General
PromptBeginner5 minmarkdown
key
value = env_var.split(=
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General
PromptBeginner5 minmarkdown
key
value = header.split(:
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General
PromptBeginner5 minmarkdown
def parse_env_vars(env_list: list[str]) -> dict[str
str]:
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General
PromptBeginner5 minmarkdown
def parse_headers(header_list: list[str]) -> dict[str
str]:
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General
PromptBeginner5 minmarkdown
tool_calls=json.dumps(result[tool_calls]
indent=2)
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General
PromptBeginner5 minmarkdown
for i
(qa_pair
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Productivity
PromptBeginner5 minmarkdown
result = await evaluate_single_task(client
model
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Writing & Content
PromptBeginner5 minmarkdown
feedback = extract_xml_content(response
feedback)
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General
PromptBeginner5 minmarkdown
for i
qa_pair in enumerate(qa_pairs):
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Writing & Content
PromptBeginner5 minmarkdown
summary = extract_xml_content(response
summary)
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General
PromptBeginner5 minmarkdown
response
tool_metrics = await agent_loop(client
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Writing & Content
PromptBeginner5 minmarkdown
response_value = extract_xml_content(response
response)
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General
PromptBeginner5 minmarkdown
tools: list[dict[str
Any]]
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General
PromptBeginner5 minmarkdown
) -> dict[str
Any]:
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General
PromptBeginner5 minmarkdown
qa_pair: dict[str
Any]
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General
PromptBeginner5 minmarkdown
return response_text
tool_metrics
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Writing & Content
PromptBeginner5 minmarkdown
(block.text for block in response.content if hasattr(block
text))
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Writing & Content
PromptBeginner5 minmarkdown
messages.append({role: assistant
content: response.content})
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General
PromptBeginner5 minmarkdown
tool_response = json.dumps(tool_result) if isinstance(tool_result
(dict
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General
PromptBeginner5 minmarkdown
tool_result = await connection.call_tool(tool_name
tool_input)
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General
PromptBeginner5 minmarkdown
tool_metrics[tool_name] = {count: 0
durations: []}
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General
PromptBeginner5 minmarkdown
) -> tuple[str
dict[str
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Writing & Content
PromptBeginner5 minmarkdown
messages.append({role: assistant
content: response.content})
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Writing & Content
PromptBeginner5 minmarkdown
messages = [{role: user
content: question}]
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