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fef7839e8a
* fix(custom_logger.py): expose new 'async_get_chat_completion_prompt' event hook * fix(custom_logger.py): langfuse_prompt_management.py remove 'headers' from custom logger 'async_get_chat_completion_prompt' and 'get_chat_completion_prompt' event hooks * feat(router.py): expose new function for prompt management based routing * feat(router.py): partial working router prompt factory logic allows load balanced model to be used for model name w/ langfuse prompt management call * feat(router.py): fix prompt management with load balanced model group * feat(langfuse_prompt_management.py): support reading in openai params from langfuse enables user to define optional params on langfuse vs. client code * test(test_Router.py): add unit test for router based langfuse prompt management * fix: fix linting errors |
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| .. | ||
| audio_utils | ||
| llm_cost_calc | ||
| llm_response_utils | ||
| prompt_templates | ||
| specialty_caches | ||
| tokenizers | ||
| asyncify.py | ||
| core_helpers.py | ||
| default_encoding.py | ||
| duration_parser.py | ||
| exception_mapping_utils.py | ||
| get_llm_provider_logic.py | ||
| get_supported_openai_params.py | ||
| health_check_utils.py | ||
| initialize_dynamic_callback_params.py | ||
| json_validation_rule.py | ||
| litellm_logging.py | ||
| llm_request_utils.py | ||
| logging_utils.py | ||
| mock_functions.py | ||
| README.md | ||
| realtime_streaming.py | ||
| redact_messages.py | ||
| response_header_helpers.py | ||
| rules.py | ||
| streaming_chunk_builder_utils.py | ||
| streaming_handler.py | ||
| token_counter.py | ||
Folder Contents
This folder contains general-purpose utilities that are used in multiple places in the codebase.
Core files:
streaming_handler.py: The core streaming logic + streaming related helper utilscore_helpers.py: code used intypes/- e.g.map_finish_reason.exception_mapping_utils.py: utils for mapping exceptions to openai-compatible error types.default_encoding.py: code for loading the default encoding (tiktoken)get_llm_provider_logic.py: code for inferring the LLM provider from a given model name.duration_parser.py: code for parsing durations - e.g. "1d", "1mo", "10s"