Basic Introduction to RAG using LangChain

What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation, or RAG, is a method used to enhance the output of a large language model (LLM) by making it reference an external or authoritative knowledge base. Instead of relying solely on its training data, RAG enables LLM to access and reference specific information from private datasets. This approach allows organisations to train models on their unique data, ensuring that users can get responses tailored to this specialised content.

Popular RAG frameworks and libraries include LangChain, LlamaIndex, and AgentGPT, among others. These tools streamline the process of connecting a language model with custom data and orchestrating the retrieval and generation of accurate responses.

Key Building Blocks of RAG in LangChain

LangChain is a robust tool that simplifies the RAG process. Here are the core components it provides for creating an effective RAG pipeline:

1. Document Loading: Importing data from various sources.
2. Document Splitting: Breaking large documents into smaller, manageable pieces.
3. Vector Stores and Embeddings: Using embeddings to convert text into vectors, allowing for efficient data retrieval.
4. Retrieval: Locating relevant information based on queries.
5. Question Answering: Generating responses based on retrieved data.
6. Chat: Interactive Q&A functionality that provides users with engaging, real-time answers.

In this blog, we’ll go over setting up a basic RAG pipeline with LangChain to retrieve data from a custom dataset and answer questions in natural language.

Building a Simple RAG with LangChain

Let’s start with a quick example using LangChain's ChatOpenAI to set up a simple RAG model. Here’s a sample code snippet:

from langchain_openai.chat_models import ChatOpenAI

model = ChatOpenAI(openai_api_key=OPENAI_API_KEY, model="gpt-3.5-turbo")
model.invoke("What MLB team won the World Series during the COVID-19 pandemic?")

In this example, we’re asking a basic question. However, when working with RAG, we’ll integrate our data first and then utilize the model to answer questions based on that specific content.

Using Custom Data as Context

To ensure the model responds with information from your custom data, we create a ChatPromptTemplate that incorporates this context before querying the model. Here’s how it looks:

from langchain.prompts import ChatPromptTemplate

template = """
Answer the question based on the context below. If you can't 
answer the question, reply "I don't know".

Context: {context}

Question: {question}
"""

prompt = ChatPromptTemplate.from_template(template)
prompt.format(context="Eniola's wife is Moyo", question="Who is Eniola's Wife?")

In this case, we use a ChatPromptTemplate to input our data as context. This lets the model know that the response should be derived specifically from the context provided.

Next, we chain the prompt with the model to ensure it answers using our data:

chain = prompt | model 
chain.invoke({
    "context": "Eniola's wife is Moyo",
    "question": "Who is Eniola's Wife?"
})

The result will reflect the context we provided, ensuring that responses are accurate and specific.

Translating Responses

We can also extend this setup to translate responses into different languages by adding a translation prompt. Here’s an example:

translation_prompt = ChatPromptTemplate.from_template(
    "Translate {answer} to {language}"
)

from operator import itemgetter

translation_chain = (
    {"answer": chain, "language": itemgetter("language")} | translation_prompt | model
)

translation_chain.invoke(
    {
        "context": "He doesn't have any more wives.",
        "question": "How many wives does Eniola have?",
        "language": "French",
    }
)

In this case, we’re translating the answer into French. The chain leverages both the contextual data and translation to provide an answer in the specified language.

Wrapping Up

RAG with LangChain opens up exciting possibilities for using private, custom datasets with LLMs, making responses more accurate and context-aware. By following the basic steps outlined here, you can start building your own RAG application to power customer support, research, or any use case requiring personalized, data-backed answers.

Ready to dive deeper? Join me as we explore the full potential of RAG with LangChain!