How to Build an LLM Rag Pipeline with Llama-2, PgVector, and LlamaIndex
Tags: ai, nlp, machinelearning, llama
Published: 2023-12-24 08:18:05


The world of Large Language Models (LLMs) has seen remarkable advancements in recent years. Among these developments is the RAG (Retrieval-Augmented Generation) pipeline, a powerful tool for enhancing the capabilities of language models like Llama-2. In this post, we’ll delve into the process of building a RAG pipeline using the transformer library, integrating the Llama-2 model, PgVector database, and the LlamaIndex library. This comprehensive guide will include installation steps, code snippets, and practical examples, ensuring you have a clear understanding of how to implement this sophisticated technology.

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**Source: GPU by E2E**

## Understanding RAG and Its Components

### What Is RAG(Retrieval-Augmented Generation)?
The Retrieval-Augmented Generation (RAG) model is a fusion of traditional language models with an information retrieval component. In essence, RAG augments the language generation process with external data, typically from a large corpus or database, to produce more informed and contextually relevant responses.

### How RAG Works
**Retrieval Phase:** When a query is input into the RAG system, it first retrieves relevant information from a database. This is where the model looks for contextual clues or additional data that might be pertinent to the query.

**Augmentation Phase:** The retrieved data is then fed into a language model, like Llama-2 in our case, which generates a response. This response is not just based on the model’s pre-trained knowledge but also on the specific information retrieved in the first phase.

### Llama-2: The Language Model


![Llama-2: The Language Model](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/h6jhsikq0lt8a9wjilnk.png)

Llama-2 stands at the forefront of language processing technology. It’s a state-of-the-art model trained on extensive datasets, enabling it to understand and generate nuanced, contextually relevant text. Llama-2’s capability stems from its sophisticated architecture, which allows it to process and produce human-like text, making it ideal for tasks such as translation, summarization, question-answering, and more.

#### Key Features:
**Versatility:** Llama-2 can handle a wide range of NLP tasks.

**Contextual Understanding:** It excels in grasping the context of a conversation or text.

**Language Generation:** Llama-2 can generate coherent and contextually appropriate responses.

**Why Llama-2 for RAG?:** Llama-2’s balance of performance and computational efficiency makes it an ideal candidate for RAG pipelines, especially when processing and generating responses based on large volumes of retrieved data.

### PgVector: Managing Vector Data Efficiently

![PgVector: Managing Vector Data Efficiently](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/04ilm5i3h1mny177qpsp.png)

PgVector is an extension of PostgreSQL, a popular open-source relational database. It’s tailored for handling high-dimensional vector data, like those produced by language models such as Llama-2. PgVector allows for efficient storage, indexing, and searching of vector data, making it an essential tool for projects involving large datasets and complex queries.

#### Key Features:
**Efficiency:** Optimized for quick retrieval of high-dimensional data.

**Integration:** Seamlessly integrates with PostgreSQL databases.

**Scalability:** Suitable for handling large-scale vector datasets.

**Importance in RAG:** For RAG, PgVector provides an optimized database environment to store and retrieve the vectorized form of the data, which is essential for the retrieval phase.

### LlamaIndex: Bridging Language and Database

![LlamaIndex: Bridging Language and Database](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/rrwam24kf3clw68i4bl5.png)

LlamaIndex is not a standalone tool but a conceptual part of our pipeline. It represents the processes and methods used to convert textual data into vectors using Llama-2 and then store these vectors in a PostgreSQL database empowered by PgVector. This conversion is crucial for enabling efficient text retrieval based on semantic similarity, rather than just keyword matching.

#### Key Features:
**Semantic Indexing:** Converts text to vectors that represent semantic meanings.

**Database Integration:** Stores and retrieves vector data from PostgreSQL.

**Enhanced Retrieval:** Facilitates efficient, context-aware search capabilities.

**Role in RAG:** LlamaIndex is crucial for efficiently searching through the embeddings stored in the PgVector database. It facilitates quick retrieval of relevant data based on the query input.

### Setting Up the Environment
Before diving into the implementation, it’s crucial to ensure that your environment is correctly set up with the necessary libraries and tools. Here’s a step-by-step guide:

### Installing Transformers Library
The transformers library by Hugging Face is a cornerstone for working with models like Llama-2. It provides easy access to pre-trained models and utilities for natural language processing tasks.

`pip install transformers`

This command installs the latest version of the transformers library, which includes the necessary functionalities to load and utilize the Llama-2 model.

### Installing PgVector
PgVector is a PostgreSQL extension that facilitates efficient handling of vector data. This is particularly important for managing the embeddings used in LLMs and enabling quick retrieval operations.

#### Download PostgreSQL
Visit the official PostgreSQL website (https://www.postgresql.org/download/) and select the appropriate version for your operating system. PostgreSQL is compatible with various platforms, including Windows, macOS, and Linux.

First, ensure that PostgreSQL is installed and running on your system. Then, install the PgVector extension:

`pip install pgvector`

Once installed, you’ll need to create a PostgreSQL database and enable the PgVector extension within it:

`CREATE DATABASE ragdb;
\c ragdb
CREATE EXTENSION pgvector;`

This sequence of SQL commands creates a new database named ragdb and activates the PgVector extension within it.

### Installing LlamaIndex Library
LlamaIndex is specifically designed for indexing and retrieving vector data, making it a vital component of the RAG pipeline.

`pip install llama-index`

This command installs the LlamaIndex library, enabling you to create and manage indexes for your vector data.

## Building the Pipeline

![Building the Pipeline](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/uwpv3y3ai9ug3mjht7z0.png)

Building the LLM RAG pipeline involves several steps: initializing Llama-2 for language processing, setting up a PostgreSQL database with PgVector for vector data management, and creating functions to integrate LlamaIndex for converting and storing text as vectors.

### Initializing Llama-2
The first step in building our RAG pipeline involves initializing the Llama-2 model using the Transformers library. This process includes setting up the model and its tokenizer, which are essential for encoding and decoding text.

`from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
`
`tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")`
`model = AutoModelForSeq2SeqLM.from_pretrained("meta-llama/Llama-2-7b-hf")`

In this snippet, we import LlamaModel from the llama-2 package and initialize it with a specific model variant (e.g., ‘llama2-large’). This model will be used for text generation and vectorization.

### Setting Up PgVector
Once the model is ready, the next step is to set up the PgVector database for storing and retrieving vectorized data.

#### PostgreSQL Database Setup:

- **Install PostgreSQL:** Ensure PostgreSQL is installed and running.

- **Create a Database and Enable PgVector:**

`CREATE DATABASE ragdb;
\c ragdb
CREATE EXTENSION pgvector;`

Python Code for Database Interaction:

`import psycopg2
conn = psycopg2.connect(dbname="ragdb", user="yourusername", password="yourpassword")
cursor = conn.cursor()
cursor.execute("CREATE TABLE embeddings (id serial PRIMARY KEY, vector vector(512));")
conn.commit()`

This code creates a connection to the PostgreSQL database and sets up a table for storing embeddings. The vector(512) data type is an example; adjust the size based on your model’s output.

### Data Preparation
For this example, let’s use a simple dataset of scientific abstracts related to renewable energy. The dataset consists of a list of abstracts, each as a string.

`data = [
    "Advances in solar panel efficiency have led to a significant reduction in cost.",
    "Wind turbines have become a major source of renewable energy in the past decade.",
    "The development of safer nuclear reactors opens new 
possibilities for clean energy.",
    # Add more abstracts as needed
]`

### Generating Embeddings
To generate embeddings from this data, we first need to load the Llama-2 model and process each abstract through it.

### Install Requirements:

`pip install torch`

After installing the torch implement the below code.

`from transformers import AutoTokenizer, AutoModel`
`import torch
`
`#Initialize the model and tokenizer
``tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")`
`model = AutoModel.from_pretrained("meta-llama/Llama-2-7b-hf")
`

`def generate_embeddings(text):`
    `inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)`
`    with torch.no_grad():`
        `outputs = model(**inputs)`
    `return outputs.last_hidden_state.mean(dim=1).numpy()`

`embeddings = [generate_embeddings(abstract) for abstract in data]`

This function processes each abstract through the Llama-2 model to produce embeddings. The embeddings are then stored in a list.

### Indexing Data with LlamaIndex
With the embeddings ready, we can now index them using LlamaIndex. This step is crucial for enabling efficient retrieval later on.

`import numpy as np
``from llama_index import VectorStoreIndex`


`# Convert the list of embeddings to a NumPy array`
`embeddings_array = np.vstack(embeddings)
`
`# Create an index for these embeddings
``index = VectorStoreIndex.from_documents(`
`    documents, service_context=embeddings_array
``)`

This code block converts the list of embeddings into a NumPy array and then creates an index named “energy_abstracts_index” using LlamaIndex.

### Integrating with PostgreSQL
Finally, to integrate this with a PostgreSQL database (assuming you have already set it up with PgVector as discussed earlier), you would store these embeddings in the database.

#### Installation Requirements:

`pip install psycopg2`

After installing ‘psycopg2’ implement the below code to store the embeddings in the database.

`import psycopg2
`
`# Connect to your PostgreSQL database
``conn = psycopg2.connect(dbname="ragdb", user="yourusername", password="yourpassword")`
`cursor = conn.cursor()
`
`# Store each embedding in the database
``for i, embedding in enumerate(embeddings_array):`
    `cursor.execute("INSERT INTO embeddings (id, vector) VALUES` `(%s, %s)", (i, embedding))`
`conn.commit()`

In this snippet, we have a list of sample texts. We loop through each text, and the index_document function converts the text into a vector and stores it in the database.

### Integrating RAG Pipeline
Having set up the individual components, integrating them into a cohesive Retrieval-Augmented Generation (RAG) pipeline is the final step. This involves creating a system where a query is processed, relevant information is retrieved from the database, and a response is generated using the Llama-2 model.

#### Creating the RAG Query Function
The core of the RAG pipeline is a function that takes a user query, retrieves relevant context from the database, and generates a response based on both the query and the retrieved context.


`def your_retrieval_condition(query_embedding, threshold=0.7):
    query_embedding_str = ','.join(map(str, query_embedding.tolist()))
    condition = f"cosine_similarity(vector, ARRAY[{query_embedding_str}]) > {threshold}"
    return condition`

Now, let’s integrate this custom retrieval logic into our RAG pipeline:


`def rag_query(query):
    input_ids = tokenizer.encode(query, return_tensors='pt')
    query_embedding = generate_embeddings(query)
    retrieval_condition = your_retrieval_condition(query_embedding)
    cursor.execute(f"SELECT vector FROM embeddings WHERE {retrieval_condition}")
    retrieved_embeddings = cursor.fetchall()
    retrieved_embeddings_tensor = torch.tensor([emb[0] for emb in retrieved_embeddings])
    combined_input = torch.cat((input_ids, retrieved_embeddings_tensor), dim=0)
    generated_response = model.generate(combined_input, max_length=512)
    return tokenizer.decode(generated_response[0], skip_special_tokens=True)`

Let’s see how our RAG pipeline would work with a sample query:

`query = "What are the latest advancements in renewable energy?"
response = rag_query(query)
print("Response:", response)`

In this scenario, the pipeline retrieves context relevant to “renewable energy” advancements, combines this with the query, and generates a comprehensive response.

## Conclusion
Building an LLM RAG pipeline with Llama-2, PgVector, and LlamaIndex opens up a realm of possibilities in the field of NLP. This pipeline not only understands and generates text but also leverages a vast database of information to augment its responses, making it incredibly powerful for various applications like chatbots, recommendation systems, and more.

The journey doesn’t end here, though. The world of NLP is rapidly advancing, and staying updated with the latest trends and technologies is crucial. The implementation discussed here is a stepping stone into a broader, more intricate world of language understanding and generation. Keep experimenting, keep learning, and most importantly, keep innovating.