### Implementing a Retrieval-Augmented Generation (RAG) Pipeline with LangChain

In this guide, we delve into the process of setting up a Retrieval-Augmented Generation (RAG) pipeline using LangChain, OpenAI’s LLM, and the Weaviate vector database. The tutorial is structured to walk you through the initial steps of collecting and loading data, specifically using President Biden’s 2022 State of the Union Address as a case study. It further guides you on chunking the document for processing, embedding the text chunks for semantic search, and finally, implementing the RAG pipeline for generating context-aware responses. This approach leverages the power of LangChain’s document loaders, text splitters, and embeddings, alongside OpenAI’s models, to create a sophisticated system capable of augmenting prompts with relevant context for enhanced question-answering capabilities.

In the rapidly evolving field of natural language processing (NLP), the integration of various AI tools to enhance the understanding and generation of human language has become a focal point for developers and researchers. A prime example of this integration is the Retrieval-Augmented Generation (RAG) pipeline, which leverages the power of large language models (LLMs) alongside vector databases for knowledge-intensive tasks. This article delves into the practical implementation of a RAG pipeline using President Biden’s 2022 State of the Union Address as a case study, showcasing the synergy between OpenAI’s LLM, Weaviate’s vector database, and LangChain’s orchestration capabilities.

### Step 1: Data Collection and Loading

The journey begins with collecting and loading the data. For this example, we utilize President Biden’s 2022 State of the Union Address, available in LangChain’s GitHub repository. The Python code snippet below demonstrates how to load the text using LangChain’s TextLoader:

“`python
import requests
from langchain.document_loaders import TextLoader

url = “https://raw.githubusercontent.com/langchain-ai/langchain/master/docs/docs/modules/state_of_the_union.txt”
res = requests.get(url)
with open(“state_of_the_union.txt”, “w”) as f:
f.write(res.text)

loader = TextLoader(‘./state_of_the_union.txt’)
documents = loader.load()
“`

### Step 2: Document Chunking

Given the length of the document, it’s necessary to chunk it into smaller pieces to fit within the LLM’s context window. LangChain provides various text splitters for this purpose. Here, we use the CharacterTextSplitter:

“`python
from langchain.text_splitter import CharacterTextSplitter

text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = text_splitter.split_documents(documents)
“`

### Step 3: Embedding and Storing Chunks

To enable semantic search across the text chunks, we generate vector embeddings for each chunk using OpenAI’s embedding model and store them in the Weaviate vector database:

“`python
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Weaviate
import weaviate
from weaviate.embedded import EmbeddedOptions

client = weaviate.Client(embedded_options = EmbeddedOptions())
vectorstore = Weaviate.from_documents(client = client, documents = chunks, embedding = OpenAIEmbeddings(), by_text = False)
“`

### Step 4: Retrieval

With the vector database populated, it acts as the retriever component, fetching additional context based on semantic similarity:

“`python
retriever = vectorstore.as_retriever()
“`

### Step 5: Augment

To augment the prompt with additional context, we prepare a prompt template that can be customized easily:

“`python
from langchain.prompts import ChatPromptTemplate

template = “””You are an assistant for question-answering tasks.
Use the following pieces of retrieved context to answer the question.
If you don’t know the answer, just say that you don’t know.
Use three sentences maximum and keep the answer concise.
Question: {question}
Context: {context}
Answer:
“””
prompt = ChatPromptTemplate.from_template(template)
“`

### Step 6: Generate

Finally, we build a RAG pipeline, chaining together the retriever, the prompt template, and the LLM. The chain is invoked with a query:

“`python
from langchain.chat_models import ChatOpenAI
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser

llm = ChatOpenAI(model_name=”gpt-3.5-turbo”, temperature=0)

rag_chain = (
{“context”: retriever, “question”: RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)

query = “What did the president say about Justice Breyer”
response = rag_chain.invoke(query)
print(response)
“`

This RAG pipeline, illustrated through the example of President Biden’s address, underscores the potential of combining different AI tools to enhance the capabilities of NLP applications. By leveraging the strengths of OpenAI’s LLM, Weaviate’s vector database, and LangChain’s orchestration, developers can create sophisticated systems capable of understanding and generating human language with remarkable accuracy and relevance.

This implementation not only showcases the practical application of the RAG concept, as presented in the 2020 paper “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” [1], but also highlights the seamless integration of cutting-edge AI technologies to solve complex problems in the realm of NLP.

[1] https://arxiv.org/abs/2005.11401