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import json |
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from llamafactory.data.formatter import EmptyFormatter, FunctionFormatter, StringFormatter, ToolFormatter |
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def test_empty_formatter(): |
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formatter = EmptyFormatter(slots=["\n"]) |
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assert formatter.apply() == ["\n"] |
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def test_string_formatter(): |
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formatter = StringFormatter(slots=["<s>", "Human: {{content}}\nAssistant:"]) |
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assert formatter.apply(content="Hi") == ["<s>", "Human: Hi\nAssistant:"] |
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def test_function_formatter(): |
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formatter = FunctionFormatter(slots=[], tool_format="default") |
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tool_calls = json.dumps({"name": "tool_name", "arguments": {"foo": "bar", "size": 10}}) |
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assert formatter.apply(content=tool_calls) == [ |
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"""Action: tool_name\nAction Input: {\"foo\": \"bar\", \"size\": 10}\n""" |
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] |
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def test_multi_function_formatter(): |
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formatter = FunctionFormatter(slots=[], tool_format="default") |
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tool_calls = json.dumps([{"name": "tool_name", "arguments": {"foo": "bar", "size": 10}}] * 2) |
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assert formatter.apply(content=tool_calls) == [ |
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"""Action: tool_name\nAction Input: {\"foo\": \"bar\", \"size\": 10}\n""", |
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"""Action: tool_name\nAction Input: {\"foo\": \"bar\", \"size\": 10}\n""", |
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] |
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def test_default_tool_formatter(): |
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formatter = ToolFormatter(tool_format="default") |
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tools = [ |
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{ |
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"name": "test_tool", |
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"description": "tool_desc", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"foo": {"type": "string", "description": "foo_desc"}, |
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"bar": {"type": "number", "description": "bar_desc"}, |
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}, |
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"required": ["foo"], |
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}, |
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} |
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] |
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assert formatter.apply(content=json.dumps(tools)) == [ |
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"You have access to the following tools:\n" |
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"> Tool Name: test_tool\n" |
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"Tool Description: tool_desc\n" |
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"Tool Args:\n" |
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" - foo (string, required): foo_desc\n" |
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" - bar (number): bar_desc\n\n" |
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"Use the following format if using a tool:\n" |
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"```\n" |
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"Action: tool name (one of [test_tool]).\n" |
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"Action Input: the input to the tool, in a JSON format representing the kwargs " |
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"""(e.g. ```{"input": "hello world", "num_beams": 5}```).\n""" |
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"```\n" |
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] |
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def test_default_tool_extractor(): |
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formatter = ToolFormatter(tool_format="default") |
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result = """Action: test_tool\nAction Input: {"foo": "bar", "size": 10}\n""" |
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assert formatter.extract(result) == [("test_tool", """{"foo": "bar", "size": 10}""")] |
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def test_default_multi_tool_extractor(): |
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formatter = ToolFormatter(tool_format="default") |
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result = ( |
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"""Action: test_tool\nAction Input: {"foo": "bar", "size": 10}\n""" |
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"""Action: another_tool\nAction Input: {"foo": "job", "size": 2}\n""" |
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) |
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assert formatter.extract(result) == [ |
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("test_tool", """{"foo": "bar", "size": 10}"""), |
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("another_tool", """{"foo": "job", "size": 2}"""), |
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] |
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def test_glm4_tool_formatter(): |
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formatter = ToolFormatter(tool_format="glm4") |
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tools = [ |
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{ |
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"name": "test_tool", |
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"description": "tool_desc", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"foo": {"type": "string", "description": "foo_desc"}, |
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"bar": {"type": "number", "description": "bar_desc"}, |
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}, |
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"required": ["foo"], |
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}, |
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} |
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] |
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assert formatter.apply(content=json.dumps(tools)) == [ |
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"你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的," |
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"你的任务是针对用户的问题和要求提供适当的答复和支持。# 可用工具\n\n" |
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"## test_tool\n\n{}\n在调用上述函数时,请使用 Json 格式表示调用的参数。".format(json.dumps(tools[0], indent=4)) |
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] |
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def test_glm4_tool_extractor(): |
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formatter = ToolFormatter(tool_format="glm4") |
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result = """test_tool\n{"foo": "bar", "size": 10}\n""" |
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assert formatter.extract(result) == [("test_tool", """{"foo": "bar", "size": 10}""")] |
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