Build RAG Chatbot with Llamaindex, Pgvector, Cohere Command, and jina-embeddings-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.)
Cohere Command: A generative AI model designed for high-accuracy text generation and instruction-following, optimized for business applications. Strengths include coherent output, robust handling of complex prompts, and enterprise-grade reliability. Ideal for automating customer interactions, generating structured content (reports, emails), data extraction, and summarization, enhancing operational efficiency and scalability.
Jina-Embeddings-v3: A state-of-the-art embedding model designed for high-dimensional vector representations of text, excelling in multilingual understanding and long-context retention. Its scalable architecture ensures robust performance in semantic search, clustering, and retrieval-augmented generation (RAG) systems. Ideal for applications requiring precise semantic analysis across diverse languages and lengthy documents, combining accuracy with efficiency.

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 Cohere Command

%pip install llama-index-llms-cohere

from llama_index.llms.cohere import Cohere

llm = Cohere(model="command", api_key=api_key)

Step 3: Install and Set Up jina-embeddings-v3

%pip install llama-index-embeddings-jinaai

You may also need other packages that do not come direcly with llama-index.

!pip install Pillow

from llama_index.embeddings.jinaai import JinaEmbedding

embed_model = JinaEmbedding(
    api_key=jinaai_api_key,
    model="jina-embeddings-v3",
    # choose `retrieval.passage` to get passage embeddings
    task="retrieval.passage",
)

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.

Cohere Command optimization tips

To optimize Cohere Command in a RAG setup, fine-tune parameters like temperature (lower for factual accuracy, higher for creativity) and top_p (narrow for precision). Use concise, structured input by chunking retrieved documents to fit context limits, and prepend clear instructions (e.g., "Answer using only the context"). Leverage Cohere’s built-in reranking to prioritize relevant passages. Regularly evaluate output quality with metrics like answer relevance and hallucination rates, and iteratively refine prompts and retrieval logic based on feedback.

Jina-embeddings-v3 optimization tips

To optimize Jina-embeddings-v3 in a RAG setup, preprocess input text by normalizing casing, removing redundant whitespace, and truncating to the model’s maximum sequence length (e.g., 8,192 tokens). Batch embedding generation for parallel processing, balancing GPU/CPU memory constraints. Use FP16 precision for faster inference if hardware supports it. Cache frequently accessed document embeddings to reduce recomputation. Experiment with dimensionality reduction (e.g., PCA) if downstream tasks tolerate lower-dimensional vectors. Regularly update to the latest model version for performance improvements. Monitor latency and adjust batch sizes dynamically for throughput-latency trade-offs.

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?

Great job making it to the end of this tutorial! You've just unlocked the blueprint to harnessing the incredible powers of LlamaIndex, Pgvector, Cohere Command, and jina-embeddings-v3 to create a robust Retrieval-Augmented Generation (RAG) system. Not only have you learned how to seamlessly integrate these components, but you’ve also discovered their distinct strengths. For instance, LlamaIndex helps you organize your data effectively, while Pgvector allows for powerful and efficient vector storage— a perfect match for dynamic queries! Using Cohere Command, you can easily harness the capabilities of a large language model to generate contextually relevant content, and jina-embeddings-v3 ensures that your embeddings are optimized for best performance.

But wait, there’s more! You explored optimization tips throughout the tutorial that can dramatically enhance the efficiency of your RAG pipeline, and you even got your hands on a free RAG cost calculator to help you estimate expenses as you build. The possibilities for innovation are endless. With your newfound knowledge, you are well-equipped to start building, optimizing, and customizing your own RAG applications. So go ahead, let your creativity flow, and experiment with these tools. The world of chatbot interactions, enhanced content generation, and personalized information retrieval is at your fingertips—now is the time to leap into action and transform your ideas into reality!

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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