File size: 9,099 Bytes
4ec8dba |
1 2 3 4 5 6 7 8 9 10 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 |
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# LiteLLM - Getting Started
https://github.com/BerriAI/litellm
## **Call 100+ LLMs using the same Input/Output Format**
## Basic usage
<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
```shell
pip install litellm
```
<Tabs>
<TabItem value="openai" label="OpenAI">
```python
from litellm import completion
import os
## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-api-key"
response = completion(
model="gpt-3.5-turbo",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
<TabItem value="anthropic" label="Anthropic">
```python
from litellm import completion
import os
## set ENV variables
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
response = completion(
model="claude-2",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
<TabItem value="vertex" label="VertexAI">
```python
from litellm import completion
import os
# auth: run 'gcloud auth application-default'
os.environ["VERTEX_PROJECT"] = "hardy-device-386718"
os.environ["VERTEX_LOCATION"] = "us-central1"
response = completion(
model="chat-bison",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
<TabItem value="hugging" label="HuggingFace">
```python
from litellm import completion
import os
os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"
# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints
response = completion(
model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0",
messages=[{ "content": "Hello, how are you?","role": "user"}],
api_base="https://my-endpoint.huggingface.cloud"
)
print(response)
```
</TabItem>
<TabItem value="azure" label="Azure OpenAI">
```python
from litellm import completion
import os
## set ENV variables
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""
# azure call
response = completion(
"azure/<your_deployment_name>",
messages = [{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
<TabItem value="ollama" label="Ollama">
```python
from litellm import completion
response = completion(
model="ollama/llama2",
messages = [{ "content": "Hello, how are you?","role": "user"}],
api_base="http://localhost:11434"
)
```
</TabItem>
<TabItem value="or" label="Openrouter">
```python
from litellm import completion
import os
## set ENV variables
os.environ["OPENROUTER_API_KEY"] = "openrouter_api_key"
response = completion(
model="openrouter/google/palm-2-chat-bison",
messages = [{ "content": "Hello, how are you?","role": "user"}],
)
```
</TabItem>
</Tabs>
## Streaming
Set `stream=True` in the `completion` args.
<Tabs>
<TabItem value="openai" label="OpenAI">
```python
from litellm import completion
import os
## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-api-key"
response = completion(
model="gpt-3.5-turbo",
messages=[{ "content": "Hello, how are you?","role": "user"}],
stream=True,
)
```
</TabItem>
<TabItem value="anthropic" label="Anthropic">
```python
from litellm import completion
import os
## set ENV variables
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
response = completion(
model="claude-2",
messages=[{ "content": "Hello, how are you?","role": "user"}],
stream=True,
)
```
</TabItem>
<TabItem value="vertex" label="VertexAI">
```python
from litellm import completion
import os
# auth: run 'gcloud auth application-default'
os.environ["VERTEX_PROJECT"] = "hardy-device-386718"
os.environ["VERTEX_LOCATION"] = "us-central1"
response = completion(
model="chat-bison",
messages=[{ "content": "Hello, how are you?","role": "user"}],
stream=True,
)
```
</TabItem>
<TabItem value="hugging" label="HuggingFace">
```python
from litellm import completion
import os
os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"
# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints
response = completion(
model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0",
messages=[{ "content": "Hello, how are you?","role": "user"}],
api_base="https://my-endpoint.huggingface.cloud",
stream=True,
)
print(response)
```
</TabItem>
<TabItem value="azure" label="Azure OpenAI">
```python
from litellm import completion
import os
## set ENV variables
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""
# azure call
response = completion(
"azure/<your_deployment_name>",
messages = [{ "content": "Hello, how are you?","role": "user"}],
stream=True,
)
```
</TabItem>
<TabItem value="ollama" label="Ollama">
```python
from litellm import completion
response = completion(
model="ollama/llama2",
messages = [{ "content": "Hello, how are you?","role": "user"}],
api_base="http://localhost:11434",
stream=True,
)
```
</TabItem>
<TabItem value="or" label="Openrouter">
```python
from litellm import completion
import os
## set ENV variables
os.environ["OPENROUTER_API_KEY"] = "openrouter_api_key"
response = completion(
model="openrouter/google/palm-2-chat-bison",
messages = [{ "content": "Hello, how are you?","role": "user"}],
stream=True,
)
```
</TabItem>
</Tabs>
## Exception handling
LiteLLM maps exceptions across all supported providers to the OpenAI exceptions. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM.
```python
from openai.error import OpenAIError
from litellm import completion
os.environ["ANTHROPIC_API_KEY"] = "bad-key"
try:
# some code
completion(model="claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}])
except OpenAIError as e:
print(e)
```
## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
LiteLLM exposes pre defined callbacks to send data to Langfuse, LLMonitor, Helicone, Promptlayer, Traceloop, Slack
```python
from litellm import completion
## set env variables for logging tools
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"
os.environ["OPENAI_API_KEY"]
# set callbacks
litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langfuse, llmonitor, supabase
#openai call
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
```
## Calculate Costs, Usage, Latency
Pass the completion response to `litellm.completion_cost(completion_response=response)` and get the cost
```python
from litellm import completion, completion_cost
import os
os.environ["OPENAI_API_KEY"] = "your-api-key"
response = completion(
model="gpt-3.5-turbo",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
cost = completion_cost(completion_response=response)
print("Cost for completion call with gpt-3.5-turbo: ", f"${float(cost):.10f}")
```
**Output**
```shell
Cost for completion call with gpt-3.5-turbo: $0.0000775000
```
### Track Costs, Usage, Latency for streaming
Use a callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback
```python
import litellm
# track_cost_callback
def track_cost_callback(
kwargs, # kwargs to completion
completion_response, # response from completion
start_time, end_time # start/end time
):
try:
# check if it has collected an entire stream response
if "complete_streaming_response" in kwargs:
# for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost
completion_response=kwargs["complete_streaming_response"]
input_text = kwargs["messages"]
output_text = completion_response["choices"][0]["message"]["content"]
response_cost = litellm.completion_cost(
model = kwargs["model"],
messages = input_text,
completion=output_text
)
print("streaming response_cost", response_cost)
except:
pass
# set callback
litellm.success_callback = [track_cost_callback] # set custom callback function
# litellm.completion() call
response = completion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": "Hi 👋 - i'm openai"
}
],
stream=True
)
```
Need a dedicated key? Email us @ krrish@berri.ai
## More details
* [exception mapping](./exception_mapping.md)
* [retries + model fallbacks for completion()](./completion/reliable_completions.md)
* [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md) |