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Build a RAG Microservice with Python

Introduction

In this article, we will explore how to build a Retrieval Augmented Generation (RAG) microservice using Python and Docker. We’ll take a comprehensive look at what RAG is, the concept of microservices, and how to implement a simple RAG pipeline with the help of libraries like Haystack and FastAPI.

Understanding RAG and Microservices

Retrieval Augmented Generation (RAG) is a hybrid modeling technique that combines retrieval-based systems with generative models such as large language models. The primary goal of RAG is to enhance the quality of knowledge-based generation by using a retrieval system that searches a large corpus of documents for the most relevant information before it is processed by a generative model.

In a typical RAG setup, the retrieval component is based on embeddings, which allows it to determine the relevance of the search query to the documents effectively. This is contrasted with traditional keyword-based techniques like Term Frequency-Inverse Document Frequency (TF-IDF), which rely on sparse representations. The advantage of RAG is that it fuses the accuracy of a retrieval system with the flexibility of generative models like OpenAI’s GPT, making it suitable for tasks that require accurate, context-aware answers from distinct datasets.

On the other hand, microservices are small, independently deployable services that perform a specific business function in an application. They can run in their own process and communicate with each other through simple protocols like HTTP. This architecture enhances modularity, resilience, and scalability, enabling different technologies to be employed across services.

Building the RAG Microservice

Prerequisites

  1. OpenAI API Key: You will need an OpenAI API key to access generative models such as GPT-4.
  2. Environment Setup: The suggested environment comprises Python and Docker.

Step-by-Step Implementation

  1. Data Preparation: We'll start by creating a corpus consisting of text files containing information about Rick and Morty episodes.

  2. Setting Up Environment: Create a Python virtual environment and install necessary dependencies like haystack for building the RAG pipeline and fastapi for serving the microservice.

    python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

  3. Creating the Haystack Pipeline:

    • Instantiate the document store.
    • Load the episodes and create an embedding retriever utilizing Sentence Transformers.
    • Set up a prompt template and a generator using OpenAI’s GPT models.
    from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import DensePassageRetriever, PromptTemplate, OpenAI
# Additional setup code...

  4. FastAPI Setup: Create a FastAPI application to expose endpoints for querying the RAG system. We'll define an endpoint that takes a user's query and responds with relevant answers based on the context derived from the embedded documents.

    from fastapi import FastAPI
app = FastAPI()

@app.post("/ask")
async def ask_question(question: str):
    # Call RAG pipeline and return results

  5. Docker Configuration: Set up the Docker environment with a Dockerfile. This helps in containerizing the microservice, allowing for easier deployment and scalability.

    FROM python:3.12
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

  6. Running the Microservice: Once the Docker image is built, we can run the microservice. The service will listen for incoming requests that can ask questions related to the episodes.

    docker build -t rag-microservice .
docker run -d -p 8000:8000 rag-microservice

  7. Testing the Microservice: Validate if the service is functioning correctly by making test queries against the defined endpoint.

  8. Cleaning Up: After testing, ensure that all Docker containers and images are removed to reclaim the resources.

docker stop <container_id>
docker rm <container_id>
docker rmi rag-microservice

Conclusion

By following these steps, we’ve successfully created a simple RAG microservice that leverages Python and Docker. This microservice can answer various queries about Rick and Morty based on the loaded episode data, showcasing the power of combining retrieval with generative capabilities.

Keywords

  • Retrieval Augmented Generation (RAG)
  • Microservices
  • Python
  • Docker
  • FastAPI
  • Haystack
  • OpenAI GPT
  • Document Store

FAQ

Q1: What is RAG in the context of machine learning?
A1: RAG stands for Retrieval Augmented Generation, a method combining the retrieval of relevant documents with the generation capabilities of language models to provide better context-aware responses.

Q2: Why use microservices architecture?
A2: Microservices allow applications to be developed in a modular way, enabling easier scalability, maintenance, and integration of different technologies for specific functions.

Q3: What libraries are needed for creating a RAG microservice?
A3: Key libraries include Haystack for implementing the RAG pipeline and FastAPI for exposing endpoints in a web server.

Q4: How do I run this microservice locally?
A4: The microservice can be run locally using Docker, which requires building a Docker image from the supplied Dockerfile and running it with defined port mappings.

Q5: Do I need an OpenAI API key to use the RAG service?
A5: Yes, an OpenAI API key is necessary for accessing the GPT models as part of the generative component in the RAG pipeline.

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