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Build a RAG System with Rig in Under 100 Lines of Code

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Build a RAG System with Rig in Under 100 Lines of Code

In the age of information overload, efficiently extracting relevant information from vast text datasets is crucial. Retrieval Augmented Generation (RAG) systems offer a powerful solution by combining information retrieval with language models to generate informative and accurate responses.

Rig, a library built on top of LangChain, provides a user-friendly interface for building and deploying RAG systems with minimal code. This article will guide you through constructing a simple RAG system with Rig in under 100 lines of code, enabling you to unlock the power of information retrieval and language modeling.

What is RAG?

RAG systems leverage the strengths of both information retrieval and large language models (LLMs) to provide a comprehensive and intelligent approach to information access:

Information Retrieval: RAG systems use techniques like keyword search, semantic search, or vector databases to efficiently identify relevant documents within a knowledge base.
Language Modeling: LLMs are trained on massive text datasets and are capable of understanding and generating human-like text. They are used to summarize, paraphrase, and reason over retrieved information to generate coherent and informative responses.

By combining these two components, RAG systems enable you to:

Query large text databases: Quickly find relevant information based on your search query.
Generate comprehensive responses: Combine retrieved information with LLM capabilities to provide detailed answers, summaries, or insights.
Personalize responses: Tailor responses based on user context and previous interactions.

Building a RAG System with Rig

Let's dive into the practical aspects of building a RAG system with Rig. We'll use a simple example to illustrate the key concepts and code structure.

Setting up the Environment

First, you need to install the necessary libraries:

pip install rig langchain openai

Loading Data

For our example, we'll use a collection of movie plot summaries stored in a simple text file. You can replace this with your own data source (e.g., a database, API, or other text files).

import rig
import os

data_dir = 'data'  # Specify your data directory
file_path = os.path.join(data_dir, 'movie_plots.txt')

with open(file_path, 'r') as f:
  data = f.readlines()

Initializing the RAG System

Rig makes it easy to set up a RAG system with minimal configuration. We'll use the Rig class and define the retriever and generator components. For simplicity, we'll use a basic keyword-based retriever and the openai LLM.

import os
import rig
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI

# Your OpenAI API key
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

retriever = rig.Retriever(
  vectorstore=FAISS.from_texts(data, OpenAIEmbeddings())  # Vector database for retrieval
)

generator = rig.Generator(
  llm=OpenAI(temperature=0.7)  # OpenAI LLM for response generation
)

Using the RAG System

Now you can interact with your RAG system using the rag method. Let's try a query about a movie:

query = "What is the movie about a robot who falls in love with a human?"

response = rig.rag(query, retriever=retriever, generator=generator)
print(response)

Rig will retrieve relevant movie plots from the database and use the LLM to summarize them, providing a comprehensive response. Adjust the temperature parameter in the OpenAI LLM to control the creativity and diversity of the generated responses.

Complete Code Example

import os
import rig
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI

# Your OpenAI API key
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

data_dir = 'data'
file_path = os.path.join(data_dir, 'movie_plots.txt')

with open(file_path, 'r') as f:
  data = f.readlines()

retriever = rig.Retriever(
  vectorstore=FAISS.from_texts(data, OpenAIEmbeddings())
)

generator = rig.Generator(
  llm=OpenAI(temperature=0.7)
)

query = "What is the movie about a robot who falls in love with a human?"

response = rig.rag(query, retriever=retriever, generator=generator)
print(response)

Key Takeaways

Rig simplifies RAG system development with its intuitive interface and powerful features.
Combining information retrieval and language models offers a comprehensive approach to accessing and understanding information.
You can customize RAG systems by choosing different retrieval and generation components based on your specific needs.

Further Exploration

This example provides a foundation for building your own RAG systems. For more advanced use cases, you can explore:

Different retrieval methods:

Experiment with semantic search techniques like SentenceTransformers or other vector database implementations.
Customizing the generator:

Explore other LLMs like GPT-3, Jurassic-1 Jumbo, or fine-tune existing models for specific tasks.
Integrating with external APIs and data sources:

Expand the knowledge base of your RAG system by connecting it to other relevant information sources.
Building conversational RAG systems:

Leverage the ConversationChain from LangChain to build interactive and engaging dialogue-based applications.

By leveraging the power of Rig and LangChain, you can quickly build and deploy effective RAG systems that unlock the potential of your knowledge base and empower you to access and understand information more efficiently.