feat: store handle in configs (#2299)

Co-authored-by: Caren Thomas <caren@caren-mac.local>

letta/providers.py

@@ -27,6 +27,10 @@ def get_model_context_window(self, model_name: str) -> Optional[int]:
     def provider_tag(self) -> str:
         """String representation of the provider for display purposes"""
         raise NotImplementedError
+
+    def get_handle(self, model_name: str) -> str:
+        return f"{self.name}/{model_name}"
+
 
 
 class LettaProvider(Provider):
@@ -40,6 +44,7 @@ def list_llm_models(self) -> List[LLMConfig]:
                 model_endpoint_type="openai",
                 model_endpoint="https://inference.memgpt.ai",
                 context_window=16384,
+                handle=self.get_handle("letta-free")
             )
         ]
 
@@ -51,6 +56,7 @@ def list_embedding_models(self):
                 embedding_endpoint="https://embeddings.memgpt.ai",
                 embedding_dim=1024,
                 embedding_chunk_size=300,
+                handle=self.get_handle("letta-free")
             )
         ]
 
@@ -115,7 +121,7 @@ def list_llm_models(self) -> List[LLMConfig]:
                 # continue
 
             configs.append(
-                LLMConfig(model=model_name, model_endpoint_type="openai", model_endpoint=self.base_url, context_window=context_window_size)
+                LLMConfig(model=model_name, model_endpoint_type="openai", model_endpoint=self.base_url, context_window=context_window_size, handle=self.get_handle(model_name))
             )
 
         # for OpenAI, sort in reverse order
@@ -135,6 +141,7 @@ def list_embedding_models(self) -> List[EmbeddingConfig]:
                 embedding_endpoint="https://api.openai.com/v1",
                 embedding_dim=1536,
                 embedding_chunk_size=300,
+                handle=self.get_handle("text-embedding-ada-002")
             )
         ]
 
@@ -163,6 +170,7 @@ def list_llm_models(self) -> List[LLMConfig]:
                     model_endpoint_type="anthropic",
                     model_endpoint=self.base_url,
                     context_window=model["context_window"],
+                    handle=self.get_handle(model["name"])
                 )
             )
         return configs
@@ -195,6 +203,7 @@ def list_llm_models(self) -> List[LLMConfig]:
                         model_endpoint_type="openai",
                         model_endpoint=self.base_url,
                         context_window=model["max_context_length"],
+                        handle=self.get_handle(model["id"])
                     )
                 )
 
@@ -250,6 +259,7 @@ def list_llm_models(self) -> List[LLMConfig]:
                     model_endpoint=self.base_url,
                     model_wrapper=self.default_prompt_formatter,
                     context_window=context_window,
+                    handle=self.get_handle(model["name"])
                 )
             )
         return configs
@@ -325,6 +335,7 @@ def list_embedding_models(self) -> List[EmbeddingConfig]:
                     embedding_endpoint=self.base_url,
                     embedding_dim=embedding_dim,
                     embedding_chunk_size=300,
+                    handle=self.get_handle(model["name"])
                 )
             )
         return configs
@@ -345,7 +356,7 @@ def list_llm_models(self) -> List[LLMConfig]:
                 continue
             configs.append(
                 LLMConfig(
-                    model=model["id"], model_endpoint_type="groq", model_endpoint=self.base_url, context_window=model["context_window"]
+                    model=model["id"], model_endpoint_type="groq", model_endpoint=self.base_url, context_window=model["context_window"], handle=self.get_handle(model["id"])
                 )
             )
         return configs
@@ -413,6 +424,7 @@ def list_llm_models(self) -> List[LLMConfig]:
                     model_endpoint=self.base_url,
                     model_wrapper=self.default_prompt_formatter,
                     context_window=context_window_size,
+                    handle=self.get_handle(model_name)
                 )
             )
 
@@ -493,6 +505,7 @@ def list_llm_models(self):
                     model_endpoint_type="google_ai",
                     model_endpoint=self.base_url,
                     context_window=self.get_model_context_window(model),
+                    handle=self.get_handle(model)
                 )
             )
         return configs
@@ -516,6 +529,7 @@ def list_embedding_models(self):
                     embedding_endpoint=self.base_url,
                     embedding_dim=768,
                     embedding_chunk_size=300,  # NOTE: max is 2048
+                    handle=self.get_handle(model)
                 )
             )
         return configs
@@ -556,7 +570,7 @@ def list_llm_models(self) -> List[LLMConfig]:
             context_window_size = self.get_model_context_window(model_name)
             model_endpoint = get_azure_chat_completions_endpoint(self.base_url, model_name, self.api_version)
             configs.append(
-                LLMConfig(model=model_name, model_endpoint_type="azure", model_endpoint=model_endpoint, context_window=context_window_size)
+                LLMConfig(model=model_name, model_endpoint_type="azure", model_endpoint=model_endpoint, context_window=context_window_size), handle=self.get_handle(model_name)
             )
         return configs
 
@@ -577,6 +591,7 @@ def list_embedding_models(self) -> List[EmbeddingConfig]:
                     embedding_endpoint=model_endpoint,
                     embedding_dim=768,
                     embedding_chunk_size=300,  # NOTE: max is 2048
+                    handle=self.get_handle(model_name)
                 )
             )
         return configs
@@ -610,6 +625,7 @@ def list_llm_models(self) -> List[LLMConfig]:
                     model_endpoint_type="openai",
                     model_endpoint=self.base_url,
                     context_window=model["max_model_len"],
+                    handle=self.get_handle(model["id"])
                 )
             )
         return configs
@@ -642,6 +658,7 @@ def list_llm_models(self) -> List[LLMConfig]:
                     model_endpoint=self.base_url,
                     model_wrapper=self.default_prompt_formatter,
                     context_window=model["max_model_len"],
+                    handle=self.get_handle(model["id"])
                 )
             )
         return configs

letta/schemas/embedding_config.py

@@ -43,6 +43,7 @@ class EmbeddingConfig(BaseModel):
     embedding_model: str = Field(..., description="The model for the embedding.")
     embedding_dim: int = Field(..., description="The dimension of the embedding.")
     embedding_chunk_size: Optional[int] = Field(300, description="The chunk size of the embedding.")
+    handle: Optional[str] = Field(None, description="The handle for this config, in the format provider/model-name.")
 
     # azure only
     azure_endpoint: Optional[str] = Field(None, description="The Azure endpoint for the model.")

letta/schemas/llm_config.py

@@ -44,6 +44,7 @@ class LLMConfig(BaseModel):
         True,
         description="Puts 'inner_thoughts' as a kwarg in the function call if this is set to True. This helps with function calling performance and also the generation of inner thoughts.",
     )
+    handle: Optional[str] = Field(None, description="The handle for this config, in the format provider/model-name.")
 
     # FIXME hack to silence pydantic protected namespace warning
     model_config = ConfigDict(protected_namespaces=())