Build RAG Chatbot with LangChain, pgvector, Anthropic Claude 3 Haiku, and nomic-embed-text-v1.5

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.
Pgvector: an open-source extension for PostgreSQL that enables efficient storage and querying of high-dimensional vector data, essential for machine learning and AI applications. Designed to handle embeddings, it supports fast approximate nearest neighbor (ANN) searches using algorithms like HNSW and IVFFlat. Since it is just a vector search add-on to traditional search 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.)
Anthropic Claude 3: This advanced AI language model from Anthropic focuses on safety and alignment, capable of generating coherent and context-aware text. It excels in creative writing, conversational AI, and insightful summarization. Ideal for creating engaging content while ensuring adherence to ethical standards and user intent.
nomic-embed-text-v1.5: This model specializes in generating high-quality text embeddings that capture semantic meaning and contextual nuances. Its strength lies in facilitating efficient similarity search and information retrieval tasks. Ideal for applications in recommendation systems, semantic search, and natural language understanding, it enhances performance in various NLP projects.

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 Anthropic Claude 3 Haiku

pip install -qU "langchain[anthropic]"

import getpass
import os

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

from langchain.chat_models import init_chat_model

llm = init_chat_model("claude-3-haiku-20240307", model_provider="anthropic")

Step 3: Install and Set Up nomic-embed-text-v1.5

pip install -qU langchain-nomic

import getpass
import os

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

from langchain_nomic import NomicEmbeddings

embeddings = NomicEmbeddings(model="nomic-embed-text-v1.5")

Step 4: Install and Set Up pgvector

pip install -qU langchain-postgres

from langchain_postgres import PGVector

vector_store = PGVector(
    embeddings=embeddings,
    collection_name="my_docs",
    connection="postgresql+psycopg://...",
)

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.

pgvector optimization tips

To optimize pgvector in a Retrieval-Augmented Generation (RAG) setup, consider indexing your vectors using GiST or IVFFlat to significantly speed up search queries and improve retrieval performance. Make sure to leverage parallelization for query execution, allowing multiple queries to be processed simultaneously, especially for large datasets. Optimize memory usage by tuning the vector storage size and using compressed embeddings where possible. To further enhance query speed, implement pre-filtering techniques to narrow down search space before querying. Regularly rebuild indexes to ensure they are up to date with any new data. Fine-tune vectorization models to reduce dimensionality without sacrificing accuracy, thus improving both storage efficiency and retrieval times. Finally, manage resource allocation carefully, utilizing horizontal scaling for larger datasets and offloading intensive operations to dedicated processing units to maintain responsiveness during high-traffic periods.

Anthropic Claude 3 Haiku optimization tips

Claude 3 Haiku is designed for efficiency, making it a great choice for low-latency RAG applications. Optimize token usage by structuring prompts concisely, removing redundant text, and leveraging system messages effectively to guide responses. Use function calling when applicable to offload structured processing tasks and improve response reliability. Batch process queries where possible to reduce API overhead and enhance throughput. If latency is critical, consider caching frequent queries and pre-generating responses for common questions. Fine-tune response control with temperature and top-p sampling; lower temperature values (e.g., 0.2-0.3) help maintain consistency in factual retrieval tasks. Use streaming mode for real-time applications to get faster partial responses while processing large prompts. Regularly evaluate and adjust model parameters based on performance benchmarks to balance speed and accuracy in your RAG pipeline.

nomic-embed-text-v1.5 optimization tips

nomic-embed-text-v1.5 is a well-rounded embedding model that performs effectively in diverse text retrieval scenarios. Optimize text preprocessing by removing stop words and redundant information before embedding to improve storage efficiency. Use hierarchical indexing structures to manage embeddings in large-scale datasets, improving retrieval speed. Leverage cosine similarity filtering to refine search results post-query. For cost-effective scaling, batch embed multiple documents at once and store embeddings in a distributed vector database like Milvus or FAISS. If dealing with rapidly changing data, implement incremental indexing rather than full reprocessing to save computation time. Regularly monitor embedding quality by validating against a benchmarked dataset to ensure relevance.

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 workflows between your data, models, and user interactions. With pgvector as your vector database, you saw firsthand how PostgreSQL transforms into a high-performance engine for storing and querying embeddings—turning unstructured data into searchable knowledge. The magic of Anthropic’s Claude 3 Haiku brought speed and affordability to the table, enabling lightning-fast, context-aware responses while keeping costs low. And let’s not forget the nomic-embed-text-v1.5 model, which turned your text into rich, multidimensional embeddings, capturing nuances that make retrieval accurate and meaningful. Together, these pieces formed a dynamic pipeline where data flows from ingestion to insight, all while you optimized performance with pro tips like chunking strategies and indexing best practices. You even explored tools like the free RAG cost calculator to estimate expenses and fine-tune your setup—proving that building smart applications doesn’t have to break the bank!

Now that you’ve seen how these components click into place, the real adventure begins. Imagine the applications you can create—chatbots that understand context deeply, research tools that surface precise answers, or personalized recommendation engines. You’ve got the blueprint; it’s time to experiment, tweak, and innovate. Play with different embedding models, test new LLMs, or scale your pgvector database for larger datasets. The flexibility of this stack means your creativity is the only limit. Remember, every iteration gets you closer to a system that’s uniquely yours. So fire up your IDE, load up those embeddings, and let Claude 3 Haiku shine. The future of intelligent applications is in your hands—build something amazing, share your wins, and keep pushing the boundaries of what RAG can do. Let’s go! 🚀

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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