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
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191 | class Contextual(OpenAILike):
"""
Generate a response using Contextual's Grounded Language Model (GLM), an LLM engineered specifically to prioritize faithfulness to in-context retrievals over parametric knowledge to reduce hallucinations in Retrieval-Augmented Generation.
The total request cannot exceed 32,000 tokens. Email [email protected] with any feedback or questions.
Examples:
`pip install llama-index-llms-contextual`
```python
from llama_index.llms.contextual import Contextual
# Set up the Contextual class with the required model and API key
llm = Contextual(model="contextual-clm", api_key="your_api_key")
# Call the complete method with a query
response = llm.complete("Explain the importance of low latency LLMs")
print(response)
```
"""
model: str = Field(
description="The model to use. Currently only supports `v1`.", default="v1"
)
api_key: str = Field(description="The API key to use.", default=None)
base_url: str = Field(
description="The base URL to use.",
default="https://api.contextual.ai/v1/generate",
)
avoid_commentary: bool = Field(
description="Flag to indicate whether the model should avoid providing additional commentary in responses. Commentary is conversational in nature and does not contain verifiable claims; therefore, commentary is not strictly grounded in available context. However, commentary may provide useful context which improves the helpfulness of responses.",
default=False,
)
client: Any = Field(default=None, description="Contextual AI Client")
def __init__(
self,
model: str,
api_key: str,
base_url: str = None,
avoid_commentary: bool = False,
**openai_llm_kwargs: Any,
) -> None:
super().__init__(
model=model,
api_key=api_key,
api_base=base_url,
is_chat_model=openai_llm_kwargs.pop("is_chat_model", True),
**openai_llm_kwargs,
)
try:
self.client = ContextualAI(api_key=api_key, base_url=base_url)
except Exception as e:
raise ValueError(f"Error initializing ContextualAI client: {e}")
@classmethod
def class_name(cls) -> str:
"""Get class name."""
return "contextual-clm"
# Synchronous Methods
@llm_completion_callback()
def complete(
self, prompt: str, knowledge: Optional[List[str]] = None, **kwargs
) -> CompletionResponse:
"""
Generate completion for the given prompt.
Args:
prompt (str): The input prompt to generate completion for.
**kwargs: Additional keyword arguments for the API request.
Returns:
str: The generated text completion.
"""
messages_list = [{"role": MessageRole.USER, "content": prompt}]
response = self._generate(
knowledge=knowledge,
messages=messages_list,
model=self.model,
system_prompt=self.system_prompt,
**kwargs,
)
return CompletionResponse(text=response)
@llm_chat_callback()
def chat(self, messages: List[ChatMessage], **kwargs) -> ChatResponse:
"""
Generate a chat response for the given messages.
"""
messages_list = [
{"role": msg.role, "content": msg.blocks[0].text} for msg in messages
]
response = self._generate(
knowledge=kwargs.get("knowledge_base"),
messages=messages_list,
model=self.model,
system_prompt=self.system_prompt,
**kwargs,
)
return ChatResponse(
message=ChatMessage(role=MessageRole.ASSISTANT, content=response)
)
@llm_chat_callback()
def stream_chat(self, messages: List[ChatMessage], **kwargs) -> ChatResponseGen:
"""
Generate a chat response for the given messages.
"""
raise NotImplementedError("stream methods not implemented in Contextual")
@llm_completion_callback()
def stream_complete(self, prompt: str, **kwargs) -> ChatResponseGen:
"""
Generate a chat response for the given messages.
"""
raise NotImplementedError("stream methods not implemented in Contextual")
# ===== Async Endpoints =====
@llm_chat_callback()
async def achat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponse:
raise NotImplementedError("async methods not implemented in Contextual")
@llm_chat_callback()
async def astream_chat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponseAsyncGen:
raise NotImplementedError("async methods not implemented in Contextual")
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
raise NotImplementedError("async methods not implemented in Contextual")
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError("async methods not implemented in Contextual")
def _generate(
self, knowledge, messages, system_prompt, **kwargs
) -> CompletionResponse:
"""
Generate completion for the given prompt.
"""
raw_message = self.client.generate.create(
messages=messages,
knowledge=knowledge or [],
model=self.model,
system_prompt=system_prompt,
avoid_commentary=self.avoid_commentary,
temperature=kwargs.get("temperature", 0.0),
max_new_tokens=kwargs.get("max_tokens", 1024),
top_p=kwargs.get("top_p", 1),
)
return raw_message.response
|
