Build RAG Chatbot with LangChain, LangChain vector store, Fireworks AI DeepSeek V3, and HuggingFace all-mpnet-base-v2

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.
LangChain in-memory vector store: an in-memory, ephemeral vector store that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance. It is intended for demos and does not yet support ids or deletion. (If you want a much more scalable solution for your apps or even enterprise projects, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvusand offers a free tier supporting up to 1 million vectors.)
Fireworks AI DeepSeek V3: This advanced AI model specializes in deep data exploration and analysis, providing powerful insights through its robust analytical capabilities. With strengths in pattern recognition and predictive analytics, it is ideal for sectors like finance and healthcare, where uncovering hidden trends and making data-driven decisions are crucial.
HuggingFace all-mpnet-base-v2: This model is a variant of MPNet designed for general-purpose NLP tasks, offering superior performance in sentence embeddings and semantic similarity. It excels in understanding contextual nuances, making it ideal for search, recommendation systems, and any application requiring robust textual comprehension and matching capabilities.

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 DeepSeek V3

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/deepseek-v3", model_provider="fireworks")

Step 3: Install and Set Up HuggingFace all-mpnet-base-v2

pip install -qU langchain-huggingface

from langchain_huggingface import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

Step 4: Install and Set Up LangChain vector store

pip install -qU langchain-core

from langchain_core.vectorstores import InMemoryVectorStore

vector_store = InMemoryVectorStore(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.

LangChain in-memory vector store optimization tips

LangChain in-memory vector store is just an ephemeral vector store that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. It has very limited features and is only intended for demos. If you plan to build a functional or even production-level solution, 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 DeepSeek V3 optimization tips

DeepSeek V3 is optimized for advanced reasoning and response quality, making it a powerful choice for RAG applications requiring deep contextual understanding. Improve retrieval by implementing multi-stage ranking, ensuring only the most relevant documents are passed as context. Use structured prompts with clear delineation between retrieved content and user queries. Adjust temperature (0.1–0.2) for accuracy and fine-tune top-k/top-p for response control. Minimize latency with precomputed embeddings and caching for commonly queried data. Take advantage of Fireworks AI’s API optimizations to batch multiple requests, reducing processing overhead. Implement dynamic scaling strategies for high-demand scenarios, ensuring model performance remains consistent under load. If used in a multi-tiered architecture, deploy DeepSeek V3 for high-value queries while leveraging smaller models for basic lookups.

HuggingFace all-mpnet-base-v2 optimization tips

HuggingFace all-mpnet-base-v2 is a robust and efficient embedding model that excels in semantic understanding for RAG tasks. Optimize retrieval by reducing text noise before embedding, ensuring that only meaningful content is processed. Use approximate nearest neighbor (ANN) search with FAISS or a similar framework to accelerate query resolution without compromising quality. Implement dimensionality reduction techniques to save memory and reduce computational overhead. Leverage caching strategies for frequently queried text embeddings to minimize API calls and improve latency. Fine-tune the embedding model on task-specific data to improve accuracy and relevance in your search results. When scaling, use parallel processing for large datasets and batch embedding operations to optimize throughput.

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? Congratulations on making it through this exciting journey of building a cutting-edge Retrieval-Augmented Generation (RAG) system! By integrating a framework like LangChain, you’ve learned how to connect the dots between various powerful components, creating a seamless workflow that enhances the capabilities of your application. The vector database, such as the LangChain vector store, enables superfast searches, delivering relevant information in the blink of an eye! With this, you can efficiently handle massive datasets, making your RAG system truly responsive and effective.

You’ve also explored how leveraging an advanced Large Language Model (LLM) fuels your system with conversational intelligence, allowing it to generate coherent, contextually relevant responses. The embedding model, like HuggingFace’s all-mpnet-base-v2, generates rich semantic representations that capture the essence of your data, ensuring your RAG system understands context and nuance like a pro!

Plus, we equipped you with some handy optimization tips and even a free cost calculator to make your development journey smoother and more efficient. So, what now? The possibilities for your RAG applications are limitless! Dive back in, start building, optimizing, and innovating. Take what you’ve learned here and turn it into something extraordinary. Your journey in advanced AI is just beginning, and I can’t wait to see what you create! Keep exploring and pushing the boundaries of what’s possible!

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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