8/24/2024

Creating a Local Knowledge Base with LangChain LLMs

Ever wanted to harness the power of Large Language Models (LLMs) such as GPT while managing your own data? Well, you’re in for a treat! In this blog, we’re diving deep into how to create a local knowledge base using LangChain alongside cutting-edge technologies like Chroma and GPT4All. It’s going to be a FUN ride, so buckle up!

What is a Knowledge Base?

A Knowledge Base is a centralized repository of information. It acts as a go-to source for individuals looking to gather expertise on a topic, whether it’s for customer service queries or detailed documentation for products. By integrating a knowledge base with an LLM, you can produce intelligent and contextually aware conversations that improve user engagement significantly.

Why Local Knowledge Base?

Utilizing a local knowledge base comes with some fantastic benefits:

Privacy: Your data stays within your infrastructure, which is crucial for companies handling sensitive information.
Customization: Tailor your responses specific to your business needs and user interactions.
Cost Efficiency: You cut down on potential API call costs associated with third-party services.

The Setup: Tools You Need

Before we start coding, let’s gather the necessary tools:

LangChain: An open-source framework that simplifies the process of building applications using LLMs.
GPT4All: A local model that runs efficiently on devices with limited resources, allowing you to leverage its functionalities without hitting the cloud.
Chroma: A vector database that allows you to manage embeddings effectively, ensuring consistency and performance.

Step 1: Environment Setup

Let’s start by setting up our Python environment.

1
pip install langchain chromadb gpt4all pytesseract

If you are using Linux, make sure to install additional packages to handle PDF conversions effectively:

1
2
3
4
5
6
7
sudo apt update
sudo apt install tesseract-ocr libtesseract-dev
dsudo apt-get install \  
libleptonica-dev \  
tesseract-ocr-dev \  
python3-pil \  
tesseract-ocr-eng

Step 2: Program Structure

Let’s create a Python file called

knowledgebase.py

. Here’s the structure we'll follow:

Loading PDF documents
Splitting text into manageable chunks
Creating embeddings
Setting up the vector database
Building a retriever
Implementing a query mechanism to interact with LLMs

Step 3: Load PDF Documents

First up is loading our documents. We’ll use LangChain’s

DirectoryLoader

to load our PDFs into our project.

1
2
3
4
5
6
7
8
9
from langchain.document_loaders import DirectoryLoader
import os

def load_pdfs(pdf_folder):
    loader = DirectoryLoader(pdf_folder)
    docs = loader.load()
    return docs

documents = load_pdfs("path/to/source_documents/")

Make sure to replace

path/to/source_documents/

with the actual path where your PDFs are stored.

Step 4: Split Documents into Chunks

Large documents can be problematic for LLMs, so we need to chunk our texts into smaller pieces. Let’s use

RecursiveCharacterTextSplitter

for this task:

1
2
3
4
5
6
7
8
from langchain.text_splitter import RecursiveCharacterTextSplitter

def split_documents(docs):
    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    chunked_docs = splitter.split_documents(docs)
    return chunked_docs

chunked_documents = split_documents(documents)

Step 5: Create Embeddings

Now that we have our chunked documents, it’s time to create embeddings. Here, we’ll utilize the

GPT4AllEmbeddings

from LangChain:

1
2
3
from langchain.embeddings import GPT4AllEmbeddings

embeddings = GPT4AllEmbeddings()

Step 6: Setting Up the Vector Database with Chroma

Next, let’s set up our vector database to store these embeddings. We’ll use Chroma for this:

1
2
3
4
5
6
7
from langchain.vectorstores import Chroma

CHROMA_DB_DIRECTORY = 'db'

vector_db = Chroma(persist_directory=CHROMA_DB_DIRECTORY, embedding_function=embeddings)
vector_db.add_documents(chunked_documents)
vector_db.persist()

Step 7: Build the Retriever

Once our embeddings are stored in the vector database, we'll create a retriever that will help us fetch relevant documents based on user queries:

1
2
3
4
5
6
def get_retriever():
    if not os.path.isdir(CHROMA_DB_DIRECTORY):
        raise NotADirectoryError("Please load the vector database first.")
    return vector_db.as_retriever()

retriever = get_retriever()

Step 8: Query Mechanism

Finally, let's set up a simple query mechanism where we can input questions, and our LLM will provide answers using the relevant knowledge from our vector database:

1
2
3
4
5
6
7
8
9
10
11
12
from langchain.chains import RetrievalQA
from langchain.llms import GPT4All

llm = GPT4All(model='ggml-gpt4all-j-v1.3-groovy.bin', backend='llama')
qa_chain = RetrievalQA.from_chain_type(llm=llm, chain_type='stuff', retriever=retriever)

while True:
    query = input("What's on your mind?")
    if query.lower() == 'exit':
        break
    result = qa_chain(query)
    print(f"Answer: ", result['result'])

Benefits of Building Your Local Knowledge Base

Immediate Access: You'll have quick access to your information without needing to make API calls.
Security: Your data remains secure within your environment, keeping your sensitive information safe.
Customization: Tailor responses based on the specific needs of your audience or project.

Arsturn: Enhance Your Engagement & Conversions

Now that you have your local knowledge base set up, don't forget about the engaging power of chatbots! Arsturn offers a seamless way to create AI chatbots that can interact with your audience, providing instant responses based on your knowledge base. This is a game changer for boosting audience engagement and enhancing conversion rates. Check out Arsturn to customize your chatbot without needing to code.

Conclusion

Creating a local knowledge base with LangChain LLMs like GPT4All and Chroma doesn’t just open new possibilities for your applications but weaves in a tapestry of customization and engagement. By following the steps laid out in this guide, you’ll be well on your way to leveraging LLMs in ways that work for you!

Feel free to drop your questions below or share your experiences about building your own knowledge bases!