Build RAG Chatbot with LangChain, OpenSearch, Fireworks AI DeepSeek R1, and Google Vertex AI textembedding-gecko@001

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.)
Fireworks AI DeepSeek R1: This advanced model specializes in deep data exploration and insight generation. With robust analytical capabilities and high accuracy, it efficiently identifies patterns and trends within large datasets. Ideal for business intelligence, market analysis, and research applications, DeepSeek R1 empowers organizations to make data-driven decisions confidently.
Google Vertex AI textembedding-gecko@001: This AI model specializes in generating high-quality text embeddings, facilitating superior semantic understanding and context capturing. Its strengths lie in efficient processing and scalability, making it ideal for applications like search, recommendation systems, and natural language understanding tasks that demand precise insights from textual data.

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 Fireworks AI DeepSeek R1

pip install -qU "langchain[fireworks]"

import getpass
import os

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

from langchain.chat_models import init_chat_model

llm = init_chat_model("accounts/fireworks/models/deepseek-r1", model_provider="fireworks")

Step 3: Install and Set Up Google Vertex AI textembedding-gecko@001

pip install -qU langchain-google-vertexai

from langchain_google_vertexai import VertexAIEmbeddings

embeddings = VertexAIEmbeddings(model="textembedding-gecko@001")

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.

Fireworks AI DeepSeek R1 optimization tips

DeepSeek R1 is a strong model for RAG applications requiring high-quality retrieval and generation. Optimize performance by using an efficient document embedding strategy, ensuring that retrieved documents are highly relevant before passing them to the model. Structure prompts effectively, emphasizing key details upfront to guide responses. Set temperature between 0.1 and 0.2 for factual accuracy and adjust top-p for response variety when needed. Minimize latency by caching frequent queries and using batched API calls for bulk processing. If running on Fireworks AI infrastructure, take advantage of GPU acceleration to improve throughput. Implement response streaming for interactive applications requiring low-latency output. Continuously monitor query performance and adjust retrieval depth based on complexity to optimize efficiency.

Google Vertex AI textembedding-gecko@001 optimization tips

Google Vertex AI textembedding-gecko@001 provides strong semantic understanding suitable for a variety of RAG workflows. To optimize retrieval, preprocess text to remove non-essential words and structure content to highlight key information. Use nearest neighbor search with techniques like HNSW or FAISS to enhance retrieval speed without sacrificing accuracy. Optimize batch processing by grouping multiple text queries together, reducing API call overhead and increasing throughput. Fine-tune temperature settings to ensure consistent responses, and adjust top-k or top-p parameters based on the desired level of output diversity. Cache embeddings for frequently used text and set up periodic updates to ensure embedding freshness. Use dimensionality reduction to manage memory usage and storage costs effectively.

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 fully functional RAG system from the ground up! You learned how LangChain acts as the glue, orchestrating workflows and seamlessly connecting components like OpenSearch for lightning-fast vector search, Fireworks AI’s DeepSeek R1 model for dynamic text generation, and Google’s textembedding-gecko@001 to transform raw data into meaningful embeddings. Each piece plays a vital role: OpenSearch stores and retrieves context efficiently, embeddings turn messy data into structured knowledge, and the LLM synthesizes answers that feel human and relevant. The tutorial also walked you through practical optimizations—like tuning chunk sizes for embeddings or balancing latency with accuracy—to make your system not just functional but exceptional. And let’s not forget the free RAG cost calculator, a handy tool to estimate expenses and scale responsibly without surprises!

Now that you’ve seen how these pieces fit together, imagine the possibilities! Whether you’re building chatbots, research assistants, or enterprise search tools, you’ve got the blueprint to innovate. Experiment with different models, tweak retrieval strategies, and fine-tune for your use case—your creativity is the limit. The world of AI is moving fast, but you’re already ahead of the curve. So fire up your IDE, start iterating, and watch your RAG applications come to life. The future is yours to build, optimize, and redefine. Let’s go make something amazing! 🚀

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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