mirror of
https://github.com/BerriAI/litellm.git
synced 2026-10-18 04:44:37 +00:00
* refactor(typing): narrow Any sources in proxy, guardrails, bedrock and responses code Turns untyped json.loads, response.json(), header lookups and forwarded kwargs into object or precise types at their source, so downstream code type-checks without reportAny / reportExplicitAny. No runtime behavior changes. * refactor(typing): narrow Any sources in caching, MCP, providers and proxy request code Decoded JSON, cache reads and header lookups now enter as object or precise types, prisma tables used by the shadow-eval logger get typed Protocol views, and a few SDK parameters move from dict[str, Any] to Mapping[str, object]. No runtime behavior changes. * refactor(typing): narrow Any sources in proxy server, router, logging and shared utils * refactor(typing): narrow Any sources in provider transformations, integrations and proxy endpoints * refactor(typing): narrow Any sources across providers, proxy and router Deepens the Any cleanup on the hottest files: typed helpers in core_helpers for attribute and item access, identity TypeAdapter(object) wraps at untyped boundaries, explicit return annotations on shared provider and proxy helpers, and Protocols in place of Any parameters. No intended behavior change. |
||
|---|---|---|
| .. | ||
| exceptions | ||
| messages | ||
| __init__.py | ||
| readme.md | ||
Use LLM API endpoints in Anthropic Interface
Note: This is called anthropic_interface because anthropic is a known python package and was failing mypy type checking.
Usage
LiteLLM Python SDK
Non-streaming example
import litellm
response = await litellm.anthropic.messages.acreate(
messages=[{"role": "user", "content": "Hello, can you tell me a short joke?"}],
api_key=api_key,
model="anthropic/claude-3-haiku-20240307",
max_tokens=100,
)
Example response:
{
"content": [
{
"text": "Hi! this is a very short joke",
"type": "text"
}
],
"id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
"model": "claude-3-7-sonnet-20250219",
"role": "assistant",
"stop_reason": "end_turn",
"stop_sequence": null,
"type": "message",
"usage": {
"input_tokens": 2095,
"output_tokens": 503,
"cache_creation_input_tokens": 2095,
"cache_read_input_tokens": 0
}
}
Streaming example
import litellm
response = await litellm.anthropic.messages.acreate(
messages=[{"role": "user", "content": "Hello, can you tell me a short joke?"}],
api_key=api_key,
model="anthropic/claude-3-haiku-20240307",
max_tokens=100,
stream=True,
)
async for chunk in response:
print(chunk)
LiteLLM Proxy Server
- Setup config.yaml
model_list:
- model_name: anthropic-claude
litellm_params:
model: claude-3-7-sonnet-latest
- Start proxy
litellm --config /path/to/config.yaml
- Test it!
import anthropic
# point anthropic sdk to litellm proxy
client = anthropic.Anthropic(
base_url="http://0.0.0.0:4000",
api_key="<your-master-key>",
)
response = client.messages.create(
messages=[{"role": "user", "content": "Hello, can you tell me a short joke?"}],
model="anthropic/claude-3-haiku-20240307",
max_tokens=100,
)
curl -L -X POST 'http://0.0.0.0:4000/v1/messages' \
-H 'content-type: application/json' \
-H 'x-api-key: $LITELLM_API_KEY' \
-H 'anthropic-version: 2023-06-01' \
-d '{
"model": "anthropic-claude",
"messages": [
{
"role": "user",
"content": "Hello, can you tell me a short joke?"
}
],
"max_tokens": 100
}'