Implement #RAG (Retrieval Augmented Generation) with #langchain #chroma #OpenAI #MicrosoftFabric

In this video, we’re taking our RAG architecture to intergalactic levels! Building on our previous video (https://youtu.be/oCHinlZRsLU), we’re removing dependencies on PaaS components like Document Intelligence and Azure AI Search. Instead, we’re leveraging LangChain to process PDF documents and using the open-source vector database, Chroma DB, as our vector store.

this is the relevant code from the notebook. First let’s install our libraries.

%pip install langchain
%pip install langchain-core
%pip install langchain-experimental
%pip install langchain_openai
%pip install langchain-chroma
%pip install langchainhub
%pip install PyPDF2

now we set up our parameters:

import os, openai#, langchain, uuid
from synapse.ml.core.platform import find_secret


openai_key = find_secret(secret_name="openaikey", keyvault="yourservice-keys")
openai_service_name = "yourservice"
openai_endpoint = "https://yourservice.openai.azure.com/"
openai_deployment_for_embeddings = "text-embedding-ada-002"
openai_deployment_for_query = "gpt-35-turbo"
openai_deployment_for_completions = "davinci-002" #"davinci-002"
openai_api_type = "azure"
openai_api_version = "2023-12-01-preview"


os.environ["OPENAI_API_TYPE"] = openai_api_type
os.environ["OPENAI_API_VERSION"] = openai_api_version
#os.environ["OPENAI_API_BASE"] = """"
os.environ["OPENAI_API_KEY"] = openai_key
os.environ["AZURE_OPENAI_ENDPOINT"] = openai_endpoint

base_path = "/lakehouse/default/Files/prohabits/"

now we have to delete the OPEN_API_BASE environment variable or our models won’t instantiate:

del os.environ['OPENAI_API_BASE'] 

now we import the stuff that we need:

import bs4
from langchain import hub
from langchain_chroma import Chroma
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import OpenAIEmbeddings
from langchain.embeddings import AzureOpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.llms import AzureOpenAI, OpenAI
from langchain_openai import AzureOpenAIEmbeddings

now we read our PDF files:

from PyPDF2 import PdfReader
from langchain.document_loaders import PyPDFLoader
from langchain.schema import Document

folder_path = base_path

def load_pdfs_from_folder(folder_path):
    documents = []
    for filename in os.listdir(folder_path):
        if filename.endswith('.pdf'):
            file_path = os.path.join(folder_path, filename)
            reader = PdfReader(file_path)
            text = ""
            for page in reader.pages:
                text += page.extract_text()
            document = Document(page_content=text, metadata={"document_name": filename})
            documents.append(document)
    return documents

# Load documents
documents = load_pdfs_from_folder(folder_path)

# Print the content of each document
for doc in documents:
    print(f"Document Name: {doc.metadata['document_name']}")
    #print(doc.page_content)
    print("\n---\n")

then we chunk our PDFs an store the chunks in the vector store, open source vector database - Chroma:

embeddings = AzureOpenAIEmbeddings() text_splitter = RecursiveCharacterTextSplitter(chunk_size=4000, chunk_overlap=200) splits = text_splitter.split_documents(documents) vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)

Let’s test if it works:

query = "what is a prohabits?"
answers = vectorstore.similarity_search(query)
display(answers[0].page_content)

and now it all comes together!

from langchain_openai import AzureChatOpenAI
from langchain.schema import HumanMessage
import openai

llm = AzureChatOpenAI(azure_deployment=openai_deployment_for_query)

retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")

message = HumanMessage(
    content="Tell me about solar eclipse."
)
result = llm.invoke([message])

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


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

rag_chain.invoke("What is Prohabits?")