Build RAG Chatbot with LangChain, Zilliz Cloud, Cohere Command, and Nomic Embed Text 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.
Zilliz Cloud: a fully managed vector database-as-a-service platform built on top of the open-source Milvus, designed to handle high-performance vector data processing at scale. It enables organizations to efficiently store, search, and analyze large volumes of unstructured data, such as text, images, or audio, by leveraging advanced vector search technology. It offers a free tier supporting up to 1 million vectors.
Cohere Command: Cohere Command is a powerful language model designed for task-oriented applications, emphasizing efficiency and scalability. It excels in generating contextual responses, deploying natural language processing tasks like text generation, summarization, and query answering. Ideal for businesses looking to enhance customer interactions and automate workflows with accurate and relevant outputs.
Nomic Embed Text V2: An open-source, multilingual text embedding model using a Mixture-of-Experts (MoE) architecture for efficient processing. Trained on 1.6 billion text pairs, it excels in retrieval tasks, supports flexible embedding dimensions, and optimizes storage and compute costs. Its training data and code are fully open-sourced for transparency.

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 Cohere Command

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.chat_models import init_chat_model

llm = init_chat_model("command", model_provider="cohere")

Step 3: Install and Set Up Nomic Embed Text V2

pip install -qU langchain-nomic

import getpass
import os

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

from langchain_nomic import NomicEmbeddings

embeddings = NomicEmbeddings(model="nomic-ai/nomic-embed-text-v2-moe")

Step 4: Install and Set Up Zilliz Cloud

pip install -qU langchain-milvus

from langchain_milvus import Zilliz

vector_store = Zilliz(
    embedding_function=embeddings,
    connection_args={
        "uri": ZILLIZ_CLOUD_URI,
        "token": ZILLIZ_CLOUD_TOKEN,
    },
)

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.

Zilliz Cloud optimization tips

Optimizing Zilliz Cloud for a RAG system involves efficient index selection, query tuning, and resource management. Use Hierarchical Navigable Small World (HNSW) indexing for high-speed, approximate nearest neighbor search while balancing recall and efficiency. Fine-tune ef_construction and M parameters based on your dataset size and query workload to optimize search accuracy and latency. Enable dynamic scaling to handle fluctuating workloads efficiently, ensuring smooth performance under varying query loads. Implement data partitioning to improve retrieval speed by grouping related data, reducing unnecessary comparisons. Regularly update and optimize embeddings to keep results relevant, particularly when dealing with evolving datasets. Use hybrid search techniques, such as combining vector and keyword search, to improve response quality. Monitor system metrics in Zilliz Cloud’s dashboard and adjust configurations accordingly to maintain low-latency, high-throughput performance.

Cohere Command optimization tips

Cohere Command is a general-purpose language model that can be optimized for RAG workflows through prompt engineering, efficient retrieval, and structured response control. To improve accuracy, use Cohere’s reranking capabilities to filter and prioritize retrieved documents before passing them into the model. Keep input prompts concise and structured, reducing token overhead while ensuring clear context for the model. Optimize response quality by adjusting parameters such as temperature (0.1–0.3 for factual accuracy) and top-p sampling to control creativity levels. Implement hybrid search techniques by combining dense and sparse retrieval methods to improve recall and precision. For cost-efficient scaling, cache frequently queried responses and precompute embeddings for common knowledge areas. Stream responses where real-time generation is required, minimizing latency while ensuring user engagement. Monitor API usage and latency through Cohere’s analytics tools to fine-tune retrieval strategies based on performance trends.

Nomic Embed Text V2 optimization tips

Nomic Embed Text V2 is a versatile embedding model suited for general-purpose text search tasks in RAG systems. Optimize efficiency by preprocessing input data to remove irrelevant noise and focus on the most meaningful content, which can help reduce computational overhead. For faster retrieval, employ approximate nearest neighbor (ANN) search algorithms like HNSW or FAISS to speed up the search process while maintaining accuracy. Consider implementing vector quantization techniques to reduce storage space without significantly affecting retrieval quality. Utilize batching to process multiple texts in parallel and minimize API latency. If working with large datasets, regularly update embeddings to reflect the most recent information, ensuring that your retrieval system remains relevant and effective.

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? You’ve embarked on an exhilarating journey to build a cutting-edge Retrieval-Augmented Generation (RAG) system, and what a thrilling ride it has been! Throughout this tutorial, you’ve discovered how seamlessly integrating a robust framework like LangChain can tie together every other component, providing a solid backbone for your innovative applications. You’ve seen how a powerful vector database like Zilliz Cloud enables lightning-fast searches, making data retrieval not just efficient but also intuitive!

With the deployment of a dynamic Language Model (LLM) like Cohere Command, your system is now equipped with conversational intelligence that can respond to users in a way that feels natural and engaging. And let’s not forget how the embedding model, Nomic Embed Text V2, generates rich semantic representations that add depth and richness to your understanding of data.

By picking up optimization tips and harnessing the free cost calculator provided, you’ve gained invaluable insights into fine-tuning your approach for both performance and budget. Now, as you stand at the threshold of immense possibilities, imagine the applications you can create! So go ahead—start building, optimizing, and innovating your RAG applications. The future is at your fingertips, and I can’t wait to see what you will create!

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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