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philschmid
2025-05-29 15:49:49 -07:00
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README.md
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# Gemini DeepSearch LangGraph Quickstart # Gemini 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 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.
@@ -63,7 +63,7 @@ _Alternatively, you can run the backend and frontend development servers separat
The core of the backend is a LangGraph agent defined in `backend/src/agent/graph.py`. It follows these steps: The core of the backend is a LangGraph agent defined in `backend/src/agent/graph.py`. It follows these steps:
![Agent Flow](./deepsearch.png) ![Agent Flow](./agent.png)
1. **Generate Initial Queries:** Based on your input, it generates a set of initial search queries using a Gemini model. 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. 2. **Web Research:** For each query, it uses the Gemini model with the Google Search API to find relevant web pages.
@@ -83,7 +83,7 @@ _Note: If you are not running the docker-compose.yml example or exposing the bac
Run the following command from the **project root directory**: Run the following command from the **project root directory**:
```bash ```bash
docker build -t deepsearch -f Dockerfile . docker build -t gemini-fullstack-langgraph -f Dockerfile .
``` ```
**2. Run the Production Server:** **2. Run the Production Server:**

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deepsearch.png → agent.png
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docker-compose.yml
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retries: 5 retries: 5
interval: 5s interval: 5s
langgraph-api: langgraph-api:
image: deepsearch image: gemini-fullstack-langgraph
ports: ports:
- "8123:8000" - "8123:8000"
depends_on: depends_on:

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frontend/src/components/WelcomeScreen.tsx
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@@ -18,7 +18,7 @@ export const WelcomeScreen: React.FC<WelcomeScreenProps> = ({
<div className="flex flex-col items-center justify-center text-center px-4 flex-1 w-full max-w-3xl mx-auto gap-4"> <div className="flex flex-col items-center justify-center text-center px-4 flex-1 w-full max-w-3xl mx-auto gap-4">
<div> <div>
<h1 className="text-5xl md:text-6xl font-semibold text-neutral-100 mb-3"> <h1 className="text-5xl md:text-6xl font-semibold text-neutral-100 mb-3">
Welcome to a new Search. Welcome.
</h1> </h1>
<p className="text-xl md:text-2xl text-neutral-400"> <p className="text-xl md:text-2xl text-neutral-400">
How can I help you today? How can I help you today?