Build RAG Chatbot with LangChain, Milvus, Fireworks AI Llama 3.1 8B Instruct, and voyage-3-large

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 8B Instruct: This model is designed to deliver precise instructions and guidance through advanced reasoning capabilities. With its 8 billion parameters, it excels in generating coherent responses across various domains, making it ideal for educational tools, virtual assistants, and interactive content creation. Its strength lies in user engagement through personalized interactions.
Voyage-3-Large: This model is designed for generative tasks, offering enhanced creativity and contextual understanding. With robust training on diverse datasets, it excels in producing coherent narratives and dialogue, making it ideal for applications in storytelling, content creation, and interactive experiences where imaginative output is essential.

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 8B 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-v3p1-8b-instruct", model_provider="fireworks")

Step 3: Install and Set Up voyage-3-large

pip install -qU langchain-voyageai

import getpass
import os

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

from langchain-voyageai import VoyageAIEmbeddings

embeddings = VoyageAIEmbeddings(model="voyage-3-large")

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 8B Instruct optimization tips

Llama 3.1 8B Instruct is a cost-efficient model that delivers strong performance in RAG applications with moderate complexity. Optimize retrieval by limiting context length to only the most relevant passages, ensuring efficient token usage. Structure prompts clearly, with short, well-organized sections that guide the model’s focus. Keep temperature around 0.1–0.3 for accuracy and fine-tune top-k and top-p for flexibility. Cache high-frequency queries to minimize redundant processing and reduce API costs. Take advantage of Fireworks AI’s infrastructure to batch requests, optimizing efficiency for large-scale operations. Use response streaming to enhance interactivity in applications requiring fast feedback. If deploying multiple models, leverage 8B for simple queries and hand off more complex tasks to larger models.

voyage-3-large optimization tips

voyage-3-large provides enhanced reasoning capabilities, making it ideal for complex RAG tasks requiring deep contextual understanding. Optimize retrieval by implementing a multi-step ranking system that prioritizes highly relevant documents while filtering out lower-quality information. Use structured prompts with clearly delineated context and user queries to improve comprehension. Adjust temperature (0.1–0.3) and fine-tune top-k and top-p settings to maintain accuracy and prevent excessive variability. Take advantage of parallelized inference and request batching to improve processing efficiency. Leverage caching for high-frequency queries to reduce costs and latency. In multi-model setups, deploy voyage-3-large for intricate reasoning tasks while using smaller models for less complex queries to balance cost and performance 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? Throughout this tutorial, you've embarked on an exciting journey to integrate a powerful framework, a top-tier vector database, a state-of-the-art language model, and an advanced embedding model to build a cutting-edge RAG system. You’ve seen how the framework seamlessly ties all these components together, creating a robust foundation for your application. With the vector database, Milvus, you've unlocked the secret to lightning-fast searches that will dramatically enhance the user experience. The conversational intelligence fueled by the Fireworks AI Llama 3.1 8B Instruct is nothing short of remarkable, enabling your application to engage users in a meaningful way. Plus, the embedding model has empowered you to generate rich semantic representations that deepen your RAG’s understanding and contextual relevance.

And let’s not forget those optimization tips we explored and the handy free cost calculator that helps you streamline your expenses as you build your application. Now it’s time to let your creativity shine! Gather your insights, experiment with innovative ideas, and start crafting your very own RAG applications. The possibilities are endless, and we can't wait to see what you'll create. Dive in, build boldly, optimize smartly, and above all, have fun innovating! Your journey has just begun!

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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