llama-stack-mirror/llama_toolchain/cli
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
..
model Nuke hardware_requirements from SKUs 2024-09-13 16:39:02 -07:00
scripts Add a script for install a pip wheel from a presigned url 2024-08-23 12:18:51 -07:00
stack Introduce a "Router" layer for providers 2024-09-16 17:04:45 -07:00
__init__.py Initial commit 2024-07-23 08:32:33 -07:00
download.py Make llama model download error message a bit better 2024-09-14 08:06:55 -07:00
llama.py API Updates: fleshing out RAG APIs, introduce "llama stack" CLI command (#51) 2024-09-03 22:39:39 -07:00
subcommand.py Initial commit 2024-07-23 08:32:33 -07:00
table.py Introduce Llama stack distributions (#22) 2024-08-08 13:38:41 -07:00