Build RAG Chatbot with LangChain, Milvus, Fireworks AI Llama 3.1 405B Instruct, and Google Vertex AI textembedding-gecko@001

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.
Milvus: An open-source vector database optimized to store, index, and search large-scale vector embeddings efficiently, perfect for use cases like RAG, semantic search, and recommender systems. If you hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on Milvus and offers a free tier supporting up to 1 million vectors.
Fireworks AI Llama 3.1 405B Instruct: This advanced model from Fireworks AI is designed for instruction-following tasks, featuring 405 billion parameters for enhanced comprehension and generation. It excels at providing detailed, context-aware responses and is ideal for customer support, educational applications, and any scenario requiring nuanced conversational abilities.
Google Vertex AI textembedding-gecko@001: This AI model specializes in generating high-quality text embeddings, facilitating superior semantic understanding and context capturing. Its strengths lie in efficient processing and scalability, making it ideal for applications like search, recommendation systems, and natural language understanding tasks that demand precise insights from textual data.

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 Fireworks AI Llama 3.1 405B Instruct

pip install -qU "langchain[fireworks]"

import getpass
import os

if not os.environ.get("FIREWORKS_API_KEY"):
  os.environ["FIREWORKS_API_KEY"] = getpass.getpass("Enter API key for Fireworks AI: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("accounts/fireworks/models/llama-v3p1-405b-instruct", model_provider="fireworks")

Step 3: Install and Set Up Google Vertex AI textembedding-gecko@001

pip install -qU langchain-google-vertexai

from langchain_google_vertexai import VertexAIEmbeddings

embeddings = VertexAIEmbeddings(model="textembedding-gecko@001")

Step 4: Install and Set Up Milvus

pip install -qU langchain-milvus

from langchain_milvus import Milvus

vector_store = Milvus(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.

Milvus optimization tips

Milvus serves as a highly efficient vector database, critical for retrieval tasks in a RAG system. To optimize its performance, ensure that indexes are properly built to balance speed and accuracy; consider utilizing HNSW (Hierarchical Navigable Small World) for efficient nearest neighbor search where response time is crucial. Partitioning data based on usage patterns can enhance query performance and reduce load times, enabling better scalability. Regularly monitor and adjust cache settings based on query frequency to avoid latency during data retrieval. Employ batch processing for vector insertions, which can minimize database lock contention and enhance overall throughput. Additionally, fine-tune the model parameters by experimenting with the dimensionality of the vectors; higher dimensions can improve retrieval accuracy but may increase search time, necessitating a balance tailored to your specific use case and hardware infrastructure.

Fireworks AI Llama 3.1 405B Instruct optimization tips

Llama 3.1 405B Instruct is a high-capacity model suited for complex RAG applications requiring detailed reasoning. Optimize retrieval by pre-ranking documents with relevance scoring before passing them as input. Structure prompts efficiently by presenting the most critical information first, reducing unnecessary token consumption. Set temperature between 0.1 and 0.2 for factual tasks and adjust top-p to refine output quality. Use caching for repeated queries to reduce API load and improve response times. Fireworks AI’s GPU infrastructure allows for efficient batching—grouping multiple queries together improves overall throughput. Implement response streaming for real-time applications, reducing perceived latency. If using 405B alongside smaller models, assign it to handle high-complexity tasks while delegating simple queries to lightweight alternatives.

Google Vertex AI textembedding-gecko@001 optimization tips

Google Vertex AI textembedding-gecko@001 provides strong semantic understanding suitable for a variety of RAG workflows. To optimize retrieval, preprocess text to remove non-essential words and structure content to highlight key information. Use nearest neighbor search with techniques like HNSW or FAISS to enhance retrieval speed without sacrificing accuracy. Optimize batch processing by grouping multiple text queries together, reducing API call overhead and increasing throughput. Fine-tune temperature settings to ensure consistent responses, and adjust top-k or top-p parameters based on the desired level of output diversity. Cache embeddings for frequently used text and set up periodic updates to ensure embedding freshness. Use dimensionality reduction to manage memory usage and storage costs effectively.

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 have you learned? Well, throughout this tutorial, you've taken significant strides in mastering the components that fuel a cutting-edge Retrieval-Augmented Generation (RAG) system. By employing LangChain as your robust framework, you’ve seamlessly tied together elements that can work in harmony to supercharge your applications. You witnessed how Milvus, your vector database, enables rapid and efficient searches, ensuring your users get the information they need without delay. Isn’t that incredibly empowering?

You also explored the capabilities of Fireworks AI Llama 3.1 405B Instruct, which brings conversational intelligence to life, enabling you to generate responses that feel naturally engaging and human-like. And let's not forget about the embedding model, Google Vertex AI textembedding-gecko@001, which generates rich semantic representations that enhance the context of your data and boost the quality of your outputs.

Along the way, you picked up valuable optimization tips and had access to a handy cost calculator, empowering you to tailor your RAG applications to be as efficient and budget-friendly as possible. So, what’s next? It’s your time to shine! Dive in, start building, optimizing, and innovating your own RAG applications. The world of AI is your oyster, and we can’t wait to see what you create! So go ahead—you've got this!

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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