Build RAG Chatbot with Llamaindex, Pgvector, DeepSeek R1, and AmazonBedrock cohere embed-english-v3

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:

Llamaindex: a data framework that connects large language models (LLMs) with various data sources, enabling efficient retrieval-augmented generation (RAG). It helps structure, index, and query private or external data, optimizing LLM applications for search, chatbots, and analytics.
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.)
DeepSeek R1: DeepSeek R1 is an open-weight large language model (LLM) designed for high-performance natural language processing, featuring a 128K context window for long-document understanding. It excels in reasoning, coding, and text generation, making it ideal for research, commercial applications, and AI-driven workflows requiring extended context retention and adaptability.
AmazonBedrock Cohere Embed-English-v3: A state-of-the-art text embedding model designed to convert English text into high-dimensional vector representations, excelling in semantic understanding and scalability. Its strengths include robust performance in semantic search, clustering, and retrieval-augmented generation (RAG), making it ideal for applications like recommendation systems, document similarity analysis, and AI-driven content organization within enterprise workflows.

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 Llamaindex

pip install llama-index

Step 2: Install and Set Up DeepSeek R1

%pip install llama-index-llms-deepseek

from llama_index.llms.deepseek import DeepSeek

# you can also set DEEPSEEK_API_KEY in your environment variables
llm = DeepSeek(model="deepseek-reasoner", api_key="you_api_key")

# You might also want to set deepseek as your default llm
# from llama_index.core import Settings
# Settings.llm = llm

Step 3: Install and Set Up AmazonBedrock cohere embed-english-v3

%pip install llama-index-embeddings-bedrock

from llama_index.embeddings.bedrock import BedrockEmbedding

ebed_model = BedrockEmbedding(model_name="cohere.embed-english-v3")

Step 4: Install and Set Up Pgvector

%pip install llama-index-vector-stores-postgres

from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.postgres import PGVectorStore

vector_store = PGVectorStore.from_params(
    database=db_name,
    host=url.host,
    password=url.password,
    port=url.port,
    user=url.username,
    table_name="your_table_name",
    embed_dim=1536,  # openai embedding dimension
    hnsw_kwargs={
        "hnsw_m": 16,
        "hnsw_ef_construction": 64,
        "hnsw_ef_search": 40,
        "hnsw_dist_method": "vector_cosine_ops",
    },
)

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 requests

from llama_index.core import SimpleDirectoryReader

# load documents
url = 'https://raw.githubusercontent.com/milvus-io/milvus-docs/refs/heads/v2.5.x/site/en/about/overview.md'
example_file = 'example_file.md'  # You can replace it with your own file paths.
response = requests.get(url)
with open(example_file, 'wb') as f:
    f.write(response.content)
documents = SimpleDirectoryReader(
    input_files=[example_file]
).load_data()

print("Document ID:", documents[0].doc_id)

storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, embed_model=embed_model
)

query_engine = index.as_query_engine(llm=llm)
res = query_engine.query("What is Milvus?")  # You can replace it with your own question.
print(res)

Example output

Milvus is a high-performance, highly scalable vector database designed to operate efficiently across various environments, from personal laptops to large-scale distributed systems. It is available as both open-source software and a cloud service. Milvus excels in managing unstructured data by converting it into numerical vectors through embeddings, which facilitates fast and scalable searches and analytics. The database supports a wide range of data types and offers robust data modeling capabilities, allowing users to organize their data effectively. Additionally, Milvus provides multiple deployment options, including a lightweight version for quick prototyping and a distributed version for handling massive data scales.

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.

LlamaIndex optimization tips

To optimize LlamaIndex for a Retrieval-Augmented Generation (RAG) setup, structure your data efficiently using hierarchical indices like tree-based or keyword-table indices for faster retrieval. Use embeddings that align with your use case to improve search relevance. Fine-tune chunk sizes to balance context length and retrieval precision. Enable caching for frequently accessed queries to enhance performance. Optimize metadata filtering to reduce unnecessary search space and improve speed. If using vector databases, ensure indexing strategies align with your query patterns. Implement async processing to handle large-scale document ingestion efficiently. Regularly monitor query performance and adjust indexing parameters as needed for optimal results.

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.

DeepSeek R1 optimization tips

To optimize DeepSeek R1 in a RAG setup, ensure that retrieval delivers high-quality, semantically relevant documents by fine-tuning your embedding model and search strategies. Use hybrid retrieval (vector + keyword search) to improve accuracy, especially for complex queries. Keep document chunks concise but rich in context to avoid exceeding token limits. Leverage caching to reduce redundant queries and enhance response speed. Experiment with prompt templates to maximize the model’s reasoning and comprehension capabilities. Monitor API latency and adjust query batching to improve efficiency. Fine-tune temperature settings to maintain consistency in generated responses.

AmazonBedrock Cohere Embed-English-v3 optimization tips

To optimize Cohere Embed-English-v3 in RAG, preprocess input text by removing redundant whitespace, normalizing casing, and filtering low-relevance content to reduce noise. Use batch embedding generation for bulk documents to minimize API calls and latency. Adjust the input_type parameter (e.g., "document" or "query") to align with use cases for context-aware embeddings. Experiment with chunk sizes (e.g., 256-512 tokens) to balance semantic capture and computational efficiency. Cache frequent or static embeddings to avoid reprocessing. Monitor embedding quality via cosine similarity checks and fine-tune retrieval thresholds for your dataset.

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?

Congratulations on making it through this tutorial! You’ve just unlocked the incredible potential of a RAG (Retrieval-Augmented Generation) system by weaving together a stellar lineup of technologies: LlamaIndex as your robust framework, Pgvector for efficient vector storage and retrieval, DeepSeek R1 as your powerful large language model, and Amazon Bedrock's cohere embed-english-v3 to enhance your embedding capabilities. By integrating these components, you’ve come to understand not only how they work individually but also how they harmonize to create a seamless RAG pipeline that can effectively drive your applications. You learned about the unique capabilities of each element—from LlamaIndex’s structural support to DeepSeek R1’s generative prowess—all designed to make your system not just functional but also truly intelligent.

What’s even more exciting is that you’ve gathered invaluable optimization tips along the way, ensuring that your RAG application runs smoothly and efficiently. You also discovered a handy RAG cost calculator to help you plan your project budget, making it accessible to anyone enthusiastic about diving in. Now that you’re equipped with this knowledge, the world of RAG applications is at your fingertips! Dive in, start building, and let your creativity flow. Use this newfound expertise to innovate beyond your wildest dreams. Get out there, experiment, and transform ideas into reality—your RAG-powered journey is just beginning!

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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