forked from phoenix/litellm-mirror
LiteLLM Minor Fixes & Improvements (10/24/2024) (#6441)
* fix(azure.py): handle /openai/deployment in azure api base * fix(factory.py): fix faulty anthropic tool result translation check Fixes https://github.com/BerriAI/litellm/issues/6422 * fix(gpt_transformation.py): add support for parallel_tool_calls to azure Fixes https://github.com/BerriAI/litellm/issues/6440 * fix(factory.py): support anthropic prompt caching for tool results * fix(vertex_ai/common_utils): don't pop non-null required field Fixes https://github.com/BerriAI/litellm/issues/6426 * feat(vertex_ai.py): support code_execution tool call for vertex ai + gemini Closes https://github.com/BerriAI/litellm/issues/6434 * build(model_prices_and_context_window.json): Add 'supports_assistant_prefill' for bedrock claude-3-5-sonnet v2 models Closes https://github.com/BerriAI/litellm/issues/6437 * fix(types/utils.py): fix linting * test: update test to include required fields * test: fix test * test: handle flaky test * test: remove e2e test - hitting gemini rate limits
This commit is contained in:
Krish Dholakia
GitHub
parent
828631d6fc
commit
f44ab00de2
16 changed files with 366 additions and 94 deletions
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@ -1173,7 +1173,6 @@ class AzureChatCompletion(BaseLLM):
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def create_azure_base_url(
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self, azure_client_params: dict, model: Optional[str]
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) -> str:
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api_base: str = azure_client_params.get(
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"azure_endpoint", ""
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) # "https://example-endpoint.openai.azure.com"
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@ -1182,16 +1181,16 @@ class AzureChatCompletion(BaseLLM):
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api_version: str = azure_client_params.get("api_version", "")
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if model is None:
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model = ""
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new_api_base = (
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api_base
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+ "/openai/deployments/"
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+ model
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+ "/images/generations"
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+ "?api-version="
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+ api_version
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)
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return new_api_base
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if "/openai/deployments/" in api_base:
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base_url_with_deployment = api_base
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else:
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base_url_with_deployment = api_base + "/openai/deployments/" + model
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base_url_with_deployment += "/images/generations"
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base_url_with_deployment += "?api-version=" + api_version
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return base_url_with_deployment
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async def aimage_generation(
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self,
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@ -18,7 +18,7 @@ class AzureOpenAIConfig:
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"""
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Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions
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The class `AzureOpenAIConfig` provides configuration for the OpenAI's Chat API interface, for use with Azure. It inherits from `OpenAIConfig`. Below are the parameters::
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The class `AzureOpenAIConfig` provides configuration for the OpenAI's Chat API interface, for use with Azure. Below are the parameters::
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- `frequency_penalty` (number or null): Defaults to 0. Allows a value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, thereby minimizing repetition.
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@ -102,6 +102,7 @@ class AzureOpenAIConfig:
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"response_format",
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"seed",
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"extra_headers",
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"parallel_tool_calls",
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]
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def map_openai_params(
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@ -65,6 +65,7 @@ def validate_environment(
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if AnthropicConfig().is_cache_control_set(messages=messages):
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cache_headers = AnthropicConfig().get_cache_control_headers()
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headers = {
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"accept": "application/json",
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"anthropic-version": "2023-06-01",
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@ -172,6 +172,8 @@ class AnthropicConfig:
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Used to check if anthropic prompt caching headers need to be set.
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"""
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for message in messages:
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if message.get("cache_control", None) is not None:
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return True
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_message_content = message.get("content")
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if _message_content is not None and isinstance(_message_content, list):
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for content in _message_content:
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@ -1224,6 +1224,10 @@ def convert_to_anthropic_tool_result(
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for content in content_list:
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if content["type"] == "text":
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content_str += content["text"]
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anthropic_tool_result: Optional[AnthropicMessagesToolResultParam] = None
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## PROMPT CACHING CHECK ##
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cache_control = message.get("cache_control", None)
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if message["role"] == "tool":
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tool_message: ChatCompletionToolMessage = message
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tool_call_id: str = tool_message["tool_call_id"]
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@ -1233,7 +1237,7 @@ def convert_to_anthropic_tool_result(
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anthropic_tool_result = AnthropicMessagesToolResultParam(
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type="tool_result", tool_use_id=tool_call_id, content=content_str
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)
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return anthropic_tool_result
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if message["role"] == "function":
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function_message: ChatCompletionFunctionMessage = message
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tool_call_id = function_message.get("tool_call_id") or str(uuid.uuid4())
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@ -1241,13 +1245,11 @@ def convert_to_anthropic_tool_result(
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type="tool_result", tool_use_id=tool_call_id, content=content_str
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)
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return anthropic_tool_result
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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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if anthropic_tool_result is None:
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raise Exception(f"Unable to parse anthropic tool result for message: {message}")
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if cache_control is not None:
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anthropic_tool_result["cache_control"] = cache_control # type: ignore
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return anthropic_tool_result
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def convert_function_to_anthropic_tool_invoke(
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@ -1384,55 +1386,73 @@ def anthropic_messages_pt( # noqa: PLR0915
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] = messages[
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msg_i
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] # type: ignore
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if user_message_types_block["content"] and isinstance(
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user_message_types_block["content"], list
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):
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for m in user_message_types_block["content"]:
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if m.get("type", "") == "image_url":
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m = cast(ChatCompletionImageObject, m)
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if isinstance(m["image_url"], str):
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image_chunk = convert_to_anthropic_image_obj(
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openai_image_url=m["image_url"]
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)
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else:
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image_chunk = convert_to_anthropic_image_obj(
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openai_image_url=m["image_url"]["url"]
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if user_message_types_block["role"] == "user":
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if isinstance(user_message_types_block["content"], list):
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for m in user_message_types_block["content"]:
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if m.get("type", "") == "image_url":
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m = cast(ChatCompletionImageObject, m)
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if isinstance(m["image_url"], str):
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image_chunk = convert_to_anthropic_image_obj(
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openai_image_url=m["image_url"]
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)
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else:
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image_chunk = convert_to_anthropic_image_obj(
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openai_image_url=m["image_url"]["url"]
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)
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_anthropic_content_element = 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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_anthropic_content_element = 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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_content_element = add_cache_control_to_content(
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anthropic_content_element=_anthropic_content_element,
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orignal_content_element=dict(m),
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)
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if "cache_control" in _content_element:
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_anthropic_content_element["cache_control"] = (
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_content_element["cache_control"]
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_content_element = add_cache_control_to_content(
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anthropic_content_element=_anthropic_content_element,
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orignal_content_element=dict(m),
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)
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user_content.append(_anthropic_content_element)
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elif m.get("type", "") == "text":
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m = cast(ChatCompletionTextObject, m)
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_anthropic_text_content_element = AnthropicMessagesTextParam(
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type="text",
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text=m["text"],
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)
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_content_element = add_cache_control_to_content(
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anthropic_content_element=_anthropic_text_content_element,
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orignal_content_element=dict(m),
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)
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_content_element = cast(
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AnthropicMessagesTextParam, _content_element
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if "cache_control" in _content_element:
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_anthropic_content_element["cache_control"] = (
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_content_element["cache_control"]
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)
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user_content.append(_anthropic_content_element)
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elif m.get("type", "") == "text":
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m = cast(ChatCompletionTextObject, m)
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_anthropic_text_content_element = (
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AnthropicMessagesTextParam(
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type="text",
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text=m["text"],
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)
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)
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_content_element = add_cache_control_to_content(
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anthropic_content_element=_anthropic_text_content_element,
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orignal_content_element=dict(m),
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)
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_content_element = cast(
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AnthropicMessagesTextParam, _content_element
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)
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user_content.append(_content_element)
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elif isinstance(user_message_types_block["content"], str):
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_anthropic_content_text_element: AnthropicMessagesTextParam = {
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"type": "text",
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"text": user_message_types_block["content"],
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}
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_content_element = add_cache_control_to_content(
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anthropic_content_element=_anthropic_content_text_element,
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orignal_content_element=dict(user_message_types_block),
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)
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if "cache_control" in _content_element:
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_anthropic_content_text_element["cache_control"] = (
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_content_element["cache_control"]
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)
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user_content.append(_content_element)
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user_content.append(_anthropic_content_text_element)
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elif (
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user_message_types_block["role"] == "tool"
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or user_message_types_block["role"] == "function"
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@ -1441,22 +1461,6 @@ def anthropic_messages_pt( # noqa: PLR0915
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user_content.append(
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convert_to_anthropic_tool_result(user_message_types_block)
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)
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elif isinstance(user_message_types_block["content"], str):
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_anthropic_content_text_element: AnthropicMessagesTextParam = {
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"type": "text",
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"text": user_message_types_block["content"],
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}
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_content_element = add_cache_control_to_content(
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anthropic_content_element=_anthropic_content_text_element,
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orignal_content_element=dict(user_message_types_block),
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)
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if "cache_control" in _content_element:
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_anthropic_content_text_element["cache_control"] = _content_element[
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"cache_control"
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]
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user_content.append(_anthropic_content_text_element)
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msg_i += 1
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@ -234,7 +234,8 @@ def convert_to_nullable(schema):
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def add_object_type(schema):
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properties = schema.get("properties", None)
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if properties is not None:
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schema.pop("required", None)
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if "required" in schema and schema["required"] is None:
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schema.pop("required", None)
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schema["type"] = "object"
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for name, value in properties.items():
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add_object_type(value)
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@ -56,6 +56,7 @@ from litellm.types.llms.vertex_ai import (
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FunctionDeclaration,
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GenerateContentResponseBody,
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GenerationConfig,
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HttpxPartType,
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PartType,
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RequestBody,
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SafetSettingsConfig,
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@ -394,6 +395,7 @@ class VertexGeminiConfig:
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def _map_function(self, value: List[dict]) -> List[Tools]:
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gtool_func_declarations = []
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googleSearchRetrieval: Optional[dict] = None
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code_execution: Optional[dict] = None
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# remove 'additionalProperties' from tools
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value = _remove_additional_properties(value)
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# remove 'strict' from tools
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@ -412,6 +414,8 @@ class VertexGeminiConfig:
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# check if grounding
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if tool.get("googleSearchRetrieval", None) is not None:
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googleSearchRetrieval = tool["googleSearchRetrieval"]
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elif tool.get("code_execution", None) is not None:
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code_execution = tool["code_execution"]
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elif openai_function_object is not None:
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gtool_func_declaration = FunctionDeclaration(
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name=openai_function_object["name"],
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@ -430,6 +434,8 @@ class VertexGeminiConfig:
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)
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if googleSearchRetrieval is not None:
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_tools["googleSearchRetrieval"] = googleSearchRetrieval
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if code_execution is not None:
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_tools["code_execution"] = code_execution
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return [_tools]
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def map_openai_params(
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@ -562,6 +568,13 @@ class VertexGeminiConfig:
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)
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return exception_string
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def get_assistant_content_message(self, parts: List[HttpxPartType]) -> str:
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content_str = ""
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for part in parts:
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if "text" in part:
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content_str += part["text"]
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return content_str
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class GoogleAIStudioGeminiConfig(
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VertexGeminiConfig
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@ -830,7 +843,7 @@ class VertexLLM(VertexBase):
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## CONTENT POLICY VIOLATION ERROR
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model_response.choices[0].finish_reason = "content_filter"
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chat_completion_message = {
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_chat_completion_message = {
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"role": "assistant",
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"content": None,
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}
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@ -838,7 +851,7 @@ class VertexLLM(VertexBase):
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choice = litellm.Choices(
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finish_reason="content_filter",
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index=0,
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message=chat_completion_message, # type: ignore
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message=_chat_completion_message,
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logprobs=None,
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enhancements=None,
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)
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@ -871,7 +884,7 @@ class VertexLLM(VertexBase):
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citation_metadata: List = []
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## GET TEXT ##
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chat_completion_message = {"role": "assistant"}
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content_str = ""
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content_str: str = ""
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tools: List[ChatCompletionToolCallChunk] = []
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functions: Optional[ChatCompletionToolCallFunctionChunk] = None
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if _candidates:
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@ -887,11 +900,12 @@ class VertexLLM(VertexBase):
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if "citationMetadata" in candidate:
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citation_metadata.append(candidate["citationMetadata"])
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if (
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"parts" in candidate["content"]
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and "text" in candidate["content"]["parts"][0]
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):
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content_str = candidate["content"]["parts"][0]["text"]
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if "parts" in candidate["content"]:
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content_str = (
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VertexGeminiConfig().get_assistant_content_message(
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parts=candidate["content"]["parts"]
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)
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)
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if (
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"parts" in candidate["content"]
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@ -4313,7 +4313,8 @@
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"litellm_provider": "bedrock",
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"mode": "chat",
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"supports_function_calling": true,
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"supports_vision": true
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"supports_vision": true,
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"supports_assistant_prefill": true
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},
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"anthropic.claude-3-haiku-20240307-v1:0": {
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"max_tokens": 4096,
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@ -4368,7 +4369,8 @@
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"litellm_provider": "bedrock",
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"mode": "chat",
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"supports_function_calling": true,
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"supports_vision": true
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"supports_vision": true,
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"supports_assistant_prefill": true
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},
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"us.anthropic.claude-3-haiku-20240307-v1:0": {
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"max_tokens": 4096,
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@ -4423,7 +4425,8 @@
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"litellm_provider": "bedrock",
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"mode": "chat",
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"supports_function_calling": true,
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"supports_vision": true
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"supports_vision": true,
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"supports_assistant_prefill": true
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},
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"eu.anthropic.claude-3-haiku-20240307-v1:0": {
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"max_tokens": 4096,
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|
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@ -78,6 +78,7 @@ class AnthropicMessagesToolResultParam(TypedDict, total=False):
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Union[AnthropicMessagesToolResultContent, AnthropicMessagesImageParam]
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],
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]
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cache_control: Optional[Union[dict, ChatCompletionCachedContent]]
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AnthropicMessagesUserMessageValues = Union[
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|
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@ -55,12 +55,24 @@ class HttpxFunctionCall(TypedDict):
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args: dict
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class HttpxExecutableCode(TypedDict):
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code: str
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language: str
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class HttpxCodeExecutionResult(TypedDict):
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outcome: str
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output: str
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class HttpxPartType(TypedDict, total=False):
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text: str
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inline_data: BlobType
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file_data: FileDataType
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functionCall: HttpxFunctionCall
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function_response: FunctionResponse
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executableCode: HttpxExecutableCode
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codeExecutionResult: HttpxCodeExecutionResult
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class HttpxContentType(TypedDict, total=False):
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@ -160,6 +172,7 @@ class GenerationConfig(TypedDict, total=False):
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class Tools(TypedDict, total=False):
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function_declarations: List[FunctionDeclaration]
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googleSearchRetrieval: dict
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code_execution: dict
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retrieval: Retrieval
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|
|
@ -4313,7 +4313,8 @@
|
|||
"litellm_provider": "bedrock",
|
||||
"mode": "chat",
|
||||
"supports_function_calling": true,
|
||||
"supports_vision": true
|
||||
"supports_vision": true,
|
||||
"supports_assistant_prefill": true
|
||||
},
|
||||
"anthropic.claude-3-haiku-20240307-v1:0": {
|
||||
"max_tokens": 4096,
|
||||
|
|
@ -4368,7 +4369,8 @@
|
|||
"litellm_provider": "bedrock",
|
||||
"mode": "chat",
|
||||
"supports_function_calling": true,
|
||||
"supports_vision": true
|
||||
"supports_vision": true,
|
||||
"supports_assistant_prefill": true
|
||||
},
|
||||
"us.anthropic.claude-3-haiku-20240307-v1:0": {
|
||||
"max_tokens": 4096,
|
||||
|
|
@ -4423,7 +4425,8 @@
|
|||
"litellm_provider": "bedrock",
|
||||
"mode": "chat",
|
||||
"supports_function_calling": true,
|
||||
"supports_vision": true
|
||||
"supports_vision": true,
|
||||
"supports_assistant_prefill": true
|
||||
},
|
||||
"eu.anthropic.claude-3-haiku-20240307-v1:0": {
|
||||
"max_tokens": 4096,
|
||||
|
|
|
|||
|
|
@ -94,3 +94,37 @@ def test_process_azure_headers_with_dict_input():
|
|||
|
||||
result = process_azure_headers(input_headers)
|
||||
assert result == expected_output, "Unexpected output for dict input"
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"api_base, model, expected_endpoint",
|
||||
[
|
||||
(
|
||||
"https://my-endpoint-sweden-berri992.openai.azure.com",
|
||||
"dall-e-3-test",
|
||||
"https://my-endpoint-sweden-berri992.openai.azure.com/openai/deployments/dall-e-3-test/images/generations?api-version=2023-12-01-preview",
|
||||
),
|
||||
(
|
||||
"https://my-endpoint-sweden-berri992.openai.azure.com/openai/deployments/my-custom-deployment",
|
||||
"dall-e-3",
|
||||
"https://my-endpoint-sweden-berri992.openai.azure.com/openai/deployments/my-custom-deployment/images/generations?api-version=2023-12-01-preview",
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_process_azure_endpoint_url(api_base, model, expected_endpoint):
|
||||
from litellm.llms.AzureOpenAI.azure import AzureChatCompletion
|
||||
|
||||
azure_chat_completion = AzureChatCompletion()
|
||||
input_args = {
|
||||
"azure_client_params": {
|
||||
"api_version": "2023-12-01-preview",
|
||||
"azure_endpoint": api_base,
|
||||
"azure_deployment": model,
|
||||
"max_retries": 2,
|
||||
"timeout": 600,
|
||||
"api_key": "f28ab7b695af4154bc53498e5bdccb07",
|
||||
},
|
||||
"model": model,
|
||||
}
|
||||
result = azure_chat_completion.create_azure_base_url(**input_args)
|
||||
assert result == expected_endpoint, "Unexpected endpoint"
|
||||
|
|
|
|||
|
|
@ -784,3 +784,21 @@ def test_unmapped_vertex_anthropic_model():
|
|||
max_retries=10,
|
||||
)
|
||||
assert "max_retries" not in optional_params
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"tools, key",
|
||||
[
|
||||
([{"googleSearchRetrieval": {}}], "googleSearchRetrieval"),
|
||||
([{"code_execution": {}}], "code_execution"),
|
||||
],
|
||||
)
|
||||
def test_vertex_tool_params(tools, key):
|
||||
|
||||
optional_params = get_optional_params(
|
||||
model="gemini-1.5-pro",
|
||||
custom_llm_provider="vertex_ai",
|
||||
tools=tools,
|
||||
)
|
||||
print(optional_params)
|
||||
assert optional_params["tools"][0][key] == {}
|
||||
|
|
|
|||
|
|
@ -54,11 +54,19 @@ def test_completion_pydantic_obj_2():
|
|||
"type": "array",
|
||||
},
|
||||
},
|
||||
"required": [
|
||||
"name",
|
||||
"date",
|
||||
"participants",
|
||||
],
|
||||
"type": "object",
|
||||
},
|
||||
"type": "array",
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"events",
|
||||
],
|
||||
"type": "object",
|
||||
},
|
||||
},
|
||||
|
|
@ -81,3 +89,31 @@ def test_completion_pydantic_obj_2():
|
|||
print(mock_post.call_args.kwargs)
|
||||
|
||||
assert mock_post.call_args.kwargs["json"] == expected_request_body
|
||||
|
||||
|
||||
def test_build_vertex_schema():
|
||||
from litellm.llms.vertex_ai_and_google_ai_studio.common_utils import (
|
||||
_build_vertex_schema,
|
||||
)
|
||||
import json
|
||||
|
||||
schema = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"recipes": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {"recipe_name": {"type": "string"}},
|
||||
"required": ["recipe_name"],
|
||||
},
|
||||
}
|
||||
},
|
||||
"required": ["recipes"],
|
||||
}
|
||||
|
||||
new_schema = _build_vertex_schema(schema)
|
||||
print(f"new_schema: {new_schema}")
|
||||
assert new_schema["type"] == schema["type"]
|
||||
assert new_schema["properties"] == schema["properties"]
|
||||
assert "required" in new_schema and new_schema["required"] == schema["required"]
|
||||
|
|
|
|||
|
|
@ -203,6 +203,7 @@ def create_async_task(**completion_kwargs):
|
|||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("stream", [False, True])
|
||||
@pytest.mark.flaky(retries=6, delay=1)
|
||||
async def test_langfuse_logging_without_request_response(stream, langfuse_client):
|
||||
try:
|
||||
import uuid
|
||||
|
|
|
|||
|
|
@ -471,3 +471,144 @@ def test_anthropic_function_call_with_no_schema(model):
|
|||
{"role": "user", "content": "What is the current temperature in New York?"}
|
||||
]
|
||||
completion(model=model, messages=messages, tools=tools, tool_choice="auto")
|
||||
|
||||
|
||||
def test_passing_tool_result_as_list():
|
||||
litellm.set_verbose = True
|
||||
model = "anthropic/claude-3-5-sonnet-20241022"
|
||||
messages = [
|
||||
{
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "You are a helpful assistant that have the ability to interact with a computer to solve tasks.",
|
||||
}
|
||||
],
|
||||
"role": "system",
|
||||
},
|
||||
{
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "Write a git commit message for the current staging area and commit the changes.",
|
||||
}
|
||||
],
|
||||
"role": "user",
|
||||
},
|
||||
{
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "I'll help you commit the changes. Let me first check the git status to see what changes are staged.",
|
||||
}
|
||||
],
|
||||
"role": "assistant",
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 1,
|
||||
"function": {
|
||||
"arguments": '{"command": "git status", "thought": "Checking git status to see staged changes"}',
|
||||
"name": "execute_bash",
|
||||
},
|
||||
"id": "toolu_01V1paXrun4CVetdAGiQaZG5",
|
||||
"type": "function",
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": 'OBSERVATION:\nOn branch master\r\n\r\nNo commits yet\r\n\r\nChanges to be committed:\r\n (use "git rm --cached <file>..." to unstage)\r\n\tnew file: hello.py\r\n\r\n\r\n[Python Interpreter: /openhands/poetry/openhands-ai-5O4_aCHf-py3.12/bin/python]\nroot@openhands-workspace:/workspace # \n[Command finished with exit code 0]',
|
||||
}
|
||||
],
|
||||
"role": "tool",
|
||||
"tool_call_id": "toolu_01V1paXrun4CVetdAGiQaZG5",
|
||||
"name": "execute_bash",
|
||||
"cache_control": {"type": "ephemeral"},
|
||||
},
|
||||
]
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "execute_bash",
|
||||
"description": 'Execute a bash command in the terminal.\n* Long running commands: For commands that may run indefinitely, it should be run in the background and the output should be redirected to a file, e.g. command = `python3 app.py > server.log 2>&1 &`.\n* Interactive: If a bash command returns exit code `-1`, this means the process is not yet finished. The assistant must then send a second call to terminal with an empty `command` (which will retrieve any additional logs), or it can send additional text (set `command` to the text) to STDIN of the running process, or it can send command=`ctrl+c` to interrupt the process.\n* Timeout: If a command execution result says "Command timed out. Sending SIGINT to the process", the assistant should retry running the command in the background.\n',
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"thought": {
|
||||
"type": "string",
|
||||
"description": "Reasoning about the action to take.",
|
||||
},
|
||||
"command": {
|
||||
"type": "string",
|
||||
"description": "The bash command to execute. Can be empty to view additional logs when previous exit code is `-1`. Can be `ctrl+c` to interrupt the currently running process.",
|
||||
},
|
||||
},
|
||||
"required": ["command"],
|
||||
},
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "finish",
|
||||
"description": "Finish the interaction.\n* Do this if the task is complete.\n* Do this if the assistant cannot proceed further with the task.\n",
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "str_replace_editor",
|
||||
"description": "Custom editing tool for viewing, creating and editing files\n* State is persistent across command calls and discussions with the user\n* If `path` is a file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep\n* The `create` command cannot be used if the specified `path` already exists as a file\n* If a `command` generates a long output, it will be truncated and marked with `<response clipped>`\n* The `undo_edit` command will revert the last edit made to the file at `path`\n\nNotes for using the `str_replace` command:\n* The `old_str` parameter should match EXACTLY one or more consecutive lines from the original file. Be mindful of whitespaces!\n* If the `old_str` parameter is not unique in the file, the replacement will not be performed. Make sure to include enough context in `old_str` to make it unique\n* The `new_str` parameter should contain the edited lines that should replace the `old_str`\n",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"command": {
|
||||
"description": "The commands to run. Allowed options are: `view`, `create`, `str_replace`, `insert`, `undo_edit`.",
|
||||
"enum": [
|
||||
"view",
|
||||
"create",
|
||||
"str_replace",
|
||||
"insert",
|
||||
"undo_edit",
|
||||
],
|
||||
"type": "string",
|
||||
},
|
||||
"path": {
|
||||
"description": "Absolute path to file or directory, e.g. `/repo/file.py` or `/repo`.",
|
||||
"type": "string",
|
||||
},
|
||||
"file_text": {
|
||||
"description": "Required parameter of `create` command, with the content of the file to be created.",
|
||||
"type": "string",
|
||||
},
|
||||
"old_str": {
|
||||
"description": "Required parameter of `str_replace` command containing the string in `path` to replace.",
|
||||
"type": "string",
|
||||
},
|
||||
"new_str": {
|
||||
"description": "Optional parameter of `str_replace` command containing the new string (if not given, no string will be added). Required parameter of `insert` command containing the string to insert.",
|
||||
"type": "string",
|
||||
},
|
||||
"insert_line": {
|
||||
"description": "Required parameter of `insert` command. The `new_str` will be inserted AFTER the line `insert_line` of `path`.",
|
||||
"type": "integer",
|
||||
},
|
||||
"view_range": {
|
||||
"description": "Optional parameter of `view` command when `path` points to a file. If none is given, the full file is shown. If provided, the file will be shown in the indicated line number range, e.g. [11, 12] will show lines 11 and 12. Indexing at 1 to start. Setting `[start_line, -1]` shows all lines from `start_line` to the end of the file.",
|
||||
"items": {"type": "integer"},
|
||||
"type": "array",
|
||||
},
|
||||
},
|
||||
"required": ["command", "path"],
|
||||
},
|
||||
},
|
||||
},
|
||||
]
|
||||
for _ in range(2):
|
||||
resp = completion(model=model, messages=messages, tools=tools)
|
||||
print(resp)
|
||||
|
||||
assert resp.usage.prompt_tokens_details.cached_tokens > 0
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue