Build RAG Chatbot with LangChain, Faiss, Fireworks AI Llama 3.1 8B Instruct, and Cohere embed-english-v2.0

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.)
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.
Cohere embed-english-v2.0: This model specializes in generating high-quality embeddings for English text, enhancing semantic understanding in various applications. Its strengths lie in capturing nuanced meanings and context, making it ideal for tasks like semantic search, recommendation systems, and clustering. Efficient and scalable, it's perfect for developers looking to integrate advanced text analysis into their 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 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 Cohere embed-english-v2.0

pip install -qU langchain-cohere

import getpass
import os

if not os.environ.get("COHERE_API_KEY"):
  os.environ["COHERE_API_KEY"] = getpass.getpass("Enter API key for Cohere: ")

from langchain_cohere import CohereEmbeddings

embeddings = CohereEmbeddings(model="embed-english-v2.0")

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.

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.

Cohere embed-english-v2.0 optimization tips

Cohere embed-english-v2.0 is a robust English-language embedding model, ideal for fast and accurate semantic retrieval in RAG systems. To optimize performance, preprocess input text by removing redundant information and normalizing formatting. Apply techniques like stemming or lemmatization to reduce the dimensionality of the input without losing meaning. Use high-performance indexing algorithms like HNSW for efficient vector search. When working with large-scale systems, batch embeddings for faster processing and minimize API latency. Consider implementing dimensionality reduction techniques to compress embeddings and reduce storage costs while retaining sufficient accuracy. For large document collections, periodically refresh the embeddings to ensure that they stay relevant to evolving data.

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 an incredible journey it has been to dive into the world of cutting-edge RAG systems! In this tutorial, we’ve seamlessly woven together a powerful framework, vector database, large language model (LLM), and embedding model, empowering you to build your own dynamic applications. The framework—think of it as the conductor of an orchestra—harmonizes all these components, ensuring they work effortlessly together.

With the vector database, powered by Faiss, you've unlocked the ability to perform lightning-fast searches, making data retrieval not just efficient but a breeze! Then, we have the LLM, the amazing Fireworks AI Llama 3.1, which enhances conversational intelligence, allowing your applications to understand context and deliver engaging responses. And let’s not forget the embedding model, Cohere's embed-english-v2.0, which generates rich semantic representations—making your data not just searchable but also meaningful and actionable.

Throughout the tutorial, we also shared some awesome optimization tips and even a handy free cost calculator to help you manage your resources. Now it's your turn! Embrace the excitement of experimentation and creativity. Start building, optimizing, and pushing the boundaries of what’s possible with your own RAG applications. The sky's the limit, so go ahead, innovate, and make your mark!

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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