Build RAG Chatbot with LangChain, LangChain vector store, Mistral AI Mistral 7B, and Azure text-embedding-ada-002

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.)
Mistral AI's Mistral 7B: A highly efficient and powerful language model with 7 billion parameters. It is designed to offer robust performance for a wide range of natural language processing tasks, delivering high-quality responses while maintaining a balance between speed and computational resource usage. Its architecture is optimized for both flexibility and scalability in AI applications.
Azure text-embedding-ada-002: This model specializes in generating high-quality text embeddings, enabling effective semantic search and natural language understanding. Its strengths lie in producing contextually rich representations that enhance tasks like text classification, clustering, and recommendation systems, making it ideal for applications in content discovery and personalized user experiences.

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 Mistral AI Mistral 7B

pip install -qU "langchain[mistralai]"

import getpass
import os

if not os.environ.get("MISTRAL_API_KEY"):
  os.environ["MISTRAL_API_KEY"] = getpass.getpass("Enter API key for Mistral AI: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("open-mistral-7b", model_provider="mistralai")

Step 3: Install and Set Up Azure text-embedding-ada-002

pip install -qU langchain-openai

import getpass
import os

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

from langchain_openai import AzureOpenAIEmbeddings

embeddings = AzureOpenAIEmbeddings(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

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.)

Mistral AI Mistral 7B optimization tips

Mistral 7B is a dense transformer-based model optimized for efficiency, making it a strong choice for RAG applications with constrained resources. Improve retrieval by using high-quality embeddings and reranking strategies to ensure only the most relevant documents are included in the context window. Optimize token usage by structuring input prompts concisely, eliminating redundant details while preserving key information. Use temperature (0.1–0.3) for consistent factual accuracy and adjust sampling techniques based on the required response diversity. Deploy Mistral 7B in a scalable infrastructure by leveraging quantization techniques (such as 4-bit or 8-bit precision) to improve inference speed without significantly impacting accuracy. Implement batch processing for handling multiple queries efficiently, reducing overall computational load. When working in real-time applications, consider response caching to minimize redundant API calls.

Azure text-embedding-ada-002 Optimization Tips

To optimize the Azure text-embedding-ada-002 component in your Retrieval-Augmented Generation (RAG) setup, ensure that you batch your requests effectively to reduce latency and maximize throughput; try grouping up to 1000 tokens per request if possible. Preprocess your input data to remove unnecessary noise and stop words, which can improve embedding quality. Adjust the model’s parameters to suit your specific use case, focusing on the length of input sequences and maintaining token limits. Utilize caching mechanisms for frequently queried inputs to speed up retrieval, and regularly fine-tune your embeddings with domain-specific data to enhance relevance. Finally, monitor and analyze performance metrics continuously to make iterative adjustments that cater to any shifts in data patterns or retrieval needs.

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?

By diving into this tutorial, you’ve unlocked the power of integrating cutting-edge tools to build a robust RAG system! You learned how LangChain acts as the glue, orchestrating workflows between components like a pro. The LangChain Vector Store stepped in as your trusty knowledge vault, efficiently storing and retrieving context using embeddings generated by Azure’s text-embedding-ada-002—transforming text into rich numerical representations that capture meaning. Then came Mistral 7B, the LLM powerhouse, which synthesized that retrieved context into coherent, human-like answers, proving that open-source models can shine in generative tasks. Together, these pieces formed a seamless pipeline: ingest data, embed it, store it smartly, retrieve what’s relevant, and generate responses that feel almost magical. Along the way, you picked up optimization gems—like tuning chunk sizes and tweaking retrieval parameters—to balance speed and accuracy, plus a nifty free RAG cost calculator to budget your experiments without surprises.

Now, armed with this toolkit, you’re ready to tackle real-world challenges! Whether you’re building a chatbot, a research assistant, or a custom knowledge hub, you’ve seen how flexible and scalable RAG can be. The best part? This is just the beginning. Experiment with different models, fine-tune your embeddings, or layer in domain-specific data—your creativity is the limit. So fire up your IDE, tweak those parameters, and watch your ideas come to life. The future of intelligent applications is in your hands, and you’ve got everything you need to build it. Let’s go make something amazing—the world’s waiting for your next breakthrough! 🚀

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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