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
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159 | class Replicate(CustomLLM):
"""Replicate LLM.
Examples:
`pip install llama-index-llms-replicate`
```python
from llama_index.llms.replicate import Replicate
# Set up the Replicate API token
import os
os.environ["REPLICATE_API_TOKEN"] = "<your API key>"
# Initialize the Replicate class
llm = Replicate(
model="replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b"
)
# Example of calling the 'complete' method with a prompt
resp = llm.complete("Who is Paul Graham?")
print(resp)
```
"""
model: str = Field(description="The Replicate model to use.")
temperature: float = Field(
default=DEFAULT_REPLICATE_TEMP,
description="The temperature to use for sampling.",
ge=0.01,
le=1.0,
)
image: str = Field(
default="", description="The image file for multimodal model to use. (optional)"
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description="The maximum number of context tokens for the model.",
gt=0,
)
prompt_key: str = Field(
default="prompt", description="The key to use for the prompt in API calls."
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the Replicate API."
)
is_chat_model: bool = Field(
default=False, description="Whether the model is a chat model."
)
@classmethod
def class_name(cls) -> str:
return "Replicate_llm"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=DEFAULT_NUM_OUTPUTS,
model_name=self.model,
is_chat_model=self.is_chat_model,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs: Dict[str, Any] = {
"temperature": self.temperature,
"max_length": self.context_window,
}
if self.image != "":
try:
base_kwargs["image"] = open(self.image, "rb")
except FileNotFoundError:
raise FileNotFoundError(
"Could not load image file. Please check whether the file exists"
)
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_input_dict(self, prompt: str, **kwargs: Any) -> Dict[str, Any]:
return {self.prompt_key: prompt, **self._model_kwargs, **kwargs}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
return stream_completion_response_to_chat_response(completion_response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
response_gen = self.stream_complete(prompt, formatted=formatted, **kwargs)
response_list = list(response_gen)
final_response = response_list[-1]
final_response.delta = None
return final_response
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
try:
import replicate
except ImportError:
raise ImportError(
"Could not import replicate library."
"Please install replicate with `pip install replicate`"
)
if not formatted:
prompt = self.completion_to_prompt(prompt)
input_dict = self._get_input_dict(prompt, **kwargs)
response_iter = replicate.stream(self.model, input=input_dict)
def gen() -> CompletionResponseGen:
text = ""
for server_event in response_iter:
delta = str(server_event)
text += delta
yield CompletionResponse(
delta=delta,
text=text,
)
return gen()
|
