Build RAG Chatbot with Llamaindex, Pgvector, Mistral Pixtral, and jina-clip-v1

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.)
• Mixtral: A high-performance, sparse mixture-of-experts (MoE) language model designed for efficient text generation and comprehension. Excelling in multilingual support and domain adaptability, it balances speed and accuracy, making it ideal for enterprise-scale applications, real-time chatbots, and resource-constrained environments requiring cost-effective AI solutions.
• Jina-CLIP-V1: A multimodal AI model that bridges text and images via shared embeddings, enabling cross-modal retrieval and understanding. Strengths include robust generalization, efficient scalability, and seamless integration for multilingual and visual-text tasks. Ideal for image-text search, content recommendation, and enhancing AI-driven applications in e-commerce, media, and digital asset management.

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 Mistral Pixtral

%pip install llama-index-llms-mistralai

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(model="pixtral-12b-2409")

Step 3: Install and Set Up jina-clip-v1

%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-clip-v1",
    # 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.

Mistral Pixtral optimization tips

To optimize Mistral Pixtral in a RAG setup, fine-tune its prompt engineering by using clear, structured instructions to guide context integration. Limit retrieved documents to the most relevant chunks (e.g., 3-5) to reduce noise and improve response quality. Adjust temperature (0.2-0.5) for balanced creativity and precision. Use dynamic batching for parallel processing to accelerate inference. Implement quantization (e.g., 4-bit) to reduce memory usage without significant performance loss. Regularly evaluate retrieval alignment with domain-specific benchmarks to refine embedding and reranking strategies.

Jina-CLIP-v1 optimization tips

To optimize Jina-CLIP-v1 in a RAG setup, preprocess inputs by normalizing text and resizing images to match the model’s expected dimensions (e.g., 224x224). Use batch inference to maximize GPU utilization and enable mixed-precision (FP16) for faster processing. Fine-tune the model on domain-specific data to improve retrieval relevance. Cache frequently accessed embeddings to reduce redundant computations. Optimize vector indexing with approximate nearest neighbor (ANN) libraries like FAISS or HNSW for efficient similarity search. Regularly validate embedding quality using downstream task metrics to ensure alignment with retrieval goals.

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?

Wow, what an incredible journey this tutorial has taken us on! You’ve not only learned how to integrate a solid framework like LlamaIndex with a robust vector database such as Pgvector but also discovered the immense potential of leveraging an LLM like Mistral Pixtral alongside an embedding model like jina-clip-v1 to create a powerful RAG (Retrieval-Augmented Generation) system! Each of these components plays a crucial role: LlamaIndex organizes your data efficiently, while Pgvector enhances your information retrieval capabilities, and Mistral Pixtral combined with jina-clip-v1 empowers your application to generate nuanced, context-aware responses. You’ve seen how these elements fit together like pieces of a puzzle, forming a seamless pipeline that can not only retrieve information but also generate insightful content.

But that’s not all! Throughout the tutorial, we’ve sprinkled in some handy optimization tips to get the most out of your RAG system, plus a fantastic free RAG cost calculator to help you estimate the efficiency of your setups. The possibilities for creativity and innovation just keep blooming! So, what are you waiting for? It’s time to roll up your sleeves and dive into building your own RAG applications. Dive deeper, experiment, and don’t shy away from optimizing; the tools and knowledge are right at your fingertips. Embrace the excitement of turning ideas into reality, and let your imagination lead the way to extraordinary solutions!

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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