on retrieved documents.
֍ Purpose:Facilitates understanding of retrieved documents’ context and generates coherent, accurate responses.
6. RAG Chain (Retrieval-Augmented Generation Chain) :
The RAG chain integrates document retrieval and text generation into a cohesive pipeline, leveraging retrieved documents as context for generation tasks.
֍ RetrievalQAWithSourcesChain:Combines document retrieval and response generation, providing context-based answers and references to the content sources.
֍ Purpose:Bridges the gap between document retrieval and response generation for context-aware information delivery.
Multi-Agentic Framework in RAG Chains
1. Collaborative Document Retrieval:Multiple agents specialize in document retrieval:
֍ Search Agents:Perform hybrid searches, combining vector (semantic) and keyword (exact match) searches to improve document quality.
֍ Meta-Agent:Supervises retrieval, dynamically balancing semantic and keyword search results to optimize document selection.
2. Task-Specific Document Processing :
Specialized agents handle distinct processing tasks:
֍ Text Preprocessing Agent:Cleans and formats raw text by removing noise and handling special characters.
֍ Summarization Agent:Extracts key information from lengthy documents.
3. Smart Planning with ReAct or Reflexion: Task management frameworks guide agents:
֍ ReAct Framework:Assigns tasks to the most suitable agents based on real-time analysis. For complex queries, additional agents may refine retrieval or processing.
֍ Reflexion Framework:Monitors and adjusts agent actions, continuously improving retrieval quality and response generation through adaptive feedback.
4. Language Model Interaction:The language model (e.g., GPT-4) generates context-aware responses, supported by retrieved documents. The multi-agent framework ensures system components collaborate seamlessly to enhance content generation.
