From Upload to Understanding: Building Context-Aware Answers with LangChain's Simple File Upload"

Imagine having a chatbot that doesn't just answer generic questions but can dive deep into specific information sources—like files you've uploaded—to provide precise and insightful answers. This is one of the most impressive capabilities made possible by Large Language Models (LLMs).

These advanced Q&A chatbots leverage Retrieval-Augmented Generation (RAG), a technique that combines the strengths of both retrieval systems and generative models. While RAG can be implemented with various frameworks, it's particularly effective when used in prototypes. However, for applications that are intended for productization, RAG frameworks like LangChain and Llama take performance to the next level, ensuring that your application is not only functional but optimized for real-world use.

In this tutorial, tells how to build a straightforward Q&A application that can handle unstructured text data using the LangChain framework.

?What is RAG?

Retrieval-Augmented Generation (RAG)? is a powerful approach that combines the strengths of retrieval systems with generative models to produce more accurate, context-aware, and informative responses. It is particularly useful in scenarios where the quality of generated content needs to be closely tied to specific and relevant information sources.

Retrieval-Augmented Generation (RAG):

• Retrieval: In this stage, relevant documents, passages, or pieces of information are retrieved from a database or knowledge base based on the input query. This retrieval process is often powered by embedding-based similarity search, where both the query and the documents are represented as vectors in a high-dimensional space.
• Generation: Once the most relevant information is retrieved, a generative model (like GPT or any other transformer-based model) is used to produce a response or generate text. The generative model leverages the retrieved documents to produce more accurate, informative, and contextually relevant output.

How RAG Works

• Input: The process starts with a user input, which could be a question or a prompt.
• Retrieval Stage: The system searches a corpus of text or a database to find documents or passages that are most relevant to the input.
• Augmentation: The retrieved content is then passed to a generative model. This model uses the additional context provided by the retrieved documents to generate a response.
• Output: The output is a text response that is typically more informed and accurate than what could be generated by a standalone generative model.

Advantages of RAG

• Enhanced Accuracy: By grounding the generation process in real data, RAG can produce more factually accurate and relevant outputs.
• Scalability: RAG systems can handle large volumes of data, making them suitable for applications that require access to vast amounts of information.
• Flexibility: It can be applied to various domains, from simple question answering to complex content generation tasks.

??In case using google colab, need to install ,

pip install langchain-chroma
pip install langchain langchain_community langchain_chroma
pip install -qU langchain_community pypdf

from langchain_chroma import Chroma #for vector db
from langchain_community.document_loaders import PyPDFLoader #for uploading pdf file
from langchain_core.output_parsers import StrOutputParser #for parsing the output
from langchain_core.runnables import RunnablePassthrough #for optimal chain
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter #splitting the file        

?import os
import openai
import sys
sys.path.append('../..')
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
openai.api_key  = os.environ['OPENAI_API_KEY']

#For google colab, we first need to upload file in the colab
from google.colab import files
uploaded = files.upload()
# then use PDF Loader from Langchain
from langchain_community.document_loaders import PyPDFLoader
#file="myfile.pdf"
loader = PyPDFLoader("myfile.pdf")
docs=loader.load()
print(docs[0].metadata)        

?Splitting the data into chunks, chunk size and chunk overlap needs to be optimized for accurate answers. Accuracy of the answers also depends on the text splitter also, here we are using recursive character text splitter

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
#creating the embeddings and loading into Chroma 
db = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())        

Retrieve and generate using the relevant snippets ?

retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 5}))
def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

#Choosing a model: We can choose chat model that takes in a sequence of messages and returns a message whereas a text-in-text-#out LLM Takes in a string and returns a string.
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o-mini")

from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages([
  ("""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:"""),
])

#We’ll use the LCEL Runnable protocol to define the chain, 
rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)
rag_chain.invoke("My question goes here")        

The process of building a simple file upload functionality with Lang Chain a file upload evolves into a powerful tool for extracting meaningful insights. This framework not only simplifies the technical aspects but also enhances the intelligence of the application.