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Merge branch 'main' into litellm_cleanup_traceback

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Krish Dholakia 2024-06-06 16:32:08 -07:00 • committed by GitHub
commit ea4334f760
12 changed files with 1105 additions and 560 deletions
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4
docs/my-website/docs/proxy/logging.md
View file

@ -41,7 +41,9 @@ litellm_settings:
**Step 3**: Set required env variables for logging to langfuse
```shell
export LANGFUSE_PUBLIC_KEY="pk_kk"
export LANGFUSE_SECRET_KEY="sk_ss
export LANGFUSE_SECRET_KEY="sk_ss"
# Optional, defaults to https://cloud.langfuse.com
export LANGFUSE_HOST="https://xxx.langfuse.com"
```
**Step 4**: Start the proxy, make a test request

72
docs/my-website/docs/scheduler.md
View file

@ -101,3 +101,75 @@ print(response)
</TabItem>
</Tabs>
## Advanced - Redis Caching
Use redis caching to do request prioritization across multiple instances of LiteLLM.
### SDK
```python
from litellm import Router
router = Router(
model_list=[
{
"model_name": "gpt-3.5-turbo",
"litellm_params": {
"model": "gpt-3.5-turbo",
"mock_response": "Hello world this is Macintosh!", # fakes the LLM API call
"rpm": 1,
},
},
],
### REDIS PARAMS ###
redis_host=os.environ["REDIS_HOST"],
redis_password=os.environ["REDIS_PASSWORD"],
redis_port=os.environ["REDIS_PORT"],
)
try:
_response = await router.schedule_acompletion( # 👈 ADDS TO QUEUE + POLLS + MAKES CALL
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hey!"}],
priority=0, # 👈 LOWER IS BETTER
)
except Exception as e:
print("didn't make request")
```
### PROXY
```yaml
model_list:
- model_name: gpt-3.5-turbo-fake-model
litellm_params:
model: gpt-3.5-turbo
mock_response: "hello world!"
api_key: my-good-key
router_settings:
redis_host; os.environ/REDIS_HOST
redis_password: os.environ/REDIS_PASSWORD
redis_port: os.environ/REDIS_PORT
```
```bash
$ litellm --config /path/to/config.yaml
# RUNNING on http://0.0.0.0:4000s
```
```bash
curl -X POST 'http://localhost:4000/queue/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-D '{
"model": "gpt-3.5-turbo-fake-model",
"messages": [
{
"role": "user",
"content": "what is the meaning of the universe? 1234"
}],
"priority": 0 👈 SET VALUE HERE
}'
```

6
docs/my-website/package-lock.json generated
View file

@ -5975,9 +5975,9 @@
}
},
"node_modules/caniuse-lite": {
"version": "1.0.30001519",
"resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001519.tgz",
"integrity": "sha512-0QHgqR+Jv4bxHMp8kZ1Kn8CH55OikjKJ6JmKkZYP1F3D7w+lnFXF70nG5eNfsZS89jadi5Ywy5UCSKLAglIRkg==",
"version": "1.0.30001629",
"resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001629.tgz",
"integrity": "sha512-c3dl911slnQhmxUIT4HhYzT7wnBK/XYpGnYLOj4nJBaRiw52Ibe7YxlDaAeRECvA786zCuExhxIUJ2K7nHMrBw==",
"funding": [
{
"type": "opencollective",

1499
docs/my-website/yarn.lock
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File diff suppressed because it is too large Load diff

13
litellm/integrations/langfuse.py
View file

@ -78,8 +78,17 @@ class LangFuseLogger:
For example if you want to append your trace to an existing `trace_id` via header, send
`headers: { ..., langfuse_existing_trace_id: your-existing-trace-id }` via proxy request.
"""
proxy_headers = litellm_params.get("proxy_server_request", {}).get(
"headers", {}
if litellm_params is None:
return metadata
if litellm_params.get("proxy_server_request") is None:
return metadata
if metadata is None:
metadata = {}
proxy_headers = (
litellm_params.get("proxy_server_request", {}).get("headers", {}) or {}
)
for metadata_param_key in proxy_headers:

15
litellm/llms/ollama.py
View file

@ -2,8 +2,9 @@ from itertools import chain
import requests, types, time # type: ignore
import json, uuid
import traceback
from typing import Optional
from typing import Optional, List
import litellm
from litellm.types.utils import ProviderField
import httpx, aiohttp, asyncio # type: ignore
from .prompt_templates.factory import prompt_factory, custom_prompt
from litellm import verbose_logger
@ -126,6 +127,18 @@ class OllamaConfig:
and v is not None
}
def get_required_params(self) -> List[ProviderField]:
"""For a given provider, return it's required fields with a description"""
return [
ProviderField(
field_name="base_url",
field_type="string",
field_description="Your Ollama API Base",
field_value="http://10.10.11.249:11434",
)
]
def get_supported_openai_params(
self,
):

4
litellm/llms/vertex_ai.py
View file

@ -647,9 +647,9 @@ def completion(
prompt = " ".join(
[
message["content"]
message.get("content")
for message in messages
if isinstance(message["content"], str)
if isinstance(message.get("content", None), str)
]
)

6
litellm/router.py
View file

@ -220,8 +220,6 @@ class Router:
[]
) # names of models under litellm_params. ex. azure/chatgpt-v-2
self.deployment_latency_map = {}
### SCHEDULER ###
self.scheduler = Scheduler(polling_interval=polling_interval)
### CACHING ###
cache_type: Literal["local", "redis"] = "local" # default to an in-memory cache
redis_cache = None
@ -259,6 +257,10 @@ class Router:
redis_cache=redis_cache, in_memory_cache=InMemoryCache()
) # use a dual cache (Redis+In-Memory) for tracking cooldowns, usage, etc.
### SCHEDULER ###
self.scheduler = Scheduler(
polling_interval=polling_interval, redis_cache=redis_cache
)
self.default_deployment = None # use this to track the users default deployment, when they want to use model = *
self.default_max_parallel_requests = default_max_parallel_requests

23
litellm/scheduler.py
View file

@ -1,13 +1,14 @@
import heapq, time
import heapq
from pydantic import BaseModel
from typing import Optional
import enum
from litellm.caching import DualCache
from litellm.caching import DualCache, RedisCache
from litellm import print_verbose
class SchedulerCacheKeys(enum.Enum):
queue = "scheduler:queue"
default_in_memory_ttl = 5 # cache queue in-memory for 5s when redis cache available
class DefaultPriorities(enum.Enum):
@ -25,18 +26,24 @@ class FlowItem(BaseModel):
class Scheduler:
cache: DualCache
def __init__(self, polling_interval: Optional[float] = None):
def __init__(
self,
polling_interval: Optional[float] = None,
redis_cache: Optional[RedisCache] = None,
):
"""
polling_interval: float or null - frequency of polling queue. Default is 3ms.
"""
self.queue: list = []
self.cache = DualCache()
default_in_memory_ttl: Optional[float] = None
if redis_cache is not None:
# if redis-cache available frequently poll that instead of using in-memory.
default_in_memory_ttl = SchedulerCacheKeys.default_in_memory_ttl.value
self.cache = DualCache(
redis_cache=redis_cache, default_in_memory_ttl=default_in_memory_ttl
)
self.polling_interval = polling_interval or 0.03 # default to 3ms
def update_variables(self, cache: Optional[DualCache] = None):
if cache is not None:
self.cache = cache
async def add_request(self, request: FlowItem):
# We use the priority directly, as lower values indicate higher priority
# get the queue

4
litellm/utils.py
View file

@ -7355,6 +7355,10 @@ def get_provider_fields(custom_llm_provider: str) -> List[ProviderField]:
if custom_llm_provider == "databricks":
return litellm.DatabricksConfig().get_required_params()
elif custom_llm_provider == "ollama":
return litellm.OllamaConfig().get_required_params()
else:
return []

14
poetry.lock generated
View file

@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
name = "aiohttp"
@ -2114,6 +2114,7 @@ files = [
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{file = "PyYAML-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6965a7bc3cf88e5a1c3bd2e0b5c22f8d677dc88a455344035f03399034eb3007"},
@ -2121,8 +2122,15 @@ files = [
{file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42f8152b8dbc4fe7d96729ec2b99c7097d656dc1213a3229ca5383f973a5ed6d"},
{file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc"},
{file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"},
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{file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"},
{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
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{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
{file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"},
{file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
@ -2139,6 +2147,7 @@ files = [
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
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{file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
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{file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
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@ -2146,6 +2155,7 @@ files = [
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@ -3140,4 +3150,4 @@ proxy = ["PyJWT", "apscheduler", "backoff", "cryptography", "fastapi", "fastapi-
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0, !=3.9.7"
content-hash = "a54d969a1a707413e7cd3ce869d14ef73dd41bb9d36ebf0fb878d9e929bc15b3"
content-hash = "6a37992b63b11d254f5f40687bd96898b1d9515728f663f30dcc81c4ef8df7b7"

3
ui/litellm-dashboard/src/components/model_dashboard.tsx
View file

@ -145,6 +145,7 @@ enum Providers {
OpenAI_Compatible = "OpenAI-Compatible Endpoints (Groq, Together AI, Mistral AI, etc.)",
Vertex_AI = "Vertex AI (Anthropic, Gemini, etc.)",
Databricks = "Databricks",
Ollama = "Ollama",
}
const provider_map: Record<string, string> = {
@ -156,6 +157,7 @@ const provider_map: Record<string, string> = {
OpenAI_Compatible: "openai",
Vertex_AI: "vertex_ai",
Databricks: "databricks",
Ollama: "ollama",
};
const retry_policy_map: Record<string, string> = {
@ -1747,6 +1749,7 @@ const ModelDashboard: React.FC<ModelDashboardProps> = ({
)}
{selectedProvider != Providers.Bedrock &&
selectedProvider != Providers.Vertex_AI &&
selectedProvider != Providers.Ollama &&
(dynamicProviderForm === undefined ||
dynamicProviderForm.fields.length == 0) && (
<Form.Item