11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135 | class LLMTextCompletionProgram(BasePydanticProgram[BaseModel]):
"""
LLM Text Completion Program.
Uses generic LLM text completion + an output parser to generate a structured output.
"""
def __init__(
self,
output_parser: BaseOutputParser,
output_cls: Type[BaseModel],
prompt: BasePromptTemplate,
llm: LLM,
verbose: bool = False,
) -> None:
self._output_parser = output_parser
self._output_cls = output_cls
self._llm = llm
self._prompt = prompt
self._verbose = verbose
self._prompt.output_parser = output_parser
@classmethod
def from_defaults(
cls,
output_parser: Optional[BaseOutputParser] = None,
output_cls: Optional[Type[BaseModel]] = None,
prompt_template_str: Optional[str] = None,
prompt: Optional[PromptTemplate] = None,
llm: Optional[LLM] = None,
verbose: bool = False,
**kwargs: Any,
) -> "LLMTextCompletionProgram":
llm = llm or Settings.llm
if prompt is None and prompt_template_str is None:
raise ValueError("Must provide either prompt or prompt_template_str.")
if prompt is not None and prompt_template_str is not None:
raise ValueError("Must provide either prompt or prompt_template_str.")
if prompt_template_str is not None:
prompt = PromptTemplate(prompt_template_str)
# decide default output class if not set
if output_cls is None:
if not isinstance(output_parser, PydanticOutputParser):
raise ValueError("Output parser must be PydanticOutputParser.")
output_cls = output_parser.output_cls
else:
if output_parser is None:
output_parser = PydanticOutputParser(output_cls=output_cls)
return cls(
output_parser,
output_cls,
prompt=cast(PromptTemplate, prompt),
llm=llm,
verbose=verbose,
)
@property
def output_cls(self) -> Type[BaseModel]:
return self._output_cls
@property
def prompt(self) -> BasePromptTemplate:
return self._prompt
@prompt.setter
def prompt(self, prompt: BasePromptTemplate) -> None:
self._prompt = prompt
def __call__(
self,
llm_kwargs: Optional[Dict[str, Any]] = None,
*args: Any,
**kwargs: Any,
) -> BaseModel:
llm_kwargs = llm_kwargs or {}
if self._llm.metadata.is_chat_model:
messages = self._prompt.format_messages(llm=self._llm, **kwargs)
response = self._llm.chat(messages, **llm_kwargs)
raw_output = response.message.content or ""
else:
formatted_prompt = self._prompt.format(llm=self._llm, **kwargs)
response = self._llm.complete(formatted_prompt, **llm_kwargs)
raw_output = response.text
output = self._output_parser.parse(raw_output)
if not isinstance(output, self._output_cls):
raise ValueError(
f"Output parser returned {type(output)} but expected {self._output_cls}"
)
return output
async def acall(
self,
llm_kwargs: Optional[Dict[str, Any]] = None,
*args: Any,
**kwargs: Any,
) -> BaseModel:
llm_kwargs = llm_kwargs or {}
if self._llm.metadata.is_chat_model:
messages = self._prompt.format_messages(llm=self._llm, **kwargs)
response = await self._llm.achat(messages, **llm_kwargs)
raw_output = response.message.content or ""
else:
formatted_prompt = self._prompt.format(llm=self._llm, **kwargs)
response = await self._llm.acomplete(formatted_prompt, **llm_kwargs)
raw_output = response.text
output = self._output_parser.parse(raw_output)
if not isinstance(output, self._output_cls):
raise ValueError(
f"Output parser returned {type(output)} but expected {self._output_cls}"
)
return output
|