Build RAG Chatbot with LangChain, OpenSearch, Cohere Command, and Cohere embed-english-light-v3.0
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.
- OpenSearch: An open-source search and analytics suite derived from Elasticsearch. It offers robust full-text search and real-time analytics, with vector search available as an add-on for similarity-based queries, extending its capabilities to handle high-dimensional data. Since it is just a vector search add-on rather than a purpose-built vector database, it lacks scalability and availability and many other advanced features required by enterprise-level applications. Therefore, if you prefer 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.)
- 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.
- Cohere embed-english-light-v3.0: This model specializes in generating high-quality embeddings for English text, optimized for efficiency and low resource usage. It excels in tasks such as semantic search, clustering, and recommendation systems. Ideal for developers needing fast and scalable text representation capabilities in resource-constrained environments.
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 Cohere embed-english-light-v3.0
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-v3.0")
Step 4: Install and Set Up OpenSearch
pip install --upgrade --quiet opensearch-py langchain-community
from langchain_community.vectorstores import OpenSearchVectorSearch
opensearch_vector_search = OpenSearchVectorSearch(
"http://localhost:9200",
"embeddings",
embedding_function
)
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.
OpenSearch optimization tips
To optimize OpenSearch in a Retrieval-Augmented Generation (RAG) setup, fine-tune indexing by enabling efficient mappings and reducing unnecessary stored fields. Use HNSW for vector search to speed up similarity queries while balancing recall and latency with appropriate ef_search
and ef_construction
values. Leverage shard and replica settings to distribute load effectively, and enable caching for frequent queries. Optimize text-based retrieval with BM25 tuning and custom analyzers for better relevance. Regularly monitor cluster health, index size, and query performance using OpenSearch Dashboards and adjust configurations accordingly.
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.
Cohere embed-english-light-v3.0 optimization tips
Cohere embed-english-light-v3.0 is optimized for lighter, faster embeddings in English language tasks. To maximize efficiency, preprocess input text by stripping out unnecessary words and standardizing formatting, which reduces computational load and speeds up embedding generation. Leverage vector compression techniques to reduce storage costs and improve retrieval performance. When managing large volumes of data, implement efficient search strategies like HNSW or product quantization to reduce time spent querying. Batch embedding operations to process multiple texts at once, minimizing the overhead from frequent API calls. Periodically update the embeddings to capture any changes in the dataset, ensuring freshness and accuracy in search results while optimizing storage.
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
What Have You Learned?
By diving into this tutorial, you’ve unlocked the power of building a RAG system that’s both intelligent and scalable! You learned how LangChain acts as the glue, seamlessly orchestrating workflows between components like OpenSearch and Cohere’s models. With OpenSearch as your vector database, you can store and retrieve dense embeddings efficiently, transforming unstructured data into searchable knowledge. Then there’s Cohere’s Command LLM, which brings your system to life by generating human-like responses grounded in the retrieved context. The Cohere embed-english-light-v3.0 model stepped in to convert text into meaningful embeddings, ensuring your RAG pipeline understands semantic relationships and delivers accurate results. Together, these tools form a dynamic pipeline: ingest data, embed it, store it, retrieve the most relevant chunks, and generate answers that feel natural and informed. You even explored optimizations like chunking strategies and metadata filtering to fine-tune performance—pro tips that make your system faster and more cost-effective!
But wait, there’s more! The tutorial also introduced a free RAG cost calculator to help you estimate expenses and scale smartly. Now that you’ve seen how these pieces fit together—and how to tweak them for efficiency—you’re ready to build your own AI-powered applications. Imagine creating chatbots that answer with precision, tools that summarize complex documents, or assistants that pull insights from massive datasets. The possibilities are endless, and you’ve got the foundation to start experimenting. So fire up your IDE, play with different datasets, and see how small tweaks can lead to big improvements. The future of intelligent apps is in your hands—go build something awesome and watch your ideas come to life! 🚀
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
We'd Love to Hear What You Think!
We’d love to hear your thoughts! 🌟 Leave your questions or comments below or join our vibrant Milvus Discord community to share your experiences, ask questions, or connect with thousands of AI enthusiasts. Your journey matters to us!
If you like this tutorial, show your support by giving our Milvus GitHub repo a star ⭐—it means the world to us and inspires us to keep creating! 💖
- Introduction to RAG
- Key Components We'll Use for This RAG Chatbot
- Step 1: Install and Set Up LangChain
- Step 2: Install and Set Up Cohere Command
- Step 3: Install and Set Up Cohere embed-english-light-v3.0
- Step 4: Install and Set Up OpenSearch
- Step 5: Build a RAG Chatbot
- Optimization Tips
- RAG Cost Calculator: A Free Tool to Calculate Your Cost in Seconds
- What Have You Learned?
- Further Resources
- We'd Love to Hear What You Think!
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