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File size: 2,564 Bytes
9c90775 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | # import os
# import sys
# import asyncio
# import logging
# sys.path.append(os.getcwd())
# from dotenv import load_dotenv
# load_dotenv()
# import logger
# import logging
# from src.entity.config_entity import (
# DataIngestionConfig,
# ContentEmbedderConfig,
# DataTransformationConfig,
# ContentTransformationConfig
# )
# from src.pipeline.Vectiorizer_pipeline import VectiorizerPipeline
# from src.pipeline.GraphRunner_pipeline import RunGraphPipeline
# from langchain_core.messages import HumanMessage
# async def main():
# thread_id = "123"
# logging.info("Starting Full Pipeline Integration Test...")
# ingestion_configs = [
# DataIngestionConfig(
# input_file_path="/home/vashuthegreat/Projects/Multi-Rag/data/growing_ai_tools.txt",
# save_file_path=f"artifacts/{thread_id}/ingestion/growing_ai_tools.pdf"
# ),
# DataIngestionConfig(
# input_file_path="/home/vashuthegreat/Projects/Multi-Rag/data/lena.png",
# save_file_path=f"artifacts/{thread_id}/ingestion/lena.pdf"
# )
# ]
# content_embedder_config = ContentEmbedderConfig(data_ingestion_configs=ingestion_configs)
# transformation_configs = [
# DataTransformationConfig(vector_store_path=f"artifacts/{thread_id}/transformation/vector_store/growing_ai_tools"),
# DataTransformationConfig(vector_store_path=f"artifacts/{thread_id}/transformation/vector_store/lena")
# ]
# content_transformation_config = ContentTransformationConfig(data_transformation_configs=transformation_configs)
# vectorizer_pipeline = VectiorizerPipeline(
# content_embedder_config=content_embedder_config,
# content_transformation_config=content_transformation_config
# )
# result = await vectorizer_pipeline.initiate(thread_id=thread_id)
# logging.info(f"Vectorizer Pipeline Result: {result}")
# vector_store_paths = [art.vector_store_path for art in result.data_transformation_artifacts]
# initial_state = {
# "messages": [HumanMessage(content="What is growing AI tools?")],
# "vector_store_file_paths": vector_store_paths,
# "queries": [],
# "retreived_results": [],
# "ai_response": ""
# }
# graph_pipeline = RunGraphPipeline()
# graph_result = await graph_pipeline.run_graph(initial_state, config={"configurable": {"thread_id": thread_id}})
# logging.info(f"Graph execution result: {graph_result}")
# if __name__ == "__main__":
# asyncio.run(main()) |