fix: patch errors with OllamaProvider (#1875)

letta/providers.py

@@ -140,9 +140,17 @@ class AnthropicProvider(Provider):
 
 
 class OllamaProvider(OpenAIProvider):
+    """Ollama provider that uses the native /api/generate endpoint
+
+    See: https://github.com/ollama/ollama/blob/main/docs/api.md#generate-a-completion
+    """
+
     name: str = "ollama"
     base_url: str = Field(..., description="Base URL for the Ollama API.")
     api_key: Optional[str] = Field(None, description="API key for the Ollama API (default: `None`).")
+    default_prompt_formatter: str = Field(
+        ..., description="Default prompt formatter (aka model wrapper) to use on a /completions style API."
+    )
 
     def list_llm_models(self) -> List[LLMConfig]:
         # https://github.com/ollama/ollama/blob/main/docs/api.md#list-local-models
@@ -156,11 +164,15 @@ class OllamaProvider(OpenAIProvider):
         configs = []
         for model in response_json["models"]:
             context_window = self.get_model_context_window(model["name"])
+            if context_window is None:
+                print(f"Ollama model {model['name']} has no context window")
+                continue
             configs.append(
                 LLMConfig(
                     model=model["name"],
                     model_endpoint_type="ollama",
                     model_endpoint=self.base_url,
+                    model_wrapper=self.default_prompt_formatter,
                     context_window=context_window,
                 )
             )
@@ -192,6 +204,10 @@ class OllamaProvider(OpenAIProvider):
         # ]
         # max_position_embeddings
         # parse model cards: nous, dolphon, llama
+        if "model_info" not in response_json:
+            if "error" in response_json:
+                print(f"Ollama fetch model info error for {model_name}: {response_json['error']}")
+            return None
         for key, value in response_json["model_info"].items():
             if "context_length" in key:
                 return value
@@ -202,6 +218,10 @@ class OllamaProvider(OpenAIProvider):
 
         response = requests.post(f"{self.base_url}/api/show", json={"name": model_name, "verbose": True})
         response_json = response.json()
+        if "model_info" not in response_json:
+            if "error" in response_json:
+                print(f"Ollama fetch model info error for {model_name}: {response_json['error']}")
+            return None
         for key, value in response_json["model_info"].items():
             if "embedding_length" in key:
                 return value
@@ -220,6 +240,7 @@ class OllamaProvider(OpenAIProvider):
         for model in response_json["models"]:
             embedding_dim = self.get_model_embedding_dim(model["name"])
             if not embedding_dim:
+                print(f"Ollama model {model['name']} has no embedding dimension")
                 continue
             configs.append(
                 EmbeddingConfig(
@@ -420,7 +441,7 @@ class VLLMCompletionsProvider(Provider):
     # NOTE: vLLM only serves one model at a time (so could configure that through env variables)
     name: str = "vllm"
     base_url: str = Field(..., description="Base URL for the vLLM API.")
-    default_prompt_formatter: str = Field(..., description="Default prompt formatter (aka model wrapper)to use on vLLM /completions API.")
+    default_prompt_formatter: str = Field(..., description="Default prompt formatter (aka model wrapper) to use on vLLM /completions API.")
 
     def list_llm_models(self) -> List[LLMConfig]:
         # not supported with vLLM