Build RAG Chatbot with LangChain, OpenSearch, Together AI Mixtral 8x7B Instruct v0.1, and Cohere embed-english-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.)
Together AI Mixtral 8x7B Instruct v0.1: This model offers a powerful blend of instruction-based learning and advanced natural language understanding. With its 8x7B architecture, it excels in generating coherent and context-aware responses. Ideal for applications like chatbots, content creation, and educational tools where user guidance and high-quality interaction are essential.
Cohere embed-english-v3.0: This model specializes in generating high-quality text embeddings for English language input. It is designed for tasks like semantic search, recommendation systems, and document similarity, providing robust performance due to its deep contextual understanding. Ideal for applications needing nuanced language comprehension and efficient information retrieval.

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 Together AI Mixtral 8x7B Instruct v0.1

pip install -qU "langchain[together]"

import getpass
import os

if not os.environ.get("TOGETHER_API_KEY"):
  os.environ["TOGETHER_API_KEY"] = getpass.getpass("Enter API key for Together AI: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("mistralai/Mixtral-8x7B-Instruct-v0.1", model_provider="together")

Step 3: Install and Set Up Cohere embed-english-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-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.

Together AI Mixtral 8x7B Instruct v0.1 optimization tips

Together AI’s Mixtral 8x7B Instruct v0.1 uses a mixture-of-experts (MoE) architecture to balance efficiency and performance. Optimize retrieval by dynamically adjusting the number of retrieved documents based on query complexity to prevent overloading the context window. Structure prompts effectively, ensuring that critical details are at the start of the input to guide the model’s focus. Use a temperature of 0.1–0.3 for factual accuracy while tweaking top-k and top-p for balanced response generation. Together AI’s inference stack allows for optimized execution, so enable expert pruning to limit active pathways when full capacity isn’t needed. Implement caching strategies for common queries to minimize redundant processing. If integrating multiple models, use Mixtral 8x7B for medium-to-high complexity reasoning while offloading simpler queries to smaller, more efficient models.

Cohere embed-english-v3.0 optimization tips

Cohere embed-english-v3.0 is a robust embedding model tailored for English language text. To optimize retrieval, preprocess input text to eliminate irrelevant noise and focus on key terms or phrases that will drive relevant matches. Use approximate nearest neighbor (ANN) algorithms like HNSW for efficient retrieval in large-scale datasets. For resource management, employ dimensionality reduction or quantization techniques to compress embeddings, reducing storage requirements without sacrificing too much performance. Implement multi-threading for batch processing to accelerate embedding generation. Use caching to store frequently accessed embeddings and reduce redundant computations. Fine-tune the model on specific data to improve performance on domain-specific queries and ensure greater relevance in retrieval.

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 from scratch! You learned how LangChain acts as the glue, seamlessly orchestrating interactions between your data, models, and user queries. OpenSearch steps in as your vector database powerhouse, storing and retrieving semantically rich embeddings generated by Cohere’s embed-english-v3.0—a model that transforms text into high-dimensional vectors with remarkable precision. Then, Together AI’s Mixtral 8x7B Instruct v0.1 takes center stage, leveraging its open-source flexibility and instruction-following prowess to synthesize accurate, context-aware answers from retrieved data. Together, these tools form a dynamic pipeline where LangChain routes workflows, OpenSearch handles lightning-fast similarity searches, Cohere ensures your data is meaningfully encoded, and Mixtral delivers human-like responses—all working in harmony to turn raw information into actionable insights.

But you didn’t just stop at the basics! The tutorial also equipped you with pro tips for optimization, like tuning chunking strategies for better retrieval and balancing cost-performance tradeoffs. And don’t forget the free RAG cost calculator—a game-changer for estimating expenses as you scale. Now that you’ve seen how these pieces fit together, imagine what’s next: tweaking parameters for niche use cases, experimenting with hybrid search approaches, or even integrating multimodal data. The tools are in your hands, and the possibilities are endless. So go ahead—build something bold, iterate fearlessly, and let your creativity shape the future of intelligent applications. The world of RAG is yours to explore, one query at a time! 🚀

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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