LangChain Tutorial – How to Build a Custom-Knowledge Chatbot

In recent months, we‘ve witnessed an explosion of AI-powered applications that are revolutionizing how we interact with information. From AI writing assistants that can generate articles and social media posts, to chatbots that can answer questions by referencing entire databases of knowledge – the possibilities seem endless.

Many of these game-changing tools are being built using LangChain – an open-source framework that allows developers to combine large language models (LLMs) like OpenAI‘s GPT with external data sources. By connecting powerful language models to up-to-date, domain-specific knowledge, LangChain enables the creation of AI agents that can engage in knowledgeable conversation and complete complex tasks.

In this in-depth tutorial, we‘ll walk through how to use LangChain to build your own custom knowledge chatbot. Whether you‘re looking to create a helpful AI study buddy, an intelligent customer support agent for your business, or an AI assistant to aid with research and analysis – the concepts covered here will give you the foundation to turn your ideas into reality. Let‘s get started!

Overview of LangChain

At its core, LangChain provides a set of modular building blocks that can be composed together to create AI applications. The key components are:

Models: LangChain provides wrappers around various LLMs, making it easy to integrate models like GPT-3.5-turbo into your application. The LLM forms the "brain" of your AI agent.

Prompts: Prompts are the instructions you give to steer the LLM‘s outputs. LangChain provides utilities for constructing and managing prompts.

Chains: Chains allow you to combine multiple prompts, models, and other components into a pipeline. They provide a way to create multi-step workflows.

Embeddings and Vector Stores: Embeddings are numerical representations of text that capture its semantic meaning. LangChain uses embeddings to create vector stores – a way to efficiently store and search over large corpora of textual data. This allows your AI to access relevant information from external sources.

Agents: An agent is an autonomous AI system that uses the other components to take in user input, break down the task, and take a sequence of steps to provide a final result. LangChain provides tools for constructing agents that can engage in dialogue, access external APIs, and more.

By composing these building blocks in various ways, LangChain provides a flexible and extensible framework for creating AI applications powered by LLMs.

Setting Up Your Environment

Before we jump into building our custom knowledge chatbot, let‘s get our environment set up. We‘ll be using Python, so make sure you have version 3.8+ installed.

First, let‘s create a new virtual environment and activate it:

python -m venv langchain-env 
source langchain-env/bin/activate  # for unix/macOS
langchain-envScriptsactivate  # for windows

Next, install the required dependencies:

pip install langchain openai pinecone-client python-dotenv

We‘ll be using OpenAI‘s language models, so you‘ll need an API key. You‘ll also need a free account with Pinecone to use their vector database. Be sure to store your API keys in a .env file:

OPENAI_API_KEY=your_openai_key_here
PINECONE_API_KEY=your_pinecone_key_here  
PINECONE_ENV=your_pinecone_env_here

With that, we‘re ready to start building!

Creating a Custom Knowledge Base

The first step in building our chatbot is to create a knowledge base that it can draw upon when answering questions. LangChain makes it easy to take a set of documents, chunk them into smaller pieces, create embeddings of those chunks, and store them in a vector database for efficient retrieval.

Let‘s say we have a collection of text files that contain information about a particular topic – for example, a set of Wikipedia articles about machine learning. We can load these into a TextSplitter to automatically break them into smaller chunks:

from langchain.text_splitter import CharacterTextSplitter

with open("machine_learning.txt") as f:
    ml_text = f.read()

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(ml_text)

Next, we convert each of these text chunks into a vector embedding. We‘ll use OpenAI‘s text-embedding-ada-002 model:

from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
embedded_texts = [embeddings.embed_query(text) for text in texts]

Finally, we‘ll store these embeddings in a Pinecone index:

import pinecone

pinecone.init(
    api_key=os.environ["PINECONE_API_KEY"],
    environment=os.environ["PINECONE_ENV"]
)

index_name = "langchain-ml-kb"
pinecone.create_index(index_name, dimension=len(embedded_texts[0]))

index = pinecone.Index(index_name)
for i, embedding in enumerate(embedded_texts):
    index.upsert((str(i), embedding))

We now have a searchable knowledge base that we can query to find the most relevant chunks of text for a given input! This will allow our chatbot to access detailed information pertaining to machine learning topics.

Retrieving Relevant Knowledge

With our knowledge base set up, we can now work on retrieving the most relevant pieces of information for a given user query. LangChain provides a VectorDBQA chain that does this for us.

from langchain.chains import VectorDBQA
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI

prompt_template = """Use the following context to answer the question at the end. If you don‘t know the answer, say that you don‘t know, don‘t try to make up an answer.

{context}

Question: {question}
Answer:"""
prompt = PromptTemplate(
    template=prompt_template,
    input_variables=["context", "question"]  
)

llm = OpenAI(temperature=0)
chain = VectorDBQA(combine_documents_chain=llm, vectorstore=index, k=4, prompt=prompt)

Here‘s what‘s happening:

1. We define a prompt template that our chatbot will use to format its responses. The {context} variable will be filled in with the relevant chunks of text retrieved from our knowledge base, and {question} will contain the user‘s input query.

2. We create an OpenAI language model that will be doing the actual text generation. We set temperature=0 so it generates predictable, factual responses.

3. We create a VectorDBQA chain, passing in the LLM, the vector index we created earlier, and the prompt template. The k parameter specifies how many chunks of context to retrieve.

Now we can pass a user question to the chain and it will output a knowledgeable response!

query = "What is a neural network?"
result = chain({"question": query})
print(result["result"])

Building a Conversational Agent

So far we‘ve seen how to retrieve relevant knowledge given a user query, but we haven‘t yet implemented the interactive, multi-turn dialogue component. This is where LangChain‘s agent framework comes in.

An agent uses an LLM to break down a user input into discrete steps, which are then executed in sequence to produce a final output. To build a conversational agent, we‘ll use a ConversationalRetrievalChain. This allows the agent to have knowledge of previous dialogue turns in addition to retrieved documents.

from langchain.chains import ConversationalRetrievalChain

qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), chain.vectorstore, return_source_documents=True)
chat_history = []

while True:
    query = input("Human: ")
    result = qa({"question": query, "chat_history": chat_history})

    print(f"Assistant: {result[‘answer‘]}")
    print(f"Relevant sources:")
    for source in result["source_documents"]:
        print(source.metadata["source"])

    chat_history.append((query, result["answer"]))

Here‘s a breakdown:

1. We create a ConversationalRetrievalChain using our existing vector store and LLM. Setting return_source_documents=True allows us to inspect which specific documents were used to generate the response.

2. We initialize an empty chat_history list to keep track of previous turns of dialogue.

3. We start a loop that prompts the user for input, passes the input and chat history to the chain, and prints out the AI‘s response along with the source documents used.

4. We append the user‘s input and the AI‘s response to the chat_history so that subsequent calls will have that context.

That‘s it! You now have a fully functioning chatbot that can engage in knowledgeable, multi-turn conversation about the topics in your knowledge base. Feel free to chat with it and see how it performs.

Next Steps

We‘ve only scratched the surface of what you can build with LangChain. Here are some ideas for taking your chatbot to the next level:

• Experiment with different prompts, chains, and agents to create chatbots with different capabilities (e.g. an agent that can browse the web and summarize articles in response to a query)
• Fine-tune your own LLM on a curated dataset for even more domain-specific knowledge
• Integrate additional tools into your agent, such as a calculator for performing quantitative reasoning
• Build a user-friendly web interface for your chatbot using a framework like Gradio or Streamlit

As you can see, the possibilities are endless. LangChain provides the building blocks – now it‘s up to you to create something amazing!

Conclusion

In this tutorial, we walked through the fundamentals of building a custom knowledge chatbot using LangChain. We covered key concepts such as:

• Chunking and embedding documents to create a searchable knowledge base
• Retrieving relevant documents in response to a user query
• Generating knowledgeable responses using an LLM and prompt templates
• Implementing a conversational agent capable of multi-turn dialogue

Armed with this foundation, you‘re well on your way to creating your own powerful AI applications. The exciting field of language models and generative AI is evolving at an incredible pace, and tools like LangChain are making it easier than ever to get started.

If you‘re eager to learn more, I encourage you to dive into the LangChain documentation, join the vibrant community of developers building with this tool, and start experimenting with your own ideas. The future of AI is wide open – who knows what incredible applications you‘ll come up with!

As always, feel free to connect with me on Twitter @yourtwitterhandle or subscribe to my AI newsletter for more tutorials, insights, and explorations in the world of artificial intelligence. Happy building!