Build an Efficient RAG Model with Haystack and Mistral 7B

Table of Contents

1. Introduction
2. Setting Up V8 Vector Database
3. Preprocessing the PDF Documents
4. Initializing the Document Store
5. Creating Embeddings with Sentence Transformer
6. Updating the Document Store with Embeddings
7. Initializing the Embedding Retriever
8. Creating the Haystack Pipeline
9. Building the FastAPI Application
10. Running the Application

📝 Article

Introduction

Welcome to the world of AI! In this video, we will be implementing a retrieval augmented generation (RAG) model using the hashtag framework and the Mistral 7B language model. This highly efficient framework and language model have proven to be incredibly effective, surpassing other benchmarks like LAMA 213B. We will be using Haystack as the orchestration framework and V8 as the vector database for this implementation. So let's dive right in and see how we can build a RAG implementation using these powerful tools.

Setting Up V8 Vector Database

To get started, we need to set up the V8 Vector Database using Docker Compose. V8 is an AI-native vector database that allows efficient storage and retrieval of data objects and vector embeddings. It is highly scalable and perfect for large-Scale applications. You can either use the V8 cloud service or set it up locally using Docker Compose. Once set up, you can access the V8 cluster on localhost:8080.

Preprocessing the PDF Documents

Next, we need to preprocess the PDF documents before creating the embeddings. We will be using the Haystack preview component along with the PiPDF converter for this task. The preprocessor will clean empty lines, clean header and footer, and split the text by WORD. We will also remove any unnecessary white spaces. Once the preprocessing is done, we will have a list of documents with their content and metadata ready for further processing.

Initializing the Document Store

Now, it's time to initialize the document store, which acts as the vector store for our embeddings. We will be using the V8 Document Store, which supports efficient storage and retrieval of vector embeddings. We will set the host to localhost:8080 and specify the embedding dimension. Once the document store is initialized, we can write the preprocessed documents into it.

Creating Embeddings with Sentence Transformer

To create the embeddings, we will be using the Sentence Transformer model. This model is highly efficient and has been trained on a large corpus of text data. We will initialize the embedding retriever class and pass the document store and the embedding model path as arguments. We can then update the document store with the embeddings created using the retriever.

Initializing the Embedding Retriever

Now that the embeddings are stored in the document store, we can initialize the embedding retriever. This retriever will be responsible for retrieving the most Relevant documents based on the user query. We will initialize the embedding retriever class and pass the document store and the embedding model as arguments. The retriever is now ready for use.

Creating the Haystack Pipeline

To create the Haystack pipeline, we will use the pipeline class from the Haystack library. This pipeline allows us to combine multiple nodes and modules together to create a seamless workflow. In the pipeline, we will add the retriever node, which will retrieve the relevant documents based on the user query. We will also add the Prompt node, which will generate the response using the retrieved documents. The pipeline is now ready for use.

Building the FastAPI Application

To build the application, we will be using the FastAPI framework. FastAPI is a modern, fast (high-performance), web framework for building APIs with Python. We will define our routes and endpoints, including the get_result and get_answer endpoints. The get_result endpoint will retrieve the user query and pass it to the get_answer function, which will use the Haystack pipeline to generate the response. The response will be returned as a JSON object.

Running the Application

With everything set up, we can now run the application. We will initialize the app using FastAPI, mount the templates and static files, define the routes, and start the server. The application will be accessible at localhost:8000. Users can enter their queries in the input box and receive the generated response in real-time. The application also includes a Swagger UI, where users can explore the API endpoints and test them with different queries.

And that's it! We have successfully implemented a retrieval augmented generation (RAG) model using the hashtag framework and the Mistral 7B language model. This powerful combination of tools allows for efficient and accurate generation of responses based on user queries. We hope you find this implementation useful and that it inspires you to explore and build on top of it.

🔗 Resources:

• V8 Vector Database
• Haystack
• Mistral 7B
• FastAPI

Highlights

• Implementing retrieval augmented generation (RAG) using hashtag framework and Mistral 7B language model
• Setting up V8 vector database for efficient storage and retrieval of embeddings
• Preprocessing PDF documents using Haystack preview component and PiPDF converter
• Initializing the document store and updating it with embeddings
• Creating embeddings with Sentence Transformer model
• Initializing the embedding retriever and Haystack pipeline
• Building the FastAPI application for real-time query response generation
• Running the application and accessing the API endpoints through Swagger UI

FAQ

Q: What is a retrieval augmented generation (RAG) model? A: A RAG model combines retrieval and generation components to generate responses based on user queries. It retrieves relevant information from a knowledge base and uses that information to generate a response.

Q: How does the V8 vector database work? A: The V8 vector database is an AI-native database that allows efficient storage and retrieval of data objects and vector embeddings. It supports scalable and high-performance operations, making it ideal for large-scale applications.

Q: What is Haystack? A: Haystack is an open-source Python library for end-to-end question answering and retrieval tasks. It provides powerful tools for building question answering systems using state-of-the-art models and techniques.

Q: What is the Mistral 7B language model? A: Mistral 7B is a large-scale language model trained on a vast amount of text data. It is known for its high performance and versatility in various natural language processing tasks.

Q: Can I use GPT 3.5 or 4.0 with Haystack? A: Yes, you can use GPT 3.5 or 4.0 with Haystack. You can specify the model name or path in the prompt node of the Haystack pipeline and use it for generation tasks.

Q: How can I access the Haystack application? A: The Haystack application can be accessed through a web browser by running the FastAPI server and navigating to the specified URL, usually localhost:8000. The Swagger UI provides a user-friendly interface for testing and exploring the API endpoints.