Build RAG Chatbot with LangChain, pgvector, Anthropic Claude 3 Haiku, and Cohere embed-english-v3.0

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.
Cohere embed-english-v3.0: This model specializes in generating high-quality text embeddings for English language input. It is designed for tasks like semantic search, recommendation systems, and document similarity, providing robust performance due to its deep contextual understanding. Ideal for applications needing nuanced language comprehension and efficient information retrieval.

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 Cohere embed-english-v3.0

pip install -qU langchain-cohere

import getpass
import os

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

from langchain_cohere import CohereEmbeddings

embeddings = CohereEmbeddings(model="embed-english-v3.0")

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.

Cohere embed-english-v3.0 optimization tips

Cohere embed-english-v3.0 is a robust embedding model tailored for English language text. To optimize retrieval, preprocess input text to eliminate irrelevant noise and focus on key terms or phrases that will drive relevant matches. Use approximate nearest neighbor (ANN) algorithms like HNSW for efficient retrieval in large-scale datasets. For resource management, employ dimensionality reduction or quantization techniques to compress embeddings, reducing storage requirements without sacrificing too much performance. Implement multi-threading for batch processing to accelerate embedding generation. Use caching to store frequently accessed embeddings and reduce redundant computations. Fine-tune the model on specific data to improve performance on domain-specific queries and ensure greater relevance in retrieval.

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 how powerful it is to combine cutting-edge tools like LangChain, pgvector, Claude 3 Haiku, and Cohere’s embed-english-v3.0 into a seamless RAG pipeline! You’ve learned how LangChain acts as the glue, orchestrating workflows to connect your data sources, vector databases, and AI models with ease. By leveraging pgvector’s PostgreSQL integration, you can store and query embeddings efficiently, turning unstructured data into searchable knowledge. Cohere’s embed-english-v3.0 model then supercharges your data retrieval by generating rich, context-aware embeddings, ensuring your system understands nuances in user queries. And with Anthropic’s Claude 3 Haiku, you’ve got a lightning-fast, cost-effective LLM that delivers precise, natural-sounding answers grounded in the retrieved context—no more generic responses! Along the way, you picked up optimization tricks like tuning chunk sizes for better retrieval and using indexing strategies in pgvector to speed up similarity searches. Plus, that free RAG cost calculator you explored? It’s a game-changer for budgeting your AI projects without surprises.

This tutorial wasn’t just about connecting dots—it was about empowering you to build intelligent systems that learn from data and adapt to real-world needs. Imagine what’s next: customizing this pipeline for your niche use cases, experimenting with hybrid search strategies, or even fine-tuning embeddings for domain-specific accuracy. The tools are here, the foundation is set, and you’ve got the skills to iterate fearlessly. So go ahead—take this blueprint, tweak it, scale it, and make it yours. Whether you’re building a next-gen chatbot, a research assistant, or an enterprise knowledge hub, you’re now equipped to innovate smarter, faster, and with confidence. The future of AI-driven applications is yours to shape. Let’s get building! 🚀

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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