| | """Main entrypoint for the app."""
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| |
|
| | import os
|
| | 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_community.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.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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| |
|
| | if os.environ.get("LANGCHAIN_DEBUG") == "true":
|
| | from langchain.globals import set_debug
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| |
|
| | set_debug(True)
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| |
|
| | from app_modules.llm_qa_chain import QAChain
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| | from app_modules.llm_chat_chain import ChatChain
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| | import nltk
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| |
|
| |
|
| | def app_init():
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| |
|
| | os.environ["CURL_CA_BUNDLE"] = ""
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| |
|
| | nltk.download("punkt")
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| |
|
| | hf_embeddings_device_type, hf_pipeline_device_type = get_device_types()
|
| | 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 = (
|
| | os.environ.get("HF_EMBEDDINGS_MODEL_NAME") or "hkunlp/instructor-xl"
|
| | )
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| |
|
| | n_threds = int(os.environ.get("NUMBER_OF_CPU_CORES") or "4")
|
| | index_path = os.environ.get("FAISS_INDEX_PATH") or os.environ.get(
|
| | "CHROMADB_INDEX_PATH"
|
| | )
|
| | using_faiss = os.environ.get("FAISS_INDEX_PATH") is not None
|
| | llm_model_type = os.environ.get("LLM_MODEL_TYPE")
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| |
|
| | qa_with_rag = os.getenv("QA_WITH_RAG", "true").lower() == "true"
|
| | print(f"qa_with_rag: {qa_with_rag}")
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| |
|
| | retrieve_from_questions_file = os.getenv("RETRIEVER_TYPE") == "questions_file"
|
| | print(f"retrieve_from_questions_file: {retrieve_from_questions_file}", flush=True)
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| |
|
| | if qa_with_rag and not retrieve_from_questions_file:
|
| | print(f"hf_embeddings_model_name: {hf_embeddings_model_name}")
|
| | start = timer()
|
| | embeddings = HuggingFaceInstructEmbeddings(
|
| | model_name=hf_embeddings_model_name,
|
| | model_kwargs={"device": hf_embeddings_device_type},
|
| | )
|
| | end = timer()
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| |
|
| | print(f"Completed in {end - start:.3f}s")
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| |
|
| | vectorstore = None
|
| | if qa_with_rag and not retrieve_from_questions_file:
|
| | 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):
|
| | raise ValueError(f"{index_path} does not exist!")
|
| | elif using_faiss:
|
| | vectorstore = FAISS.load_local(
|
| | index_path, embeddings, allow_dangerous_deserialization=True
|
| | )
|
| | else:
|
| | vectorstore = Chroma(
|
| | embedding_function=embeddings, persist_directory=index_path
|
| | )
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| |
|
| | end = timer()
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| |
|
| | print(f"Completed in {end - start:.3f}s")
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| |
|
| | start = timer()
|
| | llm_loader = LLMLoader(llm_model_type)
|
| | llm_loader.init(n_threds=n_threds, hf_pipeline_device_type=hf_pipeline_device_type)
|
| | if qa_with_rag:
|
| | qa_chain = QAChain(vectorstore, llm_loader)
|
| | else:
|
| | qa_chain = ChatChain(llm_loader)
|
| | end = timer()
|
| | print(f"Completed in {end - start:.3f}s")
|
| |
|
| | return llm_loader, qa_chain
|
| |
|
