Flask API integration for Llama3 Full Rag with Ollama, LangChain, and ChromaDB, including PDF upload

In this tutorial, we will explore how to use Llama3 Full Rag API with Ollama, LangChain, and ChromaDB, and create a Flask API that facilitates the integration of these tools. Additionally, we will also implement functionality for uploading PDF files, parsing them, and extracting text using Python’s Flask framework.

Before we get started, let’s briefly discuss each of the tools we will be using in this tutorial:

1. Llama3 Full Rag: Llama3 Full Rag is a natural language processing library that provides tools for text generation, summarization, and language generation.

2. Ollama: Ollama is a tool for language translation and generation that uses deep learning algorithms to generate high-quality translations.

3. LangChain: LangChain is a tool for creating language chains, which are sequences of words that form coherent and meaningful sentences.

4. ChromaDB: ChromaDB is a database for storing and managing linguistic data, such as text corpora, lexicons, and language models.

5. Flask: Flask is a lightweight and versatile web framework for building web applications in Python.

Now, let’s start by setting up our development environment and installing the necessary dependencies:

1. First, create a new directory for your project and navigate into it. You can do this using the following commands in your terminal:

   mkdir llama3_full_rag_api
   cd llama3_full_rag_api

2. Next, create a virtual environment for your project to isolate its dependencies. You can use the following command to create a virtual environment named ‘env’:

   python3 -m venv env

3. Activate the virtual environment using the following command:

   source env/bin/activate

4. Now, install the necessary dependencies for our project using the following command:

   pip install flask flask-restful requests PyPDF2 llamas llamas-ollama llamas-langchain llamas-chromadb

With our environment set up and dependencies installed, let’s create the Flask API for our project. We will start by defining the routes and functionality for our API:

1. Create a new Python file named ‘app.py’ in your project directory and open it in your favorite code editor.

2. Import the necessary modules and libraries at the beginning of the file:

   from flask import Flask, request, jsonify
   from flask_restful import Api, Resource
   from PyPDF2 import PdfFileReader
   from llamas import Llama3
   from llamas.components.ollama import Ollama
   from llamas.chains.langchain import LangChain
   from llamas.database.chromadb import ChromaDB

3. Instantiate the Flask application and create an instance of the Api class:

   app = Flask(__name__)
   api = Api(app)

4. Define a route for uploading PDF files and extracting text from them:

   class PDFUpload(Resource):
   def post(self):
       if 'file' not in request.files:
           return jsonify({'error': 'No file part'})
   
       file = request.files['file']
   
       if file.filename == '':
           return jsonify({'error': 'No selected file'})
   
       if file:
           pdf = PdfFileReader(file)
           text = ''
           for page_num in range(pdf.numPages):
               text += pdf.getPage(page_num).extract_text()
   
           return jsonify({'text': text})

5. Define a route for processing text using Llama3 Full Rag, Ollama, LangChain, and ChromaDB:

   class TextProcessing(Resource):
   def post(self):
       data = request.get_json()
       text = data['text']
   
       llama = Llama3()
       ollama = Ollama(llama)
       langchain = LangChain(llama)
       chromadb = ChromaDB(llama)
   
       processed_text = {
           'ollama_translation': ollama.translate(text),
           'langchain_sentence': langchain.create_sentence(text),
           'chromadb_synonyms': chromadb.get_synonyms(text)
       }
   
       return jsonify(processed_text)

6. Add the routes to the Flask application and run the application:

   
   api.add_resource(PDFUpload, '/pdf/upload')
   api.add_resource(TextProcessing, '/text/process')

if name == ‘main‘:
app.run(debug=True)

With the Flask API set up, we can now test the functionality by starting the Flask development server and making requests to the defined routes using tools such as Postman.

To start the Flask development server, run the following command in your terminal:
```bash
python app.py

You should see output indicating that the Flask application is running. Now, you can open Postman or another API testing tool, and make requests to the defined routes to upload PDF files and process text using Llama3 Full Rag, Ollama, LangChain, and ChromaDB.

In this tutorial, we have explored how to create a Flask API that integrates Llama3 Full Rag, Ollama, LangChain, and ChromaDB, and implements functionality for uploading PDF files and processing text. You can further enhance this project by adding more features and customization based on your requirements and the capabilities of the tools used.