Update project files

README.md

@@ -1,6 +1,6 @@
-# Gemini Fullstack LangGraph Quickstart
+# OpenAI-Compatible Fullstack LangGraph Quickstart
 
-This project demonstrates a fullstack application using a React frontend and a LangGraph-powered backend agent. The agent is designed to perform comprehensive research on a user's query by dynamically generating search terms, querying the web using Google Search, reflecting on the results to identify knowledge gaps, and iteratively refining its search until it can provide a well-supported answer with citations. This application serves as an example of building research-augmented conversational AI using LangGraph and Google's Gemini models.
+This project demonstrates a fullstack application using a React frontend and a LangGraph-powered backend agent. The agent performs comprehensive research on a user's query by dynamically generating search terms, querying the web with Tavily, reflecting on the results to identify knowledge gaps, and iteratively refining its search until it can provide a well-supported answer with citations. This application serves as an example of building research-augmented conversational AI using LangGraph and an OpenAI-compatible chat model.
 
 <img src="./app.png" title="Gemini Fullstack LangGraph" alt="Gemini Fullstack LangGraph" width="90%">
 
@@ -8,8 +8,8 @@ This project demonstrates a fullstack application using a React frontend and a L
 
 - 💬 Fullstack application with a React frontend and LangGraph backend.
 - 🧠 Powered by a LangGraph agent for advanced research and conversational AI.
-- 🔍 Dynamic search query generation using Google Gemini models.
-- 🌐 Integrated web research via Google Search API.
+- 🔍 Dynamic search query generation using an OpenAI-compatible chat model.
+- 🌐 Integrated web research via Tavily Search.
 - 🤔 Reflective reasoning to identify knowledge gaps and refine searches.
 - 📄 Generates answers with citations from gathered sources.
 - 🔄 Hot-reloading for both frontend and backend during development.
@@ -29,10 +29,15 @@ Follow these steps to get the application running locally for development and te
 
 -   Node.js and npm (or yarn/pnpm)
 -   Python 3.11+
--   **`GEMINI_API_KEY`**: The backend agent requires a Google Gemini API key.
+-   **`OPENAI_API_KEY`**: The backend agent requires an OpenAI-compatible API key.
+-   **`OPENAI_BASE_URL`**: Set this to `https://open.bigmodel.cn/api/paas/v4` for BigModel.
+-   **`TAVILY_API_KEY`**: Required for web search.
     1.  Navigate to the `backend/` directory.
     2.  Create a file named `.env` by copying the `backend/.env.example` file.
-    3.  Open the `.env` file and add your Gemini API key: `GEMINI_API_KEY="YOUR_ACTUAL_API_KEY"`
+    3.  Open the `.env` file and add:
+        `OPENAI_API_KEY="YOUR_ACTUAL_API_KEY"`
+        `OPENAI_BASE_URL="https://open.bigmodel.cn/api/paas/v4"`
+        `TAVILY_API_KEY="YOUR_ACTUAL_TAVILY_API_KEY"`
 
 **2. Install Dependencies:**
 
@@ -67,11 +72,11 @@ The core of the backend is a LangGraph agent defined in `backend/src/agent/graph
 
 <img src="./agent.png" title="Agent Flow" alt="Agent Flow" width="50%">
 
-1.  **Generate Initial Queries:** Based on your input, it generates a set of initial search queries using a Gemini model.
-2.  **Web Research:** For each query, it uses the Gemini model with the Google Search API to find relevant web pages.
-3.  **Reflection & Knowledge Gap Analysis:** The agent analyzes the search results to determine if the information is sufficient or if there are knowledge gaps. It uses a Gemini model for this reflection process.
+1.  **Generate Initial Queries:** Based on your input, it generates a set of initial search queries using the configured chat model.
+2.  **Web Research:** For each query, it uses Tavily Search to gather relevant web pages and snippets.
+3.  **Reflection & Knowledge Gap Analysis:** The agent analyzes the search results to determine if the information is sufficient or if there are knowledge gaps. It uses the configured chat model for this reflection process.
 4.  **Iterative Refinement:** If gaps are found or the information is insufficient, it generates follow-up queries and repeats the web research and reflection steps (up to a configured maximum number of loops).
-5.  **Finalize Answer:** Once the research is deemed sufficient, the agent synthesizes the gathered information into a coherent answer, including citations from the web sources, using a Gemini model.
+5.  **Finalize Answer:** Once the research is deemed sufficient, the agent synthesizes the gathered information into a coherent answer, including citations from the web sources, using the configured chat model.
 
 ## CLI Example
 
@@ -97,12 +102,12 @@ _Note: If you are not running the docker-compose.yml example or exposing the bac
 
    Run the following command from the **project root directory**:
    ```bash
-   docker build -t gemini-fullstack-langgraph -f Dockerfile .
+   docker build -t openai-fullstack-langgraph -f Dockerfile .
    ```
 **2. Run the Production Server:**
 
    ```bash
-   GEMINI_API_KEY=<your_gemini_api_key> LANGSMITH_API_KEY=<your_langsmith_api_key> docker-compose up
+   OPENAI_API_KEY=<your_api_key> OPENAI_BASE_URL=https://open.bigmodel.cn/api/paas/v4 TAVILY_API_KEY=<your_tavily_api_key> LANGSMITH_API_KEY=<your_langsmith_api_key> docker-compose up
    ```
 
 Open your browser and navigate to `http://localhost:8123/app/` to see the application. The API will be available at `http://localhost:8123`.
@@ -113,7 +118,8 @@ Open your browser and navigate to `http://localhost:8123/app/` to see the applic
 - [Tailwind CSS](https://tailwindcss.com/) - For styling.
 - [Shadcn UI](https://ui.shadcn.com/) - For components.
 - [LangGraph](https://github.com/langchain-ai/langgraph) - For building the backend research agent.
-- [Google Gemini](https://ai.google.dev/models/gemini) - LLM for query generation, reflection, and answer synthesis.
+- OpenAI-compatible chat model - LLM for query generation, reflection, and answer synthesis.
+- [Tavily](https://tavily.com/) - Web search for research retrieval.
 
 ## License