Delete query_engine.py

query_engine.py

@@ -1,143 +0,0 @@
-import os
-import yaml
-from dotenv import load_dotenv
-from pinecone import Pinecone
-from llama_index.vector_stores.pinecone import PineconeVectorStore
-from llama_index.core import VectorStoreIndex
-from llama_index.core.response.pprint_utils import pprint_source_node
-from llama_index.core import Settings
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from llama_index.llms.groq import Groq
-from llama_index.core.tools import QueryEngineTool
-from llama_index.core.query_engine import RouterQueryEngine
-from llama_index.core.selectors import LLMSingleSelector, LLMMultiSelector
-from llama_index.core.selectors import (
-    PydanticMultiSelector,
-    PydanticSingleSelector,
-)
-from llama_index.core import PromptTemplate
-from llama_index.core.response_synthesizers import TreeSummarize
-import nest_asyncio
-import asyncio
-nest_asyncio.apply()
-# Load environment variables from the .env file
-load_dotenv()
-
-# Function to load YAML configuration
-def load_config(config_path):
-    with open(config_path, 'r') as file:
-        config = yaml.safe_load(file)
-    return config
-def load_prompt_template(prompt_template_path):
-    with open(prompt_template_path, 'r') as file:
-        prompt_template = yaml.safe_load(file)
-    return prompt_template
-# Pinecone Index Connection
-def index_connection(config_path):
-    """
-    Initializes the Pinecone client and retrieves the index using the provided YAML configuration.
-    
-    Args:
-    config_path (str): Path to the YAML configuration file.
-    
-    Returns:
-    index: The initialized Pinecone index.
-    """
-    # Load the configuration from a YAML file
-    config = load_config(config_path)
-    embed_model_name = config['embeddings']['model_name']
-    embed_model = HuggingFaceEmbedding(model_name=embed_model_name)
-    model_name = config['model']['model_name']
-    Settings.llm = Groq(model=model_name, api_key=os.getenv('GROQ_API_KEY'))
-    Settings.embed_model = embed_model   
-    # Initialize the Pinecone client
-    pc = Pinecone(
-        api_key=os.getenv('PINECONE_API_KEY')  # Get the Pinecone API key from the environment
-    )
-    index_name = config['pinecone']['index_name']
-    summary_index_name = config['pinecone']['summary_index_name']
-    index = pc.Index(index_name) 
-    summary_index = pc.Index(summary_index_name)  # Get the Pinecone index using the index name from the config
-    return index,summary_index
-
-# Initialize Pinecone Vector Store and Retriever
-def initialize_retriever(pinecone_index,summary_index):
-    """
-    Initializes the Pinecone vector store and sets up the retriever.
-    
-    Args:
-    pinecone_index: The Pinecone index object.
-    
-    Returns:
-    retriever: The initialized retriever for querying the vector store.
-    """
-
-    # Initialize Pinecone Vector Store
-    vector_store = PineconeVectorStore(pinecone_index=pinecone_index, text_key="_node_content")
-    summary_vector_store = PineconeVectorStore(pinecone_index=summary_index, text_key="_node_content")
-    # Create the retriever using the VectorStoreIndex and configure similarity_top_k
-    index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
-    summary_index = VectorStoreIndex.from_vector_store(vector_store=summary_vector_store)   
-    return index,summary_index
-
-# Query the Pinecone Index
-def index_retrieval(index, summary_index, query_text):
-    """
-    Queries the Pinecone index using the provided retriever and query text.
-    
-    Args:
-    retriever: The initialized retriever.
-    query_text (str): The text query to search for.
-    
-    Returns:
-    str: Query result from the Pinecone index.
-    """
-    script_dir = os.path.dirname(os.path.abspath(__file__))  # Get the current script directory
-    base_dir = os.path.dirname(script_dir) 
-    prompt_template_path = os.path.join(base_dir, 'model', 'prompt_template.yaml')
-    prompt_template = load_prompt_template(prompt_template_path)
-    QA_PROMPT = PromptTemplate(prompt_template['QA_PROMPT_TMPL'])
-    # Execute the query using the retriever
-    vector_query_engine = index.as_query_engine(text_qa_template=QA_PROMPT)
-    summary_query_engine = summary_index.as_query_engine(text_qa_template=QA_PROMPT)
-
-    vector_tool = QueryEngineTool.from_defaults(
-    query_engine=vector_query_engine,
-    description="Useful for answering questions about this context",
-    )
-
-    summary_tool = QueryEngineTool.from_defaults(
-        query_engine=summary_query_engine,
-        description="Useful for answering questions about this context",
-    )
-
-    tree_summarize = TreeSummarize(
-        summary_template=PromptTemplate(prompt_template['TREE_SUMMARIZE_PROMPT_TMPL'])
-    )
-    query_engine = RouterQueryEngine(
-    selector=LLMMultiSelector.from_defaults(),
-    query_engine_tools=[
-        vector_tool,
-        summary_tool,
-    ],
-    summarizer=tree_summarize,)
-    response = query_engine.query(query_text)
-    return response
-# Example usage
-if __name__ == "__main__":
-    # Dynamically determine the path to the config file
-    script_dir = os.path.dirname(os.path.abspath(__file__))  # Get the current script directory
-    base_dir = os.path.dirname(script_dir)  # Go one level up
-    config_path = os.path.join(base_dir, 'configs', 'config.yaml')  # Path to 'config.yaml' in the 'configs' directory
-    
-    # Step 1: Initialize Pinecone Connection
-    pinecone_index,summary_index = index_connection(config_path=config_path)
-    
-    # Step 2: Initialize the Retriever
-    retriever,summary_retriever = initialize_retriever(pinecone_index,summary_index)
-    
-    # Step 3: Query the Pinecone index
-    query_text = """How much can the Minister of Health pay out of the Consolidated Revenue Fund in relation to coronavirus disease 2019 (COVID-19) tests"""
-    response = index_retrieval(retriever, summary_retriever, query_text)
-    print(response)
-    # Print the result (already printed by pprint_source_node)