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97ba71e1b3
* k * update enum imports * add functional types + model swaps * remove a log * remove kv * fully functional + robustified for kv swap * validated with hosted + cloud * ensure not updating current search settings when reindexing * add instance check * revert back to updating search settings (will need a slight refactor for endpoint) * protect advanced config override1 * run pretty * fix typing * update typing * remove unnecessary function * update model name * clearer interface names * validated foreign key constaint * proper migration * squash --------- Co-authored-by: Yuhong Sun <yuhongsun96@gmail.com>
71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
import os
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# Used for logging
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SLACK_CHANNEL_ID = "channel_id"
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MODEL_SERVER_HOST = os.environ.get("MODEL_SERVER_HOST") or "localhost"
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MODEL_SERVER_ALLOWED_HOST = os.environ.get("MODEL_SERVER_HOST") or "0.0.0.0"
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MODEL_SERVER_PORT = int(os.environ.get("MODEL_SERVER_PORT") or "9000")
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# Model server for indexing should use a separate one to not allow indexing to introduce delay
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# for inference
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INDEXING_MODEL_SERVER_HOST = (
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os.environ.get("INDEXING_MODEL_SERVER_HOST") or MODEL_SERVER_HOST
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)
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INDEXING_MODEL_SERVER_PORT = int(
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os.environ.get("INDEXING_MODEL_SERVER_PORT") or MODEL_SERVER_PORT
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)
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# Danswer custom Deep Learning Models
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INTENT_MODEL_VERSION = "danswer/hybrid-intent-token-classifier"
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INTENT_MODEL_TAG = "v1.0.3"
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# Bi-Encoder, other details
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DOC_EMBEDDING_CONTEXT_SIZE = 512
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# Used to distinguish alternative indices
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ALT_INDEX_SUFFIX = "__danswer_alt_index"
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# Used for loading defaults for automatic deployments and dev flows
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# For local, use: mixedbread-ai/mxbai-rerank-xsmall-v1
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DEFAULT_CROSS_ENCODER_MODEL_NAME = (
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os.environ.get("DEFAULT_CROSS_ENCODER_MODEL_NAME") or None
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)
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DEFAULT_CROSS_ENCODER_API_KEY = os.environ.get("DEFAULT_CROSS_ENCODER_API_KEY") or None
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DEFAULT_CROSS_ENCODER_PROVIDER_TYPE = (
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os.environ.get("DEFAULT_CROSS_ENCODER_PROVIDER_TYPE") or None
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)
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DISABLE_RERANK_FOR_STREAMING = (
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os.environ.get("DISABLE_RERANK_FOR_STREAMING", "").lower() == "true"
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)
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# This controls the minimum number of pytorch "threads" to allocate to the embedding
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# model. If torch finds more threads on its own, this value is not used.
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MIN_THREADS_ML_MODELS = int(os.environ.get("MIN_THREADS_ML_MODELS") or 1)
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# Model server that has indexing only set will throw exception if used for reranking
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# or intent classification
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INDEXING_ONLY = os.environ.get("INDEXING_ONLY", "").lower() == "true"
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# The process needs to have this for the log file to write to
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# otherwise, it will not create additional log files
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LOG_FILE_NAME = os.environ.get("LOG_FILE_NAME") or "danswer"
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# Enable generating persistent log files for local dev environments
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DEV_LOGGING_ENABLED = os.environ.get("DEV_LOGGING_ENABLED", "").lower() == "true"
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# notset, debug, info, notice, warning, error, or critical
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LOG_LEVEL = os.environ.get("LOG_LEVEL", "notice")
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# Fields which should only be set on new search setting
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PRESERVED_SEARCH_FIELDS = [
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"provider_type",
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"api_key",
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"model_name",
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"index_name",
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"multipass_indexing",
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"model_dim",
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"normalize",
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"passage_prefix",
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"query_prefix",
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]
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