Build RAG Chatbot with LangChain, Faiss, Mistral AI Codestral Mamba, and Google Vertex AI text-embedding-005

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.
Faiss: also known as Facebook AI Similarity Search, is an open-source vector search library that allows developers to quickly search for semantically similar multimedia data within a massive dataset of unstructured data. (If you want 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 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.
Google Vertex AI text-embedding-005: This model produces high-quality text embeddings, facilitating nuanced semantic understanding and similarity comparisons. Its strength lies in efficiency and scalability, making it ideal for tasks like information retrieval, recommendation systems, and multi-language support. Perfect for developers seeking to enhance their applications with powerful contextual insights.

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 Google Vertex AI text-embedding-005

pip install -qU langchain-google-vertexai

from langchain_google_vertexai import VertexAIEmbeddings

embeddings = VertexAIEmbeddings(model="text-embedding-005")

Step 4: Install and Set Up Faiss

pip install -qU langchain-community

from langchain_community.vectorstores import FAISS

vector_store = FAISS(embedding_function=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.

Faiss Optimization Tips

To enhance the performance of the Faiss library in a Retrieval-Augmented Generation (RAG) system, begin by selecting the appropriate index type based on your data volume and query speed requirements; for example, using an IVF (Inverted File) index can significantly speed up queries on large datasets by reducing the search space. Optimize your indexing process by using the nlist parameter to partition data into smaller clusters and set an appropriate number of probes (nprobe) during retrieval to balance between speed and accuracy. Ensure the vectors are properly normalized and consider using 16-bit or 8-bit quantization during indexing to reduce memory footprints for large datasets while maintaining reasonable retrieval accuracy. Additionally, consider leveraging GPU acceleration if available, as Faiss highly benefits from parallel processing, leading to faster nearest neighbor searches. Continuous fine-tuning and benchmarking with varying parameters and configurations can guide you in finding the most efficient setup specific to your data characteristics and retrieval requirements.

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.

Google Vertex AI text-embedding-005 optimization tips

Google Vertex AI text-embedding-005 is a high-performing model, optimized for generating context-aware embeddings in RAG systems. Improve retrieval accuracy by leveraging a two-stage search process: first, filter using keyword matching, then re-rank results based on embedding similarity. Use batch embedding generation to minimize latency and streamline processing. When managing large datasets, implement hierarchical vector indexing to optimize memory usage and retrieval speed. Fine-tune embeddings with domain-specific data to improve retrieval relevance for niche use cases. For high-throughput systems, employ a distributed search infrastructure to scale efficiently while maintaining low query response times. Regularly evaluate the embeddings’ relevance and update the model with new training data for optimal 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?

Wow, what a journey this tutorial has taken us on! You’ve not only explored the foundational components of a cutting-edge Retrieval-Augmented Generation (RAG) system, but you’ve also learned how to weave them together into a powerful and efficient solution. By utilizing LangChain as your foundational framework, you’ve established a robust backbone that allows these components to communicate seamlessly. The integration of the Faiss vector database significantly enhances your system by enabling lightning-fast searches through vast amounts of data, meaning you can retrieve relevant information in the blink of an eye!

With the Mistral AI's conversational capabilities powered by the large language model (LLM), you've tapped into the ability to craft engaging and human-like conversations. Meanwhile, you discovered how the Google Vertex AI's text-embedding-005 model generates rich semantic representations that elevate your understanding of the data and its context—something that transforms raw information into actionable insights.

We've also sprinkled in some optimization tips and shared a free cost calculator, ensuring that your ventures not only yield impressive outcomes but do so efficiently and economically. Now, the exciting challenge lies ahead of you! So roll up your sleeves, start experimenting, and don’t hesitate to innovate. The world of RAG applications is vast and filled with opportunities waiting for you to explore. Your creativity and ideas are crucial, so get started on building, optimizing, and pushing the boundaries of what’s possible! The future is in your hands—let's make it 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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