ChatTensorFlow

Lalan Kumar

Completed work

AI Agent Engineer

ML Engineer

AI Engineer

Google Gemini

LangChain

Artificial Intelligence

ChatTensorFlow

ChatTensorFlow is an intelligent assistant that helps users with TensorFlow related queries by utilizing the power of LangGraph based Agents, vector embeddings, and a conversational interface powered by various LLM providers.

Features

Multi-provider support: Works with multiple LLM providers (Google Gemini, OpenAI, Anthropic, Cohere)

Content processing: Splits and embeds TensorFlow documentation for semantic search

Query routing: Intelligently classifies user questions and determines next steps

Assisted research: Follows a research plan to answer complex TensorFlow queries

Context-aware responses: Provides answers with citations from official TensorFlow documentation

Architecture

ChatTensorFlow is built with a modular architecture:

Document Crawler: Efficiently crawls TensorFlow documentation using sitemap.xml

Data Ingestion: Processes web content into chunks for embedding

Vector Store: Stores embeddings for semantic search using Chroma

Retriever: Retrieves relevant content when answering questions

Researcher Graph: Follows a structured plan to research answers

Router Graph: Determines how to handle each user query

Response Generator: Creates coherent, accurate responses based on retrieved information

Graphs

Getting Started

Prerequisites

Python 3.9+

API keys for at least one supported LLM provider

Installation

Clone the repository:

git clone https://github.com/yourusername/chatTensorFlow.git
cd chatTensorFlow

Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Set up your environment variables:

cp .env.example .env
# Edit .env with your API keys

Data Collection

To crawl and process the Scikit-learn documentation:

Run the web crawler:

python -m processor.crawler

Process and ingest the data:

python -m processor.data_ingestion

This will create a Chroma vector database with the embedded documentation chunks.

Usage

Run the application:

python app/main.py

The assistant can answer questions about:

TensorFlow API usage

Deep learning concepts

Model building and training

TensorFlow data pipelines

Common errors and troubleshooting

Example queries:

"How do I compile and train a Keras model?"

"How do I use tf.data.Dataset to load and preprocess large datasets?"

"What are the parameters for a Conv2D layer?"

"How do I create and use a custom loss function?"

Configuration

The application can be configured by setting the following environment variables:

# LLM Provider (gemini, openai, anthropic, cohere)
LLM_PROVIDER=gemini

# Embedding Provider (gemini, openai, cohere)
EMBEDDING_PROVIDER=gemini

# API Keys
GOOGLE_API_KEY=your_gemini_api_key
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
COHERE_API_KEY=your_cohere_api_key

# Model selections (optional, defaults provided)
GEMINI_LLM_MODEL=gemini-2.0-flash
OPENAI_LLM_MODEL=gpt-4o
ANTHROPIC_LLM_MODEL=claude-3-sonnet-20240229
COHERE_LLM_MODEL=command

Project Structure

chatTensorFlow/
├── app/
│   ├── core/         # Core utilities and model interfaces
│   ├── graphs/       # LangGraph state management and flow
│   ├── retriever/    # Document retrieval components
│   ├── static/       # Static files (CSS, JS, images)
│   ├── templates/    # HTML templates
│   └── main.py       # Application entry point
├── config/           # Application configuration
├── DATA/             # Vector store data
├── logs/             # Application logs
├── processor/        # Web crawling and data ingestion
├── tensorflow_docs/  # Crawled TensorFlow documentation
└── tests/            # Unit and integration tests

How It Works

User Query Analysis: The system classifies the query as TensorFlow related, general, or requiring more information

Research Planning: For TensorFlow queries, a research plan is created

Document Retrieval: Relevant documentation is retrieved using semantic search

Response Generation: A detailed, accurate response is generated citing relevant documentation

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Fork the repository

Create your feature branch (git checkout -b feature/amazing-feature)

Commit your changes (git commit -m 'Add some amazing feature')

Push to the branch (git push origin feature/amazing-feature)

Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Built with LangChain and LangGraph

Uses crawl4ai for crawling

Uses ChromaDB for vector storage

Documentation content from TensorFlow

Developed by Lalan Kumar

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Completed work

Posted Jul 30, 2025

Developed ChatTensorFlow, an AI assistant for TensorFlow queries using LangGraph and LLMs.

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