Build RAG Chatbot with LangChain, pgvector, Anthropic Claude 3.7 Sonnet, and Ollama mxbai-embed-large

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 3.7 Sonnet: Anthropic Claude 3.7 Sonnet: Claude 3.7 Sonnet is an advanced AI language model developed by Anthropic, designed to offer enhanced reasoning, alignment, and safety. It excels in tasks requiring sophisticated conversational abilities, providing users with natural, context-aware responses while maintaining ethical and safe outputs. Ideal for applications in customer service, content generation, and dialogue systems where safety and clarity are paramount.
Ollama mxbai-embed-large: This advanced AI model specializes in generating high-quality embeddings for natural language processing tasks. Its strength lies in capturing nuanced meanings and relationships within text data, making it ideal for semantic search, recommendation systems, and content clustering applications.

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 3.7 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-3-7-sonnet-latest", model_provider="anthropic")

Step 3: Install and Set Up Ollama mxbai-embed-large

pip install -qU langchain-ollama

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(model="mxbai-embed-large")

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 3.7 Sonnet Optimization Tips

To optimize the use of Anthropic Claude 3.7 Sonnet in a Retrieval-Augmented Generation (RAG) setup, focus on ensuring high-quality, relevant retrievals from your document store. Preprocess and index your knowledge base effectively by removing redundancy and structuring content for easy retrieval. Additionally, fine-tune the model on domain-specific data to improve response relevance. Consider batching requests for efficiency and adjusting the temperature and top-k parameters to balance creativity and accuracy. Monitor performance closely and adjust query embeddings to fine-tune the retrieval pipeline, ensuring low-latency and high-accuracy answers.

Ollama mxbai-embed-large optimization tips

To optimize the Ollama mxbai-embed-large component in a Retrieval-Augmented Generation (RAG) setup, consider fine-tuning the embedding model on domain-specific data to enhance relevance in retrieval tasks. Utilize batching for input queries to improve throughput and efficiency, and implement caching mechanisms for frequently accessed embeddings to reduce latency. Monitor and analyze performance metrics to identify bottlenecks and iteratively adjust hyperparameters, such as learning rate and embedding size, while leveraging mixed-precision training to balance accuracy and resource utilization. Finally, regularly update your embedded database with fresh data to maintain the accuracy of retrieval results, ensuring your RAG system remains effective and responsive.

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 learned how LangChain acts as the glue that seamlessly connects every component, orchestrating workflows like document ingestion, retrieval, and response generation. With pgvector as your vector database, you saw how to store and query embeddings at scale, turning unstructured data into searchable knowledge. The Anthropic Claude 3 Sonnet model blew you away with its ability to craft human-like, context-aware answers, while Ollama’s mxbai-embed-large model transformed text into rich numerical representations, ensuring your system understands the nuances of language. Together, these tools form a dynamic pipeline where data flows from raw documents to actionable insights, all while staying efficient and scalable.

But it doesn’t stop there! You also discovered pro tips for optimizing performance—like tuning chunk sizes for better retrieval or balancing cost and accuracy with hybrid search strategies. The free RAG cost calculator you explored empowers you to make smart decisions without breaking the bank. Imagine what’s next: customizing this foundation for niche domains, experimenting with different LLMs, or adding real-time data streams. You’ve got the blueprint—now it’s time to build, iterate, and innovate. Whether you’re enhancing customer support, powering research tools, or creating AI co-pilots, the possibilities are endless. So fire up your IDE, tweak those parameters, and let your creativity run wild. The future of intelligent applications is in your hands—go make it happen! 🚀

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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