feat: list out embedding models for Google AI provider

.github/workflows/tests.yml

@@ -4,6 +4,7 @@ env:
   OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
   COMPOSIO_API_KEY: ${{ secrets.COMPOSIO_API_KEY }}
   ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+  GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
 
 on:
   push:

letta/providers.py

@@ -182,20 +182,23 @@ def get_model_context_window_size(self, model_name: str):
 class GoogleAIProvider(Provider):
     # gemini
     api_key: str = Field(..., description="API key for the Google AI API.")
-    service_endpoint: str = "generativelanguage"
+    service_endpoint: str = "generativelanguage"  # TODO: remove once old functions are refactored to just use base_url
     base_url: str = "https://generativelanguage.googleapis.com"
 
     def list_llm_models(self):
         from letta.llm_api.google_ai import google_ai_get_model_list
 
         # TODO: use base_url instead
         model_options = google_ai_get_model_list(service_endpoint=self.service_endpoint, api_key=self.api_key)
+        # filter by 'generateContent' models
+        model_options = [mo for mo in model_options if "generateContent" in mo["supportedGenerationMethods"]]
         model_options = [str(m["name"]) for m in model_options]
+
+        # filter by model names
         model_options = [mo[len("models/") :] if mo.startswith("models/") else mo for mo in model_options]
+
         # TODO remove manual filtering for gemini-pro
         model_options = [mo for mo in model_options if str(mo).startswith("gemini") and "-pro" in str(mo)]
-        # TODO: add context windows
-        # model_options = ["gemini-pro"]
 
         configs = []
         for model in model_options:
@@ -210,7 +213,27 @@ def list_llm_models(self):
         return configs
 
     def list_embedding_models(self):
-        return []
+        from letta.llm_api.google_ai import google_ai_get_model_list
+
+        # TODO: use base_url instead
+        model_options = google_ai_get_model_list(service_endpoint=self.service_endpoint, api_key=self.api_key)
+        # filter by 'generateContent' models
+        model_options = [mo for mo in model_options if "embedContent" in mo["supportedGenerationMethods"]]
+        model_options = [str(m["name"]) for m in model_options]
+        model_options = [mo[len("models/") :] if mo.startswith("models/") else mo for mo in model_options]
+
+        configs = []
+        for model in model_options:
+            configs.append(
+                EmbeddingConfig(
+                    embedding_model=model,
+                    embedding_endpoint_type="google_ai",
+                    embedding_endpoint=self.base_url,
+                    embedding_dim=768,
+                    embedding_chunk_size=300,  # NOTE: max is 2048
+                )
+            )
+        return configs
 
     def get_model_context_window(self, model_name: str):
         from letta.llm_api.google_ai import google_ai_get_model_context_window

tests/test_providers.py

@@ -1,6 +1,6 @@
 import os
 
-from letta.providers import AnthropicProvider, OpenAIProvider
+from letta.providers import AnthropicProvider, GoogleAIProvider, OpenAIProvider
 
 
 def test_openai():
@@ -30,13 +30,17 @@ def test_anthropic():
 #    print(models)
 #
 #
-# def test_googleai():
-#    provider = GoogleAIProvider(api_key=os.getenv("GEMINI_API_KEY"))
-#    models = provider.list_llm_models()
-#    print(models)
+def test_googleai():
+    provider = GoogleAIProvider(api_key=os.getenv("GEMINI_API_KEY"))
+    models = provider.list_llm_models()
+    print(models)
+
+    provider.list_embedding_models()
+
+
 #
 #
-# test_googleai()
+test_googleai()
 # test_ollama()
 # test_groq()
 # test_openai()