Build RAG Chatbot with LangChain, pgvector, Anthropic Claude 3 Sonnet, and Ollama snowflake-arctic-embed

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 Sonnet: This advanced model in the Claude 3 lineup specializes in creative and nuanced text generation. With a deep understanding of context and tone, it is well-suited for applications in creative writing, dialogue generation, and storytelling. Its ability to produce articulate and engaging prose makes it ideal for content creation and entertainment.
Ollama Snowflake-Arctic-Embed: This model specializes in generating high-dimensional embeddings for structured and unstructured data, leveraging advanced deep learning techniques. Its strengths include efficient handling of large datasets and producing contextual representations, making it ideal for applications in recommendation systems, semantic search, and personalized content delivery.

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 Sonnet

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-sonnet-20240229", model_provider="anthropic")

Step 3: Install and Set Up Ollama snowflake-arctic-embed

pip install -qU langchain-ollama

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(model="snowflake-arctic-embed")

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 Sonnet optimization tips

Claude 3 Sonnet provides a balance between speed and accuracy, making it a versatile choice for RAG systems. Optimize retrieval efficiency by limiting the number of retrieved documents per query to avoid excessive context window usage, which can slow down response times. Use semantic chunking techniques to break documents into meaningful sections, ensuring that only the most relevant information is passed to the model. Adjust temperature and sampling parameters dynamically based on the complexity of user queries, with lower values for factual responses and higher values when generating creative text. Implement structured prompting to guide the model toward more precise answers, reducing ambiguity in responses. If integrating multiple models, use Sonnet as an intermediate option for moderately complex queries while reserving Opus for high-stakes reasoning tasks. Utilize Claude’s API optimizations, such as streaming, to enhance response time and system efficiency.

Ollama snowflake-arctic-embed optimization tips

To optimize the Ollama snowflake-arctic-embed component in your Retrieval-Augmented Generation setup, ensure that you fine-tune the embedding model on domain-specific data to improve relevance and accuracy. Utilize a caching mechanism for frequently accessed embeddings to reduce computation time. Experiment with different embedding dimensions to balance performance and resource usage, and implement vector quantization techniques to save memory space without significantly impacting quality. Additionally, regularly monitor the performance metrics and adjust your hyperparameters accordingly to achieve the best outcomes in your retrieval tasks.

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 seen firsthand how powerful it is to combine cutting-edge tools like LangChain, pgvector, Anthropic Claude 3 Sonnet, and Ollama’s Snowflake-Arctic-Embed into a seamless RAG pipeline! You’ve learned how LangChain acts as the glue, orchestrating the flow of data between components—whether it’s retrieving context from pgvector’s vector database, generating rich embeddings with Snowflake-Arctic-Embed’s precision, or tapping into Claude 3 Sonnet’s advanced reasoning to craft human-like responses. This tutorial showed you not just the how but the why: pgvector’s scalability ensures your system grows with your data, while Ollama’s lightweight embedding model keeps things efficient without sacrificing accuracy. And let’s not forget those pro tips—like optimizing chunk sizes for context retention or using metadata filtering to sharpen retrieval—that transform a basic RAG setup into a high-performance powerhouse. Plus, the free RAG cost calculator you explored gives you the confidence to experiment without breaking the bank, making smart optimization decisions easier than ever.

What’s next? You’re now equipped to build systems that understand context, adapt to user needs, and deliver answers with depth and nuance. Imagine the applications: chatbots that feel alive, research tools that surface insights in seconds, or personalized assistants that learn as they go. The tools are here, the roadmap is clear, and the possibilities are limitless. So go ahead—tweak those parameters, test new datasets, and let Claude 3 Sonnet’s creativity surprise you. Remember, every iteration brings you closer to something groundbreaking. The future of intelligent applications is in your hands. Start building, keep optimizing, and most importantly, have fun shaping the next generation of AI-powered solutions. You’ve got this! 🚀

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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