Want to build a custom AI tailored to your company’s needs? This tutorial demonstrates how to create a custom large language model (LLM) or AI chat app using Python, LangChain, and OpenAI. By leveraging your own data stored in a Vector Database with Chroma DB, you can fine-tune a GPT model for specific tasks. Whether you’re a developer or an AI enthusiast, this guide will walk you through the entire process from setup to deployment.
What You’ll Learn
- Setting up a Python Flask server
- Building and fine-tuning an LLM with OpenAI
- Embedding custom data into a vector database
- Training an AI model with your own data
- Deploying and testing the final application
Step 1: Setting Up Your Project Environment
Before diving into the development, you need to set up the environment for your project. Follow these steps:
Installing Prerequisites
- Download and install VS Code.
- Ensure Python 3.9 or later is installed on your system.
- Install necessary Python libraries using pip:
pip install flask langchain chromadb openai
Getting Your OpenAI API Key
To access the GPT model, sign up for an API key on the OpenAI platform. Store this key securely, as you’ll need it to make API requests.
Step 2: Building a Flask Server
We’ll use Flask to create a lightweight server to handle requests and responses for our AI model.
Creating the Flask App
Start by creating a file named app.py and add the following code:
from flask import Flask, request, jsonify
import openai
app = Flask(__name__)
openai.api_key = "your-api-key"
@app.route('/chat', methods=['POST'])
def chat():
data = request.get_json()
prompt = data.get('prompt')
response = openai.Completion.create(
engine="text-davinci-003",
prompt=prompt,
max_tokens=150
)
return jsonify(response["choices"][0]["text"].strip())
if __name__ == '__main__':
app.run(debug=True)
Run the server using:
python app.py
Step 3: Creating the LLM
LangChain simplifies the process of connecting your AI with a vector database and fine-tuning it with your data.
Integrating LangChain
Use the following code snippet to initialize your LLM:
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
# Initialize LLM
llm = OpenAI(model_name="text-davinci-003")
# Create a prompt template
template = "Answer the following question: {question}"
prompt = PromptTemplate(template=template, input_variables=["question"])
# Build the LLM chain
chain = LLMChain(llm=llm, prompt=prompt)
# Test the LLM
print(chain.run("What is LangChain?"))
Step 4: Embedding and Indexing Custom Data
To enable the model to respond based on your specific data, embed the data into a vector database like Chroma DB.
Building the Data Embedder
Create a new file named embedder.py and include the following code:
from chromadb.utils import Embedding
from chromadb.client import Client
client = Client()
collection = client.get_or_create_collection("custom_data")
# Example: Adding a document
collection.add(
documents=["This is an example document."],
metadatas=[{"source": "example"}],
ids=["doc1"]
)
print(collection.query("example"))
Training the Model
Once the data is embedded, connect it to the LangChain pipeline to provide context-specific answers.
Step 5: Testing the Final Application
Now that the Flask server, LLM, and data embeddings are in place, test the application:
- Start the Flask server.
- Send a POST request to the
/chatendpoint with a prompt:
curl -X POST http://localhost:5000/chat \
-H "Content-Type: application/json" \
-d '{"prompt": "What is the purpose of LangChain?"}'
You should receive a detailed, context-specific response generated by your custom AI.
Conclusion
By following this guide, you’ve built a powerful custom AI using Python, LangChain, OpenAI, and Chroma DB. This AI is not only trained on general knowledge but also fine-tuned with your specific data, making it highly relevant for your business or personal projects. For the full code and additional resources, check out the GitHub repository. Continue experimenting to expand the capabilities of your AI!