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@ -47,26 +47,26 @@ python main.py
The script processes predefined queries using the RAG system and generates answers based on documents and/or live web data. The script processes predefined queries using the RAG system and generates answers based on documents and/or live web data.
``` ```
# Steps Performed: Steps Performed:
Document Processing: The documents are chunked into smaller segments for efficient retrieval. Document Processing: The documents are chunked into smaller segments for efficient retrieval.
Index Creation or Loading: An FAISS index or Chroma-based vector store is created or loaded for similarity search. Index Creation or Loading: An FAISS index or Chroma-based vector store is created or loaded for similarity search.
Query Answering: A set of queries is processed, and answers are generated using LLMs, based on the retrieved document chunks or web content. Query Answering: A set of queries is processed, and answers are generated using LLMs, based on the retrieved document chunks or web content.
Results are saved in an output file (response.txt or agent_results.txt). Results are saved in an output file (response.txt or agent_results.txt).
## Components ## Components
RAG System ### RAG System
The RAG system includes: The RAG system includes:
Document Chunking: Splitting large documents into smaller chunks to improve retrieval performance. Document Chunking: Splitting large documents into smaller chunks to improve retrieval performance.
Index Creation: Using FAISS (or Chroma) for indexing the document chunks based on their embeddings. Index Creation: Using FAISS (or Chroma) for indexing the document chunks based on their embeddings.
Similarity Search: Utilizing cosine similarity for retrieving relevant chunks during query processing. Similarity Search: Utilizing cosine similarity for retrieving relevant chunks during query processing.
Answer Generator ### Answer Generator
The Answer Generator class interacts with the RAG system to fetch the most relevant document chunks based on a given question. It then uses the LLM to generate a context-aware response. The Answer Generator class interacts with the RAG system to fetch the most relevant document chunks based on a given question. It then uses the LLM to generate a context-aware response.
Web Browsing Agent ### Web Browsing Agent
The Web Browsing Agent fetches real-time information from the web by scraping web pages. The agent can be used to get live data on current events, statistics, and more. The Web Browsing Agent fetches real-time information from the web by scraping web pages. The agent can be used to get live data on current events, statistics, and more.
Chroma-based RAG ### Chroma-based RAG
An alternative RAG implementation using Chroma for storing and querying document embeddings is also included. This utilizes LangChain's Chroma integration for efficient vector store management and querying. An alternative RAG implementation using Chroma for storing and querying document embeddings is also included. This utilizes LangChain's Chroma integration for efficient vector store management and querying.
## Results ## Results