Build RAG Chatbot with LangChain, OpenSearch, Mistral AI Mistral Large, and voyage-code-2

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.)
Mistral AI Mistral Large: This model offers a high-performance solution for a range of natural language processing tasks. With a focus on large-scale text generation and comprehension, it excels in handling complex queries and generating nuanced responses. Ideal for applications in content creation, conversational agents, and research analysis, Mistral Large combines versatility with efficiency.
Voyage Code 2: This AI model specializes in code generation and programming assistance, designed to enhance developer productivity. It offers robust support in writing, debugging, and optimizing code across various languages. Ideal for software development projects, it streamlines coding workflows and facilitates rapid prototyping and learning for both novice and experienced programmers.

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 Mistral AI Mistral Large

pip install -qU "langchain[mistralai]"

import getpass
import os

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

from langchain.chat_models import init_chat_model

llm = init_chat_model("mistral-large-latest", model_provider="mistralai")

Step 3: Install and Set Up voyage-code-2

pip install -qU langchain-voyageai

import getpass
import os

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

from langchain-voyageai import VoyageAIEmbeddings

embeddings = VoyageAIEmbeddings(model="voyage-code-2")

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.

Mistral AI Mistral Large optimization tips

Mistral Large is a high-capacity model suitable for complex reasoning and in-depth retrieval tasks in RAG applications. To improve efficiency, use advanced reranking models to prioritize top-quality retrieval results, ensuring only the most relevant information is included in the context window. Optimize token efficiency by segmenting and structuring input prompts logically, avoiding excessive context that may slow down inference. Fine-tune temperature (0.1–0.2 for factual queries, up to 0.5 for creative reasoning) and sampling parameters to maintain response quality. Use dynamic retrieval strategies—retrieving broader context for ambiguous queries and narrowing the scope for well-defined questions. Deploy caching and asynchronous processing for handling high-throughput workloads efficiently. If running Mistral Large in a cloud environment, allocate resources dynamically to balance performance and cost.

voyage-code-2 optimization tips

voyage-code-2 provides solid performance for code-related RAG tasks but requires careful retrieval optimization to ensure efficient and accurate results. Use structured embeddings to improve code snippet search and retrieval precision. Format prompts with clear structure, including specific instructions, function signatures, and constraints, to enhance output quality. Keep temperature low (0.1–0.2) for accuracy in deterministic tasks while allowing slight variation for exploratory coding tasks. Enable caching for frequently requested programming patterns to optimize efficiency. Use parallelized execution and request batching to handle large-scale queries effectively. In multi-model deployments, assign voyage-code-2 to standard code completion tasks while leveraging more advanced models for deeper analysis and architectural recommendations.

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 create a robust Retrieval-Augmented Generation (RAG) system! You’ve seen firsthand how LangChain acts as the glue that connects every piece of the puzzle, simplifying the orchestration of workflows and allowing you to focus on creativity rather than complexity. With OpenSearch as your vector database, you learned to store and retrieve embeddings efficiently, leveraging its scalability and speed to handle large datasets seamlessly. The Mistral Large model from Mistral AI brought your system to life, generating human-like responses that feel natural and context-aware, while voyage-code-2 embeddings transformed raw text into meaningful numerical representations, ensuring your RAG pipeline understands nuances and delivers accurate results. Together, these components form a dynamic pipeline that bridges retrieval and generation, empowering you to build applications that answer questions, summarize content, or even craft stories with precision and flair.

But you didn’t just stop at the basics—you also picked up pro tips for optimizing performance, like tuning retrieval parameters and balancing cost with quality. The inclusion of a free RAG cost calculator gave you a practical tool to estimate expenses and make informed decisions as you scale. Now, armed with this knowledge, you’re ready to experiment, iterate, and innovate. Whether you’re enhancing customer support bots, creating personalized learning tools, or exploring entirely new use cases, the possibilities are endless. So go ahead—fire up your IDE, tweak those embeddings, and let your ideas take flight. The future of intelligent applications is in your hands, and there’s no limit to what you’ll build next! 🚀

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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