Build RAG Chatbot with LangChain, LangChain vector store, Mistral AI Codestral Mamba, and Ollama granite-embedding

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 Codestral Mamba: A high-performance coding assistant designed to enhance software development efficiency, Codestral Mamba excels in generating and debugging code across multiple programming languages. With its advanced understanding of programming contexts and common libraries, it is ideal for developers seeking rapid prototyping, code optimization, and refactoring support.
Ollama Granite-Embedding: This AI model specializes in generating high-quality embeddings for various data types, enhancing search and recommendation systems. Its strength lies in its ability to capture complex relationships within data, making it ideal for applications like semantic search, natural language processing, or personalization in digital platforms.

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 Codestral Mamba

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-codestral-mamba", model_provider="mistralai")

Step 3: Install and Set Up Ollama granite-embedding

pip install -qU langchain-ollama

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(model="granite-embedding")

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 Codestral Mamba optimization tips

Codestral Mamba is optimized for code generation and completion, making it ideal for RAG applications that involve structured programming queries. Improve retrieval quality by using embeddings trained on code datasets to ensure retrieved context aligns well with the programming language and task. To enhance response accuracy, ensure input prompts are formatted with clear specifications, including function definitions, docstrings, and comments. Adjust temperature values dynamically—lower values (0.1–0.2) for deterministic code generation, higher values (0.3–0.5) for exploratory suggestions. Use caching for common programming patterns and frequently queried snippets to reduce latency. If deploying in an IDE or interactive coding environment, enable streaming to provide real-time feedback and suggestions. Leverage parallel inference techniques when handling multiple simultaneous code queries to optimize performance.

Ollama granite-embedding optimization tips

To optimize the Ollama granite-embedding component in your Retrieval-Augmented Generation (RAG) setup, ensure that you preprocess your text data to remove noise and irrelevant information, enhancing the quality of embeddings. Leverage batch processing to create embeddings in bulk, which can significantly improve throughput and reduce computational overhead. Experiment with different embedding dimensions to find the optimal trade-off between accuracy and performance for your specific use case. Additionally, consider fine-tuning your embeddings model on domain-specific data to enhance reactivity and relevance in your retrieval tasks. Finally, regularly monitor and evaluate performance metrics to identify bottlenecks and iteratively refine your approach.

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 magic of building a RAG system from the ground up! You learned how LangChain acts as the backbone, orchestrating the entire pipeline with its intuitive framework to connect components seamlessly. The LangChain vector store became your go-to for storing and retrieving information efficiently, transforming raw data into a searchable knowledge hub. Then came Mistral AI’s Codestral Mamba, the powerhouse LLM that breathes life into your system, generating human-like responses by leveraging the retrieved context. And let’s not forget Ollama’s granite-embedding model—your secret sauce for converting text into rich, meaningful vectors that capture the nuances of language. Together, these tools create a symphony of capabilities, turning complex queries into accurate, context-aware answers. Along the way, you picked up pro tips for optimizing performance, like tweaking chunk sizes and refining retrieval strategies, and even discovered a free RAG cost calculator to keep your projects budget-friendly!

Now that you’ve seen how these pieces fit together, the world of RAG is your playground. You’re equipped to build smarter applications, experiment with customization, and push the boundaries of what AI can achieve. Whether you’re enhancing chatbots, revolutionizing search engines, or crafting personalized tools, you’ve got the skills to make it happen. So fire up your IDE, tweak those parameters, and let your creativity run wild. The future of intelligent systems is in your hands—go build something extraordinary! 🚀

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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