router method wrapper

llama_stack/distribution/routers/routers.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any, AsyncGenerator, Dict, List, Tuple
+from typing import Any, Dict, List, Tuple
 
 from llama_stack.distribution.datatypes import Api
 
@@ -12,6 +12,10 @@ from .routing_table import RoutingTable
 from llama_stack.apis.memory import *  # noqa: F403
 from llama_stack.apis.inference import *  # noqa: F403
 
+from types import MethodType
+
+from termcolor import cprint
+
 
 class MemoryRouter(Memory):
     """Routes to an provider based on the memory bank type"""
@@ -84,9 +88,10 @@ class InferenceRouter(Inference):
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
-        provider_impl = self.routing_table.get_provider_impl(self.api, model)
-        print("InferenceRouter: chat_completion", provider_impl)
-        async for chunk in provider_impl.chat_completion(
+        # TODO: we need to fix streaming response to align provider implementations with Protocol
+        async for chunk in self.routing_table.get_provider_impl(
+            self.api, model
+        ).chat_completion(
             model=model,
             messages=messages,
             sampling_params=sampling_params,
@@ -97,3 +102,29 @@ class InferenceRouter(Inference):
             logprobs=logprobs,
         ):
             yield chunk
+
+    async def completion(
+        self,
+        model: str,
+        content: InterleavedTextMedia,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> Union[CompletionResponse, CompletionResponseStreamChunk]:
+        return await self.routing_table.get_provider_impl(self.api, model).completion(
+            model=model,
+            content=content,
+            sampling_params=sampling_params,
+            stream=stream,
+            logprobs=logprobs,
+        )
+
+    async def embeddings(
+        self,
+        model: str,
+        contents: List[InterleavedTextMedia],
+    ) -> EmbeddingsResponse:
+        return await self.routing_table.get_provider_impl(self.api, model).embeddings(
+            model=model,
+            contents=contents,
+        )

llama_stack/examples/router-table-run.yaml

@@ -20,14 +20,14 @@ provider_routing_table:
         torch_seed: null
         max_seq_len: 4096
         max_batch_size: 1
-    - routing_key: Meta-Llama3.1-8B
-      provider_id: meta-reference
-      config:
-        model: Meta-Llama3.1-8B
-        quantization: null
-        torch_seed: null
-        max_seq_len: 4096
-        max_batch_size: 1
+    # - routing_key: Meta-Llama3.1-8B
+    #   provider_id: meta-reference
+    #   config:
+    #     model: Meta-Llama3.1-8B
+    #     quantization: null
+    #     torch_seed: null
+    #     max_seq_len: 4096
+    #     max_batch_size: 1
   # memory:
   #   - routing_key: keyvalue
   #     provider_id: remote::pgvector