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@ -26,29 +26,6 @@ The system is multilingual and supports Persian language queries.
## Installation ## Installation
To set up the environment, clone the repository and install the required dependencies:
```
git clone https://github.com/yourusername/agentic-rag-system.git
cd agentic-rag-system
pip install -r requirements.txt
The requirements.txt includes dependencies such as:
faiss-cpu: For efficient similarity search.
sentence-transformers: For embedding models.
ollama: For LLM interactions.
langchain: For chaining models and agents.
chromadb: For Chroma-based document retrieval.
Install the additional dependencies for web browsing:
pip install requests beautifulsoup4
Usage
Run the following command to execute the main script:
python main.py
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.
@ -65,11 +42,19 @@ Similarity Search: Utilizing cosine similarity for retrieving relevant chunks du
### 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.
### 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.
### 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 ### Doc Search Agent
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.
### Deep Search Agent
### The Power of Agentic Search
## Results ## Results
The system successfully processes predefined questions and generates responses based on the relevant document context. Additionally, the web-browsing agent retrieves live data for real-time questions, providing a comprehensive, multi-source approach to answering queries. The system successfully processes predefined questions and generates responses based on the relevant document context. Additionally, the web-browsing agent retrieves live data for real-time questions, providing a comprehensive, multi-source approach to answering queries.