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64 lines
3.0 KiB
Python
64 lines
3.0 KiB
Python
import ast
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import os
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# FastAPI
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PROJECT_NAME = os.getenv("PROJECT_NAME", "FastAPI")
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# Resources / Computation
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USE_GPU = os.getenv("USE_GPU", "True").lower() == "true"
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GPU_NUMBER = int(os.getenv("GPU_NUMBER", 1))
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MAX_PROCESSES = int(os.getenv("MAX_PROCESSES", 0))
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BATCHSIZE = int(os.getenv("BATCHSIZE", 50))
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CONCURRENT_REQUEST_PER_WORKER = int(os.getenv("CONCURRENT_REQUEST_PER_WORKER", 4))
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# DB
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DB_HOST = os.getenv("DB_HOST", "localhost")
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DB_PORT = int(os.getenv("DB_PORT", 9200))
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DB_USER = os.getenv("DB_USER", "")
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DB_PW = os.getenv("DB_PW", "")
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DB_INDEX = os.getenv("DB_INDEX", "document")
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DB_INDEX_FEEDBACK = os.getenv("DB_INDEX_FEEDBACK", "label")
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ES_CONN_SCHEME = os.getenv("ES_CONN_SCHEME", "http")
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TEXT_FIELD_NAME = os.getenv("TEXT_FIELD_NAME", "text")
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NAME_FIELD_NAME = os.getenv("NAME_FIELD_NAME", "name")
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SEARCH_FIELD_NAME = os.getenv("SEARCH_FIELD_NAME", "text")
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FAQ_QUESTION_FIELD_NAME = os.getenv("FAQ_QUESTION_FIELD_NAME", "question")
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EMBEDDING_FIELD_NAME = os.getenv("EMBEDDING_FIELD_NAME", None)
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EMBEDDING_DIM = os.getenv("EMBEDDING_DIM", None)
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VECTOR_SIMILARITY_METRIC = os.getenv("VECTOR_SIMILARITY_METRIC", "dot_product")
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CREATE_INDEX = os.getenv("CREATE_INDEX", "True").lower() == "true"
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# Reader
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READER_MODEL_PATH = os.getenv("READER_MODEL_PATH", "deepset/roberta-base-squad2")
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READER_TYPE = os.getenv("READER_TYPE", "FARMReader") # alternative: 'TransformersReader'
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READER_TOKENIZER = os.getenv("READER_TOKENIZER", None)
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CONTEXT_WINDOW_SIZE = int(os.getenv("CONTEXT_WINDOW_SIZE", 500))
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DEFAULT_TOP_K_READER = int(os.getenv("DEFAULT_TOP_K_READER", 5))
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TOP_K_PER_CANDIDATE = int(os.getenv("TOP_K_PER_CANDIDATE", 3))
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NO_ANS_BOOST = int(os.getenv("NO_ANS_BOOST", -10))
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DOC_STRIDE = int(os.getenv("DOC_STRIDE", 128))
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MAX_SEQ_LEN = int(os.getenv("MAX_SEQ_LEN", 256))
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# Retriever
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RETRIEVER_TYPE = os.getenv("RETRIEVER_TYPE", "ElasticsearchRetriever") # alternatives: 'EmbeddingRetriever', 'ElasticsearchRetriever', 'ElasticsearchFilterOnlyRetriever', None
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DEFAULT_TOP_K_RETRIEVER = int(os.getenv("DEFAULT_TOP_K_RETRIEVER", 5))
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EXCLUDE_META_DATA_FIELDS = os.getenv("EXCLUDE_META_DATA_FIELDS", f"['question_emb','embedding']")
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if EXCLUDE_META_DATA_FIELDS:
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EXCLUDE_META_DATA_FIELDS = ast.literal_eval(EXCLUDE_META_DATA_FIELDS)
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EMBEDDING_MODEL_PATH = os.getenv("EMBEDDING_MODEL_PATH", None)
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EMBEDDING_MODEL_FORMAT = os.getenv("EMBEDDING_MODEL_FORMAT", "farm")
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# File uploads
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FILE_UPLOAD_PATH = os.getenv("FILE_UPLOAD_PATH", "file-uploads")
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REMOVE_NUMERIC_TABLES = os.getenv("REMOVE_NUMERIC_TABLES", "True").lower() == "true"
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REMOVE_WHITESPACE = os.getenv("REMOVE_WHITESPACE", "True").lower() == "true"
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REMOVE_EMPTY_LINES = os.getenv("REMOVE_EMPTY_LINES", "True").lower() == "true"
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REMOVE_HEADER_FOOTER = os.getenv("REMOVE_HEADER_FOOTER", "True").lower() == "true"
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VALID_LANGUAGES = os.getenv("VALID_LANGUAGES", None)
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if VALID_LANGUAGES:
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VALID_LANGUAGES = ast.literal_eval(VALID_LANGUAGES)
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# Monitoring
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APM_SERVER = os.getenv("APM_SERVER", None)
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APM_SERVICE_NAME = os.getenv("APM_SERVICE_NAME", "haystack-backend")
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