Build RAG Chatbot with LangChain, Milvus, Cohere Command, and IBM granite-embedding-278m-multilingual

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.
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.
IBM granite-embedding-278m-multilingual: This advanced AI model specializes in generating multilingual text embeddings, making it highly effective for tasks such as cross-linguistic information retrieval and translation. With its strength in understanding diverse languages, it excels in applications involving global datasets and multilingual customer engagement.

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 IBM granite-embedding-278m-multilingual

pip install -qU langchain-ibm

import getpass
import os

if not os.environ.get("WATSONX_APIKEY"):
  os.environ["WATSONX_APIKEY"] = getpass.getpass("Enter API key for IBM watsonx: ")

from langchain_ibm import WatsonxEmbeddings

embeddings = WatsonxEmbeddings(
    model_id="ibm/granite-embedding-278m-multilingual",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="<WATSONX PROJECT_ID>",
)

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.

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.

IBM granite-embedding-278m-multilingual optimization tips

To optimize the IBM granite-embedding-278m-multilingual for your Retrieval-Augmented Generation (RAG) setup, consider fine-tuning the model on domain-specific data relevant to your use case, which helps improve accuracy in embeddings. Use mini-batches when processing queries to balance memory efficiency and speed, ensuring you leverage GPU acceleration. Implement a caching mechanism for frequently accessed documents to reduce retrieval latency, and experiment with different similarity metrics to find the most effective one for your data. Regularly monitor the performance and iterate over hyperparameters such as learning rates and embedding dimensions to further enhance your retrieval capabilities.

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?

By diving into this tutorial, you’ve unlocked the power of combining cutting-edge tools to build a robust RAG system! You learned how LangChain acts as the glue, orchestrating the entire pipeline by seamlessly connecting your data sources, processing logic, and AI models. Milvus stepped in as your high-performance vector database, efficiently storing and retrieving the dense embeddings generated by IBM’s granite-embedding-278m-multilingual model—a lightweight yet powerful tool that captures semantic meaning across languages. Then, Cohere Command brought the magic, transforming retrieved context into human-like responses with its knack for natural language generation. Together, these tools create a dynamic system that understands queries, fetches relevant information, and crafts coherent answers—all while handling multilingual inputs gracefully. You also discovered pro tips for optimizing each stage, like tuning Milvus indexing parameters for speed or balancing Cohere’s creativity with precision. And let’s not forget the bonus: that free RAG cost calculator you explored helps estimate expenses upfront, empowering you to make smart, budget-friendly design choices.

Now, you’re equipped to build smarter, faster, and more adaptable AI applications! Whether you’re enhancing chatbots, automating research, or personalizing user experiences, this toolkit opens endless doors. Imagine tweaking the pipeline to support niche domains, experimenting with hybrid retrieval strategies, or scaling to millions of users—all within your reach. The tutorial didn’t just teach you steps; it gave you a blueprint for innovation. So go ahead—fire up your code editor, play with those API keys, and let your creativity run wild. The world of RAG is evolving fast, and you’re now part of the movement shaping it. Build something bold, optimize fearlessly, and share what you create. The future of AI-powered apps is yours to define—start today! 🚀

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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