install python version :- 3.11.9

Here is a detailed README.md file with clear explanations for each part of your chatbot project:

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🧠 Business Advisor Chatbot (FastAPI + LangGraph)

This project is a modular, scalable chatbot built with FastAPI, LangGraph, and LangChain. It supports multi-turn conversations, memory summarization, and tool integration (Tavily search). Designed for helping entrepreneurs and business users through structured prompts like executive summaries, market analysis, marketing strategies, and more.

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📁 Project Structure

my\_chatbot\_project/
│
├── main.py                  # App entry, loads FastAPI + routers
├── requirements.txt         # All dependencies
├── .env                     # API keys and secrets
│
├── config/                  # Global app settings
│   └── settings.py
│
├── routers/                 # API endpoint definitions
│   └── chat\_router.py
│
├── services/                # LangGraph logic, memory, summaries
│   ├── langgraph\_engine.py
│   ├── memory\_manager.py
│   └── summarizer.py
│
├── models/                  # Shared types
│   └── state.py
│
├── tools/                   # External tool setup (Tavily search)
│   └── tavily\_tool.py
│
├── prompts/                 # Dynamic prompt templates per chat\_type
│   ├── executive\_summary.py
│   ├── market\_analysis.py
│   ├── marketing\_strategy.py
│   ├── financial\_projection.py
│   ├── implementation\_timeline.py
│   ├── default\_prompt.py
│   └── **init**.py
│
├── utils/                   # Utilities
│   ├── serializers.py
│   └── api\_client.py
│
├── test/                    # Unit test directory
│   └── test\_chat\_stream.py
│
└── README.md                # Project documentation

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🚀 Setup Instructions

1. Clone the repo

git clone https://github.com/yourname/my_chatbot_project.git
cd my_chatbot_project

2. Install dependencies

pip install -r requirements.txt

3. Set up your .env file

Create a .env file in the root with the following keys:

OPENAI_API_KEY=your-openai-key
TAVILY_API_KEY=your-tavily-key

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🛠️ How It Works

✅ FastAPI Endpoint

/chat_stream is the main streaming endpoint using Server-Sent Events (SSE). It takes:

• message – the user's question
• checkpoint_id – resume previous context or None
• clerk_id, project_id – user/project context
• chat_type – type of session (e.g., "market_analysis")

🧠 Memory & Summarization

• Uses ConversationSummaryBufferMemory to summarize long chats.
• Automatically updates memory every round.
• Sends/receives chat summaries via REST API (utils/api_client.py).

🧩 LangGraph Integration

• Manages flow: model node → conditional → tool node → back to model.
• Asynchronously streams responses via langgraph.astream_events.

🔍 Tools

• Integrated with Tavily for real-time web search.
• You can extend tools/ for custom tools later.

💬 Prompt Templates

• Prompts are modularized in prompts/ for each type:

  • executive_summary, market_analysis, etc.

• prompts/__init__.py dynamically loads the right one.

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🔥 Example API Usage

Curl Request

curl -N "http://localhost:8000/chat_stream?message=Hi&clerk_id=123&project_id=456&chat_type=executive_summary"

Expected Response (SSE stream)

data: {"type":"checkpoint", "checkpoint_id":"uuid"}
data: {"type":"content", "content":"Hello! Let’s get started..."}
data: {"type":"end"}

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📘 Supported Chat Types

• executive_summary
• market_analysis
• marketing_strategy
• financial_projection
• implementation_timeline

These drive dynamic onboarding experiences using specialized prompts.

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🧪 Testing

You can write tests using pytest in the test/ directory.

Example test:

pytest test/test_chat_stream.py

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🧩 Extending This Bot

• Add new prompts to prompts/
• Register them in __init__.py
• Add new tools in tools/
• Enhance summarization logic in services/summarizer.py

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🙏 Credits

• LangChain
• LangGraph
• Tavily Search
• Built with ❤️ by Priya Rathor

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