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feat(vertex_httpx.py): support the 'response_schema' param for older vertex ai models - pass as prompt (user-controlled)
if 'response_schema' is not supported for vertex model (e.g. gemini-1.5-flash) pass in prompt
This commit is contained in:
Krrish Dholakia
parent
5718d1e205
commit
05dfc63b88
8 changed files with 212 additions and 112 deletions
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@ -12,6 +12,7 @@ import requests # type: ignore
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from pydantic import BaseModel
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import litellm
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from litellm._logging import verbose_logger
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from litellm.litellm_core_utils.core_helpers import map_finish_reason
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from litellm.llms.prompt_templates.factory import (
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convert_to_anthropic_image_obj,
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@ -328,80 +329,86 @@ def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]:
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contents: List[ContentType] = []
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msg_i = 0
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while msg_i < len(messages):
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user_content: List[PartType] = []
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init_msg_i = msg_i
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## MERGE CONSECUTIVE USER CONTENT ##
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while msg_i < len(messages) and messages[msg_i]["role"] in user_message_types:
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if isinstance(messages[msg_i]["content"], list):
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_parts: List[PartType] = []
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for element in messages[msg_i]["content"]:
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if isinstance(element, dict):
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if element["type"] == "text" and len(element["text"]) > 0:
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_part = PartType(text=element["text"])
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_parts.append(_part)
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elif element["type"] == "image_url":
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image_url = element["image_url"]["url"]
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_part = _process_gemini_image(image_url=image_url)
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_parts.append(_part) # type: ignore
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user_content.extend(_parts)
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elif (
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isinstance(messages[msg_i]["content"], str)
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and len(messages[msg_i]["content"]) > 0
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try:
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while msg_i < len(messages):
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user_content: List[PartType] = []
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init_msg_i = msg_i
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## MERGE CONSECUTIVE USER CONTENT ##
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while (
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msg_i < len(messages) and messages[msg_i]["role"] in user_message_types
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):
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_part = PartType(text=messages[msg_i]["content"])
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user_content.append(_part)
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if isinstance(messages[msg_i]["content"], list):
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_parts: List[PartType] = []
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for element in messages[msg_i]["content"]:
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if isinstance(element, dict):
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if element["type"] == "text" and len(element["text"]) > 0:
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_part = PartType(text=element["text"])
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_parts.append(_part)
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elif element["type"] == "image_url":
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image_url = element["image_url"]["url"]
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_part = _process_gemini_image(image_url=image_url)
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_parts.append(_part) # type: ignore
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user_content.extend(_parts)
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elif (
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isinstance(messages[msg_i]["content"], str)
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and len(messages[msg_i]["content"]) > 0
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):
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_part = PartType(text=messages[msg_i]["content"])
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user_content.append(_part)
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msg_i += 1
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msg_i += 1
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if user_content:
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contents.append(ContentType(role="user", parts=user_content))
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assistant_content = []
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## MERGE CONSECUTIVE ASSISTANT CONTENT ##
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while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
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if isinstance(messages[msg_i]["content"], list):
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_parts = []
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for element in messages[msg_i]["content"]:
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if isinstance(element, dict):
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if element["type"] == "text":
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_part = PartType(text=element["text"])
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_parts.append(_part)
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elif element["type"] == "image_url":
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image_url = element["image_url"]["url"]
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_part = _process_gemini_image(image_url=image_url)
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_parts.append(_part) # type: ignore
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assistant_content.extend(_parts)
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elif messages[msg_i].get(
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"tool_calls", []
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): # support assistant tool invoke conversion
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assistant_content.extend(
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convert_to_gemini_tool_call_invoke(messages[msg_i]["tool_calls"])
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if user_content:
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contents.append(ContentType(role="user", parts=user_content))
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assistant_content = []
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## MERGE CONSECUTIVE ASSISTANT CONTENT ##
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while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
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if isinstance(messages[msg_i]["content"], list):
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_parts = []
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for element in messages[msg_i]["content"]:
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if isinstance(element, dict):
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if element["type"] == "text":
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_part = PartType(text=element["text"])
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_parts.append(_part)
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elif element["type"] == "image_url":
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image_url = element["image_url"]["url"]
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_part = _process_gemini_image(image_url=image_url)
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_parts.append(_part) # type: ignore
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assistant_content.extend(_parts)
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elif messages[msg_i].get(
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"tool_calls", []
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): # support assistant tool invoke conversion
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assistant_content.extend(
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convert_to_gemini_tool_call_invoke(
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messages[msg_i]["tool_calls"]
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)
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)
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else:
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assistant_text = (
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messages[msg_i].get("content") or ""
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) # either string or none
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if assistant_text:
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assistant_content.append(PartType(text=assistant_text))
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msg_i += 1
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if assistant_content:
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contents.append(ContentType(role="model", parts=assistant_content))
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## APPEND TOOL CALL MESSAGES ##
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if msg_i < len(messages) and messages[msg_i]["role"] == "tool":
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_part = convert_to_gemini_tool_call_result(messages[msg_i])
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contents.append(ContentType(parts=[_part])) # type: ignore
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msg_i += 1
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if msg_i == init_msg_i: # prevent infinite loops
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raise Exception(
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"Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format(
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messages[msg_i]
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)
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)
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else:
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assistant_text = (
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messages[msg_i].get("content") or ""
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) # either string or none
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if assistant_text:
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assistant_content.append(PartType(text=assistant_text))
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msg_i += 1
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if assistant_content:
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contents.append(ContentType(role="model", parts=assistant_content))
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## APPEND TOOL CALL MESSAGES ##
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if msg_i < len(messages) and messages[msg_i]["role"] == "tool":
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_part = convert_to_gemini_tool_call_result(messages[msg_i])
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contents.append(ContentType(parts=[_part])) # type: ignore
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msg_i += 1
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if msg_i == init_msg_i: # prevent infinite loops
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raise Exception(
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"Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format(
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messages[msg_i]
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)
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)
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return contents
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return contents
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except Exception as e:
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raise e
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def _get_client_cache_key(model: str, vertex_project: str, vertex_location: str):
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