Moved llama-stack-as-library client to llama-stack

llama_stack/distribution/build.py

@@ -46,7 +46,7 @@ class ApiInput(BaseModel):
 
 
 def get_provider_dependencies(
-    config_providers: Dict[str, List[Provider]]
+    config_providers: Dict[str, List[Provider]],
 ) -> tuple[list[str], list[str]]:
     """Get normal and special dependencies from provider configuration."""
     all_providers = get_provider_registry()
@@ -92,11 +92,11 @@ def print_pip_install_help(providers: Dict[str, List[Provider]]):
     normal_deps, special_deps = get_provider_dependencies(providers)
 
     cprint(
-        f"Please install needed dependencies using the following commands:\n\n\tpip install {' '.join(normal_deps)}",
+        f"Please install needed dependencies using the following commands:\n\npip install {' '.join(normal_deps)}",
         "yellow",
     )
     for special_dep in special_deps:
-        cprint(f"\tpip install {special_dep}", "yellow")
+        cprint(f"pip install {special_dep}", "yellow")
     print()
 
 

llama_stack/distribution/library_client.py

@@ -0,0 +1,173 @@
+# 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 inspect
+from pathlib import Path
+from typing import Any, get_args, get_origin, Optional
+
+import yaml
+from llama_stack_client import LlamaStackClient, NOT_GIVEN
+from pydantic import TypeAdapter
+from rich.console import Console
+
+from termcolor import cprint
+
+from llama_stack.distribution.build import print_pip_install_help
+from llama_stack.distribution.configure import parse_and_maybe_upgrade_config
+from llama_stack.distribution.resolver import ProviderRegistry
+from llama_stack.distribution.server.endpoints import get_all_api_endpoints
+from llama_stack.distribution.stack import (
+    construct_stack,
+    get_stack_run_config_from_template,
+    replace_env_vars,
+)
+
+
+class LlamaStackAsLibraryClient(LlamaStackClient):
+    def __init__(
+        self,
+        config_path_or_template_name: str,
+        custom_provider_registry: Optional[ProviderRegistry] = None,
+    ):
+        if config_path_or_template_name.endswith(".yaml"):
+            config_path = Path(config_path_or_template_name)
+            if not config_path.exists():
+                raise ValueError(f"Config file {config_path} does not exist")
+            config_dict = replace_env_vars(yaml.safe_load(config_path.read_text()))
+            config = parse_and_maybe_upgrade_config(config_dict)
+        else:
+            # template
+            config = get_stack_run_config_from_template(config_path_or_template_name)
+
+        self.config_path_or_template_name = config_path_or_template_name
+        self.config = config
+        self.custom_provider_registry = custom_provider_registry
+
+        super().__init__()
+
+    async def initialize(self):
+        try:
+            self.impls = await construct_stack(
+                self.config, self.custom_provider_registry
+            )
+        except ModuleNotFoundError as e:
+            cprint(
+                "Using llama-stack as a library requires installing dependencies depending on the template (providers) you choose.\n",
+                "yellow",
+            )
+            print_pip_install_help(self.config.providers)
+            raise e
+
+        console = Console()
+        console.print(f"Using config [blue]{self.config_path_or_template_name}[/blue]:")
+        console.print(yaml.dump(self.config.model_dump(), indent=2))
+
+        endpoints = get_all_api_endpoints()
+        endpoint_impls = {}
+        for api, api_endpoints in endpoints.items():
+            for endpoint in api_endpoints:
+                impl = self.impls[api]
+                func = getattr(impl, endpoint.name)
+                endpoint_impls[endpoint.route] = func
+
+        self.endpoint_impls = endpoint_impls
+
+    async def get(
+        self,
+        path: str,
+        *,
+        stream=False,
+        **kwargs,
+    ):
+        if not self.endpoint_impls:
+            raise ValueError("Client not initialized")
+
+        if stream:
+            return self._call_streaming(path, "GET")
+        else:
+            return await self._call_non_streaming(path, "GET")
+
+    async def post(
+        self,
+        path: str,
+        *,
+        body: dict = None,
+        stream=False,
+        **kwargs,
+    ):
+        if not self.endpoint_impls:
+            raise ValueError("Client not initialized")
+
+        if stream:
+            return self._call_streaming(path, "POST", body)
+        else:
+            return await self._call_non_streaming(path, "POST", body)
+
+    async def _call_non_streaming(self, path: str, method: str, body: dict = None):
+        func = self.endpoint_impls.get(path)
+        if not func:
+            raise ValueError(f"No endpoint found for {path}")
+
+        body = self._convert_body(path, body)
+        return await func(**body)
+
+    async def _call_streaming(self, path: str, method: str, body: dict = None):
+        func = self.endpoint_impls.get(path)
+        if not func:
+            raise ValueError(f"No endpoint found for {path}")
+
+        body = self._convert_body(path, body)
+        async for chunk in await func(**body):
+            yield chunk
+
+    def _convert_body(self, path: str, body: Optional[dict] = None) -> dict:
+        if not body:
+            return {}
+
+        func = self.endpoint_impls[path]
+        sig = inspect.signature(func)
+
+        # Strip NOT_GIVENs to use the defaults in signature
+        body = {k: v for k, v in body.items() if v is not NOT_GIVEN}
+
+        # Convert parameters to Pydantic models where needed
+        converted_body = {}
+        for param_name, param in sig.parameters.items():
+            if param_name in body:
+                value = body.get(param_name)
+                converted_body[param_name] = self._convert_param(
+                    param.annotation, value
+                )
+        return converted_body
+
+    def _convert_param(self, annotation: Any, value: Any) -> Any:
+        if isinstance(annotation, type) and annotation in {str, int, float, bool}:
+            return value
+
+        origin = get_origin(annotation)
+        if origin is list:
+            item_type = get_args(annotation)[0]
+            try:
+                return [self._convert_param(item_type, item) for item in value]
+            except Exception:
+                return value
+
+        elif origin is dict:
+            key_type, val_type = get_args(annotation)
+            try:
+                return {k: self._convert_param(val_type, v) for k, v in value.items()}
+            except Exception:
+                return value
+
+        try:
+            # Handle Pydantic models and discriminated unions
+            return TypeAdapter(annotation).validate_python(value)
+        except Exception as e:
+            cprint(
+                f"Warning: direct client failed to convert parameter {value} into {annotation}: {e}",
+                "yellow",
+            )
+            return value

llama_stack/distribution/tests/library_client_test.py

@@ -0,0 +1,59 @@
+# 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
+
+from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
+from llama_stack_client.types import UserMessage
+
+
+async def main(config_path: str):
+    client = LlamaStackAsLibraryClient(config_path)
+    await client.initialize()
+
+    models = await client.models.list()
+    print(models)
+    if not models:
+        print("No models found, skipping chat completion test")
+        return
+
+    model_id = models[0].identifier
+    response = await client.inference.chat_completion(
+        messages=[UserMessage(content="What is the capital of France?", role="user")],
+        model_id=model_id,
+        stream=False,
+    )
+    print("\nChat completion response:")
+    print(response)
+
+    response = await client.inference.chat_completion(
+        messages=[UserMessage(content="What is the capital of France?", role="user")],
+        model_id=model_id,
+        stream=True,
+    )
+    print("\nChat completion stream response:")
+    async for chunk in response:
+        print(chunk)
+
+    response = await client.memory_banks.register(
+        memory_bank_id="memory_bank_id",
+        params={
+            "chunk_size_in_tokens": 0,
+            "embedding_model": "embedding_model",
+            "memory_bank_type": "vector",
+        },
+    )
+    print("\nRegister memory bank response:")
+    print(response)
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("config_path", help="Path to the config YAML file")
+    args = parser.parse_args()
+    asyncio.run(main(args.config_path))

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -269,7 +269,6 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
             r = await self.client.chat(**params)
         else:
             r = await self.client.generate(**params)
-        assert isinstance(r, dict)
 
         if "message" in r:
             choice = OpenAICompatCompletionChoice(