Build RAG Chatbot with LangChain, LangChain vector store, Databricks Llama 3.1, and OpenAI text-embedding-3-large

Introduction to RAG

Retrieval-Augmented Generation (RAG) is a game-changer for GenAI applications, especially in conversational AI. It combines the power of pre-trained large language models (LLMs) like OpenAI’s GPT with external knowledge sources stored in vector databases such as Milvus and Zilliz Cloud, allowing for more accurate, contextually relevant, and up-to-date response generation. A RAG pipeline usually consists of four basic components: a vector database, an embedding model, an LLM, and a framework.

Key Components We'll Use for This RAG Chatbot

This tutorial shows you how to build a simple RAG chatbot in Python using the following components:

LangChain: An open-source framework that helps you orchestrate the interaction between LLMs, vector stores, embedding models, etc, making it easier to integrate a RAG pipeline.
LangChain in-memory vector store: an in-memory, ephemeral vector store that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance. It is intended for demos and does not yet support ids or deletion. (If you want a much more scalable solution for your apps or even enterprise projects, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvusand offers a free tier supporting up to 1 million vectors.)
Databricks Llama 3.1: This advanced generative model from Databricks focuses on data-centric AI and collaborative analytics. It excels in scalable machine learning tasks, providing robust insights and predictions from large datasets. Ideal for organizations looking to leverage data for automated reporting, interactive data exploration, and enhanced decision-making processes.
text-embedding-3-large: OpenAI's text embedding model, generating embeddings with 1536 dimensions, designed for tasks like semantic search and similarity matching.

By the end of this tutorial, you’ll have a functional chatbot capable of answering questions based on a custom knowledge base.

Note: Since we may use proprietary models in our tutorials, make sure you have the required API key beforehand.

Step 1: Install and Set Up LangChain

%pip install --quiet --upgrade langchain-text-splitters langchain-community langgraph

Step 2: Install and Set Up Databricks Llama 3.1

pip install -qU "databricks-langchain"

import getpass
import os

if not os.environ.get("DATABRICKS_TOKEN"):
  os.environ["DATABRICKS_TOKEN"] = getpass.getpass("Enter API key for Databricks: ")

from databricks_langchain import ChatDatabricks

os.environ["DATABRICKS_HOST"] = "https://example.staging.cloud.databricks.com/serving-endpoints"

llm = ChatDatabricks(endpoint="databricks-meta-llama-3-1-70b-instruct")

Step 3: Install and Set Up OpenAI text-embedding-3-large

pip install -qU langchain-openai

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

Step 4: Install and Set Up LangChain vector store

pip install -qU langchain-core

from langchain_core.vectorstores import InMemoryVectorStore

vector_store = InMemoryVectorStore(embeddings)

Step 5: Build a RAG Chatbot

Now that you’ve set up all components, let’s start to build a simple chatbot. We’ll use the Milvus introduction doc as a private knowledge base. You can replace it with your own dataset to customize your RAG chatbot.

import bs4
from langchain import hub
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langgraph.graph import START, StateGraph
from typing_extensions import List, TypedDict

# Load and chunk contents of the blog
loader = WebBaseLoader(
    web_paths=("https://milvus.io/docs/overview.md",),
    bs_kwargs=dict(
        parse_only=bs4.SoupStrainer(
            class_=("doc-style doc-post-content")
        )
    ),
)

docs = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
all_splits = text_splitter.split_documents(docs)

# Index chunks
_ = vector_store.add_documents(documents=all_splits)

# Define prompt for question-answering
prompt = hub.pull("rlm/rag-prompt")


# Define state for application
class State(TypedDict):
    question: str
    context: List[Document]
    answer: str


# Define application steps
def retrieve(state: State):
    retrieved_docs = vector_store.similarity_search(state["question"])
    return {"context": retrieved_docs}


def generate(state: State):
    docs_content = "\n\n".join(doc.page_content for doc in state["context"])
    messages = prompt.invoke({"question": state["question"], "context": docs_content})
    response = llm.invoke(messages)
    return {"answer": response.content}


# Compile application and test
graph_builder = StateGraph(State).add_sequence([retrieve, generate])
graph_builder.add_edge(START, "retrieve")
graph = graph_builder.compile()

Test the Chatbot

Yeah! You've built your own chatbot. Let's ask the chatbot a question.

response = graph.invoke({"question": "What data types does Milvus support?"})
print(response["answer"])

Example Output

Milvus supports various data types including sparse vectors, binary vectors, JSON, and arrays. Additionally, it handles common numerical and character types, making it versatile for different data modeling needs. This allows users to manage unstructured or multi-modal data efficiently.

Optimization Tips

As you build your RAG system, optimization is key to ensuring peak performance and efficiency. While setting up the components is an essential first step, fine-tuning each one will help you create a solution that works even better and scales seamlessly. In this section, we’ll share some practical tips for optimizing all these components, giving you the edge to build smarter, faster, and more responsive RAG applications.

LangChain optimization tips

To optimize LangChain, focus on minimizing redundant operations in your workflow by structuring your chains and agents efficiently. Use caching to avoid repeated computations, speeding up your system, and experiment with modular design to ensure that components like models or databases can be easily swapped out. This will provide both flexibility and efficiency, allowing you to quickly scale your system without unnecessary delays or complications.

LangChain in-memory vector store optimization tips

LangChain in-memory vector store is just an ephemeral vector store that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. It has very limited features and is only intended for demos. If you plan to build a functional or even production-level solution, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvus and offers a free tier supporting up to 1 million vectors.)

Databricks Llama 3.1 optimization tips

Databricks Llama 3.1 is designed for scalable and high-performance RAG applications, making it crucial to optimize retrieval and processing efficiency. Leverage Databricks' distributed computing capabilities to parallelize retrieval and embedding computations, reducing latency for large datasets. Implement hybrid search (combining vector and keyword search) to enhance retrieval relevance. Use optimized prompt templates to minimize token usage while maximizing response quality. Fine-tune temperature (0.1–0.3) for factual consistency and adjust top-k/top-p for response control. Cache frequently queried results to reduce redundant computations, improving both cost and performance. If dealing with large-scale queries, utilize Databricks’ auto-scaling to dynamically allocate resources and avoid bottlenecks. Implement incremental indexing for real-time updates to your vector store, ensuring retrieval accuracy remains high over time.

OpenAI text-embedding-3-large optimization tips

OpenAI text-embedding-3-large is a high-capacity embedding model designed for precise and rich semantic representation, making it ideal for RAG systems with complex document retrieval needs. Optimize efficiency by preprocessing and normalizing text to reduce noise before embedding generation. Use dimensionality reduction techniques, such as PCA, if storage or computational limits become a concern. When querying, leverage HNSW-based approximate nearest neighbor (ANN) search to accelerate retrieval while maintaining accuracy. Batch process embedding requests to reduce latency and optimize resource utilization. Implement re-ranking models to further refine top results based on query context. Regularly update the embedding store with newly ingested data to maintain retrieval relevance.

By implementing these tips across your components, you'll be able to enhance the performance and functionality of your RAG system, ensuring it’s optimized for both speed and accuracy. Keep testing, iterating, and refining your setup to stay ahead in the ever-evolving world of AI development.

RAG Cost Calculator: A Free Tool to Calculate Your Cost in Seconds

Estimating the cost of a Retrieval-Augmented Generation (RAG) pipeline involves analyzing expenses across vector storage, compute resources, and API usage. Key cost drivers include vector database queries, embedding generation, and LLM inference.

RAG Cost Calculator is a free tool that quickly estimates the cost of building a RAG pipeline, including chunking, embedding, vector storage/search, and LLM generation. It also helps you identify cost-saving opportunities and achieve up to 10x cost reduction on vector databases with the serverless option.

Calculate your RAG cost now.

Calculate your RAG cost

What Have You Learned?

Wow, you’ve made it to the end of this tutorial, and what an incredible journey it has been! You've successfully integrated four powerhouse components—LangChain, the LangChain vector store, the Databricks Llama 3.1, and the OpenAI text-embedding-3-large model—creating a robust Retrieval-Augmented Generation (RAG) system that opens doors to endless possibilities.

In this adventure, you’ve seen how LangChain beautifully weaves all aspects of the system together, ensuring smooth communication and functionality. The LangChain vector store equips you with ultra-fast search capabilities, enabling you to retrieve information in a heartbeat. Meanwhile, the Databricks Llama 3.1 adds an extra layer of conversational intelligence, allowing your application to understand and engage with users in a natural, human-like manner. And let’s not forget the power of the OpenAI text-embedding-3-large model, which generates rich semantic representations, offering context that enhances understanding.

Throughout the tutorial, we even included optimization tips to fine-tune your system for performance and efficiency, plus a handy cost calculator to keep your project budget-friendly! As you reflect on what you've accomplished, remember that this is just the beginning. Every step you take in building, optimizing, and innovating your RAG applications can lead to exciting breakthroughs in your projects.

So, are you ready to dive in? Get your hands dirty with experimentation, unleash your creativity, and transform your ideas into reality! The world of RAG applications is waiting for you, and there’s no limit to the impact you can make. Go ahead—start building something amazing today!

Further Resources

🌟 In addition to this RAG tutorial, unleash your full potential with these incredible resources to level up your RAG skills.

How to Build a Multimodal RAG | Documentation
How to Enhance the Performance of Your RAG Pipeline
Graph RAG with Milvus | Documentation
How to Evaluate RAG Applications - Zilliz Learn
Generative AI Resource Hub | Zilliz

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