Build RAG Chatbot with LangChain, pgvector, Mistral AI Pixtral, and Nomic Embed Text V2

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.
Pgvector: an open-source extension for PostgreSQL that enables efficient storage and querying of high-dimensional vector data, essential for machine learning and AI applications. Designed to handle embeddings, it supports fast approximate nearest neighbor (ANN) searches using algorithms like HNSW and IVFFlat. Since it is just a vector search add-on to traditional search rather than a purpose-built vector database, it lacks scalability and availability and many other advanced features required by enterprise-level applications. Therefore, if you prefer a much more scalable solution or hate to manage your own infrastructure, 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 Pixtral: Pixtral is a cutting-edge image generation model designed for high-quality visual content creation. With a focus on artistic style transfer and detail accuracy, it excels in transforming text prompts into vibrant images. Ideal for applications in design, marketing, and creative fields, Pixtral enhances workflows with its versatility and aesthetic precision.
Nomic Embed Text V2: An open-source, multilingual text embedding model using a Mixture-of-Experts (MoE) architecture for efficient processing. Trained on 1.6 billion text pairs, it excels in retrieval tasks, supports flexible embedding dimensions, and optimizes storage and compute costs. Its training data and code are fully open-sourced for transparency.

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 Pixtral

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("pixtral-12b-2409", model_provider="mistralai")

Step 3: Install and Set Up Nomic Embed Text V2

pip install -qU langchain-nomic

import getpass
import os

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

from langchain_nomic import NomicEmbeddings

embeddings = NomicEmbeddings(model="nomic-ai/nomic-embed-text-v2-moe")

Step 4: Install and Set Up pgvector

pip install -qU langchain-postgres

from langchain_postgres import PGVector

vector_store = PGVector(
    embeddings=embeddings,
    collection_name="my_docs",
    connection="postgresql+psycopg://...",
)

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.

pgvector optimization tips

To optimize pgvector in a Retrieval-Augmented Generation (RAG) setup, consider indexing your vectors using GiST or IVFFlat to significantly speed up search queries and improve retrieval performance. Make sure to leverage parallelization for query execution, allowing multiple queries to be processed simultaneously, especially for large datasets. Optimize memory usage by tuning the vector storage size and using compressed embeddings where possible. To further enhance query speed, implement pre-filtering techniques to narrow down search space before querying. Regularly rebuild indexes to ensure they are up to date with any new data. Fine-tune vectorization models to reduce dimensionality without sacrificing accuracy, thus improving both storage efficiency and retrieval times. Finally, manage resource allocation carefully, utilizing horizontal scaling for larger datasets and offloading intensive operations to dedicated processing units to maintain responsiveness during high-traffic periods.

Mistral AI Pixtral optimization tips

Pixtral is optimized for multimodal RAG applications, requiring careful management of both textual and visual data retrieval. Improve retrieval efficiency by using specialized embeddings for different modalities—vector search for text and CLIP-based embeddings for images. Implement a multimodal ranking system to prioritize the most contextually relevant passages and images. Optimize model performance by structuring input prompts effectively, ensuring text and visual information are well-integrated without unnecessary repetition. Fine-tune temperature settings based on response requirements—lower values (0.1–0.2) for accuracy-driven applications, higher values for creative outputs. If deploying at scale, use parallel inference for handling large multimodal datasets efficiently. Streamline inference by leveraging batching and caching strategies, especially when handling frequently queried images and text pairs.

Nomic Embed Text V2 optimization tips

Nomic Embed Text V2 is a versatile embedding model suited for general-purpose text search tasks in RAG systems. Optimize efficiency by preprocessing input data to remove irrelevant noise and focus on the most meaningful content, which can help reduce computational overhead. For faster retrieval, employ approximate nearest neighbor (ANN) search algorithms like HNSW or FAISS to speed up the search process while maintaining accuracy. Consider implementing vector quantization techniques to reduce storage space without significantly affecting retrieval quality. Utilize batching to process multiple texts in parallel and minimize API latency. If working with large datasets, regularly update embeddings to reflect the most recent information, ensuring that your retrieval system remains relevant and effective.

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 combining cutting-edge tools to build a fully functional RAG system from scratch! You learned how LangChain acts as the glue, seamlessly orchestrating workflows between your data, models, and applications. With pgvector, you discovered how to store and retrieve vector embeddings efficiently, leveraging PostgreSQL’s scalability to handle similarity searches at lightning speed. The magic truly came alive when you integrated Mistral AI’s PixTral, a versatile LLM that generates insightful, context-aware responses by synthesizing retrieved data. And let’s not forget the Nomic Embed Text V2 model, which transformed raw text into rich embeddings, capturing semantic nuances to ensure your RAG system understands exactly what users are asking for. Together, these components form a robust pipeline that bridges knowledge gaps, answers complex questions, and delivers value in real time—like giving your app a supercharged brain!

But wait, there’s more! You also picked up pro tips for optimizing performance, like tuning chunk sizes for embeddings and balancing speed with accuracy in retrieval. The included free RAG cost calculator empowers you to estimate expenses upfront, making it easier to scale projects without surprises. Now that you’ve seen how these pieces fit together, imagine the possibilities: smarter chatbots, dynamic research tools, or even AI-powered knowledge hubs tailored to your needs. The tools are in your hands, the foundation is laid—so why wait? Start experimenting, tweak those parameters, and watch your ideas come to life. Whether you’re refining an existing project or building something entirely new, the future of intelligent applications is yours to shape. Let’s get building! 🚀

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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