Build Your Own AI Knowledge Base with LangChain and Pinecone

Do you want to have your own AI assistant that can answer all your questions? With LangChain and Pinecone, you can easily achieve this! In this article, we will discuss how to use these two tools to build a personal AI knowledge base, making your AI assistant smarter.

What is LangChain?

LangChain is a very useful Python library designed for developing applications based on large language models. It provides a bunch of ready-made components that allow us to easily integrate large language models with various data sources and APIs.

With LangChain, you can quickly build various flashy AI applications, such as chatbots, question-answering systems, etc.

Here’s a simple example:

from langchain import PromptTemplate, LLMChain
from langchain.llms import OpenAI

# Create an instance of the OpenAI language model
llm = OpenAI(temperature=0.9)

# Define a prompt template
prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?")

# Create LLMChain
chain = LLMChain(llm=llm, prompt=prompt)

# Use chain to generate a company name
print(chain.run("colorful socks"))

This piece of code can help the AI come up with a company name, isn’t that amazing?

What is Pinecone?

Pinecone is a vector database specifically designed to store and retrieve vector data.

In AI applications, we often need to convert text into vectors (which are just strings of numbers) and then perform similarity searches. Pinecone does just that; it allows us to quickly find the most similar vectors.

Friendly reminder: Before using Pinecone, you need to register an account and then create an index.

Steps to Build an AI Knowledge Base

Now that we know what these two tools are, let’s see how to use them to build an AI knowledge base.

1. Prepare Data

First, you need to have some data for the AI to learn from. It can be PDF documents, web pages, txt files—anything that is text will do.

1. Process Text

Cut the prepared text into smaller chunks, making it easier to process.

LangChain provides ready-made tools:

from langchain.text_splitter import CharacterTextSplitter

with open('your_text_file.txt') as f:
    raw_text = f.read()

text_splitter = CharacterTextSplitter(        
    separator = "\n\n",
    chunk_size = 1000,
    chunk_overlap  = 200,
    length_function = len,
)

texts = text_splitter.split_text(raw_text)

1. Generate Vector Embeddings

Next, we need to convert the text into vectors.

Here we use OpenAI’s embeddings model:

from langchain.embeddings.openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

1. Store Vectors in Pinecone

The generated vectors need to be stored in Pinecone:

import pinecone 
from langchain.vectorstores import Pinecone

# Initialize Pinecone
pinecone.init(
    api_key="your API key",  
    environment="your environment"  
)

index_name = "your index name"

# Create vector storage
docsearch = Pinecone.from_texts([t.page_content for t in texts], embeddings, index_name=index_name)

1. Create a Question-Answer Chain

Finally, let’s create a question-answer chain using LangChain:

from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI

chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")

1. Start Q&A

Now your AI knowledge base is set up! You can start asking questions:

query = "Your question here"
docs = docsearch.similarity_search(query)
print(chain.run(input_documents=docs, question=query))

With this, you have an AI assistant based on your own data! It can answer questions based on the materials you provide, and the quality of the answers will be better than that of a regular ChatGPT because it has specifically learned your data.

Building an AI knowledge base may seem complex, but when broken down, it’s not that difficult. The key is to understand what each step is doing and then gradually adjust it to better meet your needs.

For example, you can try different text splitting methods or use different embeddings models.

This AI knowledge base has many areas for optimization. You can add a document update feature to keep the AI’s knowledge up to date. You can also design a more user-friendly interface to make it easier for ordinary people to use. You could even deploy it to a cloud server to turn it into an online AI assistant.

In short, building an AI knowledge base with LangChain and Pinecone is just the beginning. Based on this framework, you can develop various interesting AI applications. The key is to experiment and think creatively; I believe you can create something amazing!