Build RAG Chatbot with LangChain, OpenSearch, Groq llama3-70b-8192, and IBM all-minilm-l6-v2

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.)
Groq llama3-70b-8192: The llama3-70b-8192 model is a large language model developed by Meta, featuring 70 billion parameters and an 8,192-token context window. This model is designed for general-purpose language tasks, including text generation, summarization, and translation. Groq, a company specializing in AI hardware and software solutions, offers the llama3-70b-8192 model through its API. This integration allows developers to leverage Groq's high-performance Language Processing Unit (LPU) for efficient inference. Groq's LPU is known for its deterministic, single-core streaming architecture, which provides predictable and repeatable performance for AI workloads.
IBM all-minilm-l6-v2: This model is a compact, efficient transformer-based language representation model optimized for tasks requiring fast inferencing. It excels in natural language understanding tasks such as sentiment analysis and information retrieval, making it ideal for applications in chatbots, search engines, and data annotation.

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 Groq llama3-70b-8192

pip install -qU "langchain[groq]"

import getpass
import os

if not os.environ.get("GROQ_API_KEY"):
  os.environ["GROQ_API_KEY"] = getpass.getpass("Enter API key for Groq: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("llama3-8b-8192", model_provider="groq")

Step 3: Install and Set Up IBM all-minilm-l6-v2

pip install -qU langchain-ibm

import getpass
import os

if not os.environ.get("WATSONX_APIKEY"):
  os.environ["WATSONX_APIKEY"] = getpass.getpass("Enter API key for IBM watsonx: ")

from langchain_ibm import WatsonxEmbeddings

embeddings = WatsonxEmbeddings(
    model_id="sentence-transformers/all-minilm-l6-v2",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="<WATSONX PROJECT_ID>",
)

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.

Groq llama3-70b-8192 optimization tips

To optimize Groq llama3-70b-8192 in a Retrieval-Augmented Generation (RAG) system, focus on efficient batch processing to maximize throughput by grouping multiple requests together. Utilize mixed precision for faster computation while maintaining model accuracy, and adjust input sequence length to strike a balance between context richness and computational efficiency. Leverage model parallelism to distribute workloads across multiple processing units, ensuring scalability. Regularly monitor GPU utilization and ensure that memory is efficiently managed by freeing unused tensors to prevent bottlenecks. Fine-tune the model on specific tasks or domains to improve accuracy and reduce inference time. Additionally, consider pruning or quantizing certain layers to optimize performance for production-level tasks without compromising too much on model output quality.

IBM all-minilm-l6-v2 optimization tips

To optimize the performance of IBM all-minilm-l6-v2 in a Retrieval-Augmented Generation (RAG) setup, consider implementing streamlined query preprocessing to remove stop words and normalize text, ensuring that input queries are concise and relevant. Layering caching strategies on frequently retrieved results can significantly reduce latency, while fine-tuning the model with domain-specific data enhances relevance and accuracy. Additionally, experiment with batch processing during inference to leverage parallelization, and monitor and adjust hyperparameters like learning rates and maximum token counts to refine model responses. Lastly, ensure that your retrieval system is seamlessly integrated with the generation process to maintain context and coherence in generated outputs.

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 now, you’ve unlocked the magic of building a powerful RAG system from the ground up! You’ve seen how LangChain acts as the glue, orchestrating every step of the pipeline with its intuitive framework—connecting your data to the LLM, managing prompts, and streamlining workflows. OpenSearch steps in as your trusty vector database, storing and retrieving embeddings at lightning speed, while IBM’s all-minilm-l6-v2 embedding model transforms text into rich numerical representations, making your data searchable and context-aware. Then there’s Groq’s llama3-70b-8192, the powerhouse LLM that takes those retrieved snippets and crafts human-like responses, combining accuracy with creativity. Together, these tools form a seamless loop: ingest data, retrieve context, generate answers—repeat! You’ve also picked up pro tips for optimizing performance, like tuning chunk sizes for embeddings and balancing speed with precision in retrieval. And let’s not forget the free RAG cost calculator you explored—your secret weapon for estimating expenses and scaling smarter.

This isn’t just a tutorial—it’s your launchpad. You’ve got the tools to build systems that answer questions, summarize documents, or even power chatbots that feel alive. Imagine what’s next: refining retrieval strategies, experimenting with hybrid search, or integrating domain-specific data. The sky’s the limit, and you’re equipped to soar. So fire up your IDE, tweak those parameters, and start creating. Every line of code you write brings you closer to solving real-world problems in ways that were science fiction just a few years ago. The future of intelligent applications is here, and you’re building it. Let’s go make something amazing—your RAG journey has only just begun! 🚀

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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