ChatTensorFlow by Lalan KumarChatTensorFlow by Lalan Kumar

ChatTensorFlow

Lalan Kumar

Completed work

AI Agent Engineer

ML Engineer

AI Engineer

Google Gemini

LangChain

Artificial Intelligence

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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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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