niuniu/lijiaoqiao
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

chore: initial public snapshot for github upload

Browse Source
This commit is contained in:
Your Name
2026-03-26 20:06:14 +08:00
commit 0e5ecd930e
3497 changed files with 1586236 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

659
llm-gateway-competitors/litellm-wheel-src/litellm/llms/azure_ai/agents/handler.py Normal file
View File

@@ -0,0 +1,659 @@
"""
Handler for Azure Foundry Agent Service API.
This handler executes the multi-step agent flow:
1. Create thread (or use existing)
2. Add messages to thread
3. Create and poll a run
4. Retrieve the assistant's response messages
Model format: azure_ai/agents/<agent_id>
API Base format: https://<AIFoundryResourceName>.services.ai.azure.com/api/projects/<ProjectName>
Authentication: Uses Azure AD Bearer tokens (not API keys)
Get token via: az account get-access-token --resource 'https://ai.azure.com'
Supports both polling-based and native streaming (SSE) modes.
See: https://learn.microsoft.com/en-us/azure/ai-foundry/agents/quickstart
"""
import asyncio
import json
import time
import uuid
from typing import (
TYPE_CHECKING,
Any,
AsyncIterator,
Callable,
Dict,
List,
Optional,
Tuple,
)
import httpx
from litellm._logging import verbose_logger
from litellm.llms.azure_ai.agents.transformation import (
AzureAIAgentsConfig,
AzureAIAgentsError,
)
from litellm.types.utils import ModelResponse
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
LiteLLMLoggingObj = _LiteLLMLoggingObj
else:
LiteLLMLoggingObj = Any
HTTPHandler = Any
AsyncHTTPHandler = Any
class AzureAIAgentsHandler:
"""
Handler for Azure AI Agent Service.
Executes the complete agent flow which requires multiple API calls.
"""
def __init__(self):
self.config = AzureAIAgentsConfig()
# -------------------------------------------------------------------------
# URL Builders
# -------------------------------------------------------------------------
# Azure Foundry Agents API uses /assistants, /threads, etc. directly
# See: https://learn.microsoft.com/en-us/azure/ai-foundry/agents/quickstart
# -------------------------------------------------------------------------
def _build_thread_url(self, api_base: str, api_version: str) -> str:
return f"{api_base}/threads?api-version={api_version}"
def _build_messages_url(
self, api_base: str, thread_id: str, api_version: str
) -> str:
return f"{api_base}/threads/{thread_id}/messages?api-version={api_version}"
def _build_runs_url(self, api_base: str, thread_id: str, api_version: str) -> str:
return f"{api_base}/threads/{thread_id}/runs?api-version={api_version}"
def _build_run_status_url(
self, api_base: str, thread_id: str, run_id: str, api_version: str
) -> str:
return f"{api_base}/threads/{thread_id}/runs/{run_id}?api-version={api_version}"
def _build_list_messages_url(
self, api_base: str, thread_id: str, api_version: str
) -> str:
return f"{api_base}/threads/{thread_id}/messages?api-version={api_version}"
def _build_create_thread_and_run_url(self, api_base: str, api_version: str) -> str:
"""URL for the create-thread-and-run endpoint (supports streaming)."""
return f"{api_base}/threads/runs?api-version={api_version}"
# -------------------------------------------------------------------------
# Response Helpers
# -------------------------------------------------------------------------
def _extract_content_from_messages(self, messages_data: dict) -> str:
"""Extract assistant content from the messages response."""
for msg in messages_data.get("data", []):
if msg.get("role") == "assistant":
for content_item in msg.get("content", []):
if content_item.get("type") == "text":
return content_item.get("text", {}).get("value", "")
return ""
def _build_model_response(
self,
model: str,
content: str,
model_response: ModelResponse,
thread_id: str,
messages: List[Dict[str, Any]],
) -> ModelResponse:
"""Build the ModelResponse from agent output."""
from litellm.types.utils import Choices, Message, Usage
model_response.choices = [
Choices(
finish_reason="stop",
index=0,
message=Message(content=content, role="assistant"),
)
]
model_response.model = model
# Store thread_id for conversation continuity
if (
not hasattr(model_response, "_hidden_params")
or model_response._hidden_params is None
):
model_response._hidden_params = {}
model_response._hidden_params["thread_id"] = thread_id
# Estimate token usage
try:
from litellm.utils import token_counter
prompt_tokens = token_counter(model="gpt-3.5-turbo", messages=messages)
completion_tokens = token_counter(
model="gpt-3.5-turbo", text=content, count_response_tokens=True
)
setattr(
model_response,
"usage",
Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
),
)
except Exception as e:
verbose_logger.warning(f"Failed to calculate token usage: {str(e)}")
return model_response
def _prepare_completion_params(
self,
model: str,
api_base: str,
api_key: str,
optional_params: dict,
headers: Optional[dict],
) -> tuple:
"""Prepare common parameters for completion.
Azure Foundry Agents API uses Bearer token authentication:
- Authorization: Bearer <token> (Azure AD token from 'az account get-access-token --resource https://ai.azure.com')
See: https://learn.microsoft.com/en-us/azure/ai-foundry/agents/quickstart
"""
if headers is None:
headers = {}
headers["Content-Type"] = "application/json"
# Azure Foundry Agents uses Bearer token authentication
# The api_key here is expected to be an Azure AD token
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
api_version = optional_params.get(
"api_version", self.config.DEFAULT_API_VERSION
)
agent_id = self.config._get_agent_id(model, optional_params)
thread_id = optional_params.get("thread_id")
api_base = api_base.rstrip("/")
verbose_logger.debug(
f"Azure AI Agents completion - api_base: {api_base}, agent_id: {agent_id}"
)
return headers, api_version, agent_id, thread_id, api_base
def _check_response(
self, response: httpx.Response, expected_codes: List[int], error_msg: str
):
"""Check response status and raise error if not expected."""
if response.status_code not in expected_codes:
raise AzureAIAgentsError(
status_code=response.status_code,
message=f"{error_msg}: {response.text}",
)
# -------------------------------------------------------------------------
# Sync Completion
# -------------------------------------------------------------------------
def completion(
self,
model: str,
messages: List[Dict[str, Any]],
api_base: str,
api_key: str,
model_response: ModelResponse,
logging_obj: LiteLLMLoggingObj,
optional_params: dict,
litellm_params: dict,
timeout: float,
client: Optional[HTTPHandler] = None,
headers: Optional[dict] = None,
) -> ModelResponse:
"""Execute synchronous completion using Azure Agent Service."""
from litellm.llms.custom_httpx.http_handler import _get_httpx_client
if client is None:
client = _get_httpx_client(
params={"ssl_verify": litellm_params.get("ssl_verify", None)}
)
(
headers,
api_version,
agent_id,
thread_id,
api_base,
) = self._prepare_completion_params(
model, api_base, api_key, optional_params, headers
)
def make_request(
method: str, url: str, json_data: Optional[dict] = None
) -> httpx.Response:
if method == "GET":
return client.get(url=url, headers=headers)
return client.post(
url=url,
headers=headers,
data=json.dumps(json_data) if json_data else None,
)
# Execute the agent flow
thread_id, content = self._execute_agent_flow_sync(
make_request=make_request,
api_base=api_base,
api_version=api_version,
agent_id=agent_id,
thread_id=thread_id,
messages=messages,
optional_params=optional_params,
)
return self._build_model_response(
model, content, model_response, thread_id, messages
)
def _execute_agent_flow_sync(
self,
make_request: Callable,
api_base: str,
api_version: str,
agent_id: str,
thread_id: Optional[str],
messages: List[Dict[str, Any]],
optional_params: dict,
) -> Tuple[str, str]:
"""Execute the agent flow synchronously. Returns (thread_id, content)."""
# Step 1: Create thread if not provided
if not thread_id:
verbose_logger.debug(
f"Creating thread at: {self._build_thread_url(api_base, api_version)}"
)
response = make_request(
"POST", self._build_thread_url(api_base, api_version), {}
)
self._check_response(response, [200, 201], "Failed to create thread")
thread_id = response.json()["id"]
verbose_logger.debug(f"Created thread: {thread_id}")
# At this point thread_id is guaranteed to be a string
assert thread_id is not None
# Step 2: Add messages to thread
for msg in messages:
if msg.get("role") in ["user", "system"]:
url = self._build_messages_url(api_base, thread_id, api_version)
response = make_request(
"POST", url, {"role": "user", "content": msg.get("content", "")}
)
self._check_response(response, [200, 201], "Failed to add message")
# Step 3: Create run
run_payload = {"assistant_id": agent_id}
if "instructions" in optional_params:
run_payload["instructions"] = optional_params["instructions"]
response = make_request(
"POST", self._build_runs_url(api_base, thread_id, api_version), run_payload
)
self._check_response(response, [200, 201], "Failed to create run")
run_id = response.json()["id"]
verbose_logger.debug(f"Created run: {run_id}")
# Step 4: Poll for completion
status_url = self._build_run_status_url(
api_base, thread_id, run_id, api_version
)
for _ in range(self.config.MAX_POLL_ATTEMPTS):
response = make_request("GET", status_url)
self._check_response(response, [200], "Failed to get run status")
status = response.json().get("status")
verbose_logger.debug(f"Run status: {status}")
if status == "completed":
break
elif status in ["failed", "cancelled", "expired"]:
error_msg = (
response.json()
.get("last_error", {})
.get("message", "Unknown error")
)
raise AzureAIAgentsError(
status_code=500, message=f"Run {status}: {error_msg}"
)
time.sleep(self.config.POLL_INTERVAL_SECONDS)
else:
raise AzureAIAgentsError(
status_code=408, message="Run timed out waiting for completion"
)
# Step 5: Get messages
response = make_request(
"GET", self._build_list_messages_url(api_base, thread_id, api_version)
)
self._check_response(response, [200], "Failed to get messages")
content = self._extract_content_from_messages(response.json())
return thread_id, content
# -------------------------------------------------------------------------
# Async Completion
# -------------------------------------------------------------------------
async def acompletion(
self,
model: str,
messages: List[Dict[str, Any]],
api_base: str,
api_key: str,
model_response: ModelResponse,
logging_obj: LiteLLMLoggingObj,
optional_params: dict,
litellm_params: dict,
timeout: float,
client: Optional[AsyncHTTPHandler] = None,
headers: Optional[dict] = None,
) -> ModelResponse:
"""Execute asynchronous completion using Azure Agent Service."""
import litellm
from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
if client is None:
client = get_async_httpx_client(
llm_provider=litellm.LlmProviders.AZURE_AI,
params={"ssl_verify": litellm_params.get("ssl_verify", None)},
)
(
headers,
api_version,
agent_id,
thread_id,
api_base,
) = self._prepare_completion_params(
model, api_base, api_key, optional_params, headers
)
async def make_request(
method: str, url: str, json_data: Optional[dict] = None
) -> httpx.Response:
if method == "GET":
return await client.get(url=url, headers=headers)
return await client.post(
url=url,
headers=headers,
data=json.dumps(json_data) if json_data else None,
)
# Execute the agent flow
thread_id, content = await self._execute_agent_flow_async(
make_request=make_request,
api_base=api_base,
api_version=api_version,
agent_id=agent_id,
thread_id=thread_id,
messages=messages,
optional_params=optional_params,
)
return self._build_model_response(
model, content, model_response, thread_id, messages
)
async def _execute_agent_flow_async(
self,
make_request: Callable,
api_base: str,
api_version: str,
agent_id: str,
thread_id: Optional[str],
messages: List[Dict[str, Any]],
optional_params: dict,
) -> Tuple[str, str]:
"""Execute the agent flow asynchronously. Returns (thread_id, content)."""
# Step 1: Create thread if not provided
if not thread_id:
verbose_logger.debug(
f"Creating thread at: {self._build_thread_url(api_base, api_version)}"
)
response = await make_request(
"POST", self._build_thread_url(api_base, api_version), {}
)
self._check_response(response, [200, 201], "Failed to create thread")
thread_id = response.json()["id"]
verbose_logger.debug(f"Created thread: {thread_id}")
# At this point thread_id is guaranteed to be a string
assert thread_id is not None
# Step 2: Add messages to thread
for msg in messages:
if msg.get("role") in ["user", "system"]:
url = self._build_messages_url(api_base, thread_id, api_version)
response = await make_request(
"POST", url, {"role": "user", "content": msg.get("content", "")}
)
self._check_response(response, [200, 201], "Failed to add message")
# Step 3: Create run
run_payload = {"assistant_id": agent_id}
if "instructions" in optional_params:
run_payload["instructions"] = optional_params["instructions"]
response = await make_request(
"POST", self._build_runs_url(api_base, thread_id, api_version), run_payload
)
self._check_response(response, [200, 201], "Failed to create run")
run_id = response.json()["id"]
verbose_logger.debug(f"Created run: {run_id}")
# Step 4: Poll for completion
status_url = self._build_run_status_url(
api_base, thread_id, run_id, api_version
)
for _ in range(self.config.MAX_POLL_ATTEMPTS):
response = await make_request("GET", status_url)
self._check_response(response, [200], "Failed to get run status")
status = response.json().get("status")
verbose_logger.debug(f"Run status: {status}")
if status == "completed":
break
elif status in ["failed", "cancelled", "expired"]:
error_msg = (
response.json()
.get("last_error", {})
.get("message", "Unknown error")
)
raise AzureAIAgentsError(
status_code=500, message=f"Run {status}: {error_msg}"
)
await asyncio.sleep(self.config.POLL_INTERVAL_SECONDS)
else:
raise AzureAIAgentsError(
status_code=408, message="Run timed out waiting for completion"
)
# Step 5: Get messages
response = await make_request(
"GET", self._build_list_messages_url(api_base, thread_id, api_version)
)
self._check_response(response, [200], "Failed to get messages")
content = self._extract_content_from_messages(response.json())
return thread_id, content
# -------------------------------------------------------------------------
# Streaming Completion (Native SSE)
# -------------------------------------------------------------------------
async def acompletion_stream(
self,
model: str,
messages: List[Dict[str, Any]],
api_base: str,
api_key: str,
logging_obj: LiteLLMLoggingObj,
optional_params: dict,
litellm_params: dict,
timeout: float,
headers: Optional[dict] = None,
) -> AsyncIterator:
"""Execute async streaming completion using Azure Agent Service with native SSE."""
import litellm
from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
(
headers,
api_version,
agent_id,
thread_id,
api_base,
) = self._prepare_completion_params(
model, api_base, api_key, optional_params, headers
)
# Build payload for create-thread-and-run with streaming
thread_messages = []
for msg in messages:
if msg.get("role") in ["user", "system"]:
thread_messages.append(
{"role": "user", "content": msg.get("content", "")}
)
payload: Dict[str, Any] = {
"assistant_id": agent_id,
"stream": True,
}
# Add thread with messages if we don't have an existing thread
if not thread_id:
payload["thread"] = {"messages": thread_messages}
if "instructions" in optional_params:
payload["instructions"] = optional_params["instructions"]
url = self._build_create_thread_and_run_url(api_base, api_version)
verbose_logger.debug(f"Azure AI Agents streaming - URL: {url}")
# Use LiteLLM's async HTTP client for streaming
client = get_async_httpx_client(
llm_provider=litellm.LlmProviders.AZURE_AI,
params={"ssl_verify": litellm_params.get("ssl_verify", None)},
)
response = await client.post(
url=url,
headers=headers,
data=json.dumps(payload),
stream=True,
)
if response.status_code not in [200, 201]:
error_text = await response.aread()
raise AzureAIAgentsError(
status_code=response.status_code,
message=f"Streaming request failed: {error_text.decode()}",
)
async for chunk in self._process_sse_stream(response, model):
yield chunk
async def _process_sse_stream(
self,
response: httpx.Response,
model: str,
) -> AsyncIterator:
"""Process SSE stream and yield OpenAI-compatible streaming chunks."""
from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices
response_id = f"chatcmpl-{uuid.uuid4().hex[:8]}"
created = int(time.time())
thread_id = None
current_event = None
async for line in response.aiter_lines():
line = line.strip()
if line.startswith("event:"):
current_event = line[6:].strip()
continue
if line.startswith("data:"):
data_str = line[5:].strip()
if data_str == "[DONE]":
# Send final chunk with finish_reason
final_chunk = ModelResponseStream(
id=response_id,
created=created,
model=model,
object="chat.completion.chunk",
choices=[
StreamingChoices(
finish_reason="stop",
index=0,
delta=Delta(content=None),
)
],
)
if thread_id:
final_chunk._hidden_params = {"thread_id": thread_id}
yield final_chunk
return
try:
data = json.loads(data_str)
except json.JSONDecodeError:
continue
# Extract thread_id from thread.created event
if current_event == "thread.created" and "id" in data:
thread_id = data["id"]
verbose_logger.debug(f"Stream created thread: {thread_id}")
# Process message deltas - this is where the actual content comes
if current_event == "thread.message.delta":
delta_content = data.get("delta", {}).get("content", [])
for content_item in delta_content:
if content_item.get("type") == "text":
text_value = content_item.get("text", {}).get("value", "")
if text_value:
chunk = ModelResponseStream(
id=response_id,
created=created,
model=model,
object="chat.completion.chunk",
choices=[
StreamingChoices(
finish_reason=None,
index=0,
delta=Delta(
content=text_value, role="assistant"
),
)
],
)
if thread_id:
chunk._hidden_params = {"thread_id": thread_id}
yield chunk
# Singleton instance
azure_ai_agents_handler = AzureAIAgentsHandler()