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merge: resolve conflicts with litellm_internal_staging

Browse source 
In get_config_field_info, compose both protections: run
_redact_general_setting_value first (PR #30587: redact secret config
values for non-full-admin viewers, recursing into nested secrets), then
unconditionally redact plugins[*].plugin_key to '***' (PR #30688:
shared plugin credential is never returned, and the '***' placeholder
round-trips through /config/field/update via _preserve_redacted_plugin_keys).

Test file: keep both blocks of new tests (config_field_info redaction
behavior + _preserve_redacted_plugin_keys behavior); they cover
disjoint concerns and the previous conflict was purely a co-located
insertion point.
This commit is contained in:
yucheng-berri 2026-06-22 17:07:38 -07:00
commit ba36ad8e96
474 changed files with 12786 additions and 3445 deletions
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3
backend/routes/allowlist.py
View file

@ -120,6 +120,9 @@ BACKEND_PATH_PREFIXES: tuple[str, ...] = (
"/robots.txt",
# Health (k8s probes)
"/health",
# Plugin system
"/api/plugins",
"/plugin-proxy/",
)
BACKEND_EXACT_PATHS: frozenset[str] = frozenset(

141
docs/plugin_architecture.md Normal file
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@ -0,0 +1,141 @@
# LiteLLM Plugin Architecture
Plugins let external services appear as selectable modes in the litellm UI sidebar alongside the AI Gateway.
---
## Quick start
### 1. Configure the plugin
Add a `plugins` block to your litellm `config.yaml`:
```yaml
general_settings:
master_key: sk-...
plugins:
- name: my-plugin # unique identifier (no spaces)
display_name: My Plugin # shown in the UI dropdown
url: "https://my-plugin.example.com"
plugin_key: "sk-..." # plugin's own auth credential
```
`plugin_key` is injected as `Authorization: Bearer <plugin_key>` on every
request proxied through `/plugin-proxy/my-plugin/*`. The caller's litellm
credential is stripped before forwarding so the plugin never receives a live
litellm API key.
### 2. Implement two endpoints on your service
| Endpoint | Method | Purpose |
|---|---|---|
| `GET /api/plugin-manifest` | public | Returns plugin metadata for the UI |
| `POST /api/plugin-auth` | public | Decrypts the identity claim for seamless sign-in |
#### `GET /api/plugin-manifest`
```json
{
"name": "my-plugin",
"display_name": "My Plugin",
"version": "1.0.0",
"nav_items": [
{ "key": "home", "label": "Home", "icon": "HomeOutlined", "path": "/" },
{ "key": "reports", "label": "Reports", "icon": "BarChartOutlined", "path": "/reports" }
],
"capabilities": ["reports", "data"]
}
```
#### `POST /api/plugin-auth`
Receives `{ "session_claim": "<fernet-ciphertext>" }`.
The proxy never shares `LITELLM_SALT_KEY` with your plugin. Each plugin is
provisioned with its own dedicated key, derived as
`HMAC-SHA256(LITELLM_SALT_KEY, plugin_name)`. Compute it once on the proxy
host and hand the result to your plugin as a secret (e.g. `PLUGIN_AUTH_KEY`):
```bash
python -c 'import base64,hmac,hashlib,os; \
print(base64.urlsafe_b64encode(hmac.new(os.environ["LITELLM_SALT_KEY"].encode(), b"my-plugin", hashlib.sha256).digest()).decode())'
```
A compromised plugin holding only this scoped key cannot recover
`LITELLM_SALT_KEY` or decrypt any other litellm secret.
Decrypt and validate the claim with that key:
```python
import json, os, time
from cryptography.fernet import Fernet
_CLAIM_TTL_SECONDS = 30
def plugin_auth(session_claim: str) -> dict:
cipher = Fernet(os.environ["PLUGIN_AUTH_KEY"].encode())
claim = json.loads(cipher.decrypt(session_claim.encode(), ttl=_CLAIM_TTL_SECONDS))
if claim.get("plugin") != "my-plugin":
raise ValueError("claim audience mismatch")
if int(claim.get("exp", 0)) < int(time.time()):
raise ValueError("claim expired")
return claim
```
The claim is `{ "plugin", "user_id", "user_role", "exp" }`; it carries no
litellm bearer token. Establish the plugin's own session from `user_id` /
`user_role` and authenticate API calls back to litellm through the
`/plugin-proxy/my-plugin/*` reverse proxy, which injects `plugin_key` for you.
---
## How iframe auth works
```
litellm UI
├─ GET /api/plugins/auth-token -> { session_claim }
└─ postMessage({ type:"litellm-auth", session_claim }, pluginOrigin)
│
▼
Plugin iframe browser
└─ POST /api/plugin-auth { session_claim }
│
▼
Plugin server
├─ decrypt(session_claim, PLUGIN_AUTH_KEY) -> { user_id, user_role, exp }
└─ establish plugin session -> stored in sessionStorage
```
No litellm bearer token ever leaves the proxy; the claim only conveys the
caller's identity and expires after 30 seconds. A postMessage intercept
yields ciphertext that is useless without the plugin's scoped key.
---
## Proxy routes
- `GET /api/plugins` — list registered plugins (`name`, `display_name`, `url`). `plugin_key` is **never** returned; it stays server-side. Requires an authenticated caller.
- `GET /api/plugins/auth-token?plugin_name=<name>` — short-lived encrypted identity claim for the named plugin. Requires `LITELLM_SALT_KEY` to be set (503 otherwise) and the plugin to be registered (404 otherwise).
- `ANY /plugin-proxy/{name}/{path}` — authenticated reverse proxy to the plugin backend. Restricted to `proxy_admin`.
---
## Reverse proxy behaviour
When an admin (or server-to-server caller) hits `/plugin-proxy/<name>/<path>`, the proxy authenticates the caller locally, then rewrites the request before forwarding it to the plugin's `url`:
- **Every litellm credential header is stripped** — `Authorization`, `x-api-key`, `API-Key`, `x-goog-api-key`, `Ocp-Apim-Subscription-Key`, `x-litellm-api-key`, any configured `litellm_key_header_name`, plus `Cookie`. The plugin can never be handed the caller's live litellm key.
- **`plugin_key` is injected** as `Authorization: Bearer <plugin_key>` — the only credential the plugin receives.
- **Caller identity is forwarded** as `x-litellm-user-id` and `x-litellm-user-role` so the plugin can run its own authorization. These are informational, not credentials.
- **Responses are sandboxed** — `Content-Security-Policy: sandbox` and `X-Content-Type-Options: nosniff` are set so plugin-controlled bytes served from the litellm origin cannot execute against the dashboard.
---
## Security checklist
- [ ] `LITELLM_SALT_KEY` is set on the proxy and never shared with the plugin
- [ ] The plugin holds only its derived `HMAC(LITELLM_SALT_KEY, plugin_name)` key, provisioned as a dedicated secret
- [ ] `plugin_key` is a dedicated credential scoped to the plugin (not your litellm master key)
- [ ] Plugin's `POST /api/plugin-auth` enforces the claim's `plugin` audience and `exp` (30s TTL)
- [ ] Plugin treats `x-litellm-user-id` / `x-litellm-user-role` as identity hints, not as proof of authentication
- [ ] Plugin service URL uses HTTPS in production

15
litellm/integrations/code_interpreter_interception/__init__.py Normal file
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@ -0,0 +1,15 @@
"""
Code Interpreter Interception Module
Converts the native OpenAI Responses ``code_interpreter`` tool into a function
tool, runs the model-emitted code in a sandbox, and feeds the result back into
the agentic loop.
"""
from litellm.integrations.code_interpreter_interception.handler import (
CodeInterpreterInterceptionLogger,
)
__all__ = [
"CodeInterpreterInterceptionLogger",
]

473
litellm/integrations/code_interpreter_interception/handler.py Normal file
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@ -0,0 +1,473 @@
"""
Code Interpreter Interception Handler
CustomLogger that swaps the native OpenAI Responses ``code_interpreter`` tool for
a function tool, executes the code the model emits inside a sandbox, and feeds the
captured stdout back through the typed agentic loop plan.
"""
import json
import time
import uuid
from typing import Any, cast
import litellm
from litellm._logging import verbose_logger
from litellm.integrations.custom_logger import CustomLogger
from litellm.types.integrations.code_interpreter_interception import (
CodeInterpreterInterceptionConfig,
)
from litellm.types.integrations.custom_logger import (
AgenticLoopPlan,
AgenticLoopRequestPatch,
)
from litellm.types.utils import CallTypes
LITELLM_CODE_EXECUTION_TOOL_NAME = "litellm_code_execution"
_INTERCEPTION_ACTIVE_KEY = "_code_interpreter_interception_active"
_SANDBOX_KEY = "_code_interpreter_interception_sandbox_key"
_CACHE_TTL_SECONDS = 15 * 60
def _resolve_sandbox_tool(sandbox_tool_name: str | None) -> dict[str, Any] | None:
try:
from litellm.sandbox.sandbox_tools import resolve_sandbox_tool
except ImportError:
return None
return resolve_sandbox_tool(sandbox_tool_name)
class CodeInterpreterInterceptionLogger(CustomLogger):
"""
CustomLogger that implements transparent code-interpreter execution loops.
Flow:
1. Replace the native ``code_interpreter`` tool with a function tool in the
pre-call hook so the model emits code as function-call arguments.
2. Detect ``litellm_code_execution`` function calls in the model response.
3. Run the emitted code in a sandbox (reused per request via a server-minted
sandbox key) and build a typed rerun plan that appends the
function_call_output.
"""
def __init__(
self,
enabled: bool = True,
enabled_providers: list[str] | None = None,
sandbox_tool_name: str | None = None,
sandbox_config: Any | None = None,
):
super().__init__()
self.enabled = enabled
self.enabled_providers = enabled_providers
self.sandbox_tool_name = sandbox_tool_name
self.sandbox_config = sandbox_config
self._container_cache: dict[str, tuple[Any, dict[str, Any] | None, float]] = {}
@classmethod
def from_config_yaml(
cls, config: CodeInterpreterInterceptionConfig
) -> "CodeInterpreterInterceptionLogger":
return cls(
enabled=bool(config.get("enabled", True)),
enabled_providers=config.get("enabled_providers"),
sandbox_tool_name=config.get("sandbox_tool_name"),
)
@staticmethod
def initialize_from_proxy_config(
litellm_settings: dict[str, Any],
callback_specific_params: dict[str, Any],
) -> "CodeInterpreterInterceptionLogger":
params: CodeInterpreterInterceptionConfig = {}
if "code_interpreter_interception_params" in litellm_settings:
params = litellm_settings["code_interpreter_interception_params"]
elif "code_interpreter_interception" in callback_specific_params and isinstance(
callback_specific_params["code_interpreter_interception"], dict
):
params = cast(
CodeInterpreterInterceptionConfig,
callback_specific_params["code_interpreter_interception"],
)
return CodeInterpreterInterceptionLogger.from_config_yaml(params)
async def async_pre_call_deployment_hook(
self, kwargs: dict[str, Any], call_type: CallTypes | None
) -> dict | None:
if not kwargs.get("_agentic_loop_depth"):
kwargs.pop(_INTERCEPTION_ACTIVE_KEY, None)
kwargs.pop(_SANDBOX_KEY, None)
if not self.enabled:
return None
if call_type not in (CallTypes.responses, CallTypes.aresponses):
return None
if (
self.enabled_providers is not None
and self._resolve_provider(kwargs) not in self.enabled_providers
):
return None
tools = kwargs.get("tools")
if not isinstance(tools, list):
return None
if not any(
isinstance(tool, dict) and tool.get("type") == "code_interpreter"
for tool in tools
):
return None
kwargs[_INTERCEPTION_ACTIVE_KEY] = True
kwargs[_SANDBOX_KEY] = uuid.uuid4().hex
if kwargs.get("stream"):
kwargs["stream"] = False
kwargs["_code_interpreter_interception_converted_stream"] = True
function_tool = {
"type": "function",
"name": LITELLM_CODE_EXECUTION_TOOL_NAME,
"description": "Execute python code in a sandbox and return stdout.",
"parameters": {
"type": "object",
"properties": {"code": {"type": "string"}},
"required": ["code"],
},
}
kwargs["tools"] = [
(
function_tool
if isinstance(tool, dict) and tool.get("type") == "code_interpreter"
else tool
)
for tool in tools
]
if self._tool_choice_targets_code_interpreter(kwargs.get("tool_choice")):
kwargs["tool_choice"] = {
"type": "function",
"name": LITELLM_CODE_EXECUTION_TOOL_NAME,
}
return kwargs
@staticmethod
def _tool_choice_targets_code_interpreter(tool_choice: Any) -> bool:
if not isinstance(tool_choice, dict):
return False
return (
tool_choice.get("type") == "code_interpreter"
or tool_choice.get("name") == "code_interpreter"
)
def _resolve_provider(self, kwargs: dict[str, Any]) -> str | None:
provider = kwargs.get("custom_llm_provider")
if provider:
return provider
model = kwargs.get("model")
if not isinstance(model, str):
return None
try:
return litellm.get_llm_provider(model=model)[1]
except Exception:
return None
async def async_should_run_agentic_loop(
self,
response: Any,
model: str,
messages: list[dict],
tools: list[dict] | None,
stream: bool,
custom_llm_provider: str,
kwargs: dict,
) -> tuple[bool, dict]:
if not self.enabled:
return False, {}
if not kwargs.get(_INTERCEPTION_ACTIVE_KEY):
return False, {}
if (
self.enabled_providers is not None
and custom_llm_provider not in self.enabled_providers
):
return False, {}
tool_calls = self._extract_code_execution_tool_calls(response=response)
if not tool_calls:
return False, {}
return True, {"tool_calls": tool_calls}
async def async_build_agentic_loop_plan(
self,
tools: dict,
model: str,
messages: list[dict],
response: Any,
anthropic_messages_provider_config: Any,
anthropic_messages_optional_request_params: dict,
logging_obj: Any,
stream: bool,
kwargs: dict,
) -> AgenticLoopPlan:
await self._prune_expired_cache()
tool_calls = cast(list[dict[str, Any]], tools.get("tool_calls", []))
sandbox_key = kwargs.get(_SANDBOX_KEY)
container, params = await self._get_or_create_container(cache_key=sandbox_key)
try:
container_id = getattr(container, "id", None)
input_list = self._normalize_messages(messages)
code_interpreter_calls = []
for tool_call in tool_calls:
arguments = tool_call.get("arguments", "")
code = self._parse_code(arguments)
stdout = await self._run_tool_call(
container=container, params=params, arguments=arguments
)
input_list.append(
{
"type": "function_call",
"call_id": tool_call.get("call_id"),
"name": LITELLM_CODE_EXECUTION_TOOL_NAME,
"arguments": arguments,
}
)
input_list.append(
{
"type": "function_call_output",
"call_id": tool_call.get("call_id"),
"output": stdout,
}
)
code_interpreter_calls.append(
{
"id": f"ci_{uuid.uuid4().hex}",
"type": "code_interpreter_call",
"status": "completed",
"code": code,
"container_id": container_id,
"outputs": (
[{"type": "logs", "logs": stdout}] if stdout else []
),
}
)
except Exception:
await self._delete_container_for_cache_key(sandbox_key)
raise
optional_params = anthropic_messages_optional_request_params
request_patch = AgenticLoopRequestPatch(
model=model,
messages=input_list,
tools=optional_params.get("tools"),
optional_params={k: v for k, v in optional_params.items() if k != "tools"},
kwargs={k: v for k, v in kwargs.items() if k != "litellm_logging_obj"},
)
return AgenticLoopPlan(
run_agentic_loop=True,
request_patch=request_patch,
metadata={
"tool_type": "code_interpreter",
"sandbox_key": sandbox_key or "",
"code_interpreter_calls": code_interpreter_calls,
},
)
async def async_agentic_loop_cleanup_hook(
self, plan: AgenticLoopPlan, kwargs: dict
) -> None:
metadata = plan.metadata or {} if plan else {}
await self._delete_container_for_cache_key(metadata.get("sandbox_key"))
async def async_post_agentic_loop_response_hook(
self, response: Any, plan: AgenticLoopPlan, kwargs: dict
) -> Any:
metadata = plan.metadata or {} if plan else {}
await self._delete_container_for_cache_key(metadata.get("sandbox_key"))
calls = metadata.get("code_interpreter_calls")
if not calls:
return response
is_dict = isinstance(response, dict)
output = (
response.get("output") if is_dict else getattr(response, "output", None)
)
if not isinstance(output, list):
return response
def _item_type(item: Any) -> Any:
return (
item.get("type")
if isinstance(item, dict)
else getattr(item, "type", None)
)
insert_at = next(
(i for i, item in enumerate(output) if _item_type(item) == "message"),
len(output),
)
new_output = output[:insert_at] + list(calls) + output[insert_at:]
if is_dict:
response["output"] = new_output
else:
response.output = new_output
return response
@staticmethod
def _parse_code(arguments: str) -> str:
try:
return json.loads(arguments).get("code", "") if arguments else ""
except (json.JSONDecodeError, TypeError, AttributeError):
return ""
async def _run_tool_call(
self, container: Any, params: dict[str, Any] | None, arguments: str
) -> str:
try:
code = json.loads(arguments).get("code", "") if arguments else ""
except (json.JSONDecodeError, TypeError):
return "[invalid tool arguments: could not parse code]"
result = await self._run_code(container=container, params=params, code=code)
if getattr(result, "error", None):
error = result.error
message = (
error.get("value") or error.get("name")
if isinstance(error, dict)
else str(error)
)
return f"[execution error] {message}"
return getattr(result, "stdout", "") or ""
async def _get_or_create_container(
self, cache_key: str | None
) -> tuple[Any, dict[str, Any] | None]:
if cache_key:
cached = self._container_cache.get(cache_key)
if cached is not None:
return cached[0], cached[1]
container, params = await self._create_container()
if cache_key:
self._container_cache[cache_key] = (container, params, time.time())
return container, params
async def _create_container(self) -> tuple[Any, dict[str, Any] | None]:
if self.sandbox_config is not None:
return await self.sandbox_config.acreate_sandbox(), None
params = _resolve_sandbox_tool(self.sandbox_tool_name)
if params is None:
raise ValueError(
"CodeInterpreterInterception: no sandbox available. Provide a "
"sandbox_config or configure a sandbox tool resolvable via "
"sandbox_tool_name."
)
container = await litellm.acreate_sandbox(
provider=params["sandbox_provider"],
api_key=params.get("api_key"),
api_base=params.get("api_base"),
)
return container, params
async def _run_code(
self, container: Any, params: dict[str, Any] | None, code: str
) -> Any:
if self.sandbox_config is not None:
return await self.sandbox_config.arun_code(container=container, code=code)
if params is None:
raise ValueError(
"CodeInterpreterInterception: no sandbox available to run code."
)
return await litellm.arun_code(
provider=params["sandbox_provider"],
container=container,
code=code,
api_key=params.get("api_key"),
)
async def _delete_container(
self, container: Any, params: dict[str, Any] | None
) -> None:
try:
if self.sandbox_config is not None:
await self.sandbox_config.adelete_sandbox(container=container)
return
if params is None:
return
await litellm.adelete_sandbox(
provider=params["sandbox_provider"],
container=container,
api_key=params.get("api_key"),
api_base=params.get("api_base"),
)
except Exception:
verbose_logger.exception(
"CodeInterpreterInterception: failed to delete sandbox container"
)
async def _delete_container_for_cache_key(self, cache_key: str | None) -> None:
if not cache_key:
return
cached = self._container_cache.pop(cache_key, None)
if cached is None:
return
await self._delete_container(container=cached[0], params=cached[1])
def _normalize_messages(self, messages: Any) -> list[dict[str, Any]]:
if isinstance(messages, str):
return [{"role": "user", "content": messages}]
if isinstance(messages, list):
return list(messages)
return []
def _extract_code_execution_tool_calls(self, response: Any) -> list[dict[str, Any]]:
if isinstance(response, dict):
output = response.get("output", [])
else:
output = getattr(response, "output", []) or []
if not isinstance(output, list):
return []
return [
{
"call_id": (
item.get("call_id")
if isinstance(item, dict)
else getattr(item, "call_id", None)
),
"name": LITELLM_CODE_EXECUTION_TOOL_NAME,
"arguments": (
item.get("arguments")
if isinstance(item, dict)
else getattr(item, "arguments", "")
),
}
for item in output
if self._is_code_execution_call(item)
]
def _is_code_execution_call(self, item: Any) -> bool:
if isinstance(item, dict):
return (
item.get("type") == "function_call"
and item.get("name") == LITELLM_CODE_EXECUTION_TOOL_NAME
)
return (
getattr(item, "type", None) == "function_call"
and getattr(item, "name", None) == LITELLM_CODE_EXECUTION_TOOL_NAME
)
async def _prune_expired_cache(self) -> None:
now = time.time()
expired = [
(cache_key, container, params)
for cache_key, (
container,
params,
created_at,
) in self._container_cache.items()
if now - created_at > _CACHE_TTL_SECONDS
]
for cache_key, container, params in expired:
self._container_cache.pop(cache_key, None)
await self._delete_container(container=container, params=params)

18
litellm/integrations/custom_logger.py
View file

@ -718,6 +718,24 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
"""
return response
async def async_agentic_loop_cleanup_hook(
self,
plan: AgenticLoopPlan,
kwargs: dict,
) -> None:
"""
Release resources held for an agentic-loop iteration.
Runs in a ``finally`` around the follow-up provider call, so it fires
whether the rerun returns normally, hits a loop safety abort, or raises
an upstream error. Implementations must be idempotent because the
post-response hook may already have released the same resource on the
success path. Use ``plan.metadata`` to locate what to clean up.
Default does nothing.
"""
return None
async def async_should_run_chat_completion_agentic_loop(
self,
response: Any,

6
litellm/integrations/otel/model/config.py
View file

@ -1,6 +1,7 @@
"""Typed configuration for the OpenTelemetry instrumentation."""
from enum import Enum
from functools import lru_cache
from typing import Any, List
from pydantic import AliasChoices, BaseModel, Field, field_validator, model_validator
@ -47,7 +48,12 @@ class _OTelV2Flag(BaseSettings):
enabled: bool = Field(default=False, validation_alias=AliasChoices(OTEL_V2_ENV))
@lru_cache(maxsize=1)
def is_otel_v2_enabled() -> bool:
# Resolved once at startup and cached: constructing the pydantic-settings
# model re-scans the environment and cost ~28us, which on the proxy hot path
# (auth, logging-callback setup) compounded into a measurable throughput
# regression. Tests that toggle the env must call ``is_otel_v2_enabled.cache_clear()``.
return _OTelV2Flag().enabled

4211
litellm/litellm_core_utils/exception_mapping_utils.py
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File diff suppressed because it is too large Load diff

763
litellm/litellm_core_utils/streaming_handler.py
View file

@ -6,6 +6,7 @@ import logging
import threading
import time
import traceback
from dataclasses import dataclass
from typing import (
Any,
AsyncIterator,
@ -97,6 +98,19 @@ def print_verbose(print_statement):
pass
@dataclass(frozen=True, slots=True)
class _ProviderChunkParsed:
response_obj: dict[str, Any]
@dataclass(frozen=True, slots=True)
class _ProviderChunkEarlyReturn:
value: Any
_ProviderChunkResult = Union[_ProviderChunkParsed, _ProviderChunkEarlyReturn]
class CustomStreamWrapper:
def __init__(
self,
@ -1145,381 +1159,392 @@ class CustomStreamWrapper:
del model_response.choices[0].delta.reasoning_content
return
def _dispatch_provider_chunk(
self,
chunk: Any,
model_response: ModelResponseStream,
completion_obj: dict[str, Any],
) -> _ProviderChunkResult:
response_obj: dict[str, Any] = {}
if (
isinstance(chunk, ModelResponseStream)
and self.custom_llm_provider is not None
and self.custom_llm_provider in litellm._custom_providers
):
_has_content = bool(
chunk.choices
and chunk.choices[0].delta is not None
and (
chunk.choices[0].delta.content or chunk.choices[0].delta.tool_calls
)
)
if self.received_finish_reason is not None:
if not _has_content:
raise StopIteration
if chunk.choices and chunk.choices[0].finish_reason:
self.received_finish_reason = chunk.choices[0].finish_reason
if not _has_content:
return _ProviderChunkEarlyReturn(None)
# Strip finish_reason from the content chunk so it appears
# only on the trailing empty-delta chunk (OpenAI spec).
# finish_reason_handler() will emit the proper terminal chunk.
chunk.choices[0].finish_reason = None # type: ignore[assignment]
return _ProviderChunkEarlyReturn(chunk)
if (
isinstance(chunk, dict)
and generic_chunk_has_all_required_fields(
chunk=chunk
) # check if chunk is a generic streaming chunk
) or (
self.custom_llm_provider
and self.custom_llm_provider in litellm._custom_providers
):
if self.received_finish_reason is not None:
_chunk_has_content = isinstance(chunk, dict) and (
bool(chunk.get("text", ""))
or chunk.get("tool_use") is not None
# Usage-only final chunks are valid and needed to surface
# finish_reason/usage to downstream translators.
or chunk.get("usage") is not None
)
if not _chunk_has_content and (
not isinstance(chunk, dict)
or "provider_specific_fields" not in chunk
):
raise StopIteration
anthropic_response_obj: GChunk = cast(GChunk, chunk)
completion_obj["content"] = anthropic_response_obj["text"]
if anthropic_response_obj["is_finished"]:
self.received_finish_reason = anthropic_response_obj["finish_reason"]
if anthropic_response_obj["finish_reason"]:
self.intermittent_finish_reason = anthropic_response_obj[
"finish_reason"
]
if anthropic_response_obj["usage"] is not None:
setattr(
model_response,
"usage",
litellm.Usage(**anthropic_response_obj["usage"]),
)
if (
"tool_use" in anthropic_response_obj
and anthropic_response_obj["tool_use"] is not None
):
completion_obj["tool_calls"] = [anthropic_response_obj["tool_use"]]
if (
"provider_specific_fields" in anthropic_response_obj
and anthropic_response_obj["provider_specific_fields"] is not None
):
for key, value in anthropic_response_obj[
"provider_specific_fields"
].items():
setattr(model_response, key, value)
response_obj = cast(dict[str, Any], anthropic_response_obj)
elif self.model == "replicate" or self.custom_llm_provider == "replicate":
response_obj = self.handle_replicate_chunk(chunk)
completion_obj["content"] = response_obj["text"]
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider and self.custom_llm_provider == "predibase":
response_obj = self.handle_predibase_chunk(chunk)
completion_obj["content"] = response_obj["text"]
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif (
self.custom_llm_provider and self.custom_llm_provider == "baseten"
): # baseten doesn't provide streaming
completion_obj["content"] = self.handle_baseten_chunk(chunk)
elif (
self.custom_llm_provider and self.custom_llm_provider == "ai21"
): # ai21 doesn't provide streaming
response_obj = self.handle_ai21_chunk(chunk)
completion_obj["content"] = response_obj["text"]
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider and self.custom_llm_provider == "maritalk":
response_obj = self.handle_maritalk_chunk(chunk)
completion_obj["content"] = response_obj["text"]
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider and self.custom_llm_provider == "vllm":
completion_obj["content"] = chunk[0].outputs[0].text
elif (
self.custom_llm_provider and self.custom_llm_provider == "aleph_alpha"
): # aleph alpha doesn't provide streaming
response_obj = self.handle_aleph_alpha_chunk(chunk)
completion_obj["content"] = response_obj["text"]
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider == "nlp_cloud":
try:
response_obj = self.handle_nlp_cloud_chunk(chunk)
completion_obj["content"] = response_obj["text"]
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
except Exception as e:
if self.received_finish_reason:
raise e
else:
if self.sent_first_chunk is False:
raise Exception("An unknown error occurred with the stream")
self.received_finish_reason = "stop"
elif self.custom_llm_provider == "vertex_ai" and not isinstance(
chunk, ModelResponseStream
):
chunk = cast(Any, chunk)
import proto # type: ignore
if hasattr(chunk, "candidates") is True:
try:
try:
completion_obj["content"] = chunk.text # type: ignore
except Exception as e:
original_exception = e
if "Part has no text." in str(e):
## check for function calling
function_call = (
chunk.candidates[0].content.parts[0].function_call # type: ignore
)
args_dict = {}
# Check if it's a RepeatedComposite instance
for key, val in function_call.args.items():
if isinstance(
val,
proto.marshal.collections.repeated.RepeatedComposite, # type: ignore
):
# If so, convert to list
args_dict[key] = [v for v in val]
else:
args_dict[key] = val
try:
args_str = json.dumps(args_dict)
except Exception as e:
raise e
_delta_obj = litellm.utils.Delta(
content=None,
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {
"arguments": args_str,
"name": function_call.name,
},
"type": "function",
}
],
)
_streaming_response = StreamingChoices(delta=_delta_obj)
_model_response = ModelResponseStream()
_model_response.choices = [_streaming_response]
response_obj = {"original_chunk": _model_response}
else:
raise original_exception
if (
hasattr(chunk.candidates[0], "finish_reason") # type: ignore
and chunk.candidates[0].finish_reason.name # type: ignore
!= "FINISH_REASON_UNSPECIFIED"
): # every non-final chunk in vertex ai has this
self.received_finish_reason = map_finish_reason( # type: ignore
chunk.candidates[0].finish_reason.name
)
except Exception:
if chunk.candidates[0].finish_reason.name == "SAFETY": # type: ignore
raise Exception(
f"The response was blocked by VertexAI. {str(chunk)}"
)
else:
completion_obj["content"] = str(chunk)
elif self.custom_llm_provider == "petals":
if self.completion_stream is None or len(self.completion_stream) == 0:
if self.received_finish_reason is not None:
raise StopIteration
else:
self.received_finish_reason = "stop"
chunk_size = 30
stream = cast(Any, self.completion_stream)
new_chunk = stream[:chunk_size]
completion_obj["content"] = new_chunk
self.completion_stream = stream[chunk_size:]
elif self.custom_llm_provider == "palm":
# fake streaming
response_obj = {}
if self.completion_stream is None or len(self.completion_stream) == 0:
if self.received_finish_reason is not None:
raise StopIteration
else:
self.received_finish_reason = "stop"
chunk_size = 30
stream = cast(Any, self.completion_stream)
new_chunk = stream[:chunk_size]
completion_obj["content"] = new_chunk
self.completion_stream = stream[chunk_size:]
elif self.custom_llm_provider == "triton":
response_obj = self.handle_triton_stream(chunk)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider == "text-completion-openai":
response_obj = self.handle_openai_text_completion_chunk(chunk)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
if response_obj["usage"] is not None:
setattr(
model_response,
"usage",
litellm.Usage(
prompt_tokens=response_obj["usage"].prompt_tokens,
completion_tokens=response_obj["usage"].completion_tokens,
total_tokens=response_obj["usage"].total_tokens,
),
)
elif self.custom_llm_provider == "text-completion-codestral":
if not isinstance(chunk, str):
raise ValueError(f"chunk is not a string: {chunk}")
response_obj = cast(
dict[str, Any],
litellm.CodestralTextCompletionConfig()._chunk_parser(chunk),
)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
if "usage" in response_obj is not None:
setattr(
model_response,
"usage",
litellm.Usage(
prompt_tokens=response_obj["usage"].prompt_tokens,
completion_tokens=response_obj["usage"].completion_tokens,
total_tokens=response_obj["usage"].total_tokens,
),
)
elif self.custom_llm_provider == "azure_text":
response_obj = self.handle_azure_text_completion_chunk(chunk)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider == "cached_response":
chunk = cast(ModelResponseStream, chunk)
response_obj = {
"text": chunk.choices[0].delta.content,
"is_finished": True,
"finish_reason": chunk.choices[0].finish_reason,
"original_chunk": chunk,
"tool_calls": (
chunk.choices[0].delta.tool_calls
if hasattr(chunk.choices[0].delta, "tool_calls")
else None
),
}
completion_obj["content"] = response_obj["text"]
if response_obj["tool_calls"] is not None:
completion_obj["tool_calls"] = response_obj["tool_calls"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if hasattr(chunk, "id"):
model_response.id = chunk.id
self.response_id = chunk.id
if hasattr(chunk, "system_fingerprint"):
self.system_fingerprint = chunk.system_fingerprint
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
else: # openai / azure chat model
if self.custom_llm_provider in [
LlmProviders.AZURE.value,
LlmProviders.AZURE_AI.value,
]:
if isinstance(chunk, BaseModel) and hasattr(chunk, "model"):
# for azure, we need to pass the model from the original chunk
self.model = getattr(chunk, "model", self.model)
response_obj = self.handle_openai_chat_completion_chunk(chunk)
if response_obj is None:
return _ProviderChunkEarlyReturn(None)
completion_obj["content"] = response_obj["text"]
self.intermittent_finish_reason = response_obj.get("finish_reason", None)
if response_obj["is_finished"]:
if response_obj["finish_reason"] == "error":
raise Exception(
"{} raised a streaming error - finish_reason: error, no content string given. Received Chunk={}".format(
self.custom_llm_provider, response_obj
)
)
self.received_finish_reason = response_obj["finish_reason"]
if response_obj.get("original_chunk", None) is not None:
if hasattr(response_obj["original_chunk"], "id"):
model_response = self.set_model_id(
response_obj["original_chunk"].id, model_response
)
if hasattr(response_obj["original_chunk"], "system_fingerprint"):
model_response.system_fingerprint = response_obj[
"original_chunk"
].system_fingerprint
self.system_fingerprint = response_obj[
"original_chunk"
].system_fingerprint
if response_obj["logprobs"] is not None:
model_response.choices[0].logprobs = response_obj["logprobs"]
if response_obj["usage"] is not None:
if isinstance(response_obj["usage"], dict):
setattr(
model_response,
"usage",
litellm.Usage(
prompt_tokens=response_obj["usage"].get(
"prompt_tokens", None
)
or None,
completion_tokens=response_obj["usage"].get(
"completion_tokens", None
)
or None,
total_tokens=response_obj["usage"].get("total_tokens", None)
or None,
),
)
elif isinstance(response_obj["usage"], Usage):
setattr(
model_response,
"usage",
response_obj["usage"],
)
elif isinstance(response_obj["usage"], BaseModel):
setattr(
model_response,
"usage",
litellm.Usage(**response_obj["usage"].model_dump()),
)
return _ProviderChunkParsed(response_obj)
def chunk_creator(self, chunk: Any): # type: ignore
if hasattr(chunk, "id"):
self.response_id = chunk.id
model_response = self.model_response_creator()
response_obj: Dict[str, Any] = {}
response_obj: dict[str, Any] = {}
try:
# return this for all models
completion_obj: Dict[str, Any] = {"content": ""}
from litellm.types.utils import GenericStreamingChunk as GChunk
if (
isinstance(chunk, ModelResponseStream)
and self.custom_llm_provider is not None
and self.custom_llm_provider in litellm._custom_providers
):
_has_content = bool(
chunk.choices
and chunk.choices[0].delta is not None
and (
chunk.choices[0].delta.content
or chunk.choices[0].delta.tool_calls
)
)
if self.received_finish_reason is not None:
if not _has_content:
raise StopIteration
if chunk.choices and chunk.choices[0].finish_reason:
self.received_finish_reason = chunk.choices[0].finish_reason
if not _has_content:
return None
# Strip finish_reason from the content chunk so it appears
# only on the trailing empty-delta chunk (OpenAI spec).
# finish_reason_handler() will emit the proper terminal chunk.
chunk.choices[0].finish_reason = None # type: ignore[assignment]
return chunk
if (
isinstance(chunk, dict)
and generic_chunk_has_all_required_fields(
chunk=chunk
) # check if chunk is a generic streaming chunk
) or (
self.custom_llm_provider
and self.custom_llm_provider in litellm._custom_providers
):
if self.received_finish_reason is not None:
_chunk_has_content = isinstance(chunk, dict) and (
bool(chunk.get("text", ""))
or chunk.get("tool_use") is not None
# Usage-only final chunks are valid and needed to surface
# finish_reason/usage to downstream translators.
or chunk.get("usage") is not None
)
if not _chunk_has_content and (
not isinstance(chunk, dict)
or "provider_specific_fields" not in chunk
):
raise StopIteration
anthropic_response_obj: GChunk = cast(GChunk, chunk)
completion_obj["content"] = anthropic_response_obj["text"]
if anthropic_response_obj["is_finished"]:
self.received_finish_reason = anthropic_response_obj[
"finish_reason"
]
if anthropic_response_obj["finish_reason"]:
self.intermittent_finish_reason = anthropic_response_obj[
"finish_reason"
]
if anthropic_response_obj["usage"] is not None:
setattr(
model_response,
"usage",
litellm.Usage(**anthropic_response_obj["usage"]),
)
if (
"tool_use" in anthropic_response_obj
and anthropic_response_obj["tool_use"] is not None
):
completion_obj["tool_calls"] = [anthropic_response_obj["tool_use"]]
if (
"provider_specific_fields" in anthropic_response_obj
and anthropic_response_obj["provider_specific_fields"] is not None
):
for key, value in anthropic_response_obj[
"provider_specific_fields"
].items():
setattr(model_response, key, value)
response_obj = cast(Dict[str, Any], anthropic_response_obj)
elif self.model == "replicate" or self.custom_llm_provider == "replicate":
response_obj = self.handle_replicate_chunk(chunk)
completion_obj["content"] = response_obj["text"]
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider and self.custom_llm_provider == "predibase":
response_obj = self.handle_predibase_chunk(chunk)
completion_obj["content"] = response_obj["text"]
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif (
self.custom_llm_provider and self.custom_llm_provider == "baseten"
): # baseten doesn't provide streaming
completion_obj["content"] = self.handle_baseten_chunk(chunk)
elif (
self.custom_llm_provider and self.custom_llm_provider == "ai21"
): # ai21 doesn't provide streaming
response_obj = self.handle_ai21_chunk(chunk)
completion_obj["content"] = response_obj["text"]
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider and self.custom_llm_provider == "maritalk":
response_obj = self.handle_maritalk_chunk(chunk)
completion_obj["content"] = response_obj["text"]
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider and self.custom_llm_provider == "vllm":
completion_obj["content"] = chunk[0].outputs[0].text
elif (
self.custom_llm_provider and self.custom_llm_provider == "aleph_alpha"
): # aleph alpha doesn't provide streaming
response_obj = self.handle_aleph_alpha_chunk(chunk)
completion_obj["content"] = response_obj["text"]
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider == "nlp_cloud":
try:
response_obj = self.handle_nlp_cloud_chunk(chunk)
completion_obj["content"] = response_obj["text"]
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
except Exception as e:
if self.received_finish_reason:
raise e
else:
if self.sent_first_chunk is False:
raise Exception("An unknown error occurred with the stream")
self.received_finish_reason = "stop"
elif self.custom_llm_provider == "vertex_ai" and not isinstance(
chunk, ModelResponseStream
):
import proto # type: ignore
if hasattr(chunk, "candidates") is True:
try:
try:
completion_obj["content"] = chunk.text # type: ignore
except Exception as e:
original_exception = e
if "Part has no text." in str(e):
## check for function calling
function_call = (
chunk.candidates[0].content.parts[0].function_call # type: ignore
)
args_dict = {}
# Check if it's a RepeatedComposite instance
for key, val in function_call.args.items():
if isinstance(
val,
proto.marshal.collections.repeated.RepeatedComposite, # type: ignore
):
# If so, convert to list
args_dict[key] = [v for v in val]
else:
args_dict[key] = val
try:
args_str = json.dumps(args_dict)
except Exception as e:
raise e
_delta_obj = litellm.utils.Delta(
content=None,
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {
"arguments": args_str,
"name": function_call.name,
},
"type": "function",
}
],
)
_streaming_response = StreamingChoices(delta=_delta_obj)
_model_response = ModelResponseStream()
_model_response.choices = [_streaming_response]
response_obj = {"original_chunk": _model_response}
else:
raise original_exception
if (
hasattr(chunk.candidates[0], "finish_reason") # type: ignore
and chunk.candidates[0].finish_reason.name # type: ignore
!= "FINISH_REASON_UNSPECIFIED"
): # every non-final chunk in vertex ai has this
self.received_finish_reason = map_finish_reason( # type: ignore
chunk.candidates[0].finish_reason.name
)
except Exception:
if chunk.candidates[0].finish_reason.name == "SAFETY": # type: ignore
raise Exception(
f"The response was blocked by VertexAI. {str(chunk)}"
)
else:
completion_obj["content"] = str(chunk)
elif self.custom_llm_provider == "petals":
if self.completion_stream is None or len(self.completion_stream) == 0:
if self.received_finish_reason is not None:
raise StopIteration
else:
self.received_finish_reason = "stop"
chunk_size = 30
new_chunk = self.completion_stream[:chunk_size] # type: ignore[index]
completion_obj["content"] = new_chunk
self.completion_stream = self.completion_stream[chunk_size:] # type: ignore[index]
elif self.custom_llm_provider == "palm":
# fake streaming
response_obj = {}
if self.completion_stream is None or len(self.completion_stream) == 0:
if self.received_finish_reason is not None:
raise StopIteration
else:
self.received_finish_reason = "stop"
chunk_size = 30
new_chunk = self.completion_stream[:chunk_size] # type: ignore[index]
completion_obj["content"] = new_chunk
self.completion_stream = self.completion_stream[chunk_size:] # type: ignore[index]
elif self.custom_llm_provider == "triton":
response_obj = self.handle_triton_stream(chunk)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider == "text-completion-openai":
response_obj = self.handle_openai_text_completion_chunk(chunk)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
if response_obj["usage"] is not None:
setattr(
model_response,
"usage",
litellm.Usage(
prompt_tokens=response_obj["usage"].prompt_tokens,
completion_tokens=response_obj["usage"].completion_tokens,
total_tokens=response_obj["usage"].total_tokens,
),
)
elif self.custom_llm_provider == "text-completion-codestral":
if not isinstance(chunk, str):
raise ValueError(f"chunk is not a string: {chunk}")
response_obj = cast(
Dict[str, Any],
litellm.CodestralTextCompletionConfig()._chunk_parser(chunk),
)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
if "usage" in response_obj is not None:
setattr(
model_response,
"usage",
litellm.Usage(
prompt_tokens=response_obj["usage"].prompt_tokens,
completion_tokens=response_obj["usage"].completion_tokens,
total_tokens=response_obj["usage"].total_tokens,
),
)
elif self.custom_llm_provider == "azure_text":
response_obj = self.handle_azure_text_completion_chunk(chunk)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider == "cached_response":
chunk = cast(ModelResponseStream, chunk)
response_obj = {
"text": chunk.choices[0].delta.content,
"is_finished": True,
"finish_reason": chunk.choices[0].finish_reason,
"original_chunk": chunk,
"tool_calls": (
chunk.choices[0].delta.tool_calls
if hasattr(chunk.choices[0].delta, "tool_calls")
else None
),
}
completion_obj["content"] = response_obj["text"]
if response_obj["tool_calls"] is not None:
completion_obj["tool_calls"] = response_obj["tool_calls"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if hasattr(chunk, "id"):
model_response.id = chunk.id
self.response_id = chunk.id
if hasattr(chunk, "system_fingerprint"):
self.system_fingerprint = chunk.system_fingerprint
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
else: # openai / azure chat model
if self.custom_llm_provider in [
LlmProviders.AZURE.value,
LlmProviders.AZURE_AI.value,
]:
if isinstance(chunk, BaseModel) and hasattr(chunk, "model"):
# for azure, we need to pass the model from the original chunk
self.model = getattr(chunk, "model", self.model)
response_obj = self.handle_openai_chat_completion_chunk(chunk)
if response_obj is None:
return
completion_obj["content"] = response_obj["text"]
self.intermittent_finish_reason = response_obj.get(
"finish_reason", None
)
if response_obj["is_finished"]:
if response_obj["finish_reason"] == "error":
raise Exception(
"{} raised a streaming error - finish_reason: error, no content string given. Received Chunk={}".format(
self.custom_llm_provider, response_obj
)
)
self.received_finish_reason = response_obj["finish_reason"]
if response_obj.get("original_chunk", None) is not None:
if hasattr(response_obj["original_chunk"], "id"):
model_response = self.set_model_id(
response_obj["original_chunk"].id, model_response
)
if hasattr(response_obj["original_chunk"], "system_fingerprint"):
model_response.system_fingerprint = response_obj[
"original_chunk"
].system_fingerprint
self.system_fingerprint = response_obj[
"original_chunk"
].system_fingerprint
if response_obj["logprobs"] is not None:
model_response.choices[0].logprobs = response_obj["logprobs"]
if response_obj["usage"] is not None:
if isinstance(response_obj["usage"], dict):
setattr(
model_response,
"usage",
litellm.Usage(
prompt_tokens=response_obj["usage"].get(
"prompt_tokens", None
)
or None,
completion_tokens=response_obj["usage"].get(
"completion_tokens", None
)
or None,
total_tokens=response_obj["usage"].get(
"total_tokens", None
)
or None,
),
)
elif isinstance(response_obj["usage"], Usage):
setattr(
model_response,
"usage",
response_obj["usage"],
)
elif isinstance(response_obj["usage"], BaseModel):
setattr(
model_response,
"usage",
litellm.Usage(**response_obj["usage"].model_dump()),
)
completion_obj: dict[str, Any] = {"content": ""}
dispatch_result = self._dispatch_provider_chunk(
chunk=chunk,
model_response=model_response,
completion_obj=completion_obj,
)
if isinstance(dispatch_result, _ProviderChunkEarlyReturn):
return dispatch_result.value
response_obj = dispatch_result.response_obj
model_response.model = self.model
## FUNCTION CALL PARSING

24
litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py
View file

@ -84,8 +84,14 @@ class AdvisorOrchestrationHandler(MessagesInterceptor):
)
# Optional routing overrides for the advisor sub-call (e.g. proxy routing).
# If not set in the tool definition, litellm resolves from env vars.
advisor_api_key: Optional[str] = advisor_tool.get("api_key")
advisor_api_base: Optional[str] = advisor_tool.get("api_base")
# The advisor tool is caller-controlled; only honor a client-supplied
# api_base/api_key when the proxy has enabled clientside credentials,
# otherwise let litellm resolve from server config.
advisor_api_key: Optional[str] = None
advisor_api_base: Optional[str] = None
if _allow_client_side_advisor_credentials():
advisor_api_key = advisor_tool.get("api_key")
advisor_api_base = advisor_tool.get("api_base")
# Build the synthetic tool definition the provider will receive.
synthetic_advisor_tool = _make_synthetic_advisor_tool()
@ -181,6 +187,20 @@ class AdvisorOrchestrationHandler(MessagesInterceptor):
# ---------------------------------------------------------------------------
def _allow_client_side_advisor_credentials() -> bool:
"""Whether a caller-supplied advisor api_base/api_key may be honored.
Gated on the proxy's ``allow_client_side_credentials`` opt-in. When the
interceptor runs outside the proxy (SDK use), there is no admin boundary
to protect, so client-supplied routing is allowed.
"""
try:
from litellm.proxy.proxy_server import general_settings
except (ImportError, ModuleNotFoundError):
return True
return general_settings.get("allow_client_side_credentials") is True
def _make_synthetic_advisor_tool() -> Dict:
"""Build a regular tool definition the executor provider can understand."""
return {

62
litellm/llms/bedrock/base_aws_llm.py
View file

@ -10,7 +10,6 @@ from typing import (
Callable,
ClassVar,
Dict,
List,
Literal,
Optional,
Tuple,
@ -210,32 +209,11 @@ class BaseAWSLLM:
"""
Return a boto3.Credentials object
"""
## CHECK IS 'os.environ/' passed in
params_to_check: List[Optional[str]] = [
aws_access_key_id,
aws_secret_access_key,
aws_session_token,
aws_region_name,
aws_session_name,
aws_profile_name,
aws_role_name,
aws_web_identity_token,
aws_sts_endpoint,
aws_external_id,
]
# Iterate over parameters and update if needed
for i, param in enumerate(params_to_check):
if param and param.startswith("os.environ/"):
_v = get_secret(param)
if _v is not None and isinstance(_v, str):
params_to_check[i] = _v
elif param is None: # check if uppercase value in env
key = self.aws_authentication_params[i]
if key.upper() in os.environ:
params_to_check[i] = os.getenv(key.upper())
# Assign updated values back to parameters
# Only config-sourced credentials are expanded against the environment.
# os.environ/<VAR> references in the model config are resolved at load time,
# so any reference still present at this point is caller-supplied input and is
# left as-is rather than expanded into a process environment variable. Each
# unset param falls back to its matching fixed AWS_* ambient env var.
(
aws_access_key_id,
aws_secret_access_key,
@ -247,7 +225,21 @@ class BaseAWSLLM:
aws_web_identity_token,
aws_sts_endpoint,
aws_external_id,
) = params_to_check
) = tuple(
value if value is not None else os.getenv(env_var)
for value, env_var in (
(aws_access_key_id, "AWS_ACCESS_KEY_ID"),
(aws_secret_access_key, "AWS_SECRET_ACCESS_KEY"),
(aws_session_token, "AWS_SESSION_TOKEN"),
(aws_region_name, "AWS_REGION_NAME"),
(aws_session_name, "AWS_SESSION_NAME"),
(aws_profile_name, "AWS_PROFILE_NAME"),
(aws_role_name, "AWS_ROLE_NAME"),
(aws_web_identity_token, "AWS_WEB_IDENTITY_TOKEN"),
(aws_sts_endpoint, "AWS_STS_ENDPOINT"),
(aws_external_id, "AWS_EXTERNAL_ID"),
)
)
verbose_logger.debug(
"in get credentials\n"
@ -845,6 +837,20 @@ class BaseAWSLLM:
f"IN Web Identity Token: {aws_web_identity_token} | Role Name: {aws_role_name} | Session Name: {aws_session_name}"
)
# get_secret() expands environment-variable references (an os.environ/<VAR>
# prefix, or a bare name matching an environment variable). Config-sourced
# references are expanded at load time, so such a reference reaching here is
# caller-supplied input; reject it rather than expanding a process-environment
# value for use as the token.
if (
aws_web_identity_token.startswith("os.environ/")
or aws_web_identity_token in os.environ
):
raise AwsAuthError(
message="Invalid web identity token reference.",
status_code=400,
)
oidc_token = get_secret(aws_web_identity_token)
if oidc_token is None:

197
litellm/llms/custom_httpx/llm_http_handler.py
View file

@ -2407,11 +2407,17 @@ class BaseLLMHTTPHandler:
provider_config=responses_api_provider_config,
)
return responses_api_provider_config.transform_response_api_response(
model=model,
raw_response=response,
logging_obj=logging_obj,
initial_response = (
responses_api_provider_config.transform_response_api_response(
model=model,
raw_response=response,
logging_obj=logging_obj,
)
)
# Responses agentic interception (e.g. code interpreter) runs the follow-up
# loop via the async hook, so it is async-only for now; the sync path returns
# the initial response unchanged.
return initial_response
async def async_response_api_handler(
self,
@ -2570,12 +2576,44 @@ class BaseLLMHTTPHandler:
provider_config=responses_api_provider_config,
)
return responses_api_provider_config.transform_response_api_response(
model=model,
raw_response=response,
logging_obj=logging_obj,
initial_response = (
responses_api_provider_config.transform_response_api_response(
model=model,
raw_response=response,
logging_obj=logging_obj,
)
)
final_response = await self._call_agentic_completion_hooks(
response=initial_response,
model=model,
messages=(
input
if isinstance(input, list)
else [{"role": "user", "content": input}]
),
anthropic_messages_provider_config=responses_api_provider_config,
anthropic_messages_optional_request_params=response_api_optional_request_params,
logging_obj=logging_obj,
stream=False,
custom_llm_provider=custom_llm_provider,
kwargs=dict(litellm_params),
api_surface="responses",
)
result = final_response if final_response is not None else initial_response
if litellm_params.get(
"_code_interpreter_interception_converted_stream"
) and not litellm_params.get("_agentic_loop_depth"):
return self._wrap_responses_response_as_fake_stream(
result=result,
model=model,
responses_api_provider_config=responses_api_provider_config,
logging_obj=logging_obj,
custom_llm_provider=custom_llm_provider,
)
return result
async def async_delete_response_api_handler(
self,
response_id: str,
@ -4875,6 +4913,132 @@ class BaseLLMHTTPHandler:
return response
async def _execute_responses_agentic_plan(
self,
plan: AgenticLoopPlan,
model: str,
response_api_optional_request_params: dict,
logging_obj: "LiteLLMLoggingObj",
kwargs: dict,
depth: int,
max_loops: int,
fingerprints: list[str],
fingerprint: str,
callback: Any | None = None,
) -> Any:
patch = plan.request_patch or AgenticLoopRequestPatch()
if patch.messages is None:
raise ValueError("Agentic loop plan missing patched responses input")
optional_params = dict(response_api_optional_request_params)
optional_params.update(patch.optional_params)
if patch.tools is not None:
optional_params["tools"] = patch.tools
optional_params = {
k: v
for k, v in optional_params.items()
if k != "stream" and k != "_code_interpreter_interception_converted_stream"
}
internal_keys = {"litellm_logging_obj"}
kwargs_for_followup = {
k: v
for k, v in kwargs.items()
if not k.startswith("_websearch_interception")
and not k.startswith("_compression_interception")
and k != "_code_interpreter_interception_converted_stream"
and k not in internal_keys
and k not in optional_params
}
kwargs_for_followup.update(patch.kwargs)
kwargs_for_followup["_agentic_loop_depth"] = depth + 1
kwargs_for_followup["max_agentic_loops"] = max_loops
kwargs_for_followup["_agentic_loop_fingerprints"] = fingerprints + [fingerprint]
try:
response = await litellm.aresponses(
model=patch.model or model,
input=patch.messages,
**optional_params,
**kwargs_for_followup,
)
if callback is not None:
try:
response = await callback.async_post_agentic_loop_response_hook(
response=response, plan=plan, kwargs=kwargs
)
except Exception as e:
_call_id = getattr(logging_obj, "litellm_call_id", "unknown")
verbose_logger.exception(
"LiteLLM.AgenticHookError: Exception in "
"async_post_agentic_loop_response_hook [call_id=%s model=%s]: %s",
_call_id,
model,
str(e),
)
return response
finally:
if callback is not None:
await self._run_agentic_loop_cleanup(
callback=callback,
plan=plan,
kwargs=kwargs,
logging_obj=logging_obj,
model=model,
)
@staticmethod
async def _run_agentic_loop_cleanup(
callback: Any,
plan: AgenticLoopPlan,
kwargs: dict,
logging_obj: "LiteLLMLoggingObj",
model: str,
) -> None:
try:
await callback.async_agentic_loop_cleanup_hook(plan=plan, kwargs=kwargs)
except Exception as e:
_call_id = getattr(logging_obj, "litellm_call_id", "unknown")
verbose_logger.exception(
"LiteLLM.AgenticHookError: Exception in "
"async_agentic_loop_cleanup_hook [call_id=%s model=%s]: %s",
_call_id,
model,
str(e),
)
def _wrap_responses_response_as_fake_stream(
self,
result: Any,
model: str,
responses_api_provider_config: Any,
logging_obj: "LiteLLMLoggingObj",
custom_llm_provider: str,
) -> Any:
"""
Wrap a completed responses result as a synthetic stream.
Used when an interceptor forced stream=False to run the agentic loop on
the non-streaming path, but the caller originally asked for streaming.
"""
import httpx
from litellm.responses.streaming_iterator import (
MockResponsesAPIStreamingIterator,
)
payload = result.model_dump() if hasattr(result, "model_dump") else result
raw_response = httpx.Response(status_code=200, json=payload)
return MockResponsesAPIStreamingIterator(
response=raw_response,
model=model,
responses_api_provider_config=responses_api_provider_config,
logging_obj=logging_obj,
custom_llm_provider=custom_llm_provider,
)
async def _execute_chat_completion_agentic_plan(
self,
plan: AgenticLoopPlan,
@ -4940,6 +5104,7 @@ class BaseLLMHTTPHandler:
stream: bool,
custom_llm_provider: str,
kwargs: Dict,
api_surface: str = "anthropic_messages",
) -> Optional[Any]:
"""
Call agentic completion hooks for all custom loggers (Anthropic Messages API).
@ -5046,6 +5211,20 @@ class BaseLLMHTTPHandler:
if not plan.run_agentic_loop:
continue
if api_surface == "responses":
return await self._execute_responses_agentic_plan(
plan=plan,
model=model,
response_api_optional_request_params=anthropic_messages_optional_request_params,
logging_obj=logging_obj,
kwargs=kwargs_with_provider,
depth=depth,
max_loops=max_loops,
fingerprints=fingerprints,
fingerprint=fingerprint,
callback=callback,
)
return await self._execute_anthropic_agentic_plan(
plan=plan,
model=model,
@ -5083,7 +5262,7 @@ class BaseLLMHTTPHandler:
else False
)
if websearch_converted_stream:
if api_surface == "anthropic_messages" and websearch_converted_stream:
from typing import cast
from litellm._logging import verbose_logger

9
litellm/llms/e2b/sandbox/transformation.py
View file

@ -51,11 +51,13 @@ class E2BSandboxConfig(BaseSandboxConfig):
timeout: int | None = None,
allow_internet_access: bool = True,
api_key: str | None = None,
api_base: str | None = None,
metadata: dict | None = None,
client: AsyncHTTPHandler | None = None,
**kwargs,
) -> ContainerHandle:
key = self.validate_environment(api_key=api_key)
base = api_base or E2B_API_BASE
body = {
"templateID": template or E2B_DEFAULT_TEMPLATE,
"timeout": timeout if timeout is not None else DEFAULT_SANDBOX_TIMEOUT,
@ -68,7 +70,7 @@ class E2BSandboxConfig(BaseSandboxConfig):
response = cast(
httpx.Response,
await self._http(client).post(
url=f"{E2B_API_BASE}/sandboxes",
url=f"{base}/sandboxes",
headers={"X-API-Key": key, "Content-Type": "application/json"},
json=body,
),
@ -84,6 +86,7 @@ class E2BSandboxConfig(BaseSandboxConfig):
"envd_access_token": data.get("envdAccessToken"),
"traffic_access_token": data.get("trafficAccessToken"),
"api_key": key,
"api_base": base,
}
return handle
@ -130,6 +133,7 @@ class E2BSandboxConfig(BaseSandboxConfig):
*,
container: Union[ContainerHandle, str],
api_key: str | None = None,
api_base: str | None = None,
client: AsyncHTTPHandler | None = None,
**kwargs,
) -> bool:
@ -139,11 +143,12 @@ class E2BSandboxConfig(BaseSandboxConfig):
or handle._hidden_params.get("api_key")
or self.validate_environment()
)
base = api_base or handle._hidden_params.get("api_base") or E2B_API_BASE
try:
response = cast(
httpx.Response,
await self._http(client).delete(
url=f"{E2B_API_BASE}/sandboxes/{handle.id}",
url=f"{base}/sandboxes/{handle.id}",
headers={"X-API-Key": key},
),
)

276
litellm/llms/fireworks_ai/chat/transformation.py
View file

@ -1,5 +1,15 @@
import json
from typing import Any, List, Literal, Optional, Tuple, Union, cast
from typing import (
Any,
AsyncIterator,
Iterator,
List,
Literal,
Optional,
Tuple,
Union,
cast,
)
import httpx
@ -15,7 +25,6 @@ from litellm.litellm_core_utils.llm_response_utils.get_headers import (
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import (
AllMessageValues,
ChatCompletionImageObject,
ChatCompletionToolParam,
OpenAIChatCompletionToolParam,
)
@ -25,6 +34,7 @@ from litellm.types.utils import (
Function,
Message,
ModelResponse,
ModelResponseStream,
ProviderSpecificModelInfo,
)
from litellm.utils import (
@ -34,10 +44,34 @@ from litellm.utils import (
supports_tool_choice,
)
from ...openai.chat.gpt_transformation import OpenAIGPTConfig
from ...openai.chat.gpt_transformation import (
OpenAIChatCompletionStreamingHandler,
OpenAIGPTConfig,
)
from ..common_utils import FireworksAIException
def _extract_fireworks_hidden_params(payload: dict) -> dict:
"""
Collect Fireworks-specific response fields (perf_metrics, prompt_token_ids,
per-choice raw_output and token_ids) from a non-streaming completion payload
or a single streaming chunk, so the same data lands in ``_hidden_params`` on
both response paths.
"""
choices = [c for c in (payload.get("choices") or []) if isinstance(c, dict)]
top_level = {
f"fireworks_{field}": payload[field]
for field in ("perf_metrics", "prompt_token_ids")
if field in payload
}
per_choice = {
f"fireworks_{dest}": [c[field] for c in choices if field in c]
for field, dest in (("raw_output", "raw_outputs"), ("token_ids", "token_ids"))
if any(field in c for c in choices)
}
return {**top_level, **per_choice}
class FireworksAIConfig(OpenAIGPTConfig):
"""
Reference: https://docs.fireworks.ai/api-reference/post-chatcompletions
@ -60,8 +94,7 @@ class FireworksAIConfig(OpenAIGPTConfig):
logprobs: Optional[int] = None
reasoning_effort: Optional[str] = None
# Non OpenAI parameters - Fireworks AI only params
prompt_truncate_length: Optional[int] = None
prompt_truncate_len: Optional[int] = None
context_length_exceeded_behavior: Optional[Literal["error", "truncate"]] = None
def __init__(
@ -80,7 +113,7 @@ class FireworksAIConfig(OpenAIGPTConfig):
user: Optional[str] = None,
logprobs: Optional[int] = None,
reasoning_effort: Optional[str] = None,
prompt_truncate_length: Optional[int] = None,
prompt_truncate_len: Optional[int] = None,
context_length_exceeded_behavior: Optional[Literal["error", "truncate"]] = None,
) -> None:
locals_ = locals().copy()
@ -108,8 +141,30 @@ class FireworksAIConfig(OpenAIGPTConfig):
"response_format",
"user",
"logprobs",
"prompt_truncate_length",
"prompt_truncate_len",
"context_length_exceeded_behavior",
"seed",
"top_logprobs",
"min_p",
"typical_p",
"repetition_penalty",
"mirostat_target",
"mirostat_lr",
"logit_bias",
"echo",
"echo_last",
"ignore_eos",
"prompt_cache_key",
"prompt_cache_isolation_key",
"raw_output",
"perf_metrics_in_response",
"return_token_ids",
"safe_tokenization",
"service_tier",
"speculation",
"prediction",
"stream_options",
"sampling_mask",
]
# Only add tools for models that support function calling
@ -133,9 +188,11 @@ class FireworksAIConfig(OpenAIGPTConfig):
if supports_tool_choice(model=model, custom_llm_provider="fireworks_ai"):
supported_params.append("tool_choice")
# Only add reasoning_effort for models that support it
# Only add reasoning params for models that support it
if supports_reasoning(model=model, custom_llm_provider="fireworks_ai"):
supported_params.append("reasoning_effort")
supported_params.append("reasoning_history")
supported_params.append("thinking")
return supported_params
@ -151,6 +208,18 @@ class FireworksAIConfig(OpenAIGPTConfig):
param == "tools" and value is not None
for param, value in non_default_params.items()
)
if (
non_default_params.get("thinking") is not None
and non_default_params.get("reasoning_effort") is not None
):
raise litellm.BadRequestError(
message=(
"Fireworks AI chat completions does not support specifying both "
"`thinking` and `reasoning_effort` in the same request."
),
model=model,
llm_provider="fireworks_ai",
)
for param, value in non_default_params.items():
if param == "tool_choice":
@ -174,40 +243,19 @@ class FireworksAIConfig(OpenAIGPTConfig):
optional_params["response_format"] = value
elif param == "max_completion_tokens":
optional_params["max_tokens"] = value
elif param == "reasoning_effort":
if value is True:
optional_params["reasoning_effort"] = "medium"
elif value is False:
optional_params["reasoning_effort"] = "none"
else:
optional_params["reasoning_effort"] = value
elif param in supported_openai_params:
if value is not None:
optional_params[param] = value
return optional_params
def _add_transform_inline_image_block(
self,
content: ChatCompletionImageObject,
model: str,
disable_add_transform_inline_image_block: Optional[bool],
) -> ChatCompletionImageObject:
"""
Add transform_inline to the image_url (allows non-vision models to parse documents/images/etc.)
- ignore if model is a vision model
- ignore if user has disabled this feature
"""
if (
"vision" in model or disable_add_transform_inline_image_block
): # allow user to toggle this feature.
return content
if isinstance(content["image_url"], str):
# Skip base64 data URLs — appending #transform=inline corrupts the
# base64 payload and causes an "Incorrect padding" decode error on
# the Fireworks side. Data URLs are already inlined by definition.
# Lower-case before checking: URI schemes are case-insensitive (RFC 3986).
if not content["image_url"].lower().startswith("data:"):
content["image_url"] = f"{content['image_url']}#transform=inline"
elif isinstance(content["image_url"], dict):
url = content["image_url"]["url"]
if not url.lower().startswith("data:"):
content["image_url"]["url"] = f"{url}#transform=inline"
return content
def _transform_tools(
self, tools: List[OpenAIChatCompletionToolParam]
) -> List[OpenAIChatCompletionToolParam]:
@ -225,36 +273,46 @@ class FireworksAIConfig(OpenAIGPTConfig):
self, messages: List[AllMessageValues], model: str, litellm_params: dict
) -> List[AllMessageValues]:
"""
Add 'transform=inline' to the url of the image_url
Strip fields not permitted by FireworksAI from messages.
"""
from litellm.litellm_core_utils.prompt_templates.common_utils import (
filter_value_from_dict,
migrate_file_to_image_url,
)
disable_add_transform_inline_image_block = cast(
Optional[bool],
litellm_params.get("disable_add_transform_inline_image_block")
or litellm.disable_add_transform_inline_image_block,
supports_vision_value = self._get_model_cost_capability_exact(
model=model, capability="supports_vision"
)
## For any 'file' message type with pdf content, move to 'image_url' message type
for message in messages:
if message["role"] == "user":
_message_content = message.get("content")
if _message_content is not None and isinstance(_message_content, list):
for idx, content in enumerate(_message_content):
if content["type"] == "file":
_message_content[idx] = migrate_file_to_image_url(content)
for message in messages:
if message["role"] == "user":
_message_content = message.get("content")
if _message_content is not None and isinstance(_message_content, list):
for content in _message_content:
if content["type"] == "image_url":
content = self._add_transform_inline_image_block(
content=content,
if not isinstance(content, dict):
continue
if content.get("type") == "file":
raise litellm.BadRequestError(
message=(
"Fireworks AI chat completions does not support "
"file content blocks. For PDFs, convert pages to "
"images and send image_url blocks to a Fireworks "
"vision model, or extract text before calling a "
"text-only model."
),
model=model,
disable_add_transform_inline_image_block=disable_add_transform_inline_image_block,
llm_provider="fireworks_ai",
)
if (
content.get("type") == "image_url"
and supports_vision_value is False
):
raise litellm.BadRequestError(
message=(
f"Fireworks AI model {model} does not support "
"image inputs. Use a Fireworks vision model or "
"remove image_url content blocks."
),
model=model,
llm_provider="fireworks_ai",
)
filter_value_from_dict(cast(dict, message), "cache_control")
# Remove fields not permitted by FireworksAI (additionalProperties: false
@ -317,43 +375,55 @@ class FireworksAIConfig(OpenAIGPTConfig):
return True
return ("-" + key_short + "-") in short_name
def _get_model_cost_capability(self, model: str, capability: str) -> Optional[bool]:
@staticmethod
def _short_model_name(model: str) -> str:
short_name = model
if short_name.startswith("fireworks_ai/"):
short_name = short_name[len("fireworks_ai/") :]
if short_name.startswith("accounts/fireworks/models/"):
short_name = short_name[len("accounts/fireworks/models/") :]
return short_name
candidate_keys = [
def _get_model_cost_capability_exact(
self, model: str, capability: str
) -> Optional[bool]:
short_name = self._short_model_name(model)
candidate_keys = (
model,
f"fireworks_ai/{short_name}",
f"fireworks_ai/accounts/fireworks/models/{short_name}",
]
)
for candidate_key in candidate_keys:
model_info = litellm.model_cost.get(candidate_key)
if model_info is not None and model_info.get(capability) is not None:
return cast(Optional[bool], model_info.get(capability))
return None
# Fallback: preserve historical substring matching for model name
# variants (e.g. fine-tuned or regionally-suffixed versions of a
# known model). Pick the *longest* matching entry so a more specific
# known model (e.g. "qwen3-8b-instruct") wins over a less specific
# one (e.g. "qwen3-8b") when the query model is more specific still.
# Use hyphen-aligned matching to avoid false positives where a short
# known model name is an unrelated substring of a longer one.
best_match_short: Optional[str] = None
best_match_value: Optional[bool] = None
for key_short, model_info in self._get_fireworks_index():
if model_info.get(capability) is None:
continue
if not self._matches_on_hyphen_boundary(short_name, key_short):
continue
if best_match_short is None or len(key_short) > len(best_match_short):
best_match_short = key_short
best_match_value = cast(Optional[bool], model_info.get(capability))
def _get_model_cost_capability(self, model: str, capability: str) -> Optional[bool]:
exact = self._get_model_cost_capability_exact(
model=model, capability=capability
)
if exact is not None:
return exact
return best_match_value
# Fallback: substring matching for model name variants (e.g. fine-tuned
# or regionally-suffixed versions of a known model). Pick the *longest*
# matching entry so a more specific known model (e.g. "qwen3-8b-instruct")
# wins over a less specific one (e.g. "qwen3-8b"). Hyphen-aligned matching
# avoids false positives where a short known name is an unrelated
# substring of a longer one. This stays a soft signal: capability-gated
# hard rejections use the exact lookup so a fuzzy match never blocks a
# custom deployment.
short_name = self._short_model_name(model)
matches = [
(key_short, cast(Optional[bool], model_info.get(capability)))
for key_short, model_info in self._get_fireworks_index()
if model_info.get(capability) is not None
and self._matches_on_hyphen_boundary(short_name, key_short)
]
if not matches:
return None
return max(matches, key=lambda match: len(match[0]))[1]
def get_provider_info(self, model: str) -> ProviderSpecificModelInfo:
supports_function_calling_value = self._get_model_cost_capability(
@ -362,12 +432,16 @@ class FireworksAIConfig(OpenAIGPTConfig):
supports_reasoning_value = self._get_model_cost_capability(
model=model, capability="supports_reasoning"
)
supports_vision_value = self._get_model_cost_capability(
model=model, capability="supports_vision"
)
supports_pdf_input_value = self._get_model_cost_capability(
model=model, capability="supports_pdf_input"
)
provider_specific_model_info: ProviderSpecificModelInfo = {
"supports_function_calling": True,
"supports_prompt_caching": True, # https://docs.fireworks.ai/guides/prompt-caching
"supports_pdf_input": True, # via document inlining
"supports_vision": True, # via document inlining
}
if supports_function_calling_value is not None:
@ -381,6 +455,14 @@ class FireworksAIConfig(OpenAIGPTConfig):
supports_reasoning_value
)
if supports_vision_value is not None:
provider_specific_model_info["supports_vision"] = supports_vision_value
if supports_pdf_input_value is not None:
provider_specific_model_info["supports_pdf_input"] = (
supports_pdf_input_value
)
return provider_specific_model_info
def transform_request(
@ -402,6 +484,15 @@ class FireworksAIConfig(OpenAIGPTConfig):
if "tools" in optional_params and optional_params["tools"] is not None:
tools = self._transform_tools(tools=optional_params["tools"])
optional_params["tools"] = tools
if optional_params.get("stream"):
stream_options = optional_params.get("stream_options")
if stream_options is None:
optional_params["stream_options"] = {"include_usage": True}
elif stream_options.get("include_usage") is not False:
optional_params["stream_options"] = {
**stream_options,
"include_usage": True,
}
return super().transform_request(
model=model,
messages=messages,
@ -494,10 +585,25 @@ class FireworksAIConfig(OpenAIGPTConfig):
)
)
response._hidden_params = {"additional_headers": additional_headers}
response._hidden_params = {
"additional_headers": additional_headers,
**_extract_fireworks_hidden_params(completion_response),
}
return response
def get_model_response_iterator(
self,
streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
sync_stream: bool,
json_mode: Optional[bool] = False,
) -> Any:
return FireworksAIChatCompletionStreamingHandler(
streaming_response=streaming_response,
sync_stream=sync_stream,
json_mode=json_mode,
)
def _get_openai_compatible_provider_info(
self, api_base: Optional[str], api_key: Optional[str]
) -> Tuple[Optional[str], Optional[str]]:
@ -554,3 +660,15 @@ class FireworksAIConfig(OpenAIGPTConfig):
or get_secret_str("FIREWORKSAI_API_KEY")
or get_secret_str("FIREWORKS_AI_TOKEN")
)
class FireworksAIChatCompletionStreamingHandler(OpenAIChatCompletionStreamingHandler):
def chunk_parser(self, chunk: dict) -> ModelResponseStream:
parsed = super().chunk_parser(chunk)
fireworks_fields = _extract_fireworks_hidden_params(chunk)
if fireworks_fields:
parsed.provider_specific_fields = {
**(getattr(parsed, "provider_specific_fields", None) or {}),
**fireworks_fields,
}
return parsed

4
litellm/model_prices_and_context_window_backup.json
View file

@ -15011,7 +15011,7 @@
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": false
"supports_vision": true
},
"fireworks_ai/accounts/fireworks/models/mixtral-8x22b-instruct-hf": {
"input_cost_per_token": 1.2e-06,
@ -15314,7 +15314,7 @@
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": false
"supports_vision": true
},
"fireworks_ai/qwen3p7-plus": {
"cache_read_input_token_cost": 8e-08,

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