Build RAG Chatbot with LangChain, Milvus, Cohere Command R, and Cohere embed-english-light-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.
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 R: This model is designed for high-performance retrieval tasks, offering advanced capabilities in understanding and generating natural language. Its strengths lie in semantic search and document summarization, making it ideal for applications such as customer support, content generation, and knowledge management, where accuracy and context relevance are paramount.
Cohere embed-english-light-v2.0: This lightweight embedding model is designed for efficient English text encoding, providing high-quality representations for various natural language processing tasks. It excels in scenarios requiring embedding generation with minimal computational resources, such as document similarity, clustering, and recommendation systems, ensuring fast performance without compromising accuracy.

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 R

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-r", model_provider="cohere")

Step 3: Install and Set Up Cohere embed-english-light-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-light-v2.0")

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 R optimization tips

Cohere Command R is designed for retrieval-augmented generation, making efficient context retrieval and ranking critical for system performance. Optimize retrieval pipelines by using Cohere’s embedding-based search to identify and rank the most relevant documents, reducing unnecessary input context while maintaining accuracy. Improve response quality by fine-tuning temperature settings; lower values (0.1–0.2) work best for structured, fact-based queries, while higher values introduce more variability in generated responses. Utilize prompt templates to maintain consistent formatting, ensuring clarity in output. Implement batch processing where multiple queries need similar context, reducing redundant API calls. To enhance efficiency, cache top query results and leverage incremental context updates instead of repeatedly sending full document sets. If deploying at scale, monitor latency and response consistency with real-time metrics, adjusting retrieval thresholds dynamically for optimal balance between speed and completeness.

Cohere embed-english-light-v2.0 optimization tips

Cohere embed-english-light-v2.0 is designed for faster, more resource-efficient embedding generation in English-language tasks. To optimize processing, preprocess text by removing stopwords, punctuation, and unnecessary formatting to minimize the complexity of the input. Leverage vector compression techniques, such as quantization or PCA, to reduce storage requirements without sacrificing significant accuracy. For retrieval, implement hybrid search strategies that combine keyword search with dense vector search to achieve faster and more relevant results. Use multi-threading or parallel processing to handle large batches of embeddings efficiently. Apply caching for frequently queried embeddings to minimize re-processing and reduce query latency, especially for high-traffic RAG systems.

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 a journey this has been! As we wrap up this tutorial, it's inspiring to see how seamlessly all four components—LangChain, Milvus, Cohere Command R, and the Cohere embed-english-light-v2.0—fit together to create a powerful RAG (Retrieval-Augmented Generation) system. You've learned how LangChain serves as the backbone, orchestrating the entire setup while making it user-friendly and intuitive. The vector database, Milvus, ensures that your queries are lightning-fast, enabling you to perform searches with unmatched efficiency. Isn’t it exciting to imagine the speed at which you can retrieve pertinent information?

The LLM, powered by Cohere Command R, injects a conversational intelligence that can truly transform user interactions, making them more dynamic and engaging. Meanwhile, the embedding model from Cohere offers a way to generate rich, nuanced semantic representations, enhancing the understanding of context in your conversations.

We even explored some optimization tips and introduced a handy cost calculator to make sure your innovations can grow sustainably. So, now that you've grasped how each component fits into the bigger picture, it’s time to take action! Dive into building your RAG applications, experiment with configurations, and innovate like never before! The possibilities are endless, and your next big idea could be just around the corner. Get started today, and let's change the future together!

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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