436 lines
18 KiB
Python
436 lines
18 KiB
Python
import uuid
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from typing import List, Optional, Tuple
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import requests
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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
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from letta.helpers.json_helpers import json_dumps
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from letta.llm_api.helpers import make_post_request
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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.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.utils import get_tool_call_id
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def get_gemini_endpoint_and_headers(
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base_url: str, model: Optional[str], api_key: str, key_in_header: bool = True, generate_content: bool = False
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) -> Tuple[str, dict]:
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"""
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Dynamically generate the model endpoint and headers.
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"""
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url = f"{base_url}/v1beta/models"
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# Add the model
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if model is not None:
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url += f"/{model}"
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# Add extension for generating content if we're hitting the LM
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if generate_content:
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url += ":generateContent"
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# Decide if api key should be in header or not
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# Two ways to pass the key: https://ai.google.dev/tutorials/setup
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if key_in_header:
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headers = {"Content-Type": "application/json", "x-goog-api-key": api_key}
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else:
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url += f"?key={api_key}"
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headers = {"Content-Type": "application/json"}
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return url, headers
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def google_ai_get_model_details(base_url: str, api_key: str, model: str, key_in_header: bool = True) -> List[dict]:
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from letta.utils import printd
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url, headers = get_gemini_endpoint_and_headers(base_url, model, api_key, key_in_header)
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try:
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response = requests.get(url, headers=headers)
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printd(f"response = {response}")
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response.raise_for_status() # Raises HTTPError for 4XX/5XX status
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response = response.json() # convert to dict from string
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printd(f"response.json = {response}")
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# Grab the models out
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return response
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except requests.exceptions.HTTPError as http_err:
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# Handle HTTP errors (e.g., response 4XX, 5XX)
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printd(f"Got HTTPError, exception={http_err}")
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# Print the HTTP status code
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print(f"HTTP Error: {http_err.response.status_code}")
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# Print the response content (error message from server)
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print(f"Message: {http_err.response.text}")
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raise http_err
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except requests.exceptions.RequestException as req_err:
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# Handle other requests-related errors (e.g., connection error)
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printd(f"Got RequestException, exception={req_err}")
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raise req_err
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except Exception as e:
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# Handle other potential errors
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printd(f"Got unknown Exception, exception={e}")
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raise e
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def google_ai_get_model_context_window(base_url: str, api_key: str, model: str, key_in_header: bool = True) -> int:
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model_details = google_ai_get_model_details(base_url=base_url, api_key=api_key, model=model, key_in_header=key_in_header)
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# TODO should this be:
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# return model_details["inputTokenLimit"] + model_details["outputTokenLimit"]
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return int(model_details["inputTokenLimit"])
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def google_ai_get_model_list(base_url: str, api_key: str, key_in_header: bool = True) -> List[dict]:
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from letta.utils import printd
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url, headers = get_gemini_endpoint_and_headers(base_url, None, api_key, key_in_header)
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try:
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response = requests.get(url, headers=headers)
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response.raise_for_status() # Raises HTTPError for 4XX/5XX status
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response = response.json() # convert to dict from string
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# Grab the models out
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model_list = response["models"]
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return model_list
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except requests.exceptions.HTTPError as http_err:
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# Handle HTTP errors (e.g., response 4XX, 5XX)
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printd(f"Got HTTPError, exception={http_err}")
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# Print the HTTP status code
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print(f"HTTP Error: {http_err.response.status_code}")
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# Print the response content (error message from server)
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print(f"Message: {http_err.response.text}")
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raise http_err
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except requests.exceptions.RequestException as req_err:
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# Handle other requests-related errors (e.g., connection error)
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printd(f"Got RequestException, exception={req_err}")
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raise req_err
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except Exception as e:
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# Handle other potential errors
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printd(f"Got unknown Exception, exception={e}")
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raise e
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def add_dummy_model_messages(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 = {"role": "model", "parts": [{"text": f"{NON_USER_MSG_PREFIX}Function call returned, waiting for user response."}]}
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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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# TODO use pydantic model as input
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def to_google_ai(openai_message_dict: dict) -> dict:
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# TODO supports "parts" as part of multimodal support
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assert not isinstance(openai_message_dict["content"], list), "Multi-part content is message not yet supported"
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if openai_message_dict["role"] == "user":
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google_ai_message_dict = {
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"role": "user",
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"parts": [{"text": openai_message_dict["content"]}],
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}
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elif openai_message_dict["role"] == "assistant":
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google_ai_message_dict = {
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"role": "model", # NOTE: diff
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"parts": [{"text": openai_message_dict["content"]}],
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}
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elif openai_message_dict["role"] == "tool":
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google_ai_message_dict = {
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"role": "function", # NOTE: diff
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"parts": [{"text": openai_message_dict["content"]}],
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}
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else:
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raise ValueError(f"Unsupported conversion (OpenAI -> Google AI) from role {openai_message_dict['role']}")
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# TODO convert return type to pydantic
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def convert_tools_to_google_ai_format(tools: List[Tool], inner_thoughts_in_kwargs: Optional[bool] = True) -> 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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# Correct casing + add inner thoughts if needed
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for func in function_list:
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func["parameters"]["type"] = "OBJECT"
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for param_name, param_fields in func["parameters"]["properties"].items():
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param_fields["type"] = param_fields["type"].upper()
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# Add inner thoughts
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if inner_thoughts_in_kwargs:
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from letta.local_llm.constants import INNER_THOUGHTS_KWARG, INNER_THOUGHTS_KWARG_DESCRIPTION
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func["parameters"]["properties"][INNER_THOUGHTS_KWARG] = {
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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)
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return [{"functionDeclarations": function_list}]
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def convert_google_ai_response_to_chatcompletion(
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response_json: dict, # REST response from Google AI API
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model: str, # Required since not returned
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input_messages: Optional[List[dict]] = None, # Required if the API doesn't return UsageMetadata
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pull_inner_thoughts_from_args: Optional[bool] = True,
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) -> ChatCompletionResponse:
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"""Google AI API response format is not the same as ChatCompletion, requires unpacking
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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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try:
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choices = []
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index = 0
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for candidate in response_json["candidates"]:
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content = candidate["content"]
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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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# 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 "functionCall" in response_message and response_message["functionCall"] is not None:
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function_call = response_message["functionCall"]
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assert isinstance(function_call, dict), function_call
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function_name = function_call["name"]
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assert isinstance(function_name, str), function_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 pull_inner_thoughts_from_args:
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from letta.local_llm.constants import INNER_THOUGHTS_KWARG
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assert INNER_THOUGHTS_KWARG in function_args, f"Couldn't find inner thoughts in function args:\n{function_call}"
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inner_thoughts = function_args.pop(INNER_THOUGHTS_KWARG)
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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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# 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["finishReason"]
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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 "usageMetadata" in response_json:
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usage = UsageStatistics(
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prompt_tokens=response_json["usageMetadata"]["promptTokenCount"],
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completion_tokens=response_json["usageMetadata"]["candidatesTokenCount"],
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total_tokens=response_json["usageMetadata"]["totalTokenCount"],
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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=model, # NOTE: Google API doesn't pass back model in the response
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created=get_utc_time(),
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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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# TODO convert 'data' type to pydantic
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def google_ai_chat_completions_request(
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base_url: str,
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model: str,
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api_key: str,
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data: dict,
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key_in_header: bool = True,
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add_postfunc_model_messages: bool = True,
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# NOTE: Google AI API doesn't support mixing parts 'text' and 'function',
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# so there's no clean way to put inner thoughts in the same message as a function call
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inner_thoughts_in_kwargs: bool = True,
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) -> ChatCompletionResponse:
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"""https://ai.google.dev/docs/function_calling
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From https://ai.google.dev/api/rest#service-endpoint:
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"A service endpoint is a base URL that specifies the network address of an API service.
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One service might have multiple service endpoints.
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This service has the following service endpoint and all URIs below are relative to this service endpoint:
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https://xxx.googleapis.com
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"""
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assert api_key is not None, "Missing api_key when calling Google AI"
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url, headers = get_gemini_endpoint_and_headers(base_url, model, api_key, key_in_header, generate_content=True)
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# data["contents"][-1]["role"] = "model"
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if add_postfunc_model_messages:
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data["contents"] = add_dummy_model_messages(data["contents"])
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response_json = make_post_request(url, headers, data)
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try:
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return convert_google_ai_response_to_chatcompletion(
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response_json=response_json,
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model=data.get("model"),
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input_messages=data["contents"],
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pull_inner_thoughts_from_args=inner_thoughts_in_kwargs,
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)
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except Exception as conversion_error:
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print(f"Error during response conversion: {conversion_error}")
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raise conversion_error
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