API Updates (#73)

* API Keys passed from Client instead of distro configuration

* delete distribution registry

* Rename the "package" word away

* 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.

* update `apis_to_serve`

* llama_toolchain -> llama_stack

* Codemod from llama_toolchain -> llama_stack

- added providers/registry
- cleaned up api/ subdirectories and moved impls away
- restructured api/api.py
- from llama_stack.apis.<api> import foo should work now
- update imports to do llama_stack.apis.<api>
- update many other imports
- added __init__, fixed some registry imports
- updated registry imports
- create_agentic_system -> create_agent
- AgenticSystem -> Agent

* Moved some stuff out of common/; re-generated OpenAPI spec

* llama-toolchain -> llama-stack (hyphens)

* add control plane API

* add redis adapter + sqlite provider

* move core -> distribution

* Some more toolchain -> stack changes

* small naming shenanigans

* Removing custom tool and agent utilities and moving them client side

* Move control plane to distribution server for now

* Remove control plane from API list

* no codeshield dependency randomly plzzzzz

* Add "fire" as a dependency

* add back event loggers

* stack configure fixes

* use brave instead of bing in the example client

* add init file so it gets packaged

* add init files so it gets packaged

* Update MANIFEST

* bug fix

---------

Co-authored-by: Hardik Shah <hjshah@fb.com>
Co-authored-by: Xi Yan <xiyan@meta.com>
Co-authored-by: Ashwin Bharambe <ashwin@meta.com>

llama_stack/cli/model/describe.py

@@ -0,0 +1,75 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import argparse
+import json
+
+from llama_models.sku_list import resolve_model
+
+from termcolor import colored
+
+from llama_stack.cli.subcommand import Subcommand
+from llama_stack.cli.table import print_table
+from llama_stack.distribution.utils.serialize import EnumEncoder
+
+
+class ModelDescribe(Subcommand):
+    """Show details about a model"""
+
+    def __init__(self, subparsers: argparse._SubParsersAction):
+        super().__init__()
+        self.parser = subparsers.add_parser(
+            "describe",
+            prog="llama model describe",
+            description="Show details about a llama model",
+            formatter_class=argparse.RawTextHelpFormatter,
+        )
+        self._add_arguments()
+        self.parser.set_defaults(func=self._run_model_describe_cmd)
+
+    def _add_arguments(self):
+        self.parser.add_argument(
+            "-m",
+            "--model-id",
+            type=str,
+            required=True,
+        )
+
+    def _run_model_describe_cmd(self, args: argparse.Namespace) -> None:
+        model = resolve_model(args.model_id)
+        if model is None:
+            self.parser.error(
+                f"Model {args.model_id} not found; try 'llama model list' for a list of available models."
+            )
+            return
+
+        rows = [
+            (
+                colored("Model", "white", attrs=["bold"]),
+                colored(model.descriptor(), "white", attrs=["bold"]),
+            ),
+            ("HuggingFace ID", model.huggingface_repo or "<Not Available>"),
+            ("Description", model.description_markdown),
+            ("Context Length", f"{model.max_seq_length // 1024}K tokens"),
+            ("Weights format", model.quantization_format.value),
+            ("Model params.json", json.dumps(model.model_args, indent=4)),
+        ]
+
+        if model.recommended_sampling_params is not None:
+            sampling_params = model.recommended_sampling_params.dict()
+            for k in ("max_tokens", "repetition_penalty"):
+                del sampling_params[k]
+            rows.append(
+                (
+                    "Recommended sampling params",
+                    json.dumps(sampling_params, cls=EnumEncoder, indent=4),
+                )
+            )
+
+        print_table(
+            rows,
+            separate_rows=True,
+        )