Deploying LlamaIndex with openGauss

LlamaIndex (formerly GPT Index) is a data framework designed specifically for large language model (LLM) applications. When developing with LlamaIndex, you typically need to combine its core functionality with selected integration plugins.

Containerized Deployment of openGauss

For details, see Container Image Installation.

LlamaIndex Deployment

Install Dependencies

Install dependencies from source:

bash

pip install llama-index-vector-stores-opengauss

Write the Code

LlamaIndex supports custom workflows. The following is a demo of building a RAG application with openGauss and ollama: Note: Create a data folder in the same directory and place documents in the folder.

Install the required dependencies:

bash

pip install llama-index-llms-ollama
pip install llama-index-embeddings-ollama

python

import os
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext
from llama_index.core import Settings
from llama_index.llms.ollama import Ollama
from llama_index.embeddings.ollama import OllamaEmbedding
from llama_index.vector_stores.openGauss import OpenGaussStore

def build_rag_app():
    vector_store = OpenGaussStore.from_params(
        database="postgres",
        host="127.0.0.1",
        password="xxxxxx",
        port=8888,
        user="postgres",
        table_name="paul_graham_essay",
        embed_dim=768  # openai embedding dimension
    )

    # 2. Create the storage context
    storage_context = StorageContext.from_defaults(vector_store=vector_store)

    # 3. Load documents
    documents = SimpleDirectoryReader("data").load_data()

    # 4. Create the index
    index = VectorStoreIndex.from_documents(
        documents,
        storage_context=storage_context,
        show_progress=True
    )

    # 5. Create the query engine
    query_engine = index.as_query_engine(
        similarity_top_k=3,
        vector_store_kwargs={
            "hybrid_search": True,
            "text_search_config": "english"
        }
    )
    return query_engine

if __name__ == "__main__":
    # Configure the local model
    Settings.llm = Ollama(
        model="llama3:latest",
        base_url="http://127.0.0.1:11434",
        request_timeout=300.0,
        
        temperature=0.3,
        top_p=0.9,
        top_k=40,
        
        num_ctx=2048,
        num_gpu=1,
        num_thread=8,
        
        keep_alive="5m",
        tfs_z=1.0,
        
        headers={
            "Content-Type": "application/json",
            "Accept": "application/json",
            "Connection": "keep-alive"
        },
        
        max_retries=3,
        retry_delay=5,
        
        format="json",
        stream=False
    )
    
    Settings.embed_model = OllamaEmbedding(
        model_name="nomic-embed-text:latest",
        base_url="http://127.0.0.1:11434",
        ollama_additional_kwargs={
            "embedding_only": True,
            "options": {
                "num_ctx": 2048,
                "num_gpu": 1,
                "use_mlock": True
            }
        },
        request_timeout=300,
        headers={
            "Content-Type": "application/json",
            "Accept": "application/json"
        }
    )

    # Initialize the RAG application
    rag_engine = build_rag_app()
    
    # Example query
    query = "What did the author have for breakfast?"
    response = rag_engine.query(query)
    
    print(f"Question: {query}")
    print(f"Answer: {str(response).strip()}")

Deploying LlamaIndex with openGauss ​

Containerized Deployment of openGauss ​

LlamaIndex Deployment ​

Install Dependencies ​

Write the Code ​

Deploying LlamaIndex with openGauss

Containerized Deployment of openGauss

LlamaIndex Deployment

Install Dependencies

Write the Code