Build RAG Chatbot with LangChain, pgvector, Mistral AI Mistral Large, and Google Vertex AI textembedding-gecko@003

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 Mistral Large: This model offers a high-performance solution for a range of natural language processing tasks. With a focus on large-scale text generation and comprehension, it excels in handling complex queries and generating nuanced responses. Ideal for applications in content creation, conversational agents, and research analysis, Mistral Large combines versatility with efficiency.
Google Vertex AI textembedding-gecko@003: This model specializes in generating high-quality text embeddings for diverse applications, including semantic search and content recommendation. It leverages advanced techniques for contextual understanding, ensuring accurate representations of intricate text. Ideal for integration into systems needing scalable and efficient NLP solutions, enhancing user experience in real-time applications.

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 Large

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("mistral-large-latest", model_provider="mistralai")

Step 3: Install and Set Up Google Vertex AI textembedding-gecko@003

pip install -qU langchain-google-vertexai

from langchain_google_vertexai import VertexAIEmbeddings

embeddings = VertexAIEmbeddings(model="textembedding-gecko@003")

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 Mistral Large optimization tips

Mistral Large is a high-capacity model suitable for complex reasoning and in-depth retrieval tasks in RAG applications. To improve efficiency, use advanced reranking models to prioritize top-quality retrieval results, ensuring only the most relevant information is included in the context window. Optimize token efficiency by segmenting and structuring input prompts logically, avoiding excessive context that may slow down inference. Fine-tune temperature (0.1–0.2 for factual queries, up to 0.5 for creative reasoning) and sampling parameters to maintain response quality. Use dynamic retrieval strategies—retrieving broader context for ambiguous queries and narrowing the scope for well-defined questions. Deploy caching and asynchronous processing for handling high-throughput workloads efficiently. If running Mistral Large in a cloud environment, allocate resources dynamically to balance performance and cost.

Google Vertex AI textembedding-gecko@003 optimization tips

Google Vertex AI textembedding-gecko@003 is designed for advanced text understanding, making it ideal for high-accuracy RAG applications. Optimize embedding generation by removing noisy data and focusing on the most relevant content within documents. Use efficient vector search algorithms, such as FAISS with IVF or HNSW, to ensure fast and accurate document retrieval. Batch text embeddings for large volumes of data to speed up processing and minimize latency. Implement caching for high-frequency queries and periodically refresh embeddings to keep up with changes in the data landscape. Fine-tune the model on domain-specific tasks to improve relevance in specialized RAG applications. Consider deploying a multi-stage search strategy with semantic and keyword-based approaches for optimal accuracy and performance.

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 now, 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 the entire pipeline with its flexible framework. It seamlessly connects to pgvector, your vector database powerhouse, which stores and retrieves embeddings efficiently, ensuring your system delivers relevant context lightning-fast. Speaking of embeddings, Google’s Vertex AI textembedding-gecko@003 transformed your raw text into rich numerical representations, capturing semantic meaning like a pro. And Mistral AI’s Mistral Large stepped in as the brainy LLM, synthesizing retrieved information into coherent, human-like responses—proving how modern models can balance creativity with factual accuracy. Together, these tools form a dynamic RAG workflow that’s greater than the sum of its parts!

But wait—there’s more! You also picked up optimization tricks, like tweaking chunking strategies and indexing methods to boost performance, and even discovered how the free RAG cost calculator can help you budget compute resources wisely. Imagine what’s next: refining your pipeline for niche use cases, experimenting with hybrid retrieval methods, or scaling this setup to handle millions of queries. The skills you’ve gained aren’t just theoretical—they’re a launchpad for real-world AI applications. So go ahead—dive into your next project, customize this toolkit, and watch your ideas come to life. The future of intelligent systems is yours to shape. Build boldly, iterate fearlessly, and let your innovations surprise even you! 🚀

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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