Build RAG Chatbot with LangChain, Zilliz Cloud, Together AI Mixtral 8x7B Instruct v0.1, 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.
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.
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-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 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-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 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.

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-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 an incredible journey we’ve been on together! Throughout this tutorial, you’ve unlocked the secrets to building a cutting-edge RAG (Retrieval-Augmented Generation) system by integrating a powerful framework, a lightning-fast vector database, a conversationally adept LLM, and an intuitive embedding model. Each of these components plays a unique role: the framework seamlessly ties everything together, orchestrating interactions between them with grace and efficiency. The Zilliz Cloud vector database serves as your high-speed engine, enabling rapid searches through massive datasets, ensuring you get answers in the blink of an eye. Meanwhile, the Together AI Mixtral 8x7B Instruct LLM injects your applications with conversational intelligence, transforming interactions into engaging dialogues. Lastly, the Cohere embed-english-light-v2.0 embedding model crafts rich, semantic representations, setting the stage for insightful retrievals.

Plus, don’t forget the valuable optimization tips and the handy free cost calculator we explored! These tools empower you to refine your RAG applications even further, making them smarter and more efficient.

Now, it’s your turn! Take all that knowledge and excitement into your future projects. Start building, optimizing, and innovating your RAG applications. The sky's the limit, and we can't wait to see what amazing solutions you come up with. Go for it—your journey of creativity starts now!

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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