fix: refactor Google AI Provider / helper functions and add endpoint test (#1850)

Co-authored-by: Matt Zhou <mattzhou@Matts-MacBook-Pro.local>

letta/credentials.py

@@ -76,7 +76,7 @@ class LettaCredentials:
                 "azure_embedding_deployment": get_field(config, "azure", "embedding_deployment"),
                 # gemini
                 "google_ai_key": get_field(config, "google_ai", "key"),
-                "google_ai_service_endpoint": get_field(config, "google_ai", "service_endpoint"),
+                # "google_ai_service_endpoint": get_field(config, "google_ai", "service_endpoint"),
                 # anthropic
                 "anthropic_key": get_field(config, "anthropic", "key"),
                 # cohere
@@ -117,7 +117,7 @@ class LettaCredentials:
 
         # gemini
         set_field(config, "google_ai", "key", self.google_ai_key)
-        set_field(config, "google_ai", "service_endpoint", self.google_ai_service_endpoint)
+        # set_field(config, "google_ai", "service_endpoint", self.google_ai_service_endpoint)
 
         # anthropic
         set_field(config, "anthropic", "key", self.anthropic_key)

letta/llm_api/google_ai.py

@@ -1,9 +1,10 @@
 import uuid
-from typing import List, Optional
+from typing import List, Optional, Tuple
 
 import requests
 
 from letta.constants import NON_USER_MSG_PREFIX
+from letta.llm_api.helpers import make_post_request
 from letta.local_llm.json_parser import clean_json_string_extra_backslash
 from letta.local_llm.utils import count_tokens
 from letta.schemas.openai.chat_completion_request import Tool
@@ -15,27 +16,41 @@ from letta.schemas.openai.chat_completion_response import (
     ToolCall,
     UsageStatistics,
 )
-from letta.utils import get_tool_call_id, get_utc_time
-
-# from letta.data_types import ToolCall
+from letta.utils import get_tool_call_id, get_utc_time, json_dumps
 
 
-SUPPORTED_MODELS = [
-    "gemini-pro",
-]
+def get_gemini_endpoint_and_headers(
+    base_url: str, model: Optional[str], api_key: str, key_in_header: bool = True, generate_content: bool = False
+) -> Tuple[str, dict]:
+    """
+    Dynamically generate the model endpoint and headers.
+    """
+    url = f"{base_url}/v1beta/models"
 
+    # Add the model
+    if model is not None:
+        url += f"/{model}"
 
-def google_ai_get_model_details(service_endpoint: str, api_key: str, model: str, key_in_header: bool = True) -> List[dict]:
-    from letta.utils import printd
+    # Add extension for generating content if we're hitting the LM
+    if generate_content:
+        url += ":generateContent"
 
+    # Decide if api key should be in header or not
     # Two ways to pass the key: https://ai.google.dev/tutorials/setup
     if key_in_header:
-        url = f"https://{service_endpoint}.googleapis.com/v1beta/models/{model}"
         headers = {"Content-Type": "application/json", "x-goog-api-key": api_key}
     else:
-        url = f"https://{service_endpoint}.googleapis.com/v1beta/models/{model}?key={api_key}"
+        url += f"?key={api_key}"
         headers = {"Content-Type": "application/json"}
 
+    return url, headers
+
+
+def google_ai_get_model_details(base_url: str, api_key: str, model: str, key_in_header: bool = True) -> List[dict]:
+    from letta.utils import printd
+
+    url, headers = get_gemini_endpoint_and_headers(base_url, model, api_key, key_in_header)
+
     try:
         response = requests.get(url, headers=headers)
         printd(f"response = {response}")
@@ -66,25 +81,17 @@ def google_ai_get_model_details(service_endpoint: str, api_key: str, model: str,
         raise e
 
 
-def google_ai_get_model_context_window(service_endpoint: str, api_key: str, model: str, key_in_header: bool = True) -> int:
-    model_details = google_ai_get_model_details(
-        service_endpoint=service_endpoint, api_key=api_key, model=model, key_in_header=key_in_header
-    )
+def google_ai_get_model_context_window(base_url: str, api_key: str, model: str, key_in_header: bool = True) -> int:
+    model_details = google_ai_get_model_details(base_url=base_url, api_key=api_key, model=model, key_in_header=key_in_header)
     # TODO should this be:
     # return model_details["inputTokenLimit"] + model_details["outputTokenLimit"]
     return int(model_details["inputTokenLimit"])
 
 
-def google_ai_get_model_list(service_endpoint: str, api_key: str, key_in_header: bool = True) -> List[dict]:
+def google_ai_get_model_list(base_url: str, api_key: str, key_in_header: bool = True) -> List[dict]:
     from letta.utils import printd
 
-    # Two ways to pass the key: https://ai.google.dev/tutorials/setup
-    if key_in_header:
-        url = f"https://{service_endpoint}.googleapis.com/v1beta/models"
-        headers = {"Content-Type": "application/json", "x-goog-api-key": api_key}
-    else:
-        url = f"https://{service_endpoint}.googleapis.com/v1beta/models?key={api_key}"
-        headers = {"Content-Type": "application/json"}
+    url, headers = get_gemini_endpoint_and_headers(base_url, None, api_key, key_in_header)
 
     try:
         response = requests.get(url, headers=headers)
@@ -396,7 +403,7 @@ def convert_google_ai_response_to_chatcompletion(
 
 # TODO convert 'data' type to pydantic
 def google_ai_chat_completions_request(
-    service_endpoint: str,
+    base_url: str,
     model: str,
     api_key: str,
     data: dict,
@@ -414,55 +421,23 @@ def google_ai_chat_completions_request(
     This service has the following service endpoint and all URIs below are relative to this service endpoint:
     https://xxx.googleapis.com
     """
-    from letta.utils import printd
 
-    assert service_endpoint is not None, "Missing service_endpoint when calling Google AI"
     assert api_key is not None, "Missing api_key when calling Google AI"
-    assert model in SUPPORTED_MODELS, f"Model '{model}' not in supported models: {', '.join(SUPPORTED_MODELS)}"
 
-    # Two ways to pass the key: https://ai.google.dev/tutorials/setup
-    if key_in_header:
-        url = f"https://{service_endpoint}.googleapis.com/v1beta/models/{model}:generateContent"
-        headers = {"Content-Type": "application/json", "x-goog-api-key": api_key}
-    else:
-        url = f"https://{service_endpoint}.googleapis.com/v1beta/models/{model}:generateContent?key={api_key}"
-        headers = {"Content-Type": "application/json"}
+    url, headers = get_gemini_endpoint_and_headers(base_url, model, api_key, key_in_header, generate_content=True)
 
     # data["contents"][-1]["role"] = "model"
     if add_postfunc_model_messages:
         data["contents"] = add_dummy_model_messages(data["contents"])
 
-    printd(f"Sending request to {url}")
+    response_json = make_post_request(url, headers, data)
     try:
-        response = requests.post(url, headers=headers, json=data)
-        printd(f"response = {response}")
-        response.raise_for_status()  # Raises HTTPError for 4XX/5XX status
-        response = response.json()  # convert to dict from string
-        printd(f"response.json = {response}")
-
-        # Convert Google AI response to ChatCompletion style
         return convert_google_ai_response_to_chatcompletion(
-            response_json=response,
-            model=model,
+            response_json=response_json,
+            model=data.get("model"),
             input_messages=data["contents"],
-            pull_inner_thoughts_from_args=inner_thoughts_in_kwargs,
+            pull_inner_thoughts_from_args=data.get("inner_thoughts_in_kwargs", False),
         )
-
-    except requests.exceptions.HTTPError as http_err:
-        # Handle HTTP errors (e.g., response 4XX, 5XX)
-        printd(f"Got HTTPError, exception={http_err}, payload={data}")
-        # Print the HTTP status code
-        print(f"HTTP Error: {http_err.response.status_code}")
-        # Print the response content (error message from server)
-        print(f"Message: {http_err.response.text}")
-        raise http_err
-
-    except requests.exceptions.RequestException as req_err:
-        # Handle other requests-related errors (e.g., connection error)
-        printd(f"Got RequestException, exception={req_err}")
-        raise req_err
-
-    except Exception as e:
-        # Handle other potential errors
-        printd(f"Got unknown Exception, exception={e}")
-        raise e
+    except Exception as conversion_error:
+        print(f"Error during response conversion: {conversion_error}")
+        raise conversion_error

letta/llm_api/helpers.py

@@ -1,14 +1,69 @@
 import copy
 import json
 import warnings
-from typing import List, Union
+from typing import Any, List, Union
 
 import requests
 
 from letta.constants import OPENAI_CONTEXT_WINDOW_ERROR_SUBSTRING
 from letta.schemas.enums import OptionState
 from letta.schemas.openai.chat_completion_response import ChatCompletionResponse, Choice
-from letta.utils import json_dumps
+from letta.utils import json_dumps, printd
+
+
+def make_post_request(url: str, headers: dict[str, str], data: dict[str, Any]) -> dict[str, Any]:
+    printd(f"Sending request to {url}")
+    try:
+        # Make the POST request
+        response = requests.post(url, headers=headers, json=data)
+        printd(f"Response status code: {response.status_code}")
+
+        # Raise for 4XX/5XX HTTP errors
+        response.raise_for_status()
+
+        # Ensure the content is JSON before parsing
+        if response.headers.get("Content-Type") == "application/json":
+            response_data = response.json()  # Convert to dict from JSON
+            printd(f"Response JSON: {response_data}")
+        else:
+            error_message = f"Unexpected content type returned: {response.headers.get('Content-Type')}"
+            printd(error_message)
+            raise ValueError(error_message)
+
+        # Process the response using the callback function
+        return response_data
+
+    except requests.exceptions.HTTPError as http_err:
+        # HTTP errors (4XX, 5XX)
+        error_message = f"HTTP error occurred: {http_err}"
+        if http_err.response is not None:
+            error_message += f" | Status code: {http_err.response.status_code}, Message: {http_err.response.text}"
+        printd(error_message)
+        raise requests.exceptions.HTTPError(error_message) from http_err
+
+    except requests.exceptions.Timeout as timeout_err:
+        # Handle timeout errors
+        error_message = f"Request timed out: {timeout_err}"
+        printd(error_message)
+        raise requests.exceptions.Timeout(error_message) from timeout_err
+
+    except requests.exceptions.RequestException as req_err:
+        # Non-HTTP errors (e.g., connection, SSL errors)
+        error_message = f"Request failed: {req_err}"
+        printd(error_message)
+        raise requests.exceptions.RequestException(error_message) from req_err
+
+    except ValueError as val_err:
+        # Handle content-type or non-JSON response issues
+        error_message = f"ValueError: {val_err}"
+        printd(error_message)
+        raise ValueError(error_message) from val_err
+
+    except Exception as e:
+        # Catch any other unknown exceptions
+        error_message = f"An unexpected error occurred: {e}"
+        printd(error_message)
+        raise Exception(error_message) from e
 
 
 # TODO update to use better types

letta/llm_api/llm_api_tools.py

@@ -28,7 +28,6 @@ from letta.local_llm.constants import (
     INNER_THOUGHTS_KWARG,
     INNER_THOUGHTS_KWARG_DESCRIPTION,
 )
-from letta.providers import GoogleAIProvider
 from letta.schemas.enums import OptionState
 from letta.schemas.llm_config import LLMConfig
 from letta.schemas.message import Message
@@ -231,7 +230,7 @@ def create(
 
         return google_ai_chat_completions_request(
             inner_thoughts_in_kwargs=google_ai_inner_thoughts_in_kwarg,
-            service_endpoint=GoogleAIProvider(model_settings.gemini_api_key).service_endpoint,
+            base_url=llm_config.model_endpoint,
             model=llm_config.model,
             api_key=model_settings.gemini_api_key,
             # see structure of payload here: https://ai.google.dev/docs/function_calling

letta/llm_api/openai.py

@@ -9,7 +9,7 @@ from httpx_sse._exceptions import SSEError
 
 from letta.constants import OPENAI_CONTEXT_WINDOW_ERROR_SUBSTRING
 from letta.errors import LLMError
-from letta.llm_api.helpers import add_inner_thoughts_to_functions
+from letta.llm_api.helpers import add_inner_thoughts_to_functions, make_post_request
 from letta.local_llm.constants import (
     INNER_THOUGHTS_KWARG,
     INNER_THOUGHTS_KWARG_DESCRIPTION,
@@ -483,58 +483,14 @@ def openai_chat_completions_request(
         data.pop("tools")
         data.pop("tool_choice", None)  # extra safe,  should exist always (default="auto")
 
-    printd(f"Sending request to {url}")
-    try:
-        response = requests.post(url, headers=headers, json=data)
-        printd(f"response = {response}, response.text = {response.text}")
-        # print(json.dumps(data, indent=4))
-        # raise requests.exceptions.HTTPError
-        response.raise_for_status()  # Raises HTTPError for 4XX/5XX status
-
-        response = response.json()  # convert to dict from string
-        printd(f"response.json = {response}")
-
-        response = ChatCompletionResponse(**response)  # convert to 'dot-dict' style which is the openai python client default
-        return response
-    except requests.exceptions.HTTPError as http_err:
-        # Handle HTTP errors (e.g., response 4XX, 5XX)
-        printd(f"Got HTTPError, exception={http_err}, payload={data}")
-        raise http_err
-    except requests.exceptions.RequestException as req_err:
-        # Handle other requests-related errors (e.g., connection error)
-        printd(f"Got RequestException, exception={req_err}")
-        raise req_err
-    except Exception as e:
-        # Handle other potential errors
-        printd(f"Got unknown Exception, exception={e}")
-        raise e
+    response_json = make_post_request(url, headers, data)
+    return ChatCompletionResponse(**response_json)
 
 
 def openai_embeddings_request(url: str, api_key: str, data: dict) -> EmbeddingResponse:
     """https://platform.openai.com/docs/api-reference/embeddings/create"""
-    from letta.utils import printd
 
     url = smart_urljoin(url, "embeddings")
     headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
-
-    printd(f"Sending request to {url}")
-    try:
-        response = requests.post(url, headers=headers, json=data)
-        printd(f"response = {response}")
-        response.raise_for_status()  # Raises HTTPError for 4XX/5XX status
-        response = response.json()  # convert to dict from string
-        printd(f"response.json = {response}")
-        response = EmbeddingResponse(**response)  # convert to 'dot-dict' style which is the openai python client default
-        return response
-    except requests.exceptions.HTTPError as http_err:
-        # Handle HTTP errors (e.g., response 4XX, 5XX)
-        printd(f"Got HTTPError, exception={http_err}, payload={data}")
-        raise http_err
-    except requests.exceptions.RequestException as req_err:
-        # Handle other requests-related errors (e.g., connection error)
-        printd(f"Got RequestException, exception={req_err}")
-        raise req_err
-    except Exception as e:
-        # Handle other potential errors
-        printd(f"Got unknown Exception, exception={e}")
-        raise e
+    response_json = make_post_request(url, headers, data)
+    return EmbeddingResponse(**response_json)

letta/providers.py

@@ -217,14 +217,12 @@ class GroqProvider(OpenAIProvider):
 class GoogleAIProvider(Provider):
     # gemini
     api_key: str = Field(..., description="API key for the Google AI API.")
-    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)
+        model_options = google_ai_get_model_list(base_url=self.base_url, 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]
@@ -251,7 +249,7 @@ class GoogleAIProvider(Provider):
         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)
+        model_options = google_ai_get_model_list(base_url=self.base_url, 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]
@@ -273,8 +271,7 @@ class GoogleAIProvider(Provider):
     def get_model_context_window(self, model_name: str):
         from letta.llm_api.google_ai import google_ai_get_model_context_window
 
-        # TODO: use base_url instead
-        return google_ai_get_model_context_window(self.service_endpoint, self.api_key, model_name)
+        return google_ai_get_model_context_window(self.base_url, self.api_key, model_name)
 
 
 class AzureProvider(Provider):

letta/schemas/openai/chat_completion_response.py

@@ -74,6 +74,9 @@ class ChatCompletionResponse(BaseModel):
     object: Literal["chat.completion"] = "chat.completion"
     usage: UsageStatistics
 
+    def __str__(self):
+        return self.model_dump_json(indent=4)
+
 
 class FunctionCallDelta(BaseModel):
     # arguments: Optional[str] = None

tests/configs/llm_model_configs/gemini-pro.json

@@ -0,0 +1,7 @@
+{
+    "context_window": 2097152,
+    "model": "gemini-1.5-pro-latest",
+    "model_endpoint_type": "google_ai",
+    "model_endpoint": "https://generativelanguage.googleapis.com",
+    "model_wrapper": null
+}

tests/test_endpoints.py

@@ -273,3 +273,13 @@ def test_groq_llama31_70b_edit_core_memory():
     response = check_agent_edit_core_memory(filename)
     # Log out successful response
     print(f"Got successful response from client: \n\n{response}")
+
+
+# ======================================================================================================================
+# GEMINI TESTS
+# ======================================================================================================================
+def test_gemini_pro_15_returns_valid_first_message():
+    filename = os.path.join(llm_config_dir, "gemini-pro.json")
+    response = check_first_response_is_valid_for_llm_endpoint(filename)
+    # Log out successful response
+    print(f"Got successful response from client: \n\n{response}")