o Á¿i´ã@s†ddlmZddlmZddlmZdZdZdZdZdZd Z d Z eeeee e fZ d e d e fd d„Z de fdd„Zded dfdd„ZdS)é)Ú"CACHE_READ_INPUT_TOKENS_METRIC_KEY)Ú#CACHE_WRITE_INPUT_TOKENS_METRIC_KEY)ÚINPUT_TOKENS_METRIC_KEY)úfoundation-model/ú custom-model/zprovisioned-model/zimported-model/zprompt/z endpoint/úinference-profile/zdefault-prompt-router/Úai21ÚamazonÚ anthropicÚcohereÚmetaÚ stabilityÚoriginal_model_idÚreturncCstD] }||vr |SqdS)zbIf the original model ID contains a known model provider, return it, otherwise fallback to custom.Úcustom)Ú_MODEL_PROVIDERS)rÚprovider©rú^/home/ubuntu/.local/lib/python3.10/site-packages/ddtrace/llmobs/_integrations/bedrock_utils.pyÚ_fallback_providers ÿrÚmodel_idcCsÄ|}| d¡s"| d¡}t|ƒdkrt|ƒ|dfS|d|dfStD]7}||vr+q$| |d¡d}|dvrT| d¡}t|ƒdkrJt|ƒ|fS|d|dfSt|ƒ|fSt|ƒd fS) a`Best effort to extract and return the model provider and model name from the bedrock model ID. model_id can be a 1/2 period-separated string or a full AWS ARN, based on the following formats: 1. Base model: "{model_provider}.{model_name}" 2. Cross-region model: "{region}.{model_provider}.{model_name}" 3. Other: Prefixed by AWS ARN "arn:aws{+region?}:bedrock:{region}:{account-id}:" a. Foundation model: ARN prefix + "foundation-model/{region?}.{model_provider}.{model_name}" b. Custom model: ARN prefix + "custom-model/{model_provider}.{model_name}" c. Provisioned model: ARN prefix + "provisioned-model/{model-id}" d. Imported model: ARN prefix + "imported-module/{model-id}" e. Prompt management: ARN prefix + "prompt/{prompt-id}" f. Sagemaker: ARN prefix + "endpoint/{model-id}" g. Inference profile: ARN prefix + "{application-?}inference-profile/{model-id}" h. Default prompt router: ARN prefix + "default-prompt-router/{prompt-id}" If model provider cannot be inferred from the model_id formatting, then default to "custom" zarn:awsÚ.éréþÿÿÿéÿÿÿÿé)rrrr)Ú startswithÚsplitÚlenrÚ_MODEL_TYPE_IDENTIFIERSÚrsplit)rrÚ model_metaÚ identifierrrrÚparse_model_id"s"      r#Ú usage_metricsNcCsLt|vst|vr$| td¡}| td¡}| td¡}||||t<dSdS)a8 `input_tokens` in bedrock's response usage metadata is the number of non-cached tokens. We normalize it to mean the total tokens sent to the model to be consistent with other model providers. Args: usage_metrics: Dictionary containing token usage metrics that will be modified in-place rN)rrÚgetr)r$Ú input_tokensÚcache_read_tokensÚcache_write_tokensrrrÚnormalize_input_tokensGs    ür))Úddtrace.llmobs._constantsrrrrÚ_AI21Ú_AMAZONÚ _ANTHROPICÚ_COHEREÚ_METAÚ _STABILITYrÚstrrr#Údictr)rrrrÚs   %