Data Agent API with Langchain, Langchain Hub by LangSmith and FastAPI

Overview

This repository contains the code for an API developed to interface with a specialist agent in the field of data using Langchain, LangSmith for prompt versioning, and FastAPI. The agent is designed to provide in-depth responses to questions related to Machine Learning, Data Engineering, MLOps, and Data Science.

Workflow

Features

• Specialist Knowledge Areas: The API leverages specific templates for various domains, including Data Engineering, MLOps, Machine Learning Engineering, and Data Science.
• Dynamic Prompt Templating: Supports dynamic templating for prompts using Langchain and LangSmith, ensuring versatility and relevance of the responses.
• Error Handling: Robust error handling to manage and respond to execution failures.

Installation

Prerequisites

• Python 3.10+
• pip

Setting Up Environment

1. Clone the repository:

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

2. Install the required dependencies:

   pip install -r requirements/requirements.txt

Environment Variables

Set the following environment variables before running the API:

• OPENAI_BASE: Azure endpoint for OpenAI services.
• OPENAI_VERSION: API version for the OpenAI service.
• OPENAI_KEY: API key for accessing OpenAI services.
• OPENAI_TYPE: Type of OpenAI service (e.g., chat).
• OPENAI_DEPLOYMENT_NAME: Deployment name on Azure.

These variables can be set in your shell or through an environment file.

Running the API

To run the API locally, use the following command:

uvicorn app.api:app --reload

The --reload option enables auto-reloading of the server on code changes.

API Usage

Endpoint: /agent/invoke

• Method: POST
• Input: JSON containing input_text field with the user's query.
• Response: JSON containing model_response field with the agent's response.

Example Request

curl -X 'POST' \
  'http://localhost:8000/agent/invoke' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{"input_text": "What is MLOps?"}'

Example Response

{
  "model_response": "MLOps is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. The MLOps practices involve automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment, and infrastructure management."
}

Deployment

Refer to the deployment directory for Docker and Kubernetes configurations to deploy the API to a production environment.

Docker

Build and run the Docker container using:

docker build -t langsmith-agent-api ./deployment/docker
docker run -p 8000:8000 langsmith-agent-api

Kubernetes

Apply the Kubernetes configurations:

kubectl apply -f deployment/kubernetes

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues to propose changes or add features.