Update README.md

README.md

@@ -12,7 +12,7 @@ pinned: false
 
 # AgentGraph: Intelligent SQL-agent Q&A and RAG System for Chatting with Multiple Databases
 
-This project demonstrates how to build an agentic system using Large Language Models (LLMs) that can interact with multiple databases and utilize various tools. It highlights the use of SQL agents to efficiently query large databases. The key frameworks used in this project include OpenAI, LangChain, LangGraph, LangSmith, and Gradio. The end product is an end-to-end chatbot, designed to perform these tasks, with LangSmith used to monitor the performance of the agents.
+This project demonstrates how to build an agentic system using Large Language Models (LLMs) that can interact with multiple databases and utilize various tools. It highlights the use of SQL agents to efficiently query large databases. The key frameworks used in this project include GroqAI, LangChain, LangGraph, LangSmith, and Gradio. The end product is an end-to-end chatbot, designed to perform these tasks, with LangSmith used to monitor the performance of the agents.
 
 ---
 
@@ -26,7 +26,7 @@ Automating LLM Agents to Chat with Multiple/Large Databases (Combining RAG and S
 ## Requirements
 
 - **Operating System:** Linux or Windows (Tested on Windows 11 with Python 3.9.11)
-- **OpenAI API Key:** Required for GPT functionality.
+- **Groq API Key:** Required for GPT functionality.
 - **Tavily Credentials:** Required for search tools (Free from your Tavily profile).
 - **LangChain Credentials:** Required for LangSmith (Free from your LangChain profile).
 - **Dependencies:** The necessary libraries are provided in `requirements.txt` file.
@@ -61,7 +61,7 @@ To set up the project, follow these steps:
 
 6. Download the chinook SQL database from this link and paste it into the `data` folder.
 
-7. Prepare the `.env` file and add your `OPEN_AI_API_KEY`, `TAVILY_API_KEY`, and `LANGCHAIN_API_KEY`.
+7. Prepare the `.env` file and add your `GROQ_API_KEY`, `TAVILY_API_KEY`, and `LANGCHAIN_API_KEY`.
 
 8. Run `prepare_vector_db.py` module once to prepare both vector databases.
    ```bash
@@ -154,7 +154,7 @@ Once your databases are ready, you can either connect the current agents to the
 - **LangGraph**
 - **LangSmith**
 - **Gradio:** [Documentation](https://www.gradio.app/docs/interface)
-- **OpenAI:** [Developer Quickstart](https://platform.openai.com/docs/quickstart?context=python)
+- **GroqAI** 
 - **Tavily Search**
 ---
 
@@ -168,11 +168,9 @@ docker build -t querymind .
 ### Run the container
 ```bash
 docker run -p 7860:7860 \
-  -e OPENAI_API_KEY=your_key \
   -e GROQ_API_KEY=your_key \
   -e TAVILY_API_KEY=your_key \
-  -e LANGSMITH_API_KEY=your_key \
-  -e LANGCHAIN_TRACING_V2=true \
+  -e LANGCHAIN_API_KEY=your_key \
   querymind
 ```