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"""Main entrypoint for the app.""" |
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import os |
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from timeit import default_timer as timer |
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from typing import List, Optional |
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from dotenv import find_dotenv, load_dotenv |
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from langchain.embeddings import HuggingFaceInstructEmbeddings |
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from langchain.vectorstores.chroma import Chroma |
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from langchain.vectorstores.faiss import FAISS |
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from app_modules.llm_loader import LLMLoader |
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from app_modules.llm_qa_chain import QAChain |
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from app_modules.utils import get_device_types, init_settings |
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found_dotenv = find_dotenv(".env") |
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if len(found_dotenv) == 0: |
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found_dotenv = find_dotenv(".env.example") |
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print(f"loading env vars from: {found_dotenv}") |
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load_dotenv(found_dotenv, override=False) |
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init_settings() |
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def app_init(lc_serve: bool = False): |
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os.environ["CURL_CA_BUNDLE"] = "" |
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hf_embeddings_device_type, hf_pipeline_device_type = get_device_types() |
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print(f"hf_embeddings_device_type: {hf_embeddings_device_type}") |
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print(f"hf_pipeline_device_type: {hf_pipeline_device_type}") |
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hf_embeddings_model_name = ( |
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os.environ.get("HF_EMBEDDINGS_MODEL_NAME") or "hkunlp/instructor-xl" |
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) |
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n_threds = int(os.environ.get("NUMBER_OF_CPU_CORES") or "4") |
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index_path = os.environ.get("FAISS_INDEX_PATH") or os.environ.get( |
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"CHROMADB_INDEX_PATH" |
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) |
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using_faiss = os.environ.get("FAISS_INDEX_PATH") is not None |
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llm_model_type = os.environ.get("LLM_MODEL_TYPE") |
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start = timer() |
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embeddings = HuggingFaceInstructEmbeddings( |
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model_name=hf_embeddings_model_name, |
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model_kwargs={"device": hf_embeddings_device_type}, |
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) |
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end = timer() |
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print(f"Completed in {end - start:.3f}s") |
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start = timer() |
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print(f"Load index from {index_path} with {'FAISS' if using_faiss else 'Chroma'}") |
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if not os.path.isdir(index_path): |
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raise ValueError(f"{index_path} does not exist!") |
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elif using_faiss: |
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vectorstore = FAISS.load_local(index_path, embeddings) |
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else: |
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vectorstore = Chroma( |
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embedding_function=embeddings, persist_directory=index_path |
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) |
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end = timer() |
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print(f"Completed in {end - start:.3f}s") |
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start = timer() |
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llm_loader = LLMLoader(llm_model_type, lc_serve) |
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llm_loader.init(n_threds=n_threds, hf_pipeline_device_type=hf_pipeline_device_type) |
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qa_chain = QAChain(vectorstore, llm_loader) |
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end = timer() |
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print(f"Completed in {end - start:.3f}s") |
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return llm_loader, qa_chain |
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