Build RAG Chatbot with LangChain, LangChain vector store, Mistral AI Ministral 8B, and Ollama nomic-embed-text

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 Ministral 8B: This model is designed for high-performance generative tasks with 8 billion parameters, offering a balance of efficiency and capability. It excels in document generation, conversational AI, and creative content production. Ideal for developers seeking a powerful yet manageable model for sophisticated language applications without the resource demands of larger architectures.
Ollama nomic-embed-text: This model specializes in generating high-quality text embeddings, designed to enhance semantic understanding in various NLP tasks. Its strengths lie in contextual representation and scalability, making it suitable for applications like semantic search, recommendation systems, and clustering. Ideal for developers looking to integrate profound text analysis into their projects.

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 Ministral 8B

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("ministral-8b-latest", model_provider="mistralai")

Step 3: Install and Set Up Ollama nomic-embed-text

pip install -qU langchain-ollama

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(model="nomic-embed-text")

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 Ministral 8B optimization tips

Ministral 8B provides a balance between efficiency and reasoning power, making it a good mid-range choice for RAG applications. Optimize retrieval by leveraging hybrid search (combining vector and keyword search) to improve accuracy. Use prompt engineering techniques, such as structured input formatting and logical ordering of context, to enhance response quality. Adjust temperature (0.1–0.3) and top-k values to balance factual accuracy with creative flexibility. Implement response caching for commonly accessed queries to improve latency. If deploying at scale, use model parallelism to distribute workloads efficiently across multiple GPUs or nodes. Optimize memory management by using half-precision (FP16) or quantized models to improve throughput. Fine-tune retrieval granularity, ensuring that only the most relevant and concise context is included in each query.

Ollama nomic-embed-text optimization tips

Ollama nomic-embed-text is designed for robust text embedding generation, making it essential to optimize how embeddings are stored and queried in a RAG pipeline. Preprocess input text by stripping unnecessary metadata and normalizing case to maintain consistency. Choose an optimized vector indexing strategy, such as IVF-PQ for balanced speed and accuracy, depending on dataset size. Use approximate nearest neighbor search to accelerate retrieval while maintaining a high recall rate. Cache commonly accessed embeddings to avoid redundant computations. If embeddings are used for long-term retrieval tasks, periodically refresh and retrain on new data to prevent model drift. Optimize database queries to quickly retrieve relevant embeddings and minimize I/O bottlenecks.

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?

What an amazing journey you’ve just completed! Throughout this tutorial, we’ve explored the exciting integration of a powerful framework, a lightning-fast vector database, a sophisticated LLM, and an insightful embedding model to build a state-of-the-art Retrieval-Augmented Generation (RAG) system. By leveraging LangChain as our central framework, you’ve learned how to effectively tie all these components together, creating a seamless workflow that enhances your application’s performance and usability.

The vector database, specifically LangChain’s vector store, gives your application the speed and efficiency it needs to perform fast and reliable searches—imagine transforming your application into a supercharged information hub! With Mistral AI's Ministral 8B at the helm, your system gains extraordinary conversational intelligence, allowing it to process and generate natural responses that truly engage users. Meanwhile, the nomic-embed-text embedding model has empowered you to produce rich semantic representations, ensuring that your application understands context and nuance in a way that really resonates with users.

We’ve also sprinkled in some optimization tips and introduced a fantastic free cost calculator to help you manage resources effectively. Now it’s time for you to unleash your creativity! Get out there and start building, optimizing, and innovating your own RAG applications. The possibilities are endless, and the only limit is your imagination. Let’s go create something incredible together!

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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