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Merge pull request #4746 from BerriAI/litellm_anthropic_translation
fix(factory.py): use stronger typing for anthropic translation
This commit is contained in:
Krish Dholakia
GitHub
commit
606466d5fc
2 changed files with 110 additions and 47 deletions
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@ -1081,7 +1081,7 @@ def convert_to_gemini_tool_call_result(
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return _part
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def convert_to_anthropic_tool_result(message: dict) -> dict:
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def convert_to_anthropic_tool_result(message: dict) -> AnthropicMessagesToolResultParam:
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"""
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OpenAI message with a tool result looks like:
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{
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@ -1114,44 +1114,50 @@ def convert_to_anthropic_tool_result(message: dict) -> dict:
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}
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"""
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if message["role"] == "tool":
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tool_call_id = message.get("tool_call_id")
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content = message.get("content")
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tool_call_id: str = message.get("tool_call_id") # type: ignore
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content: str = message.get("content") # type: ignore
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# We can't determine from openai message format whether it's a successful or
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# error call result so default to the successful result template
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anthropic_tool_result = {
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"type": "tool_result",
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"tool_use_id": tool_call_id,
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"content": content,
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}
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anthropic_tool_result = AnthropicMessagesToolResultParam(
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type="tool_result", tool_use_id=tool_call_id, content=content
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)
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return anthropic_tool_result
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elif message["role"] == "function":
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content = message.get("content")
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anthropic_tool_result = {
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"type": "tool_result",
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"tool_use_id": str(uuid.uuid4()),
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"content": content,
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}
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if message["role"] == "function":
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content = message.get("content") # type: ignore
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anthropic_tool_result = AnthropicMessagesToolResultParam(
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type="tool_result", tool_use_id=str(uuid.uuid4()), content=content
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)
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return anthropic_tool_result
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return {}
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else:
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raise Exception(
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"Invalid role={}. Only 'tool' or 'function' are accepted for tool result blocks.".format(
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message.get("content")
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)
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)
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def convert_function_to_anthropic_tool_invoke(function_call):
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def convert_function_to_anthropic_tool_invoke(
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function_call,
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) -> List[AnthropicMessagesToolUseParam]:
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try:
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anthropic_tool_invoke = [
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{
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"type": "tool_use",
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"id": str(uuid.uuid4()),
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"name": get_attribute_or_key(function_call, "name"),
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"input": json.loads(get_attribute_or_key(function_call, "arguments")),
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}
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AnthropicMessagesToolUseParam(
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type="tool_use",
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id=str(uuid.uuid4()),
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name=get_attribute_or_key(function_call, "name"),
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input=json.loads(get_attribute_or_key(function_call, "arguments")),
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)
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]
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return anthropic_tool_invoke
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except Exception as e:
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raise e
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def convert_to_anthropic_tool_invoke(tool_calls: list) -> list:
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def convert_to_anthropic_tool_invoke(
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tool_calls: list,
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) -> List[AnthropicMessagesToolUseParam]:
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"""
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OpenAI tool invokes:
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{
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@ -1189,18 +1195,16 @@ def convert_to_anthropic_tool_invoke(tool_calls: list) -> list:
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}
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"""
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anthropic_tool_invoke = [
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{
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"type": "tool_use",
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"id": get_attribute_or_key(tool, "id"),
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"name": get_attribute_or_key(
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get_attribute_or_key(tool, "function"), "name"
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),
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"input": json.loads(
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AnthropicMessagesToolUseParam(
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type="tool_use",
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id=get_attribute_or_key(tool, "id"),
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name=get_attribute_or_key(get_attribute_or_key(tool, "function"), "name"),
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input=json.loads(
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get_attribute_or_key(
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get_attribute_or_key(tool, "function"), "arguments"
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)
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),
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}
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)
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for tool in tool_calls
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if get_attribute_or_key(tool, "type") == "function"
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]
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@ -1212,7 +1216,12 @@ def anthropic_messages_pt(
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messages: list,
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model: str,
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llm_provider: str,
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):
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) -> List[
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Union[
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AnthropicMessagesUserMessageParam,
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AnthopicMessagesAssistantMessageParam,
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]
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]:
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"""
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format messages for anthropic
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1. Anthropic supports roles like "user" and "assistant" (system prompt sent separately)
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@ -1225,24 +1234,33 @@ def anthropic_messages_pt(
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# add role=tool support to allow function call result/error submission
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user_message_types = {"user", "tool", "function"}
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# reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, merge them.
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new_messages: list = []
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new_messages: List[
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Union[
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AnthropicMessagesUserMessageParam,
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AnthopicMessagesAssistantMessageParam,
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]
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] = []
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msg_i = 0
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tool_use_param = False
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while msg_i < len(messages):
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user_content = []
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user_content: List[AnthropicMessagesUserMessageValues] = []
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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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for m in messages[msg_i]["content"]:
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if m.get("type", "") == "image_url":
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image_chunk = convert_to_anthropic_image_obj(
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m["image_url"]["url"]
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)
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user_content.append(
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{
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"type": "image",
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"source": convert_to_anthropic_image_obj(
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m["image_url"]["url"]
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AnthropicMessagesImageParam(
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type="image",
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source=AnthropicImageParamSource(
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type="base64",
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media_type=image_chunk["media_type"],
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data=image_chunk["data"],
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),
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}
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)
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)
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elif m.get("type", "") == "text":
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user_content.append({"type": "text", "text": m["text"]})
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@ -1262,14 +1280,25 @@ def anthropic_messages_pt(
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if user_content:
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new_messages.append({"role": "user", "content": user_content})
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assistant_content = []
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assistant_content: List[AnthropicMessagesAssistantMessageValues] = []
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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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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({"type": "text", "text": assistant_text})
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if "content" in messages[msg_i] and isinstance(
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messages[msg_i]["content"], list
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):
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for m in messages[msg_i]["content"]:
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# handle text
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if m.get("type", "") == "text":
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anthropic_message = AnthropicMessagesTextParam(
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type="text", text=m.get("text")
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)
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assistant_content.append(anthropic_message)
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elif "content" in messages[msg_i] and isinstance(
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messages[msg_i]["content"], str
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):
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assistant_content.append(
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{"type": "text", "text": messages[msg_i]["content"]}
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)
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if messages[msg_i].get(
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"tool_calls", []
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@ -7,6 +7,8 @@ import pytest
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sys.path.insert(0, os.path.abspath("../.."))
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from typing import Union
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# from litellm.llms.prompt_templates.factory import prompt_factory
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import litellm
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from litellm import completion
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@ -138,6 +140,38 @@ def test_anthropic_messages_pt():
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assert "Invalid first message" in str(err.value)
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def test_anthropic_messages_nested_pt():
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from litellm.types.llms.anthropic import (
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AnthopicMessagesAssistantMessageParam,
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AnthropicMessagesUserMessageParam,
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)
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messages = [
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{"content": [{"text": "here is a task", "type": "text"}], "role": "user"},
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{
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"content": [{"text": "sure happy to help", "type": "text"}],
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"role": "assistant",
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},
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{
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"content": [
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{
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"text": "Here is a screenshot of the current desktop with the "
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"mouse coordinates (500, 350). Please select an action "
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"from the provided schema.",
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"type": "text",
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}
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],
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"role": "user",
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},
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]
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new_messages = anthropic_messages_pt(
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messages, model="claude-3-sonnet-20240229", llm_provider="anthropic"
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)
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assert isinstance(new_messages[1]["content"][0]["text"], str)
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# codellama_prompt_format()
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def test_bedrock_tool_calling_pt():
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tools = [
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