487 lines
22 KiB
Python
487 lines
22 KiB
Python
import json
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import uuid
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from typing import List, Optional
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from google import genai
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from google.genai.types import FunctionCallingConfig, FunctionCallingConfigMode, GenerateContentResponse, ThinkingConfig, ToolConfig
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from letta.constants import NON_USER_MSG_PREFIX
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from letta.helpers.datetime_helpers import get_utc_time_int
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from letta.helpers.json_helpers import json_dumps, json_loads
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from letta.llm_api.llm_client_base import LLMClientBase
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from letta.local_llm.json_parser import clean_json_string_extra_backslash
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from letta.local_llm.utils import count_tokens
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from letta.log import get_logger
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from letta.otel.tracing import trace_method
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from letta.schemas.llm_config import LLMConfig
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from letta.schemas.message import Message as PydanticMessage
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from letta.schemas.openai.chat_completion_request import Tool
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from letta.schemas.openai.chat_completion_response import ChatCompletionResponse, Choice, FunctionCall, Message, ToolCall, UsageStatistics
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from letta.settings import model_settings, settings
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from letta.utils import get_tool_call_id
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logger = get_logger(__name__)
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class GoogleVertexClient(LLMClientBase):
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def _get_client(self):
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return genai.Client(
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vertexai=True,
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project=model_settings.google_cloud_project,
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location=model_settings.google_cloud_location,
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http_options={"api_version": "v1"},
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)
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@trace_method
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def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
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"""
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Performs underlying request to llm and returns raw response.
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"""
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client = self._get_client()
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response = client.models.generate_content(
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model=llm_config.model,
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contents=request_data["contents"],
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config=request_data["config"],
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)
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return response.model_dump()
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@trace_method
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async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
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"""
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Performs underlying request to llm and returns raw response.
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"""
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client = self._get_client()
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response = await client.aio.models.generate_content(
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model=llm_config.model,
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contents=request_data["contents"],
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config=request_data["config"],
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)
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return response.model_dump()
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def add_dummy_model_messages(self, messages: List[dict]) -> List[dict]:
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"""Google AI API requires all function call returns are immediately followed by a 'model' role message.
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In Letta, the 'model' will often call a function (e.g. send_message) that itself yields to the user,
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so there is no natural follow-up 'model' role message.
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To satisfy the Google AI API restrictions, we can add a dummy 'yield' message
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with role == 'model' that is placed in-betweeen and function output
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(role == 'tool') and user message (role == 'user').
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"""
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dummy_yield_message = {
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"role": "model",
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"parts": [{"text": f"{NON_USER_MSG_PREFIX}Function call returned, waiting for user response."}],
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}
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messages_with_padding = []
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for i, message in enumerate(messages):
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messages_with_padding.append(message)
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# Check if the current message role is 'tool' and the next message role is 'user'
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if message["role"] in ["tool", "function"] and (i + 1 < len(messages) and messages[i + 1]["role"] == "user"):
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messages_with_padding.append(dummy_yield_message)
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return messages_with_padding
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def _clean_google_ai_schema_properties(self, schema_part: dict):
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"""Recursively clean schema parts to remove unsupported Google AI keywords."""
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if not isinstance(schema_part, dict):
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return
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# Per https://ai.google.dev/gemini-api/docs/function-calling?example=meeting#notes_and_limitations
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# * Only a subset of the OpenAPI schema is supported.
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# * Supported parameter types in Python are limited.
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unsupported_keys = ["default", "exclusiveMaximum", "exclusiveMinimum", "additionalProperties"]
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keys_to_remove_at_this_level = [key for key in unsupported_keys if key in schema_part]
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for key_to_remove in keys_to_remove_at_this_level:
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logger.warning(f"Removing unsupported keyword '{key_to_remove}' from schema part.")
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del schema_part[key_to_remove]
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if schema_part.get("type") == "string" and "format" in schema_part:
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allowed_formats = ["enum", "date-time"]
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if schema_part["format"] not in allowed_formats:
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logger.warning(f"Removing unsupported format '{schema_part['format']}' for string type. Allowed: {allowed_formats}")
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del schema_part["format"]
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# Check properties within the current level
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if "properties" in schema_part and isinstance(schema_part["properties"], dict):
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for prop_name, prop_schema in schema_part["properties"].items():
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self._clean_google_ai_schema_properties(prop_schema)
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# Check items within arrays
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if "items" in schema_part and isinstance(schema_part["items"], dict):
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self._clean_google_ai_schema_properties(schema_part["items"])
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# Check within anyOf, allOf, oneOf lists
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for key in ["anyOf", "allOf", "oneOf"]:
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if key in schema_part and isinstance(schema_part[key], list):
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for item_schema in schema_part[key]:
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self._clean_google_ai_schema_properties(item_schema)
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def convert_tools_to_google_ai_format(self, tools: List[Tool], llm_config: LLMConfig) -> List[dict]:
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"""
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OpenAI style:
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"tools": [{
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"type": "function",
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"function": {
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"name": "find_movies",
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"description": "find ....",
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"parameters": {
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"type": "object",
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"properties": {
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PARAM: {
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"type": PARAM_TYPE, # eg "string"
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"description": PARAM_DESCRIPTION,
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},
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...
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},
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"required": List[str],
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}
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}
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}
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]
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Google AI style:
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"tools": [{
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"functionDeclarations": [{
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"name": "find_movies",
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"description": "find movie titles currently playing in theaters based on any description, genre, title words, etc.",
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"parameters": {
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"type": "OBJECT",
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"properties": {
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"location": {
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"type": "STRING",
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"description": "The city and state, e.g. San Francisco, CA or a zip code e.g. 95616"
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},
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"description": {
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"type": "STRING",
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"description": "Any kind of description including category or genre, title words, attributes, etc."
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}
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},
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"required": ["description"]
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}
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}, {
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"name": "find_theaters",
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...
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"""
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function_list = [
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dict(
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name=t.function.name,
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description=t.function.description,
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parameters=t.function.parameters, # TODO need to unpack
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)
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for t in tools
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]
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# Add inner thoughts if needed
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for func in function_list:
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# Note: Google AI API used to have weird casing requirements, but not any more
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# Google AI API only supports a subset of OpenAPI 3.0, so unsupported params must be cleaned
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if "parameters" in func and isinstance(func["parameters"], dict):
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self._clean_google_ai_schema_properties(func["parameters"])
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# Add inner thoughts
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if llm_config.put_inner_thoughts_in_kwargs:
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from letta.local_llm.constants import INNER_THOUGHTS_KWARG_DESCRIPTION, INNER_THOUGHTS_KWARG_VERTEX
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func["parameters"]["properties"][INNER_THOUGHTS_KWARG_VERTEX] = {
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"type": "string",
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"description": INNER_THOUGHTS_KWARG_DESCRIPTION,
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}
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func["parameters"]["required"].append(INNER_THOUGHTS_KWARG_VERTEX)
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return [{"functionDeclarations": function_list}]
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@trace_method
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def build_request_data(
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self,
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messages: List[PydanticMessage],
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llm_config: LLMConfig,
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tools: List[dict],
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force_tool_call: Optional[str] = None,
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) -> dict:
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"""
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Constructs a request object in the expected data format for this client.
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"""
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if tools:
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tool_objs = [Tool(type="function", function=t) for t in tools]
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tool_names = [t.function.name for t in tool_objs]
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# Convert to the exact payload style Google expects
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formatted_tools = self.convert_tools_to_google_ai_format(tool_objs, llm_config)
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else:
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formatted_tools = []
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tool_names = []
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contents = self.add_dummy_model_messages(
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[m.to_google_ai_dict() for m in messages],
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)
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request_data = {
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"contents": contents,
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"config": {
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"temperature": llm_config.temperature,
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"max_output_tokens": llm_config.max_tokens,
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"tools": formatted_tools,
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},
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}
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if len(tool_names) == 1 and settings.use_vertex_structured_outputs_experimental:
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request_data["config"]["response_mime_type"] = "application/json"
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request_data["config"]["response_schema"] = self.get_function_call_response_schema(tools[0])
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del request_data["config"]["tools"]
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else:
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tool_config = ToolConfig(
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function_calling_config=FunctionCallingConfig(
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# ANY mode forces the model to predict only function calls
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mode=FunctionCallingConfigMode.ANY,
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# Provide the list of tools (though empty should also work, it seems not to)
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allowed_function_names=tool_names,
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)
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)
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request_data["config"]["tool_config"] = tool_config.model_dump()
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# Add thinking_config for flash
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# If enable_reasoner is False, set thinking_budget to 0
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# Otherwise, use the value from max_reasoning_tokens
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if "flash" in llm_config.model:
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thinking_config = ThinkingConfig(
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thinking_budget=llm_config.max_reasoning_tokens if llm_config.enable_reasoner else 0,
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)
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request_data["config"]["thinking_config"] = thinking_config.model_dump()
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return request_data
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@trace_method
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def convert_response_to_chat_completion(
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self,
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response_data: dict,
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input_messages: List[PydanticMessage],
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llm_config: LLMConfig,
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) -> ChatCompletionResponse:
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"""
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Converts custom response format from llm client into an OpenAI
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ChatCompletionsResponse object.
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Example:
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{
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"candidates": [
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{
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"content": {
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"parts": [
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{
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"text": " OK. Barbie is showing in two theaters in Mountain View, CA: AMC Mountain View 16 and Regal Edwards 14."
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}
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]
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}
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}
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],
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"usageMetadata": {
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"promptTokenCount": 9,
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"candidatesTokenCount": 27,
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"totalTokenCount": 36
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}
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}
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"""
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# print(response_data)
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response = GenerateContentResponse(**response_data)
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try:
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choices = []
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index = 0
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for candidate in response.candidates:
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content = candidate.content
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if content.role is None or content.parts is None:
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# This means the response is malformed like MALFORMED_FUNCTION_CALL
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# NOTE: must be a ValueError to trigger a retry
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if candidate.finish_reason == "MALFORMED_FUNCTION_CALL":
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raise ValueError(f"Error in response data from LLM: {candidate.finish_message[:350]}...")
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else:
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raise ValueError(f"Error in response data from LLM: {response_data}")
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role = content.role
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assert role == "model", f"Unknown role in response: {role}"
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parts = content.parts
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# NOTE: we aren't properly supported multi-parts here anyways (we're just appending choices),
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# so let's disable it for now
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# NOTE(Apr 9, 2025): there's a very strange bug on 2.5 where the response has a part with broken text
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# {'candidates': [{'content': {'parts': [{'functionCall': {'name': 'send_message', 'args': {'request_heartbeat': False, 'message': 'Hello! How can I make your day better?', 'inner_thoughts': 'User has initiated contact. Sending a greeting.'}}}], 'role': 'model'}, 'finishReason': 'STOP', 'avgLogprobs': -0.25891534213362066}], 'usageMetadata': {'promptTokenCount': 2493, 'candidatesTokenCount': 29, 'totalTokenCount': 2522, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2493}], 'candidatesTokensDetails': [{'modality': 'TEXT', 'tokenCount': 29}]}, 'modelVersion': 'gemini-1.5-pro-002'}
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# To patch this, if we have multiple parts we can take the last one
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if len(parts) > 1:
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logger.warning(f"Unexpected multiple parts in response from Google AI: {parts}")
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parts = [parts[-1]]
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# TODO support parts / multimodal
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# TODO support parallel tool calling natively
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# TODO Alternative here is to throw away everything else except for the first part
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for response_message in parts:
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# Convert the actual message style to OpenAI style
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if response_message.function_call:
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function_call = response_message.function_call
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function_name = function_call.name
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function_args = function_call.args
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assert isinstance(function_args, dict), function_args
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# NOTE: this also involves stripping the inner monologue out of the function
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if llm_config.put_inner_thoughts_in_kwargs:
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from letta.local_llm.constants import INNER_THOUGHTS_KWARG_VERTEX
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assert (
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INNER_THOUGHTS_KWARG_VERTEX in function_args
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), f"Couldn't find inner thoughts in function args:\n{function_call}"
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inner_thoughts = function_args.pop(INNER_THOUGHTS_KWARG_VERTEX)
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assert inner_thoughts is not None, f"Expected non-null inner thoughts function arg:\n{function_call}"
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else:
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inner_thoughts = None
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# Google AI API doesn't generate tool call IDs
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openai_response_message = Message(
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role="assistant", # NOTE: "model" -> "assistant"
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content=inner_thoughts,
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tool_calls=[
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ToolCall(
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id=get_tool_call_id(),
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type="function",
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function=FunctionCall(
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name=function_name,
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arguments=clean_json_string_extra_backslash(json_dumps(function_args)),
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),
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)
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],
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)
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else:
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try:
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# Structured output tool call
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function_call = json_loads(response_message.text)
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function_name = function_call["name"]
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function_args = function_call["args"]
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assert isinstance(function_args, dict), function_args
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# NOTE: this also involves stripping the inner monologue out of the function
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if llm_config.put_inner_thoughts_in_kwargs:
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from letta.local_llm.constants import INNER_THOUGHTS_KWARG_VERTEX
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assert (
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INNER_THOUGHTS_KWARG_VERTEX in function_args
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), f"Couldn't find inner thoughts in function args:\n{function_call}"
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inner_thoughts = function_args.pop(INNER_THOUGHTS_KWARG_VERTEX)
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assert inner_thoughts is not None, f"Expected non-null inner thoughts function arg:\n{function_call}"
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else:
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inner_thoughts = None
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# Google AI API doesn't generate tool call IDs
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openai_response_message = Message(
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role="assistant", # NOTE: "model" -> "assistant"
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content=inner_thoughts,
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tool_calls=[
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ToolCall(
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id=get_tool_call_id(),
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type="function",
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function=FunctionCall(
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name=function_name,
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arguments=clean_json_string_extra_backslash(json_dumps(function_args)),
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),
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)
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],
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)
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except json.decoder.JSONDecodeError:
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if candidate.finish_reason == "MAX_TOKENS":
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raise ValueError(f"Could not parse response data from LLM: exceeded max token limit")
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# Inner thoughts are the content by default
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inner_thoughts = response_message.text
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# Google AI API doesn't generate tool call IDs
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openai_response_message = Message(
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role="assistant", # NOTE: "model" -> "assistant"
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content=inner_thoughts,
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)
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# Google AI API uses different finish reason strings than OpenAI
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# OpenAI: 'stop', 'length', 'function_call', 'content_filter', null
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# see: https://platform.openai.com/docs/guides/text-generation/chat-completions-api
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# Google AI API: FINISH_REASON_UNSPECIFIED, STOP, MAX_TOKENS, SAFETY, RECITATION, OTHER
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# see: https://ai.google.dev/api/python/google/ai/generativelanguage/Candidate/FinishReason
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finish_reason = candidate.finish_reason.value
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if finish_reason == "STOP":
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openai_finish_reason = (
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"function_call"
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if openai_response_message.tool_calls is not None and len(openai_response_message.tool_calls) > 0
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else "stop"
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)
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elif finish_reason == "MAX_TOKENS":
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openai_finish_reason = "length"
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elif finish_reason == "SAFETY":
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openai_finish_reason = "content_filter"
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elif finish_reason == "RECITATION":
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openai_finish_reason = "content_filter"
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else:
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raise ValueError(f"Unrecognized finish reason in Google AI response: {finish_reason}")
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choices.append(
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Choice(
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finish_reason=openai_finish_reason,
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index=index,
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message=openai_response_message,
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)
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)
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index += 1
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# if len(choices) > 1:
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# raise UserWarning(f"Unexpected number of candidates in response (expected 1, got {len(choices)})")
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# NOTE: some of the Google AI APIs show UsageMetadata in the response, but it seems to not exist?
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# "usageMetadata": {
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# "promptTokenCount": 9,
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# "candidatesTokenCount": 27,
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# "totalTokenCount": 36
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# }
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if response.usage_metadata:
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usage = UsageStatistics(
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prompt_tokens=response.usage_metadata.prompt_token_count,
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completion_tokens=response.usage_metadata.candidates_token_count,
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total_tokens=response.usage_metadata.total_token_count,
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)
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else:
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# Count it ourselves
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assert input_messages is not None, f"Didn't get UsageMetadata from the API response, so input_messages is required"
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prompt_tokens = count_tokens(json_dumps(input_messages)) # NOTE: this is a very rough approximation
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completion_tokens = count_tokens(json_dumps(openai_response_message.model_dump())) # NOTE: this is also approximate
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total_tokens = prompt_tokens + completion_tokens
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usage = UsageStatistics(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=total_tokens,
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)
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response_id = str(uuid.uuid4())
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return ChatCompletionResponse(
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id=response_id,
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choices=choices,
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model=llm_config.model, # NOTE: Google API doesn't pass back model in the response
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created=get_utc_time_int(),
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usage=usage,
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)
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except KeyError as e:
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raise e
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def get_function_call_response_schema(self, tool: dict) -> dict:
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return {
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"type": "OBJECT",
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"properties": {
|
|
"name": {"type": "STRING", "enum": [tool["name"]]},
|
|
"args": {
|
|
"type": "OBJECT",
|
|
"properties": tool["parameters"]["properties"],
|
|
"required": tool["parameters"]["required"],
|
|
},
|
|
},
|
|
"propertyOrdering": ["name", "args"],
|
|
"required": ["name", "args"],
|
|
}
|