Build RAG Chatbot with LangChain, Milvus, Fireworks AI Llama 3.1 70B Instruct, 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.
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 70B Instruct: This advanced model is designed for instruction-following tasks, featuring a vast 70 billion parameters for enhanced comprehension and context. It excels in generating coherent, contextually relevant outputs for complex queries, making it ideal for interactive applications, educational tools, and customer support systems requiring detailed and precise information.
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 Fireworks AI Llama 3.1 70B 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-v3p1-70b-instruct", model_provider="fireworks")

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 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 70B Instruct optimization tips

Llama 3.1 70B Instruct is a powerful model with a balance of speed and reasoning capabilities. Enhance retrieval performance by using hybrid search methods that incorporate both semantic and keyword-based matching. Keep prompts structured with clear sections, ensuring optimal focus on key information. Set temperature between 0.1 and 0.3, fine-tuning top-k and top-p values to control output variability. Implement caching strategies for frequently used queries to optimize cost and efficiency. Fireworks AI's auto-scaling capabilities allow for dynamic resource allocation—use this to handle peak workloads effectively. Streaming responses can improve user experience for real-time applications. If deploying 70B alongside larger models, use it as an intermediate option for moderately complex queries while reserving the largest models for deep analytical reasoning.

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?

What have we learned? Wow, what a journey we’ve taken together! In this tutorial, we've explored how to integrate a fantastic framework, a powerful vector database, a remarkable language model (LLM), and an innovative embedding model to create a cutting-edge Retrieval-Augmented Generation (RAG) system. It’s like crafting a dream team where each player excels in their own arena! The framework, LangChain, serves as the glue that holds everything together, orchestrating seamless interactions and ensuring that our system runs like a well-oiled machine. Meanwhile, the Milvus vector database supercharges our ability to perform incredibly fast searches, allowing us to retrieve information at lightning speed, which is essential for building responsive applications.

The conversational intelligence powered by the Fireworks AI Llama 3.1, with its 70 billion parameters, takes our user interactions to another level. Add the Google Vertex AI text-embedding-005 model into the mix, and you’re creating rich semantic representations that capture the essence of your data. We've even thrown in optimization tips and a free cost calculator to assist you along the way!

Now, it’s time to channel that enthusiasm into action. Dive into building, optimizing, and innovating your own RAG applications. The potential is limitless, and you’re already equipped with the knowledge to unleash your creativity! Go forth and explore the possibilities—your adventure in RAG awaits!

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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