Ashwin Bharambe b6a3ef51da Introduce a "Router" layer for providers ... Some providers need to be factorized and considered as thin routing layers on top of other providers. Consider two examples: - The inference API should be a routing layer over inference providers, routed using the "model" key - The memory banks API is another instance where various memory bank types will be provided by independent providers (e.g., a vector store is served by Chroma while a keyvalue memory can be served by Redis or PGVector) This commit introduces a generalized routing layer for this purpose.		2024-09-16 17:04:45 -07:00
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__init__.py	Initial commit	2024-07-23 08:32:33 -07:00
config_dirs.py	API Updates: fleshing out RAG APIs, introduce "llama stack" CLI command (#51)	2024-09-03 22:39:39 -07:00
deployment_types.py	Enable Bing search (#59)	2024-09-10 12:34:29 -07:00
exec.py	Introduce Llama stack distributions (#22)	2024-08-08 13:38:41 -07:00
model_utils.py	Add a --manifest-file option to llama download	2024-08-17 10:08:42 -07:00
prompt_for_config.py	Introduce a "Router" layer for providers	2024-09-16 17:04:45 -07:00
serialize.py	API Updates: fleshing out RAG APIs, introduce "llama stack" CLI command (#51)	2024-09-03 22:39:39 -07:00
training_types.py	llama_models.llama3_1 -> llama_models.llama3	2024-08-19 10:55:37 -07:00