+ curl https://api.lakera.ai/v1/prompt_injection \
+ -X POST \
+ -H "Authorization: Bearer $LAKERA_GUARD_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{"input": "Your content goes here"}'
+ """
+
+ response = await self.async_handler.post(
+ url="https://api.lakera.ai/v1/prompt_injection",
+ data=_json_data,
+ headers={
+ "Authorization": "Bearer " + self.lakera_api_key,
+ "Content-Type": "application/json",
+ },
+ )
+ verbose_proxy_logger.debug("Lakera AI response: %s", response.text)
+ if response.status_code == 200:
+ # check if the response was flagged
+ """
+ Example Response from Lakera AI
+
+ {
+ "model": "lakera-guard-1",
+ "results": [
+ {
+ "categories": {
+ "prompt_injection": true,
+ "jailbreak": false
+ },
+ "category_scores": {
+ "prompt_injection": 1.0,
+ "jailbreak": 0.0
+ },
+ "flagged": true,
+ "payload": {}
+ }
+ ],
+ "dev_info": {
+ "git_revision": "784489d3",
+ "git_timestamp": "2024-05-22T16:51:26+00:00"
+ }
+ }
+ """
+ _json_response = response.json()
+ _results = _json_response.get("results", [])
+ if len(_results) <= 0:
+ return
+
+ flagged = _results[0].get("flagged", False)
+
+ if flagged == True:
+ raise HTTPException(
+ status_code=400, detail={"error": "Violated content safety policy"}
+ )
+
+ pass
diff --git a/enterprise/enterprise_hooks/openai_moderation.py b/enterprise/enterprise_hooks/openai_moderation.py
new file mode 100644
index 000000000..0fa375fb2
--- /dev/null
+++ b/enterprise/enterprise_hooks/openai_moderation.py
@@ -0,0 +1,68 @@
+# +-------------------------------------------------------------+
+#
+# Use OpenAI /moderations for your LLM calls
+#
+# +-------------------------------------------------------------+
+# Thank you users! We ❤️ you! - Krrish & Ishaan
+
+import sys, os
+
+sys.path.insert(
+ 0, os.path.abspath("../..")
+) # Adds the parent directory to the system path
+from typing import Optional, Literal, Union
+import litellm, traceback, sys, uuid
+from litellm.caching import DualCache
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.integrations.custom_logger import CustomLogger
+from fastapi import HTTPException
+from litellm._logging import verbose_proxy_logger
+from litellm.utils import (
+ ModelResponse,
+ EmbeddingResponse,
+ ImageResponse,
+ StreamingChoices,
+)
+from datetime import datetime
+import aiohttp, asyncio
+from litellm._logging import verbose_proxy_logger
+
+litellm.set_verbose = True
+
+
+class _ENTERPRISE_OpenAI_Moderation(CustomLogger):
+ def __init__(self):
+ self.model_name = (
+ litellm.openai_moderations_model_name or "text-moderation-latest"
+ ) # pass the model_name you initialized on litellm.Router()
+ pass
+
+ #### CALL HOOKS - proxy only ####
+
+ async def async_moderation_hook( ### 👈 KEY CHANGE ###
+ self,
+ data: dict,
+ user_api_key_dict: UserAPIKeyAuth,
+ call_type: Literal["completion", "embeddings", "image_generation"],
+ ):
+ if "messages" in data and isinstance(data["messages"], list):
+ text = ""
+ for m in data["messages"]: # assume messages is a list
+ if "content" in m and isinstance(m["content"], str):
+ text += m["content"]
+
+ from litellm.proxy.proxy_server import llm_router
+
+ if llm_router is None:
+ return
+
+ moderation_response = await llm_router.amoderation(
+ model=self.model_name, input=text
+ )
+
+ verbose_proxy_logger.debug("Moderation response: %s", moderation_response)
+ if moderation_response.results[0].flagged == True:
+ raise HTTPException(
+ status_code=403, detail={"error": "Violated content safety policy"}
+ )
+ pass
diff --git a/litellm/__init__.py b/litellm/__init__.py
index 0db5d365a..9fa801318 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -6,7 +6,13 @@ warnings.filterwarnings("ignore", message=".*conflict with protected namespace.*
import threading, requests, os
from typing import Callable, List, Optional, Dict, Union, Any, Literal
from litellm.caching import Cache
-from litellm._logging import set_verbose, _turn_on_debug, verbose_logger, json_logs
+from litellm._logging import (
+ set_verbose,
+ _turn_on_debug,
+ verbose_logger,
+ json_logs,
+ _turn_on_json,
+)
from litellm.proxy._types import (
KeyManagementSystem,
KeyManagementSettings,
@@ -27,8 +33,8 @@ input_callback: List[Union[str, Callable]] = []
success_callback: List[Union[str, Callable]] = []
failure_callback: List[Union[str, Callable]] = []
service_callback: List[Union[str, Callable]] = []
-callbacks: List[Callable] = []
-_custom_logger_compatible_callbacks: list = ["openmeter"]
+_custom_logger_compatible_callbacks_literal = Literal["lago", "openmeter"]
+callbacks: List[Union[Callable, _custom_logger_compatible_callbacks_literal]] = []
_langfuse_default_tags: Optional[
List[
Literal[
@@ -69,6 +75,7 @@ retry = True
### AUTH ###
api_key: Optional[str] = None
openai_key: Optional[str] = None
+databricks_key: Optional[str] = None
azure_key: Optional[str] = None
anthropic_key: Optional[str] = None
replicate_key: Optional[str] = None
@@ -97,6 +104,7 @@ ssl_verify: bool = True
disable_streaming_logging: bool = False
### GUARDRAILS ###
llamaguard_model_name: Optional[str] = None
+openai_moderations_model_name: Optional[str] = None
presidio_ad_hoc_recognizers: Optional[str] = None
google_moderation_confidence_threshold: Optional[float] = None
llamaguard_unsafe_content_categories: Optional[str] = None
@@ -219,7 +227,7 @@ default_team_settings: Optional[List] = None
max_user_budget: Optional[float] = None
max_end_user_budget: Optional[float] = None
#### RELIABILITY ####
-request_timeout: Optional[float] = 6000
+request_timeout: float = 6000
num_retries: Optional[int] = None # per model endpoint
default_fallbacks: Optional[List] = None
fallbacks: Optional[List] = None
@@ -296,6 +304,7 @@ api_base = None
headers = None
api_version = None
organization = None
+project = None
config_path = None
####### COMPLETION MODELS ###################
open_ai_chat_completion_models: List = []
@@ -615,6 +624,7 @@ provider_list: List = [
"watsonx",
"triton",
"predibase",
+ "databricks",
"custom", # custom apis
]
@@ -724,9 +734,14 @@ from .utils import (
get_supported_openai_params,
get_api_base,
get_first_chars_messages,
+ ModelResponse,
+ ImageResponse,
+ ImageObject,
+ get_provider_fields,
)
from .llms.huggingface_restapi import HuggingfaceConfig
from .llms.anthropic import AnthropicConfig
+from .llms.databricks import DatabricksConfig, DatabricksEmbeddingConfig
from .llms.predibase import PredibaseConfig
from .llms.anthropic_text import AnthropicTextConfig
from .llms.replicate import ReplicateConfig
@@ -758,7 +773,12 @@ from .llms.bedrock import (
AmazonMistralConfig,
AmazonBedrockGlobalConfig,
)
-from .llms.openai import OpenAIConfig, OpenAITextCompletionConfig, MistralConfig
+from .llms.openai import (
+ OpenAIConfig,
+ OpenAITextCompletionConfig,
+ MistralConfig,
+ DeepInfraConfig,
+)
from .llms.azure import AzureOpenAIConfig, AzureOpenAIError
from .llms.watsonx import IBMWatsonXAIConfig
from .main import * # type: ignore
@@ -784,3 +804,4 @@ from .budget_manager import BudgetManager
from .proxy.proxy_cli import run_server
from .router import Router
from .assistants.main import *
+from .batches.main import *
diff --git a/litellm/_logging.py b/litellm/_logging.py
index f31ee41f8..a8121d9a8 100644
--- a/litellm/_logging.py
+++ b/litellm/_logging.py
@@ -1,19 +1,33 @@
-import logging
+import logging, os, json
+from logging import Formatter
set_verbose = False
-json_logs = False
+json_logs = bool(os.getenv("JSON_LOGS", False))
# Create a handler for the logger (you may need to adapt this based on your needs)
handler = logging.StreamHandler()
handler.setLevel(logging.DEBUG)
+
+class JsonFormatter(Formatter):
+ def __init__(self):
+ super(JsonFormatter, self).__init__()
+
+ def format(self, record):
+ json_record = {}
+ json_record["message"] = record.getMessage()
+ return json.dumps(json_record)
+
+
# Create a formatter and set it for the handler
-formatter = logging.Formatter(
- "\033[92m%(asctime)s - %(name)s:%(levelname)s\033[0m: %(filename)s:%(lineno)s - %(message)s",
- datefmt="%H:%M:%S",
-)
+if json_logs:
+ handler.setFormatter(JsonFormatter())
+else:
+ formatter = logging.Formatter(
+ "\033[92m%(asctime)s - %(name)s:%(levelname)s\033[0m: %(filename)s:%(lineno)s - %(message)s",
+ datefmt="%H:%M:%S",
+ )
-
-handler.setFormatter(formatter)
+ handler.setFormatter(formatter)
verbose_proxy_logger = logging.getLogger("LiteLLM Proxy")
verbose_router_logger = logging.getLogger("LiteLLM Router")
@@ -25,6 +39,16 @@ verbose_proxy_logger.addHandler(handler)
verbose_logger.addHandler(handler)
+def _turn_on_json():
+ handler = logging.StreamHandler()
+ handler.setLevel(logging.DEBUG)
+ handler.setFormatter(JsonFormatter())
+
+ verbose_router_logger.addHandler(handler)
+ verbose_proxy_logger.addHandler(handler)
+ verbose_logger.addHandler(handler)
+
+
def _turn_on_debug():
verbose_logger.setLevel(level=logging.DEBUG) # set package log to debug
verbose_router_logger.setLevel(level=logging.DEBUG) # set router logs to debug
diff --git a/litellm/batches/main.py b/litellm/batches/main.py
new file mode 100644
index 000000000..4043606d5
--- /dev/null
+++ b/litellm/batches/main.py
@@ -0,0 +1,589 @@
+"""
+Main File for Batches API implementation
+
+https://platform.openai.com/docs/api-reference/batch
+
+- create_batch()
+- retrieve_batch()
+- cancel_batch()
+- list_batch()
+
+"""
+
+import os
+import asyncio
+from functools import partial
+import contextvars
+from typing import Literal, Optional, Dict, Coroutine, Any, Union
+import httpx
+
+import litellm
+from litellm import client
+from litellm.utils import supports_httpx_timeout
+from ..types.router import *
+from ..llms.openai import OpenAIBatchesAPI, OpenAIFilesAPI
+from ..types.llms.openai import (
+ CreateBatchRequest,
+ RetrieveBatchRequest,
+ CancelBatchRequest,
+ CreateFileRequest,
+ FileTypes,
+ FileObject,
+ Batch,
+ FileContentRequest,
+ HttpxBinaryResponseContent,
+)
+
+####### ENVIRONMENT VARIABLES ###################
+openai_batches_instance = OpenAIBatchesAPI()
+openai_files_instance = OpenAIFilesAPI()
+#################################################
+
+
+async def acreate_file(
+ file: FileTypes,
+ purpose: Literal["assistants", "batch", "fine-tune"],
+ custom_llm_provider: Literal["openai"] = "openai",
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Coroutine[Any, Any, FileObject]:
+ """
+ Async: Files are used to upload documents that can be used with features like Assistants, Fine-tuning, and Batch API.
+
+ LiteLLM Equivalent of POST: POST https://api.openai.com/v1/files
+ """
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["acreate_file"] = True
+
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ create_file,
+ file,
+ purpose,
+ custom_llm_provider,
+ extra_headers,
+ extra_body,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response # type: ignore
+
+ return response
+ except Exception as e:
+ raise e
+
+
+def create_file(
+ file: FileTypes,
+ purpose: Literal["assistants", "batch", "fine-tune"],
+ custom_llm_provider: Literal["openai"] = "openai",
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Union[FileObject, Coroutine[Any, Any, FileObject]]:
+ """
+ Files are used to upload documents that can be used with features like Assistants, Fine-tuning, and Batch API.
+
+ LiteLLM Equivalent of POST: POST https://api.openai.com/v1/files
+ """
+ try:
+ optional_params = GenericLiteLLMParams(**kwargs)
+ if custom_llm_provider == "openai":
+ # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+ ### TIMEOUT LOGIC ###
+ timeout = (
+ optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ )
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ _create_file_request = CreateFileRequest(
+ file=file,
+ purpose=purpose,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ )
+
+ _is_async = kwargs.pop("acreate_file", False) is True
+
+ response = openai_files_instance.create_file(
+ _is_async=_is_async,
+ api_base=api_base,
+ api_key=api_key,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ organization=organization,
+ create_file_data=_create_file_request,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ return response
+ except Exception as e:
+ raise e
+
+
+async def afile_content(
+ file_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Coroutine[Any, Any, HttpxBinaryResponseContent]:
+ """
+ Async: Get file contents
+
+ LiteLLM Equivalent of GET https://api.openai.com/v1/files
+ """
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["afile_content"] = True
+
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ file_content,
+ file_id,
+ custom_llm_provider,
+ extra_headers,
+ extra_body,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response # type: ignore
+
+ return response
+ except Exception as e:
+ raise e
+
+
+def file_content(
+ file_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Union[HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent]]:
+ """
+ Returns the contents of the specified file.
+
+ LiteLLM Equivalent of POST: POST https://api.openai.com/v1/files
+ """
+ try:
+ optional_params = GenericLiteLLMParams(**kwargs)
+ if custom_llm_provider == "openai":
+ # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+ ### TIMEOUT LOGIC ###
+ timeout = (
+ optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ )
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ _file_content_request = FileContentRequest(
+ file_id=file_id,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ )
+
+ _is_async = kwargs.pop("afile_content", False) is True
+
+ response = openai_files_instance.file_content(
+ _is_async=_is_async,
+ file_content_request=_file_content_request,
+ api_base=api_base,
+ api_key=api_key,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ organization=organization,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ return response
+ except Exception as e:
+ raise e
+
+
+async def acreate_batch(
+ completion_window: Literal["24h"],
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"],
+ input_file_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ metadata: Optional[Dict[str, str]] = None,
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Coroutine[Any, Any, Batch]:
+ """
+ Async: Creates and executes a batch from an uploaded file of request
+
+ LiteLLM Equivalent of POST: https://api.openai.com/v1/batches
+ """
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["acreate_batch"] = True
+
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ create_batch,
+ completion_window,
+ endpoint,
+ input_file_id,
+ custom_llm_provider,
+ metadata,
+ extra_headers,
+ extra_body,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response # type: ignore
+
+ return response
+ except Exception as e:
+ raise e
+
+
+def create_batch(
+ completion_window: Literal["24h"],
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings"],
+ input_file_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ metadata: Optional[Dict[str, str]] = None,
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Union[Batch, Coroutine[Any, Any, Batch]]:
+ """
+ Creates and executes a batch from an uploaded file of request
+
+ LiteLLM Equivalent of POST: https://api.openai.com/v1/batches
+ """
+ try:
+ optional_params = GenericLiteLLMParams(**kwargs)
+ if custom_llm_provider == "openai":
+
+ # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+ ### TIMEOUT LOGIC ###
+ timeout = (
+ optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ )
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ _is_async = kwargs.pop("acreate_batch", False) is True
+
+ _create_batch_request = CreateBatchRequest(
+ completion_window=completion_window,
+ endpoint=endpoint,
+ input_file_id=input_file_id,
+ metadata=metadata,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ )
+
+ response = openai_batches_instance.create_batch(
+ api_base=api_base,
+ api_key=api_key,
+ organization=organization,
+ create_batch_data=_create_batch_request,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ _is_async=_is_async,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ return response
+ except Exception as e:
+ raise e
+
+
+async def aretrieve_batch(
+ batch_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ metadata: Optional[Dict[str, str]] = None,
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Coroutine[Any, Any, Batch]:
+ """
+ Async: Retrieves a batch.
+
+ LiteLLM Equivalent of GET https://api.openai.com/v1/batches/{batch_id}
+ """
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["aretrieve_batch"] = True
+
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ retrieve_batch,
+ batch_id,
+ custom_llm_provider,
+ metadata,
+ extra_headers,
+ extra_body,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response # type: ignore
+
+ return response
+ except Exception as e:
+ raise e
+
+
+def retrieve_batch(
+ batch_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ metadata: Optional[Dict[str, str]] = None,
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Union[Batch, Coroutine[Any, Any, Batch]]:
+ """
+ Retrieves a batch.
+
+ LiteLLM Equivalent of GET https://api.openai.com/v1/batches/{batch_id}
+ """
+ try:
+ optional_params = GenericLiteLLMParams(**kwargs)
+ if custom_llm_provider == "openai":
+
+ # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+ ### TIMEOUT LOGIC ###
+ timeout = (
+ optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ )
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ _retrieve_batch_request = RetrieveBatchRequest(
+ batch_id=batch_id,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ )
+
+ _is_async = kwargs.pop("aretrieve_batch", False) is True
+
+ response = openai_batches_instance.retrieve_batch(
+ _is_async=_is_async,
+ retrieve_batch_data=_retrieve_batch_request,
+ api_base=api_base,
+ api_key=api_key,
+ organization=organization,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ return response
+ except Exception as e:
+ raise e
+
+
+def cancel_batch():
+ pass
+
+
+def list_batch():
+ pass
+
+
+async def acancel_batch():
+ pass
+
+
+async def alist_batch():
+ pass
diff --git a/litellm/caching.py b/litellm/caching.py
index 8c9157e53..c8c1736d8 100644
--- a/litellm/caching.py
+++ b/litellm/caching.py
@@ -1190,6 +1190,15 @@ class DualCache(BaseCache):
)
self.default_redis_ttl = default_redis_ttl or litellm.default_redis_ttl
+ def update_cache_ttl(
+ self, default_in_memory_ttl: Optional[float], default_redis_ttl: Optional[float]
+ ):
+ if default_in_memory_ttl is not None:
+ self.default_in_memory_ttl = default_in_memory_ttl
+
+ if default_redis_ttl is not None:
+ self.default_redis_ttl = default_redis_ttl
+
def set_cache(self, key, value, local_only: bool = False, **kwargs):
# Update both Redis and in-memory cache
try:
@@ -1441,7 +1450,9 @@ class DualCache(BaseCache):
class Cache:
def __init__(
self,
- type: Optional[Literal["local", "redis", "redis-semantic", "s3", "disk"]] = "local",
+ type: Optional[
+ Literal["local", "redis", "redis-semantic", "s3", "disk"]
+ ] = "local",
host: Optional[str] = None,
port: Optional[str] = None,
password: Optional[str] = None,
diff --git a/litellm/exceptions.py b/litellm/exceptions.py
index 5eb66743b..abddb108a 100644
--- a/litellm/exceptions.py
+++ b/litellm/exceptions.py
@@ -177,6 +177,32 @@ class ContextWindowExceededError(BadRequestError): # type: ignore
) # Call the base class constructor with the parameters it needs
+# sub class of bad request error - meant to help us catch guardrails-related errors on proxy.
+class RejectedRequestError(BadRequestError): # type: ignore
+ def __init__(
+ self,
+ message,
+ model,
+ llm_provider,
+ request_data: dict,
+ litellm_debug_info: Optional[str] = None,
+ ):
+ self.status_code = 400
+ self.message = message
+ self.model = model
+ self.llm_provider = llm_provider
+ self.litellm_debug_info = litellm_debug_info
+ self.request_data = request_data
+ request = httpx.Request(method="POST", url="https://api.openai.com/v1")
+ response = httpx.Response(status_code=500, request=request)
+ super().__init__(
+ message=self.message,
+ model=self.model, # type: ignore
+ llm_provider=self.llm_provider, # type: ignore
+ response=response,
+ ) # Call the base class constructor with the parameters it needs
+
+
class ContentPolicyViolationError(BadRequestError): # type: ignore
# Error code: 400 - {'error': {'code': 'content_policy_violation', 'message': 'Your request was rejected as a result of our safety system. Image descriptions generated from your prompt may contain text that is not allowed by our safety system. If you believe this was done in error, your request may succeed if retried, or by adjusting your prompt.', 'param': None, 'type': 'invalid_request_error'}}
def __init__(
@@ -288,6 +314,7 @@ class BudgetExceededError(Exception):
self.current_cost = current_cost
self.max_budget = max_budget
message = f"Budget has been exceeded! Current cost: {current_cost}, Max budget: {max_budget}"
+ self.message = message
super().__init__(message)
diff --git a/litellm/integrations/athina.py b/litellm/integrations/athina.py
index 660dd51ef..28da73806 100644
--- a/litellm/integrations/athina.py
+++ b/litellm/integrations/athina.py
@@ -1,6 +1,5 @@
import datetime
-
class AthinaLogger:
def __init__(self):
import os
@@ -29,7 +28,18 @@ class AthinaLogger:
import traceback
try:
- response_json = response_obj.model_dump() if response_obj else {}
+ is_stream = kwargs.get("stream", False)
+ if is_stream:
+ if "complete_streaming_response" in kwargs:
+ # Log the completion response in streaming mode
+ completion_response = kwargs["complete_streaming_response"]
+ response_json = completion_response.model_dump() if completion_response else {}
+ else:
+ # Skip logging if the completion response is not available
+ return
+ else:
+ # Log the completion response in non streaming mode
+ response_json = response_obj.model_dump() if response_obj else {}
data = {
"language_model_id": kwargs.get("model"),
"request": kwargs,
diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py
index d50882592..e192cdaea 100644
--- a/litellm/integrations/custom_logger.py
+++ b/litellm/integrations/custom_logger.py
@@ -4,7 +4,6 @@ import dotenv, os
from litellm.proxy._types import UserAPIKeyAuth
from litellm.caching import DualCache
-
from typing import Literal, Union, Optional
import traceback
@@ -64,8 +63,17 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
user_api_key_dict: UserAPIKeyAuth,
cache: DualCache,
data: dict,
- call_type: Literal["completion", "embeddings", "image_generation"],
- ):
+ call_type: Literal[
+ "completion",
+ "text_completion",
+ "embeddings",
+ "image_generation",
+ "moderation",
+ "audio_transcription",
+ ],
+ ) -> Optional[
+ Union[Exception, str, dict]
+ ]: # raise exception if invalid, return a str for the user to receive - if rejected, or return a modified dictionary for passing into litellm
pass
async def async_post_call_failure_hook(
diff --git a/litellm/integrations/lago.py b/litellm/integrations/lago.py
new file mode 100644
index 000000000..e6d38f530
--- /dev/null
+++ b/litellm/integrations/lago.py
@@ -0,0 +1,179 @@
+# What is this?
+## On Success events log cost to Lago - https://github.com/BerriAI/litellm/issues/3639
+
+import dotenv, os, json
+import litellm
+import traceback, httpx
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+import uuid
+from typing import Optional, Literal
+
+
+def get_utc_datetime():
+ import datetime as dt
+ from datetime import datetime
+
+ if hasattr(dt, "UTC"):
+ return datetime.now(dt.UTC) # type: ignore
+ else:
+ return datetime.utcnow() # type: ignore
+
+
+class LagoLogger(CustomLogger):
+ def __init__(self) -> None:
+ super().__init__()
+ self.validate_environment()
+ self.async_http_handler = AsyncHTTPHandler()
+ self.sync_http_handler = HTTPHandler()
+
+ def validate_environment(self):
+ """
+ Expects
+ LAGO_API_BASE,
+ LAGO_API_KEY,
+ LAGO_API_EVENT_CODE,
+
+ Optional:
+ LAGO_API_CHARGE_BY
+
+ in the environment
+ """
+ missing_keys = []
+ if os.getenv("LAGO_API_KEY", None) is None:
+ missing_keys.append("LAGO_API_KEY")
+
+ if os.getenv("LAGO_API_BASE", None) is None:
+ missing_keys.append("LAGO_API_BASE")
+
+ if os.getenv("LAGO_API_EVENT_CODE", None) is None:
+ missing_keys.append("LAGO_API_EVENT_CODE")
+
+ if len(missing_keys) > 0:
+ raise Exception("Missing keys={} in environment.".format(missing_keys))
+
+ def _common_logic(self, kwargs: dict, response_obj) -> dict:
+ call_id = response_obj.get("id", kwargs.get("litellm_call_id"))
+ dt = get_utc_datetime().isoformat()
+ cost = kwargs.get("response_cost", None)
+ model = kwargs.get("model")
+ usage = {}
+
+ if (
+ isinstance(response_obj, litellm.ModelResponse)
+ or isinstance(response_obj, litellm.EmbeddingResponse)
+ ) and hasattr(response_obj, "usage"):
+ usage = {
+ "prompt_tokens": response_obj["usage"].get("prompt_tokens", 0),
+ "completion_tokens": response_obj["usage"].get("completion_tokens", 0),
+ "total_tokens": response_obj["usage"].get("total_tokens"),
+ }
+
+ litellm_params = kwargs.get("litellm_params", {}) or {}
+ proxy_server_request = litellm_params.get("proxy_server_request") or {}
+ end_user_id = proxy_server_request.get("body", {}).get("user", None)
+ user_id = litellm_params["metadata"].get("user_api_key_user_id", None)
+ team_id = litellm_params["metadata"].get("user_api_key_team_id", None)
+ org_id = litellm_params["metadata"].get("user_api_key_org_id", None)
+
+ charge_by: Literal["end_user_id", "team_id", "user_id"] = "end_user_id"
+ external_customer_id: Optional[str] = None
+
+ if os.getenv("LAGO_API_CHARGE_BY", None) is not None and isinstance(
+ os.environ["LAGO_API_CHARGE_BY"], str
+ ):
+ if os.environ["LAGO_API_CHARGE_BY"] in [
+ "end_user_id",
+ "user_id",
+ "team_id",
+ ]:
+ charge_by = os.environ["LAGO_API_CHARGE_BY"] # type: ignore
+ else:
+ raise Exception("invalid LAGO_API_CHARGE_BY set")
+
+ if charge_by == "end_user_id":
+ external_customer_id = end_user_id
+ elif charge_by == "team_id":
+ external_customer_id = team_id
+ elif charge_by == "user_id":
+ external_customer_id = user_id
+
+ if external_customer_id is None:
+ raise Exception("External Customer ID is not set")
+
+ return {
+ "event": {
+ "transaction_id": str(uuid.uuid4()),
+ "external_customer_id": external_customer_id,
+ "code": os.getenv("LAGO_API_EVENT_CODE"),
+ "properties": {"model": model, "response_cost": cost, **usage},
+ }
+ }
+
+ def log_success_event(self, kwargs, response_obj, start_time, end_time):
+ _url = os.getenv("LAGO_API_BASE")
+ assert _url is not None and isinstance(
+ _url, str
+ ), "LAGO_API_BASE missing or not set correctly. LAGO_API_BASE={}".format(_url)
+ if _url.endswith("/"):
+ _url += "api/v1/events"
+ else:
+ _url += "/api/v1/events"
+
+ api_key = os.getenv("LAGO_API_KEY")
+
+ _data = self._common_logic(kwargs=kwargs, response_obj=response_obj)
+ _headers = {
+ "Content-Type": "application/json",
+ "Authorization": "Bearer {}".format(api_key),
+ }
+
+ try:
+ response = self.sync_http_handler.post(
+ url=_url,
+ data=json.dumps(_data),
+ headers=_headers,
+ )
+
+ response.raise_for_status()
+ except Exception as e:
+ if hasattr(response, "text"):
+ litellm.print_verbose(f"\nError Message: {response.text}")
+ raise e
+
+ async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+ try:
+ _url = os.getenv("LAGO_API_BASE")
+ assert _url is not None and isinstance(
+ _url, str
+ ), "LAGO_API_BASE missing or not set correctly. LAGO_API_BASE={}".format(
+ _url
+ )
+ if _url.endswith("/"):
+ _url += "api/v1/events"
+ else:
+ _url += "/api/v1/events"
+
+ api_key = os.getenv("LAGO_API_KEY")
+
+ _data = self._common_logic(kwargs=kwargs, response_obj=response_obj)
+ _headers = {
+ "Content-Type": "application/json",
+ "Authorization": "Bearer {}".format(api_key),
+ }
+ except Exception as e:
+ raise e
+
+ response: Optional[httpx.Response] = None
+ try:
+ response = await self.async_http_handler.post(
+ url=_url,
+ data=json.dumps(_data),
+ headers=_headers,
+ )
+
+ response.raise_for_status()
+ except Exception as e:
+ if response is not None and hasattr(response, "text"):
+ litellm.print_verbose(f"\nError Message: {response.text}")
+ raise e
diff --git a/litellm/integrations/langfuse.py b/litellm/integrations/langfuse.py
index f4a581eb9..12b20f3d3 100644
--- a/litellm/integrations/langfuse.py
+++ b/litellm/integrations/langfuse.py
@@ -93,6 +93,7 @@ class LangFuseLogger:
)
litellm_params = kwargs.get("litellm_params", {})
+ litellm_call_id = kwargs.get("litellm_call_id", None)
metadata = (
litellm_params.get("metadata", {}) or {}
) # if litellm_params['metadata'] == None
@@ -161,6 +162,7 @@ class LangFuseLogger:
response_obj,
level,
print_verbose,
+ litellm_call_id,
)
elif response_obj is not None:
self._log_langfuse_v1(
@@ -255,6 +257,7 @@ class LangFuseLogger:
response_obj,
level,
print_verbose,
+ litellm_call_id,
) -> tuple:
import langfuse
@@ -318,7 +321,7 @@ class LangFuseLogger:
session_id = clean_metadata.pop("session_id", None)
trace_name = clean_metadata.pop("trace_name", None)
- trace_id = clean_metadata.pop("trace_id", None)
+ trace_id = clean_metadata.pop("trace_id", litellm_call_id)
existing_trace_id = clean_metadata.pop("existing_trace_id", None)
update_trace_keys = clean_metadata.pop("update_trace_keys", [])
debug = clean_metadata.pop("debug_langfuse", None)
@@ -351,9 +354,13 @@ class LangFuseLogger:
# Special keys that are found in the function arguments and not the metadata
if "input" in update_trace_keys:
- trace_params["input"] = input if not mask_input else "redacted-by-litellm"
+ trace_params["input"] = (
+ input if not mask_input else "redacted-by-litellm"
+ )
if "output" in update_trace_keys:
- trace_params["output"] = output if not mask_output else "redacted-by-litellm"
+ trace_params["output"] = (
+ output if not mask_output else "redacted-by-litellm"
+ )
else: # don't overwrite an existing trace
trace_params = {
"id": trace_id,
@@ -375,7 +382,9 @@ class LangFuseLogger:
if level == "ERROR":
trace_params["status_message"] = output
else:
- trace_params["output"] = output if not mask_output else "redacted-by-litellm"
+ trace_params["output"] = (
+ output if not mask_output else "redacted-by-litellm"
+ )
if debug == True or (isinstance(debug, str) and debug.lower() == "true"):
if "metadata" in trace_params:
@@ -412,7 +421,6 @@ class LangFuseLogger:
if "cache_hit" in kwargs:
if kwargs["cache_hit"] is None:
kwargs["cache_hit"] = False
- tags.append(f"cache_hit:{kwargs['cache_hit']}")
clean_metadata["cache_hit"] = kwargs["cache_hit"]
if existing_trace_id is None:
trace_params.update({"tags": tags})
@@ -447,8 +455,13 @@ class LangFuseLogger:
}
generation_name = clean_metadata.pop("generation_name", None)
if generation_name is None:
- # just log `litellm-{call_type}` as the generation name
+ # if `generation_name` is None, use sensible default values
+ # If using litellm proxy user `key_alias` if not None
+ # If `key_alias` is None, just log `litellm-{call_type}` as the generation name
+ _user_api_key_alias = clean_metadata.get("user_api_key_alias", None)
generation_name = f"litellm-{kwargs.get('call_type', 'completion')}"
+ if _user_api_key_alias is not None:
+ generation_name = f"litellm:{_user_api_key_alias}"
if response_obj is not None and "system_fingerprint" in response_obj:
system_fingerprint = response_obj.get("system_fingerprint", None)
diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py
index 92e440215..3e25b4ee7 100644
--- a/litellm/integrations/langsmith.py
+++ b/litellm/integrations/langsmith.py
@@ -44,6 +44,8 @@ class LangsmithLogger:
print_verbose(
f"Langsmith Logging - project_name: {project_name}, run_name {run_name}"
)
+ langsmith_base_url = os.getenv("LANGSMITH_BASE_URL", "https://api.smith.langchain.com")
+
try:
print_verbose(
f"Langsmith Logging - Enters logging function for model {kwargs}"
@@ -86,8 +88,12 @@ class LangsmithLogger:
"end_time": end_time,
}
+ url = f"{langsmith_base_url}/runs"
+ print_verbose(
+ f"Langsmith Logging - About to send data to {url} ..."
+ )
response = requests.post(
- "https://api.smith.langchain.com/runs",
+ url=url,
json=data,
headers={"x-api-key": self.langsmith_api_key},
)
diff --git a/litellm/integrations/logfire_logger.py b/litellm/integrations/logfire_logger.py
new file mode 100644
index 000000000..e27d848fb
--- /dev/null
+++ b/litellm/integrations/logfire_logger.py
@@ -0,0 +1,178 @@
+#### What this does ####
+# On success + failure, log events to Logfire
+
+import dotenv, os
+
+dotenv.load_dotenv() # Loading env variables using dotenv
+import traceback
+import uuid
+from litellm._logging import print_verbose, verbose_logger
+
+from enum import Enum
+from typing import Any, Dict, NamedTuple
+from typing_extensions import LiteralString
+
+
+class SpanConfig(NamedTuple):
+ message_template: LiteralString
+ span_data: Dict[str, Any]
+
+
+class LogfireLevel(str, Enum):
+ INFO = "info"
+ ERROR = "error"
+
+
+class LogfireLogger:
+ # Class variables or attributes
+ def __init__(self):
+ try:
+ verbose_logger.debug(f"in init logfire logger")
+ import logfire
+
+ # only setting up logfire if we are sending to logfire
+ # in testing, we don't want to send to logfire
+ if logfire.DEFAULT_LOGFIRE_INSTANCE.config.send_to_logfire:
+ logfire.configure(token=os.getenv("LOGFIRE_TOKEN"))
+ except Exception as e:
+ print_verbose(f"Got exception on init logfire client {str(e)}")
+ raise e
+
+ def _get_span_config(self, payload) -> SpanConfig:
+ if (
+ payload["call_type"] == "completion"
+ or payload["call_type"] == "acompletion"
+ ):
+ return SpanConfig(
+ message_template="Chat Completion with {request_data[model]!r}",
+ span_data={"request_data": payload},
+ )
+ elif (
+ payload["call_type"] == "embedding" or payload["call_type"] == "aembedding"
+ ):
+ return SpanConfig(
+ message_template="Embedding Creation with {request_data[model]!r}",
+ span_data={"request_data": payload},
+ )
+ elif (
+ payload["call_type"] == "image_generation"
+ or payload["call_type"] == "aimage_generation"
+ ):
+ return SpanConfig(
+ message_template="Image Generation with {request_data[model]!r}",
+ span_data={"request_data": payload},
+ )
+ else:
+ return SpanConfig(
+ message_template="Litellm Call with {request_data[model]!r}",
+ span_data={"request_data": payload},
+ )
+
+ async def _async_log_event(
+ self,
+ kwargs,
+ response_obj,
+ start_time,
+ end_time,
+ print_verbose,
+ level: LogfireLevel,
+ ):
+ self.log_event(
+ kwargs=kwargs,
+ response_obj=response_obj,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ level=level,
+ )
+
+ def log_event(
+ self,
+ kwargs,
+ start_time,
+ end_time,
+ print_verbose,
+ level: LogfireLevel,
+ response_obj,
+ ):
+ try:
+ import logfire
+
+ verbose_logger.debug(
+ f"logfire Logging - Enters logging function for model {kwargs}"
+ )
+
+ if not response_obj:
+ response_obj = {}
+ litellm_params = kwargs.get("litellm_params", {})
+ metadata = (
+ litellm_params.get("metadata", {}) or {}
+ ) # if litellm_params['metadata'] == None
+ messages = kwargs.get("messages")
+ optional_params = kwargs.get("optional_params", {})
+ call_type = kwargs.get("call_type", "completion")
+ cache_hit = kwargs.get("cache_hit", False)
+ usage = response_obj.get("usage", {})
+ id = response_obj.get("id", str(uuid.uuid4()))
+ try:
+ response_time = (end_time - start_time).total_seconds()
+ except:
+ response_time = None
+
+ # Clean Metadata before logging - never log raw metadata
+ # the raw metadata can contain circular references which leads to infinite recursion
+ # we clean out all extra litellm metadata params before logging
+ clean_metadata = {}
+ if isinstance(metadata, dict):
+ for key, value in metadata.items():
+ # clean litellm metadata before logging
+ if key in [
+ "endpoint",
+ "caching_groups",
+ "previous_models",
+ ]:
+ continue
+ else:
+ clean_metadata[key] = value
+
+ # Build the initial payload
+ payload = {
+ "id": id,
+ "call_type": call_type,
+ "cache_hit": cache_hit,
+ "startTime": start_time,
+ "endTime": end_time,
+ "responseTime (seconds)": response_time,
+ "model": kwargs.get("model", ""),
+ "user": kwargs.get("user", ""),
+ "modelParameters": optional_params,
+ "spend": kwargs.get("response_cost", 0),
+ "messages": messages,
+ "response": response_obj,
+ "usage": usage,
+ "metadata": clean_metadata,
+ }
+ logfire_openai = logfire.with_settings(custom_scope_suffix="openai")
+ message_template, span_data = self._get_span_config(payload)
+ if level == LogfireLevel.INFO:
+ logfire_openai.info(
+ message_template,
+ **span_data,
+ )
+ elif level == LogfireLevel.ERROR:
+ logfire_openai.error(
+ message_template,
+ **span_data,
+ _exc_info=True,
+ )
+ print_verbose(f"\ndd Logger - Logging payload = {payload}")
+
+ print_verbose(
+ f"Logfire Layer Logging - final response object: {response_obj}"
+ )
+ except Exception as e:
+ traceback.print_exc()
+ verbose_logger.debug(
+ f"Logfire Layer Error - {str(e)}\n{traceback.format_exc()}"
+ )
+ pass
diff --git a/litellm/integrations/slack_alerting.py b/litellm/integrations/slack_alerting.py
index 40258af6b..49a8d0e2c 100644
--- a/litellm/integrations/slack_alerting.py
+++ b/litellm/integrations/slack_alerting.py
@@ -1,20 +1,47 @@
#### What this does ####
# Class for sending Slack Alerts #
-import dotenv, os
-from litellm.proxy._types import UserAPIKeyAuth
+import dotenv, os, traceback
+from litellm.proxy._types import UserAPIKeyAuth, CallInfo, AlertType
from litellm._logging import verbose_logger, verbose_proxy_logger
import litellm, threading
-from typing import List, Literal, Any, Union, Optional, Dict
+from typing import List, Literal, Any, Union, Optional, Dict, Set
from litellm.caching import DualCache
-import asyncio
+import asyncio, time
import aiohttp
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
import datetime
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
from enum import Enum
from datetime import datetime as dt, timedelta, timezone
from litellm.integrations.custom_logger import CustomLogger
+from litellm.proxy._types import WebhookEvent
import random
+from typing import TypedDict
+from openai import APIError
+
+import litellm.types
+from litellm.types.router import LiteLLM_Params
+
+
+class BaseOutageModel(TypedDict):
+ alerts: List[int]
+ minor_alert_sent: bool
+ major_alert_sent: bool
+ last_updated_at: float
+
+
+class OutageModel(BaseOutageModel):
+ model_id: str
+
+
+class ProviderRegionOutageModel(BaseOutageModel):
+ provider_region_id: str
+ deployment_ids: Set[str]
+
+
+# we use this for the email header, please send a test email if you change this. verify it looks good on email
+LITELLM_LOGO_URL = "https://litellm-listing.s3.amazonaws.com/litellm_logo.png"
+LITELLM_SUPPORT_CONTACT = "support@berri.ai"
class LiteLLMBase(BaseModel):
@@ -30,12 +57,55 @@ class LiteLLMBase(BaseModel):
return self.dict()
+class SlackAlertingArgsEnum(Enum):
+ daily_report_frequency: int = 12 * 60 * 60
+ report_check_interval: int = 5 * 60
+ budget_alert_ttl: int = 24 * 60 * 60
+ outage_alert_ttl: int = 1 * 60
+ region_outage_alert_ttl: int = 1 * 60
+ minor_outage_alert_threshold: int = 1 * 5
+ major_outage_alert_threshold: int = 1 * 10
+ max_outage_alert_list_size: int = 1 * 10
+
+
class SlackAlertingArgs(LiteLLMBase):
- default_daily_report_frequency: int = 12 * 60 * 60 # 12 hours
- daily_report_frequency: int = int(
- os.getenv("SLACK_DAILY_REPORT_FREQUENCY", default_daily_report_frequency)
+ daily_report_frequency: int = Field(
+ default=int(
+ os.getenv(
+ "SLACK_DAILY_REPORT_FREQUENCY",
+ SlackAlertingArgsEnum.daily_report_frequency.value,
+ )
+ ),
+ description="Frequency of receiving deployment latency/failure reports. Default is 12hours. Value is in seconds.",
)
- report_check_interval: int = 5 * 60 # 5 minutes
+ report_check_interval: int = Field(
+ default=SlackAlertingArgsEnum.report_check_interval.value,
+ description="Frequency of checking cache if report should be sent. Background process. Default is once per hour. Value is in seconds.",
+ ) # 5 minutes
+ budget_alert_ttl: int = Field(
+ default=SlackAlertingArgsEnum.budget_alert_ttl.value,
+ description="Cache ttl for budgets alerts. Prevents spamming same alert, each time budget is crossed. Value is in seconds.",
+ ) # 24 hours
+ outage_alert_ttl: int = Field(
+ default=SlackAlertingArgsEnum.outage_alert_ttl.value,
+ description="Cache ttl for model outage alerts. Sets time-window for errors. Default is 1 minute. Value is in seconds.",
+ ) # 1 minute ttl
+ region_outage_alert_ttl: int = Field(
+ default=SlackAlertingArgsEnum.region_outage_alert_ttl.value,
+ description="Cache ttl for provider-region based outage alerts. Alert sent if 2+ models in same region report errors. Sets time-window for errors. Default is 1 minute. Value is in seconds.",
+ ) # 1 minute ttl
+ minor_outage_alert_threshold: int = Field(
+ default=SlackAlertingArgsEnum.minor_outage_alert_threshold.value,
+ description="The number of errors that count as a model/region minor outage. ('400' error code is not counted).",
+ )
+ major_outage_alert_threshold: int = Field(
+ default=SlackAlertingArgsEnum.major_outage_alert_threshold.value,
+ description="The number of errors that countas a model/region major outage. ('400' error code is not counted).",
+ )
+ max_outage_alert_list_size: int = Field(
+ default=SlackAlertingArgsEnum.max_outage_alert_list_size.value,
+ description="Maximum number of errors to store in cache. For a given model/region. Prevents memory leaks.",
+ ) # prevent memory leak
class DeploymentMetrics(LiteLLMBase):
@@ -79,22 +149,17 @@ class SlackAlerting(CustomLogger):
internal_usage_cache: Optional[DualCache] = None,
alerting_threshold: float = 300, # threshold for slow / hanging llm responses (in seconds)
alerting: Optional[List] = [],
- alert_types: List[
- Literal[
- "llm_exceptions",
- "llm_too_slow",
- "llm_requests_hanging",
- "budget_alerts",
- "db_exceptions",
- "daily_reports",
- ]
- ] = [
+ alert_types: List[AlertType] = [
"llm_exceptions",
"llm_too_slow",
"llm_requests_hanging",
"budget_alerts",
"db_exceptions",
"daily_reports",
+ "spend_reports",
+ "cooldown_deployment",
+ "new_model_added",
+ "outage_alerts",
],
alert_to_webhook_url: Optional[
Dict
@@ -111,6 +176,7 @@ class SlackAlerting(CustomLogger):
self.is_running = False
self.alerting_args = SlackAlertingArgs(**alerting_args)
self.default_webhook_url = default_webhook_url
+ self.llm_router: Optional[litellm.Router] = None
def update_values(
self,
@@ -119,6 +185,7 @@ class SlackAlerting(CustomLogger):
alert_types: Optional[List] = None,
alert_to_webhook_url: Optional[Dict] = None,
alerting_args: Optional[Dict] = None,
+ llm_router: Optional[litellm.Router] = None,
):
if alerting is not None:
self.alerting = alerting
@@ -134,6 +201,8 @@ class SlackAlerting(CustomLogger):
self.alert_to_webhook_url = alert_to_webhook_url
else:
self.alert_to_webhook_url.update(alert_to_webhook_url)
+ if llm_router is not None:
+ self.llm_router = llm_router
async def deployment_in_cooldown(self):
pass
@@ -158,13 +227,28 @@ class SlackAlerting(CustomLogger):
) -> Optional[str]:
"""
Returns langfuse trace url
+
+ - check:
+ -> existing_trace_id
+ -> trace_id
+ -> litellm_call_id
"""
# do nothing for now
- if (
- request_data is not None
- and request_data.get("metadata", {}).get("trace_id", None) is not None
- ):
- trace_id = request_data["metadata"]["trace_id"]
+ if request_data is not None:
+ trace_id = None
+ if (
+ request_data.get("metadata", {}).get("existing_trace_id", None)
+ is not None
+ ):
+ trace_id = request_data["metadata"]["existing_trace_id"]
+ elif request_data.get("metadata", {}).get("trace_id", None) is not None:
+ trace_id = request_data["metadata"]["trace_id"]
+ elif request_data.get("litellm_logging_obj", None) is not None and hasattr(
+ request_data["litellm_logging_obj"], "model_call_details"
+ ):
+ trace_id = request_data["litellm_logging_obj"].model_call_details[
+ "litellm_call_id"
+ ]
if litellm.utils.langFuseLogger is not None:
base_url = litellm.utils.langFuseLogger.Langfuse.base_url
return f"{base_url}/trace/{trace_id}"
@@ -347,6 +431,9 @@ class SlackAlerting(CustomLogger):
keys=combined_metrics_keys
) # [1, 2, None, ..]
+ if combined_metrics_values is None:
+ return False
+
all_none = True
for val in combined_metrics_values:
if val is not None and val > 0:
@@ -398,7 +485,7 @@ class SlackAlerting(CustomLogger):
]
# format alert -> return the litellm model name + api base
- message = f"\n\nHere are today's key metrics 📈: \n\n"
+ message = f"\n\nTime: `{time.time()}`s\nHere are today's key metrics 📈: \n\n"
message += "\n\n*❗️ Top Deployments with Most Failed Requests:*\n\n"
if not top_5_failed:
@@ -449,6 +536,8 @@ class SlackAlerting(CustomLogger):
cache_list=combined_metrics_cache_keys
)
+ message += f"\n\nNext Run is in: `{time.time() + self.alerting_args.daily_report_frequency}`s"
+
# send alert
await self.send_alert(message=message, level="Low", alert_type="daily_reports")
@@ -549,127 +638,468 @@ class SlackAlerting(CustomLogger):
alert_type="llm_requests_hanging",
)
+ async def failed_tracking_alert(self, error_message: str):
+ """Raise alert when tracking failed for specific model"""
+ _cache: DualCache = self.internal_usage_cache
+ message = "Failed Tracking Cost for" + error_message
+ _cache_key = "budget_alerts:failed_tracking:{}".format(message)
+ result = await _cache.async_get_cache(key=_cache_key)
+ if result is None:
+ await self.send_alert(
+ message=message, level="High", alert_type="budget_alerts"
+ )
+ await _cache.async_set_cache(
+ key=_cache_key,
+ value="SENT",
+ ttl=self.alerting_args.budget_alert_ttl,
+ )
+
async def budget_alerts(
self,
type: Literal[
"token_budget",
"user_budget",
- "user_and_proxy_budget",
- "failed_budgets",
- "failed_tracking",
+ "team_budget",
+ "proxy_budget",
"projected_limit_exceeded",
],
- user_max_budget: float,
- user_current_spend: float,
- user_info=None,
- error_message="",
+ user_info: CallInfo,
):
+ ## PREVENTITIVE ALERTING ## - https://github.com/BerriAI/litellm/issues/2727
+ # - Alert once within 24hr period
+ # - Cache this information
+ # - Don't re-alert, if alert already sent
+ _cache: DualCache = self.internal_usage_cache
+
if self.alerting is None or self.alert_types is None:
# do nothing if alerting is not switched on
return
if "budget_alerts" not in self.alert_types:
return
_id: str = "default_id" # used for caching
- if type == "user_and_proxy_budget":
- user_info = dict(user_info)
- user_id = user_info["user_id"]
- _id = user_id
- max_budget = user_info["max_budget"]
- spend = user_info["spend"]
- user_email = user_info["user_email"]
- user_info = f"""\nUser ID: {user_id}\nMax Budget: ${max_budget}\nSpend: ${spend}\nUser Email: {user_email}"""
+ user_info_json = user_info.model_dump(exclude_none=True)
+ for k, v in user_info_json.items():
+ user_info_str = "\n{}: {}\n".format(k, v)
+
+ event: Optional[
+ Literal["budget_crossed", "threshold_crossed", "projected_limit_exceeded"]
+ ] = None
+ event_group: Optional[
+ Literal["internal_user", "team", "key", "proxy", "customer"]
+ ] = None
+ event_message: str = ""
+ webhook_event: Optional[WebhookEvent] = None
+ if type == "proxy_budget":
+ event_group = "proxy"
+ event_message += "Proxy Budget: "
+ elif type == "user_budget":
+ event_group = "internal_user"
+ event_message += "User Budget: "
+ _id = user_info.user_id or _id
+ elif type == "team_budget":
+ event_group = "team"
+ event_message += "Team Budget: "
+ _id = user_info.team_id or _id
elif type == "token_budget":
- token_info = dict(user_info)
- token = token_info["token"]
- _id = token
- spend = token_info["spend"]
- max_budget = token_info["max_budget"]
- user_id = token_info["user_id"]
- user_info = f"""\nToken: {token}\nSpend: ${spend}\nMax Budget: ${max_budget}\nUser ID: {user_id}"""
- elif type == "failed_tracking":
- user_id = str(user_info)
- _id = user_id
- user_info = f"\nUser ID: {user_id}\n Error {error_message}"
- message = "Failed Tracking Cost for" + user_info
- await self.send_alert(
- message=message, level="High", alert_type="budget_alerts"
- )
- return
- elif type == "projected_limit_exceeded" and user_info is not None:
- """
- Input variables:
- user_info = {
- "key_alias": key_alias,
- "projected_spend": projected_spend,
- "projected_exceeded_date": projected_exceeded_date,
- }
- user_max_budget=soft_limit,
- user_current_spend=new_spend
- """
- message = f"""\n🚨 `ProjectedLimitExceededError` 💸\n\n`Key Alias:` {user_info["key_alias"]} \n`Expected Day of Error`: {user_info["projected_exceeded_date"]} \n`Current Spend`: {user_current_spend} \n`Projected Spend at end of month`: {user_info["projected_spend"]} \n`Soft Limit`: {user_max_budget}"""
- await self.send_alert(
- message=message, level="High", alert_type="budget_alerts"
- )
- return
- else:
- user_info = str(user_info)
+ event_group = "key"
+ event_message += "Key Budget: "
+ _id = user_info.token
+ elif type == "projected_limit_exceeded":
+ event_group = "key"
+ event_message += "Key Budget: Projected Limit Exceeded"
+ event = "projected_limit_exceeded"
+ _id = user_info.token
# percent of max_budget left to spend
- if user_max_budget > 0:
- percent_left = (user_max_budget - user_current_spend) / user_max_budget
+ if user_info.max_budget is None:
+ return
+
+ if user_info.max_budget > 0:
+ percent_left = (
+ user_info.max_budget - user_info.spend
+ ) / user_info.max_budget
else:
percent_left = 0
- verbose_proxy_logger.debug(
- f"Budget Alerts: Percent left: {percent_left} for {user_info}"
- )
-
- ## PREVENTITIVE ALERTING ## - https://github.com/BerriAI/litellm/issues/2727
- # - Alert once within 28d period
- # - Cache this information
- # - Don't re-alert, if alert already sent
- _cache: DualCache = self.internal_usage_cache
# check if crossed budget
- if user_current_spend >= user_max_budget:
- verbose_proxy_logger.debug("Budget Crossed for %s", user_info)
- message = "Budget Crossed for" + user_info
- result = await _cache.async_get_cache(key=message)
- if result is None:
- await self.send_alert(
- message=message, level="High", alert_type="budget_alerts"
- )
- await _cache.async_set_cache(key=message, value="SENT", ttl=2419200)
- return
+ if user_info.spend >= user_info.max_budget:
+ event = "budget_crossed"
+ event_message += f"Budget Crossed\n Total Budget:`{user_info.max_budget}`"
+ elif percent_left <= 0.05:
+ event = "threshold_crossed"
+ event_message += "5% Threshold Crossed "
+ elif percent_left <= 0.15:
+ event = "threshold_crossed"
+ event_message += "15% Threshold Crossed"
- # check if 5% of max budget is left
- if percent_left <= 0.05:
- message = "5% budget left for" + user_info
- cache_key = "alerting:{}".format(_id)
- result = await _cache.async_get_cache(key=cache_key)
+ if event is not None and event_group is not None:
+ _cache_key = "budget_alerts:{}:{}".format(event, _id)
+ result = await _cache.async_get_cache(key=_cache_key)
if result is None:
+ webhook_event = WebhookEvent(
+ event=event,
+ event_group=event_group,
+ event_message=event_message,
+ **user_info_json,
+ )
await self.send_alert(
- message=message, level="Medium", alert_type="budget_alerts"
+ message=event_message + "\n\n" + user_info_str,
+ level="High",
+ alert_type="budget_alerts",
+ user_info=webhook_event,
+ )
+ await _cache.async_set_cache(
+ key=_cache_key,
+ value="SENT",
+ ttl=self.alerting_args.budget_alert_ttl,
)
- await _cache.async_set_cache(key=cache_key, value="SENT", ttl=2419200)
-
return
-
- # check if 15% of max budget is left
- if percent_left <= 0.15:
- message = "15% budget left for" + user_info
- result = await _cache.async_get_cache(key=message)
- if result is None:
- await self.send_alert(
- message=message, level="Low", alert_type="budget_alerts"
- )
- await _cache.async_set_cache(key=message, value="SENT", ttl=2419200)
- return
-
return
- async def model_added_alert(self, model_name: str, litellm_model_name: str):
- model_info = litellm.model_cost.get(litellm_model_name, {})
+ async def customer_spend_alert(
+ self,
+ token: Optional[str],
+ key_alias: Optional[str],
+ end_user_id: Optional[str],
+ response_cost: Optional[float],
+ max_budget: Optional[float],
+ ):
+ if end_user_id is not None and token is not None and response_cost is not None:
+ # log customer spend
+ event = WebhookEvent(
+ spend=response_cost,
+ max_budget=max_budget,
+ token=token,
+ customer_id=end_user_id,
+ user_id=None,
+ team_id=None,
+ user_email=None,
+ key_alias=key_alias,
+ projected_exceeded_date=None,
+ projected_spend=None,
+ event="spend_tracked",
+ event_group="customer",
+ event_message="Customer spend tracked. Customer={}, spend={}".format(
+ end_user_id, response_cost
+ ),
+ )
+
+ await self.send_webhook_alert(webhook_event=event)
+
+ def _count_outage_alerts(self, alerts: List[int]) -> str:
+ """
+ Parameters:
+ - alerts: List[int] -> list of error codes (either 408 or 500+)
+
+ Returns:
+ - str -> formatted string. This is an alert message, giving a human-friendly description of the errors.
+ """
+ error_breakdown = {"Timeout Errors": 0, "API Errors": 0, "Unknown Errors": 0}
+ for alert in alerts:
+ if alert == 408:
+ error_breakdown["Timeout Errors"] += 1
+ elif alert >= 500:
+ error_breakdown["API Errors"] += 1
+ else:
+ error_breakdown["Unknown Errors"] += 1
+
+ error_msg = ""
+ for key, value in error_breakdown.items():
+ if value > 0:
+ error_msg += "\n{}: {}\n".format(key, value)
+
+ return error_msg
+
+ def _outage_alert_msg_factory(
+ self,
+ alert_type: Literal["Major", "Minor"],
+ key: Literal["Model", "Region"],
+ key_val: str,
+ provider: str,
+ api_base: Optional[str],
+ outage_value: BaseOutageModel,
+ ) -> str:
+ """Format an alert message for slack"""
+ headers = {f"{key} Name": key_val, "Provider": provider}
+ if api_base is not None:
+ headers["API Base"] = api_base # type: ignore
+
+ headers_str = "\n"
+ for k, v in headers.items():
+ headers_str += f"*{k}:* `{v}`\n"
+ return f"""\n\n
+*⚠️ {alert_type} Service Outage*
+
+{headers_str}
+
+*Errors:*
+{self._count_outage_alerts(alerts=outage_value["alerts"])}
+
+*Last Check:* `{round(time.time() - outage_value["last_updated_at"], 4)}s ago`\n\n
+"""
+
+ async def region_outage_alerts(
+ self,
+ exception: APIError,
+ deployment_id: str,
+ ) -> None:
+ """
+ Send slack alert if specific provider region is having an outage.
+
+ Track for 408 (Timeout) and >=500 Error codes
+ """
+ ## CREATE (PROVIDER+REGION) ID ##
+ if self.llm_router is None:
+ return
+
+ deployment = self.llm_router.get_deployment(model_id=deployment_id)
+
+ if deployment is None:
+ return
+
+ model = deployment.litellm_params.model
+ ### GET PROVIDER ###
+ provider = deployment.litellm_params.custom_llm_provider
+ if provider is None:
+ model, provider, _, _ = litellm.get_llm_provider(model=model)
+
+ ### GET REGION ###
+ region_name = deployment.litellm_params.region_name
+ if region_name is None:
+ region_name = litellm.utils._get_model_region(
+ custom_llm_provider=provider, litellm_params=deployment.litellm_params
+ )
+
+ if region_name is None:
+ return
+
+ ### UNIQUE CACHE KEY ###
+ cache_key = provider + region_name
+
+ outage_value: Optional[ProviderRegionOutageModel] = (
+ await self.internal_usage_cache.async_get_cache(key=cache_key)
+ )
+
+ if (
+ getattr(exception, "status_code", None) is None
+ or (
+ exception.status_code != 408 # type: ignore
+ and exception.status_code < 500 # type: ignore
+ )
+ or self.llm_router is None
+ ):
+ return
+
+ if outage_value is None:
+ _deployment_set = set()
+ _deployment_set.add(deployment_id)
+ outage_value = ProviderRegionOutageModel(
+ provider_region_id=cache_key,
+ alerts=[exception.status_code], # type: ignore
+ minor_alert_sent=False,
+ major_alert_sent=False,
+ last_updated_at=time.time(),
+ deployment_ids=_deployment_set,
+ )
+
+ ## add to cache ##
+ await self.internal_usage_cache.async_set_cache(
+ key=cache_key,
+ value=outage_value,
+ ttl=self.alerting_args.region_outage_alert_ttl,
+ )
+ return
+
+ if len(outage_value["alerts"]) < self.alerting_args.max_outage_alert_list_size:
+ outage_value["alerts"].append(exception.status_code) # type: ignore
+ else: # prevent memory leaks
+ pass
+ _deployment_set = outage_value["deployment_ids"]
+ _deployment_set.add(deployment_id)
+ outage_value["deployment_ids"] = _deployment_set
+ outage_value["last_updated_at"] = time.time()
+
+ ## MINOR OUTAGE ALERT SENT ##
+ if (
+ outage_value["minor_alert_sent"] == False
+ and len(outage_value["alerts"])
+ >= self.alerting_args.minor_outage_alert_threshold
+ and len(_deployment_set) > 1 # make sure it's not just 1 bad deployment
+ ):
+ msg = self._outage_alert_msg_factory(
+ alert_type="Minor",
+ key="Region",
+ key_val=region_name,
+ api_base=None,
+ outage_value=outage_value,
+ provider=provider,
+ )
+ # send minor alert
+ await self.send_alert(
+ message=msg, level="Medium", alert_type="outage_alerts"
+ )
+ # set to true
+ outage_value["minor_alert_sent"] = True
+
+ ## MAJOR OUTAGE ALERT SENT ##
+ elif (
+ outage_value["major_alert_sent"] == False
+ and len(outage_value["alerts"])
+ >= self.alerting_args.major_outage_alert_threshold
+ and len(_deployment_set) > 1 # make sure it's not just 1 bad deployment
+ ):
+ msg = self._outage_alert_msg_factory(
+ alert_type="Major",
+ key="Region",
+ key_val=region_name,
+ api_base=None,
+ outage_value=outage_value,
+ provider=provider,
+ )
+
+ # send minor alert
+ await self.send_alert(message=msg, level="High", alert_type="outage_alerts")
+ # set to true
+ outage_value["major_alert_sent"] = True
+
+ ## update cache ##
+ await self.internal_usage_cache.async_set_cache(
+ key=cache_key, value=outage_value
+ )
+
+ async def outage_alerts(
+ self,
+ exception: APIError,
+ deployment_id: str,
+ ) -> None:
+ """
+ Send slack alert if model is badly configured / having an outage (408, 401, 429, >=500).
+
+ key = model_id
+
+ value = {
+ - model_id
+ - threshold
+ - alerts []
+ }
+
+ ttl = 1hr
+ max_alerts_size = 10
+ """
+ try:
+ outage_value: Optional[OutageModel] = await self.internal_usage_cache.async_get_cache(key=deployment_id) # type: ignore
+ if (
+ getattr(exception, "status_code", None) is None
+ or (
+ exception.status_code != 408 # type: ignore
+ and exception.status_code < 500 # type: ignore
+ )
+ or self.llm_router is None
+ ):
+ return
+
+ ### EXTRACT MODEL DETAILS ###
+ deployment = self.llm_router.get_deployment(model_id=deployment_id)
+ if deployment is None:
+ return
+
+ model = deployment.litellm_params.model
+ provider = deployment.litellm_params.custom_llm_provider
+ if provider is None:
+ try:
+ model, provider, _, _ = litellm.get_llm_provider(model=model)
+ except Exception as e:
+ provider = ""
+ api_base = litellm.get_api_base(
+ model=model, optional_params=deployment.litellm_params
+ )
+
+ if outage_value is None:
+ outage_value = OutageModel(
+ model_id=deployment_id,
+ alerts=[exception.status_code], # type: ignore
+ minor_alert_sent=False,
+ major_alert_sent=False,
+ last_updated_at=time.time(),
+ )
+
+ ## add to cache ##
+ await self.internal_usage_cache.async_set_cache(
+ key=deployment_id,
+ value=outage_value,
+ ttl=self.alerting_args.outage_alert_ttl,
+ )
+ return
+
+ if (
+ len(outage_value["alerts"])
+ < self.alerting_args.max_outage_alert_list_size
+ ):
+ outage_value["alerts"].append(exception.status_code) # type: ignore
+ else: # prevent memory leaks
+ pass
+
+ outage_value["last_updated_at"] = time.time()
+
+ ## MINOR OUTAGE ALERT SENT ##
+ if (
+ outage_value["minor_alert_sent"] == False
+ and len(outage_value["alerts"])
+ >= self.alerting_args.minor_outage_alert_threshold
+ ):
+ msg = self._outage_alert_msg_factory(
+ alert_type="Minor",
+ key="Model",
+ key_val=model,
+ api_base=api_base,
+ outage_value=outage_value,
+ provider=provider,
+ )
+ # send minor alert
+ await self.send_alert(
+ message=msg, level="Medium", alert_type="outage_alerts"
+ )
+ # set to true
+ outage_value["minor_alert_sent"] = True
+ elif (
+ outage_value["major_alert_sent"] == False
+ and len(outage_value["alerts"])
+ >= self.alerting_args.major_outage_alert_threshold
+ ):
+ msg = self._outage_alert_msg_factory(
+ alert_type="Major",
+ key="Model",
+ key_val=model,
+ api_base=api_base,
+ outage_value=outage_value,
+ provider=provider,
+ )
+ # send minor alert
+ await self.send_alert(
+ message=msg, level="High", alert_type="outage_alerts"
+ )
+ # set to true
+ outage_value["major_alert_sent"] = True
+
+ ## update cache ##
+ await self.internal_usage_cache.async_set_cache(
+ key=deployment_id, value=outage_value
+ )
+ except Exception as e:
+ pass
+
+ async def model_added_alert(
+ self, model_name: str, litellm_model_name: str, passed_model_info: Any
+ ):
+ base_model_from_user = getattr(passed_model_info, "base_model", None)
+ model_info = {}
+ base_model = ""
+ if base_model_from_user is not None:
+ model_info = litellm.model_cost.get(base_model_from_user, {})
+ base_model = f"Base Model: `{base_model_from_user}`\n"
+ else:
+ model_info = litellm.model_cost.get(litellm_model_name, {})
model_info_str = ""
for k, v in model_info.items():
if k == "input_cost_per_token" or k == "output_cost_per_token":
@@ -681,6 +1111,7 @@ class SlackAlerting(CustomLogger):
message = f"""
*🚅 New Model Added*
Model Name: `{model_name}`
+{base_model}
Usage OpenAI Python SDK:
```
@@ -707,28 +1138,228 @@ Model Info:
```
"""
- await self.send_alert(
+ alert_val = self.send_alert(
message=message, level="Low", alert_type="new_model_added"
)
- pass
+
+ if alert_val is not None and asyncio.iscoroutine(alert_val):
+ await alert_val
async def model_removed_alert(self, model_name: str):
pass
+ async def send_webhook_alert(self, webhook_event: WebhookEvent) -> bool:
+ """
+ Sends structured alert to webhook, if set.
+
+ Currently only implemented for budget alerts
+
+ Returns -> True if sent, False if not.
+ """
+
+ webhook_url = os.getenv("WEBHOOK_URL", None)
+ if webhook_url is None:
+ raise Exception("Missing webhook_url from environment")
+
+ payload = webhook_event.model_dump_json()
+ headers = {"Content-type": "application/json"}
+
+ response = await self.async_http_handler.post(
+ url=webhook_url,
+ headers=headers,
+ data=payload,
+ )
+ if response.status_code == 200:
+ return True
+ else:
+ print("Error sending webhook alert. Error=", response.text) # noqa
+
+ return False
+
+ async def _check_if_using_premium_email_feature(
+ self,
+ premium_user: bool,
+ email_logo_url: Optional[str] = None,
+ email_support_contact: Optional[str] = None,
+ ):
+ from litellm.proxy.proxy_server import premium_user
+ from litellm.proxy.proxy_server import CommonProxyErrors
+
+ if premium_user is not True:
+ if email_logo_url is not None or email_support_contact is not None:
+ raise ValueError(
+ f"Trying to Customize Email Alerting\n {CommonProxyErrors.not_premium_user.value}"
+ )
+
+ async def send_key_created_email(self, webhook_event: WebhookEvent) -> bool:
+ from litellm.proxy.utils import send_email
+
+ if self.alerting is None or "email" not in self.alerting:
+ # do nothing if user does not want email alerts
+ return False
+ from litellm.proxy.proxy_server import premium_user, prisma_client
+
+ email_logo_url = os.getenv("SMTP_SENDER_LOGO", None)
+ email_support_contact = os.getenv("EMAIL_SUPPORT_CONTACT", None)
+ await self._check_if_using_premium_email_feature(
+ premium_user, email_logo_url, email_support_contact
+ )
+ if email_logo_url is None:
+ email_logo_url = LITELLM_LOGO_URL
+ if email_support_contact is None:
+ email_support_contact = LITELLM_SUPPORT_CONTACT
+
+ event_name = webhook_event.event_message
+ recipient_email = webhook_event.user_email
+ recipient_user_id = webhook_event.user_id
+ if (
+ recipient_email is None
+ and recipient_user_id is not None
+ and prisma_client is not None
+ ):
+ user_row = await prisma_client.db.litellm_usertable.find_unique(
+ where={"user_id": recipient_user_id}
+ )
+
+ if user_row is not None:
+ recipient_email = user_row.user_email
+
+ key_name = webhook_event.key_alias
+ key_token = webhook_event.token
+ key_budget = webhook_event.max_budget
+
+ email_html_content = "Alert from LiteLLM Server"
+ if recipient_email is None:
+ verbose_proxy_logger.error(
+ "Trying to send email alert to no recipient", extra=webhook_event.dict()
+ )
+ email_html_content = f"""
+ Hi {recipient_email},
+ Key: {key_token}
+
+
Usage Example
+
+ Detailed Documentation on Usage with OpenAI Python SDK, Langchain, LlamaIndex, Curl
+
+
+
+ import openai
+ client = openai.OpenAI(
+ api_key="{key_token}",
+ base_url={os.getenv("PROXY_BASE_URL", "http://0.0.0.0:4000")}
+ )
+
+ response = client.chat.completions.create(
+ model="gpt-3.5-turbo", # model to send to the proxy
+ messages = [
+ {{
+ "role": "user",
+ "content": "this is a test request, write a short poem"
+ }}
+ ]
+ )
+
+
+
+
+ If you have any questions, please send an email to {email_support_contact} Hi {user_name}, monthly budget of ${max_budget} {function_arguments_str} "
+ )
+ function_arguments = parse_xml_params(
+ function_arguments_str,
+ json_schema=json_schemas.get(
+ function_name, None
+ ), # check if we have a json schema for this function name)
+ )
+ _message = litellm.Message(
+ tool_calls=[
+ {
+ "id": f"call_{uuid.uuid4()}",
+ "type": "function",
+ "function": {
+ "name": function_name,
+ "arguments": json.dumps(function_arguments),
+ },
+ }
+ ],
+ content=None,
+ )
+ model_response.choices[0].message = _message # type: ignore
+ model_response._hidden_params["original_response"] = (
+ outputText # allow user to access raw anthropic tool calling response
+ )
+ if (
+ _is_function_call == True
+ and stream is not None
+ and stream == True
+ ):
+ print_verbose(
+ f"INSIDE BEDROCK STREAMING TOOL CALLING CONDITION BLOCK"
+ )
+ # return an iterator
+ streaming_model_response = ModelResponse(stream=True)
+ streaming_model_response.choices[0].finish_reason = getattr(
+ model_response.choices[0], "finish_reason", "stop"
+ )
+ # streaming_model_response.choices = [litellm.utils.StreamingChoices()]
+ streaming_choice = litellm.utils.StreamingChoices()
+ streaming_choice.index = model_response.choices[0].index
+ _tool_calls = []
+ print_verbose(
+ f"type of model_response.choices[0]: {type(model_response.choices[0])}"
+ )
+ print_verbose(
+ f"type of streaming_choice: {type(streaming_choice)}"
+ )
+ if isinstance(model_response.choices[0], litellm.Choices):
+ if getattr(
+ model_response.choices[0].message, "tool_calls", None
+ ) is not None and isinstance(
+ model_response.choices[0].message.tool_calls, list
+ ):
+ for tool_call in model_response.choices[
+ 0
+ ].message.tool_calls:
+ _tool_call = {**tool_call.dict(), "index": 0}
+ _tool_calls.append(_tool_call)
+ delta_obj = litellm.utils.Delta(
+ content=getattr(
+ model_response.choices[0].message, "content", None
+ ),
+ role=model_response.choices[0].message.role,
+ tool_calls=_tool_calls,
+ )
+ streaming_choice.delta = delta_obj
+ streaming_model_response.choices = [streaming_choice]
+ completion_stream = ModelResponseIterator(
+ model_response=streaming_model_response
+ )
+ print_verbose(
+ f"Returns anthropic CustomStreamWrapper with 'cached_response' streaming object"
+ )
+ return litellm.CustomStreamWrapper(
+ completion_stream=completion_stream,
+ model=model,
+ custom_llm_provider="cached_response",
+ logging_obj=logging_obj,
+ )
+
+ model_response["finish_reason"] = map_finish_reason(
+ completion_response.get("stop_reason", "")
+ )
+ _usage = litellm.Usage(
+ prompt_tokens=completion_response["usage"]["input_tokens"],
+ completion_tokens=completion_response["usage"]["output_tokens"],
+ total_tokens=completion_response["usage"]["input_tokens"]
+ + completion_response["usage"]["output_tokens"],
+ )
+ setattr(model_response, "usage", _usage)
+ else:
+ outputText = completion_response["completion"]
+
+ model_response["finish_reason"] = completion_response["stop_reason"]
+ elif provider == "ai21":
+ outputText = (
+ completion_response.get("completions")[0].get("data").get("text")
+ )
+ elif provider == "meta":
+ outputText = completion_response["generation"]
+ elif provider == "mistral":
+ outputText = completion_response["outputs"][0]["text"]
+ model_response["finish_reason"] = completion_response["outputs"][0][
+ "stop_reason"
+ ]
+ else: # amazon titan
+ outputText = completion_response.get("results")[0].get("outputText")
except Exception as e:
- raise BedrockError(message=response.text, status_code=422)
+ raise BedrockError(
+ message="Error processing={}, Received error={}".format(
+ response.text, str(e)
+ ),
+ status_code=422,
+ )
+
+ try:
+ if (
+ len(outputText) > 0
+ and hasattr(model_response.choices[0], "message")
+ and getattr(model_response.choices[0].message, "tool_calls", None)
+ is None
+ ):
+ model_response["choices"][0]["message"]["content"] = outputText
+ elif (
+ hasattr(model_response.choices[0], "message")
+ and getattr(model_response.choices[0].message, "tool_calls", None)
+ is not None
+ ):
+ pass
+ else:
+ raise Exception()
+ except:
+ raise BedrockError(
+ message=json.dumps(outputText), status_code=response.status_code
+ )
+
+ if stream and provider == "ai21":
+ streaming_model_response = ModelResponse(stream=True)
+ streaming_model_response.choices[0].finish_reason = model_response.choices[ # type: ignore
+ 0
+ ].finish_reason
+ # streaming_model_response.choices = [litellm.utils.StreamingChoices()]
+ streaming_choice = litellm.utils.StreamingChoices()
+ streaming_choice.index = model_response.choices[0].index
+ delta_obj = litellm.utils.Delta(
+ content=getattr(model_response.choices[0].message, "content", None),
+ role=model_response.choices[0].message.role,
+ )
+ streaming_choice.delta = delta_obj
+ streaming_model_response.choices = [streaming_choice]
+ mri = ModelResponseIterator(model_response=streaming_model_response)
+ return CustomStreamWrapper(
+ completion_stream=mri,
+ model=model,
+ custom_llm_provider="cached_response",
+ logging_obj=logging_obj,
+ )
## CALCULATING USAGE - bedrock returns usage in the headers
- prompt_tokens = int(
- response.headers.get(
- "x-amzn-bedrock-input-token-count",
- len(encoding.encode("".join(m.get("content", "") for m in messages))),
- )
+ bedrock_input_tokens = response.headers.get(
+ "x-amzn-bedrock-input-token-count", None
)
+ bedrock_output_tokens = response.headers.get(
+ "x-amzn-bedrock-output-token-count", None
+ )
+
+ prompt_tokens = int(
+ bedrock_input_tokens or litellm.token_counter(messages=messages)
+ )
+
completion_tokens = int(
- response.headers.get(
- "x-amzn-bedrock-output-token-count",
- len(
- encoding.encode(
- model_response.choices[0].message.content, # type: ignore
- disallowed_special=(),
- )
- ),
+ bedrock_output_tokens
+ or litellm.token_counter(
+ text=model_response.choices[0].message.content, # type: ignore
+ count_response_tokens=True,
)
)
@@ -331,6 +525,16 @@ class BedrockLLM(BaseLLM):
return model_response
+ def encode_model_id(self, model_id: str) -> str:
+ """
+ Double encode the model ID to ensure it matches the expected double-encoded format.
+ Args:
+ model_id (str): The model ID to encode.
+ Returns:
+ str: The double-encoded model ID.
+ """
+ return urllib.parse.quote(model_id, safe="")
+
def completion(
self,
model: str,
@@ -359,6 +563,13 @@ class BedrockLLM(BaseLLM):
## SETUP ##
stream = optional_params.pop("stream", None)
+ modelId = optional_params.pop("model_id", None)
+ if modelId is not None:
+ modelId = self.encode_model_id(model_id=modelId)
+ else:
+ modelId = model
+
+ provider = model.split(".")[0]
## CREDENTIALS ##
# pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them
@@ -414,19 +625,18 @@ class BedrockLLM(BaseLLM):
else:
endpoint_url = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com"
- if stream is not None and stream == True:
- endpoint_url = f"{endpoint_url}/model/{model}/invoke-with-response-stream"
+ if (stream is not None and stream == True) and provider != "ai21":
+ endpoint_url = f"{endpoint_url}/model/{modelId}/invoke-with-response-stream"
else:
- endpoint_url = f"{endpoint_url}/model/{model}/invoke"
+ endpoint_url = f"{endpoint_url}/model/{modelId}/invoke"
sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name)
- provider = model.split(".")[0]
prompt, chat_history = self.convert_messages_to_prompt(
model, messages, provider, custom_prompt_dict
)
inference_params = copy.deepcopy(optional_params)
-
+ json_schemas: dict = {}
if provider == "cohere":
if model.startswith("cohere.command-r"):
## LOAD CONFIG
@@ -453,8 +663,114 @@ class BedrockLLM(BaseLLM):
True # cohere requires stream = True in inference params
)
data = json.dumps({"prompt": prompt, **inference_params})
+ elif provider == "anthropic":
+ if model.startswith("anthropic.claude-3"):
+ # Separate system prompt from rest of message
+ system_prompt_idx: list[int] = []
+ system_messages: list[str] = []
+ for idx, message in enumerate(messages):
+ if message["role"] == "system":
+ system_messages.append(message["content"])
+ system_prompt_idx.append(idx)
+ if len(system_prompt_idx) > 0:
+ inference_params["system"] = "\n".join(system_messages)
+ messages = [
+ i for j, i in enumerate(messages) if j not in system_prompt_idx
+ ]
+ # Format rest of message according to anthropic guidelines
+ messages = prompt_factory(
+ model=model, messages=messages, custom_llm_provider="anthropic_xml"
+ ) # type: ignore
+ ## LOAD CONFIG
+ config = litellm.AmazonAnthropicClaude3Config.get_config()
+ for k, v in config.items():
+ if (
+ k not in inference_params
+ ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+ inference_params[k] = v
+ ## Handle Tool Calling
+ if "tools" in inference_params:
+ _is_function_call = True
+ for tool in inference_params["tools"]:
+ json_schemas[tool["function"]["name"]] = tool["function"].get(
+ "parameters", None
+ )
+ tool_calling_system_prompt = construct_tool_use_system_prompt(
+ tools=inference_params["tools"]
+ )
+ inference_params["system"] = (
+ inference_params.get("system", "\n")
+ + tool_calling_system_prompt
+ ) # add the anthropic tool calling prompt to the system prompt
+ inference_params.pop("tools")
+ data = json.dumps({"messages": messages, **inference_params})
+ else:
+ ## LOAD CONFIG
+ config = litellm.AmazonAnthropicConfig.get_config()
+ for k, v in config.items():
+ if (
+ k not in inference_params
+ ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+ inference_params[k] = v
+ data = json.dumps({"prompt": prompt, **inference_params})
+ elif provider == "ai21":
+ ## LOAD CONFIG
+ config = litellm.AmazonAI21Config.get_config()
+ for k, v in config.items():
+ if (
+ k not in inference_params
+ ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+ inference_params[k] = v
+
+ data = json.dumps({"prompt": prompt, **inference_params})
+ elif provider == "mistral":
+ ## LOAD CONFIG
+ config = litellm.AmazonMistralConfig.get_config()
+ for k, v in config.items():
+ if (
+ k not in inference_params
+ ): # completion(top_k=3) > amazon_config(top_k=3) <- allows for dynamic variables to be passed in
+ inference_params[k] = v
+
+ data = json.dumps({"prompt": prompt, **inference_params})
+ elif provider == "amazon": # amazon titan
+ ## LOAD CONFIG
+ config = litellm.AmazonTitanConfig.get_config()
+ for k, v in config.items():
+ if (
+ k not in inference_params
+ ): # completion(top_k=3) > amazon_config(top_k=3) <- allows for dynamic variables to be passed in
+ inference_params[k] = v
+
+ data = json.dumps(
+ {
+ "inputText": prompt,
+ "textGenerationConfig": inference_params,
+ }
+ )
+ elif provider == "meta":
+ ## LOAD CONFIG
+ config = litellm.AmazonLlamaConfig.get_config()
+ for k, v in config.items():
+ if (
+ k not in inference_params
+ ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+ inference_params[k] = v
+ data = json.dumps({"prompt": prompt, **inference_params})
else:
- raise Exception("UNSUPPORTED PROVIDER")
+ ## LOGGING
+ logging_obj.pre_call(
+ input=messages,
+ api_key="",
+ additional_args={
+ "complete_input_dict": inference_params,
+ },
+ )
+ raise Exception(
+ "Bedrock HTTPX: Unsupported provider={}, model={}".format(
+ provider, model
+ )
+ )
## COMPLETION CALL
@@ -482,7 +798,7 @@ class BedrockLLM(BaseLLM):
if acompletion:
if isinstance(client, HTTPHandler):
client = None
- if stream:
+ if stream == True and provider != "ai21":
return self.async_streaming(
model=model,
messages=messages,
@@ -511,7 +827,7 @@ class BedrockLLM(BaseLLM):
encoding=encoding,
logging_obj=logging_obj,
optional_params=optional_params,
- stream=False,
+ stream=stream, # type: ignore
litellm_params=litellm_params,
logger_fn=logger_fn,
headers=prepped.headers,
@@ -528,7 +844,7 @@ class BedrockLLM(BaseLLM):
self.client = HTTPHandler(**_params) # type: ignore
else:
self.client = client
- if stream is not None and stream == True:
+ if (stream is not None and stream == True) and provider != "ai21":
response = self.client.post(
url=prepped.url,
headers=prepped.headers, # type: ignore
@@ -541,7 +857,7 @@ class BedrockLLM(BaseLLM):
status_code=response.status_code, message=response.text
)
- decoder = AWSEventStreamDecoder()
+ decoder = AWSEventStreamDecoder(model=model)
completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024))
streaming_response = CustomStreamWrapper(
@@ -550,15 +866,24 @@ class BedrockLLM(BaseLLM):
custom_llm_provider="bedrock",
logging_obj=logging_obj,
)
+
+ ## LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key="",
+ original_response=streaming_response,
+ additional_args={"complete_input_dict": data},
+ )
return streaming_response
- response = self.client.post(url=prepped.url, headers=prepped.headers, data=data) # type: ignore
-
try:
+ response = self.client.post(url=prepped.url, headers=prepped.headers, data=data) # type: ignore
response.raise_for_status()
except httpx.HTTPStatusError as err:
error_code = err.response.status_code
raise BedrockError(status_code=error_code, message=response.text)
+ except httpx.TimeoutException as e:
+ raise BedrockError(status_code=408, message="Timeout error occurred.")
return self.process_response(
model=model,
@@ -591,7 +916,7 @@ class BedrockLLM(BaseLLM):
logger_fn=None,
headers={},
client: Optional[AsyncHTTPHandler] = None,
- ) -> ModelResponse:
+ ) -> Union[ModelResponse, CustomStreamWrapper]:
if client is None:
_params = {}
if timeout is not None:
@@ -602,12 +927,20 @@ class BedrockLLM(BaseLLM):
else:
self.client = client # type: ignore
- response = await self.client.post(api_base, headers=headers, data=data) # type: ignore
+ try:
+ response = await self.client.post(api_base, headers=headers, data=data) # type: ignore
+ response.raise_for_status()
+ except httpx.HTTPStatusError as err:
+ error_code = err.response.status_code
+ raise BedrockError(status_code=error_code, message=response.text)
+ except httpx.TimeoutException as e:
+ raise BedrockError(status_code=408, message="Timeout error occurred.")
+
return self.process_response(
model=model,
response=response,
model_response=model_response,
- stream=stream,
+ stream=stream if isinstance(stream, bool) else False,
logging_obj=logging_obj,
api_key="",
data=data,
@@ -650,7 +983,7 @@ class BedrockLLM(BaseLLM):
if response.status_code != 200:
raise BedrockError(status_code=response.status_code, message=response.text)
- decoder = AWSEventStreamDecoder()
+ decoder = AWSEventStreamDecoder(model=model)
completion_stream = decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024))
streaming_response = CustomStreamWrapper(
@@ -659,6 +992,15 @@ class BedrockLLM(BaseLLM):
custom_llm_provider="bedrock",
logging_obj=logging_obj,
)
+
+ ## LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key="",
+ original_response=streaming_response,
+ additional_args={"complete_input_dict": data},
+ )
+
return streaming_response
def embedding(self, *args, **kwargs):
@@ -676,11 +1018,70 @@ def get_response_stream_shape():
class AWSEventStreamDecoder:
- def __init__(self) -> None:
+ def __init__(self, model: str) -> None:
from botocore.parsers import EventStreamJSONParser
+ self.model = model
self.parser = EventStreamJSONParser()
+ def _chunk_parser(self, chunk_data: dict) -> GenericStreamingChunk:
+ text = ""
+ is_finished = False
+ finish_reason = ""
+ if "outputText" in chunk_data:
+ text = chunk_data["outputText"]
+ # ai21 mapping
+ if "ai21" in self.model: # fake ai21 streaming
+ text = chunk_data.get("completions")[0].get("data").get("text") # type: ignore
+ is_finished = True
+ finish_reason = "stop"
+ ######## bedrock.anthropic mappings ###############
+ elif "completion" in chunk_data: # not claude-3
+ text = chunk_data["completion"] # bedrock.anthropic
+ stop_reason = chunk_data.get("stop_reason", None)
+ if stop_reason != None:
+ is_finished = True
+ finish_reason = stop_reason
+ elif "delta" in chunk_data:
+ if chunk_data["delta"].get("text", None) is not None:
+ text = chunk_data["delta"]["text"]
+ stop_reason = chunk_data["delta"].get("stop_reason", None)
+ if stop_reason != None:
+ is_finished = True
+ finish_reason = stop_reason
+ ######## bedrock.mistral mappings ###############
+ elif "outputs" in chunk_data:
+ if (
+ len(chunk_data["outputs"]) == 1
+ and chunk_data["outputs"][0].get("text", None) is not None
+ ):
+ text = chunk_data["outputs"][0]["text"]
+ stop_reason = chunk_data.get("stop_reason", None)
+ if stop_reason != None:
+ is_finished = True
+ finish_reason = stop_reason
+ ######## bedrock.cohere mappings ###############
+ # meta mapping
+ elif "generation" in chunk_data:
+ text = chunk_data["generation"] # bedrock.meta
+ # cohere mapping
+ elif "text" in chunk_data:
+ text = chunk_data["text"] # bedrock.cohere
+ # cohere mapping for finish reason
+ elif "finish_reason" in chunk_data:
+ finish_reason = chunk_data["finish_reason"]
+ is_finished = True
+ elif chunk_data.get("completionReason", None):
+ is_finished = True
+ finish_reason = chunk_data["completionReason"]
+ return GenericStreamingChunk(
+ **{
+ "text": text,
+ "is_finished": is_finished,
+ "finish_reason": finish_reason,
+ }
+ )
+
def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[GenericStreamingChunk]:
"""Given an iterator that yields lines, iterate over it & yield every event encountered"""
from botocore.eventstream import EventStreamBuffer
@@ -693,12 +1094,7 @@ class AWSEventStreamDecoder:
if message:
# sse_event = ServerSentEvent(data=message, event="completion")
_data = json.loads(message)
- streaming_chunk: GenericStreamingChunk = GenericStreamingChunk(
- text=_data.get("text", ""),
- is_finished=_data.get("is_finished", False),
- finish_reason=_data.get("finish_reason", ""),
- )
- yield streaming_chunk
+ yield self._chunk_parser(chunk_data=_data)
async def aiter_bytes(
self, iterator: AsyncIterator[bytes]
@@ -713,12 +1109,7 @@ class AWSEventStreamDecoder:
message = self._parse_message_from_event(event)
if message:
_data = json.loads(message)
- streaming_chunk: GenericStreamingChunk = GenericStreamingChunk(
- text=_data.get("text", ""),
- is_finished=_data.get("is_finished", False),
- finish_reason=_data.get("finish_reason", ""),
- )
- yield streaming_chunk
+ yield self._chunk_parser(chunk_data=_data)
def _parse_message_from_event(self, event) -> Optional[str]:
response_dict = event.to_response_dict()
diff --git a/litellm/llms/clarifai.py b/litellm/llms/clarifai.py
index e07a8d9e8..4610911e1 100644
--- a/litellm/llms/clarifai.py
+++ b/litellm/llms/clarifai.py
@@ -14,28 +14,25 @@ class ClarifaiError(Exception):
def __init__(self, status_code, message, url):
self.status_code = status_code
self.message = message
- self.request = httpx.Request(
- method="POST", url=url
- )
+ self.request = httpx.Request(method="POST", url=url)
self.response = httpx.Response(status_code=status_code, request=self.request)
- super().__init__(
- self.message
- )
+ super().__init__(self.message)
+
class ClarifaiConfig:
"""
Reference: https://clarifai.com/meta/Llama-2/models/llama2-70b-chat
- TODO fill in the details
"""
+
max_tokens: Optional[int] = None
temperature: Optional[int] = None
top_k: Optional[int] = None
def __init__(
- self,
- max_tokens: Optional[int] = None,
- temperature: Optional[int] = None,
- top_k: Optional[int] = None,
+ self,
+ max_tokens: Optional[int] = None,
+ temperature: Optional[int] = None,
+ top_k: Optional[int] = None,
) -> None:
locals_ = locals()
for key, value in locals_.items():
@@ -60,6 +57,7 @@ class ClarifaiConfig:
and v is not None
}
+
def validate_environment(api_key):
headers = {
"accept": "application/json",
@@ -69,42 +67,37 @@ def validate_environment(api_key):
headers["Authorization"] = f"Bearer {api_key}"
return headers
-def completions_to_model(payload):
- # if payload["n"] != 1:
- # raise HTTPException(
- # status_code=422,
- # detail="Only one generation is supported. Please set candidate_count to 1.",
- # )
- params = {}
- if temperature := payload.get("temperature"):
- params["temperature"] = temperature
- if max_tokens := payload.get("max_tokens"):
- params["max_tokens"] = max_tokens
- return {
- "inputs": [{"data": {"text": {"raw": payload["prompt"]}}}],
- "model": {"output_info": {"params": params}},
-}
-
+def completions_to_model(payload):
+ # if payload["n"] != 1:
+ # raise HTTPException(
+ # status_code=422,
+ # detail="Only one generation is supported. Please set candidate_count to 1.",
+ # )
+
+ params = {}
+ if temperature := payload.get("temperature"):
+ params["temperature"] = temperature
+ if max_tokens := payload.get("max_tokens"):
+ params["max_tokens"] = max_tokens
+ return {
+ "inputs": [{"data": {"text": {"raw": payload["prompt"]}}}],
+ "model": {"output_info": {"params": params}},
+ }
+
+
def process_response(
- model,
- prompt,
- response,
- model_response,
- api_key,
- data,
- encoding,
- logging_obj
- ):
+ model, prompt, response, model_response, api_key, data, encoding, logging_obj
+):
logging_obj.post_call(
- input=prompt,
- api_key=api_key,
- original_response=response.text,
- additional_args={"complete_input_dict": data},
- )
- ## RESPONSE OBJECT
+ input=prompt,
+ api_key=api_key,
+ original_response=response.text,
+ additional_args={"complete_input_dict": data},
+ )
+ ## RESPONSE OBJECT
try:
- completion_response = response.json()
+ completion_response = response.json()
except Exception:
raise ClarifaiError(
message=response.text, status_code=response.status_code, url=model
@@ -119,7 +112,7 @@ def process_response(
message_obj = Message(content=None)
choice_obj = Choices(
finish_reason="stop",
- index=idx + 1, #check
+ index=idx + 1, # check
message=message_obj,
)
choices_list.append(choice_obj)
@@ -143,53 +136,56 @@ def process_response(
)
return model_response
+
def convert_model_to_url(model: str, api_base: str):
user_id, app_id, model_id = model.split(".")
return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs"
+
def get_prompt_model_name(url: str):
clarifai_model_name = url.split("/")[-2]
if "claude" in clarifai_model_name:
return "anthropic", clarifai_model_name.replace("_", ".")
- if ("llama" in clarifai_model_name)or ("mistral" in clarifai_model_name):
+ if ("llama" in clarifai_model_name) or ("mistral" in clarifai_model_name):
return "", "meta-llama/llama-2-chat"
else:
return "", clarifai_model_name
+
async def async_completion(
- model: str,
- prompt: str,
- api_base: str,
- custom_prompt_dict: dict,
- model_response: ModelResponse,
- print_verbose: Callable,
- encoding,
- api_key,
- logging_obj,
- data=None,
- optional_params=None,
- litellm_params=None,
- logger_fn=None,
- headers={}):
-
- async_handler = AsyncHTTPHandler(
- timeout=httpx.Timeout(timeout=600.0, connect=5.0)
- )
+ model: str,
+ prompt: str,
+ api_base: str,
+ custom_prompt_dict: dict,
+ model_response: ModelResponse,
+ print_verbose: Callable,
+ encoding,
+ api_key,
+ logging_obj,
+ data=None,
+ optional_params=None,
+ litellm_params=None,
+ logger_fn=None,
+ headers={},
+):
+
+ async_handler = AsyncHTTPHandler(timeout=httpx.Timeout(timeout=600.0, connect=5.0))
response = await async_handler.post(
- api_base, headers=headers, data=json.dumps(data)
- )
-
- return process_response(
- model=model,
- prompt=prompt,
- response=response,
- model_response=model_response,
- api_key=api_key,
- data=data,
- encoding=encoding,
- logging_obj=logging_obj,
+ api_base, headers=headers, data=json.dumps(data)
)
+ return process_response(
+ model=model,
+ prompt=prompt,
+ response=response,
+ model_response=model_response,
+ api_key=api_key,
+ data=data,
+ encoding=encoding,
+ logging_obj=logging_obj,
+ )
+
+
def completion(
model: str,
messages: list,
@@ -207,14 +203,12 @@ def completion(
):
headers = validate_environment(api_key)
model = convert_model_to_url(model, api_base)
- prompt = " ".join(message["content"] for message in messages) # TODO
+ prompt = " ".join(message["content"] for message in messages) # TODO
## Load Config
config = litellm.ClarifaiConfig.get_config()
for k, v in config.items():
- if (
- k not in optional_params
- ):
+ if k not in optional_params:
optional_params[k] = v
custom_llm_provider, orig_model_name = get_prompt_model_name(model)
@@ -223,14 +217,14 @@ def completion(
model=orig_model_name,
messages=messages,
api_key=api_key,
- custom_llm_provider="clarifai"
+ custom_llm_provider="clarifai",
)
else:
prompt = prompt_factory(
model=orig_model_name,
messages=messages,
api_key=api_key,
- custom_llm_provider=custom_llm_provider
+ custom_llm_provider=custom_llm_provider,
)
# print(prompt); exit(0)
@@ -240,7 +234,6 @@ def completion(
}
data = completions_to_model(data)
-
## LOGGING
logging_obj.pre_call(
input=prompt,
@@ -251,7 +244,7 @@ def completion(
"api_base": api_base,
},
)
- if acompletion==True:
+ if acompletion == True:
return async_completion(
model=model,
prompt=prompt,
@@ -271,15 +264,17 @@ def completion(
else:
## COMPLETION CALL
response = requests.post(
- model,
- headers=headers,
- data=json.dumps(data),
- )
+ model,
+ headers=headers,
+ data=json.dumps(data),
+ )
# print(response.content); exit()
if response.status_code != 200:
- raise ClarifaiError(status_code=response.status_code, message=response.text, url=model)
-
+ raise ClarifaiError(
+ status_code=response.status_code, message=response.text, url=model
+ )
+
if "stream" in optional_params and optional_params["stream"] == True:
completion_stream = response.iter_lines()
stream_response = CustomStreamWrapper(
@@ -287,11 +282,11 @@ def completion(
model=model,
custom_llm_provider="clarifai",
logging_obj=logging_obj,
- )
+ )
return stream_response
-
+
else:
- return process_response(
+ return process_response(
model=model,
prompt=prompt,
response=response,
@@ -299,8 +294,9 @@ def completion(
api_key=api_key,
data=data,
encoding=encoding,
- logging_obj=logging_obj)
-
+ logging_obj=logging_obj,
+ )
+
class ModelResponseIterator:
def __init__(self, model_response):
@@ -325,4 +321,4 @@ class ModelResponseIterator:
if self.is_done:
raise StopAsyncIteration
self.is_done = True
- return self.model_response
\ No newline at end of file
+ return self.model_response
diff --git a/litellm/llms/cohere.py b/litellm/llms/cohere.py
index 0ebdf38f1..14a66b54a 100644
--- a/litellm/llms/cohere.py
+++ b/litellm/llms/cohere.py
@@ -117,6 +117,7 @@ class CohereConfig:
def validate_environment(api_key):
headers = {
+ "Request-Source":"unspecified:litellm",
"accept": "application/json",
"content-type": "application/json",
}
diff --git a/litellm/llms/cohere_chat.py b/litellm/llms/cohere_chat.py
index e4de6ddcb..8ae839243 100644
--- a/litellm/llms/cohere_chat.py
+++ b/litellm/llms/cohere_chat.py
@@ -112,6 +112,7 @@ class CohereChatConfig:
def validate_environment(api_key):
headers = {
+ "Request-Source":"unspecified:litellm",
"accept": "application/json",
"content-type": "application/json",
}
diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py
index 0adbd95bf..8b5f11398 100644
--- a/litellm/llms/custom_httpx/http_handler.py
+++ b/litellm/llms/custom_httpx/http_handler.py
@@ -7,8 +7,12 @@ _DEFAULT_TIMEOUT = httpx.Timeout(timeout=5.0, connect=5.0)
class AsyncHTTPHandler:
def __init__(
- self, timeout: httpx.Timeout = _DEFAULT_TIMEOUT, concurrent_limit=1000
+ self,
+ timeout: Optional[Union[float, httpx.Timeout]] = None,
+ concurrent_limit=1000,
):
+ if timeout is None:
+ timeout = _DEFAULT_TIMEOUT
# Create a client with a connection pool
self.client = httpx.AsyncClient(
timeout=timeout,
@@ -39,12 +43,13 @@ class AsyncHTTPHandler:
self,
url: str,
data: Optional[Union[dict, str]] = None, # type: ignore
+ json: Optional[dict] = None,
params: Optional[dict] = None,
headers: Optional[dict] = None,
stream: bool = False,
):
req = self.client.build_request(
- "POST", url, data=data, params=params, headers=headers # type: ignore
+ "POST", url, data=data, json=json, params=params, headers=headers # type: ignore
)
response = await self.client.send(req, stream=stream)
return response
@@ -59,7 +64,7 @@ class AsyncHTTPHandler:
class HTTPHandler:
def __init__(
self,
- timeout: Optional[httpx.Timeout] = None,
+ timeout: Optional[Union[float, httpx.Timeout]] = None,
concurrent_limit=1000,
client: Optional[httpx.Client] = None,
):
diff --git a/litellm/llms/databricks.py b/litellm/llms/databricks.py
new file mode 100644
index 000000000..7b2013710
--- /dev/null
+++ b/litellm/llms/databricks.py
@@ -0,0 +1,696 @@
+# What is this?
+## Handler file for databricks API https://docs.databricks.com/en/machine-learning/foundation-models/api-reference.html#chat-request
+import os, types
+import json
+from enum import Enum
+import requests, copy # type: ignore
+import time
+from typing import Callable, Optional, List, Union, Tuple, Literal
+from litellm.utils import (
+ ModelResponse,
+ Usage,
+ map_finish_reason,
+ CustomStreamWrapper,
+ EmbeddingResponse,
+)
+import litellm
+from .prompt_templates.factory import prompt_factory, custom_prompt
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from .base import BaseLLM
+import httpx # type: ignore
+from litellm.types.llms.databricks import GenericStreamingChunk
+from litellm.types.utils import ProviderField
+
+
+class DatabricksError(Exception):
+ def __init__(self, status_code, message):
+ self.status_code = status_code
+ self.message = message
+ self.request = httpx.Request(method="POST", url="https://docs.databricks.com/")
+ self.response = httpx.Response(status_code=status_code, request=self.request)
+ super().__init__(
+ self.message
+ ) # Call the base class constructor with the parameters it needs
+
+
+class DatabricksConfig:
+ """
+ Reference: https://docs.databricks.com/en/machine-learning/foundation-models/api-reference.html#chat-request
+ """
+
+ max_tokens: Optional[int] = None
+ temperature: Optional[int] = None
+ top_p: Optional[int] = None
+ top_k: Optional[int] = None
+ stop: Optional[Union[List[str], str]] = None
+ n: Optional[int] = None
+
+ def __init__(
+ self,
+ max_tokens: Optional[int] = None,
+ temperature: Optional[int] = None,
+ top_p: Optional[int] = None,
+ top_k: Optional[int] = None,
+ stop: Optional[Union[List[str], str]] = None,
+ n: Optional[int] = None,
+ ) -> None:
+ locals_ = locals()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_required_params(self) -> List[ProviderField]:
+ """For a given provider, return it's required fields with a description"""
+ return [
+ ProviderField(
+ field_name="api_key",
+ field_type="string",
+ field_description="Your Databricks API Key.",
+ field_value="dapi...",
+ ),
+ ProviderField(
+ field_name="api_base",
+ field_type="string",
+ field_description="Your Databricks API Base.",
+ field_value="https://adb-..",
+ ),
+ ]
+
+ def get_supported_openai_params(self):
+ return ["stream", "stop", "temperature", "top_p", "max_tokens", "n"]
+
+ def map_openai_params(self, non_default_params: dict, optional_params: dict):
+ for param, value in non_default_params.items():
+ if param == "max_tokens":
+ optional_params["max_tokens"] = value
+ if param == "n":
+ optional_params["n"] = value
+ if param == "stream" and value == True:
+ optional_params["stream"] = value
+ if param == "temperature":
+ optional_params["temperature"] = value
+ if param == "top_p":
+ optional_params["top_p"] = value
+ if param == "stop":
+ optional_params["stop"] = value
+ return optional_params
+
+ def _chunk_parser(self, chunk_data: str) -> GenericStreamingChunk:
+ try:
+ text = ""
+ is_finished = False
+ finish_reason = None
+ logprobs = None
+ usage = None
+ original_chunk = None # this is used for function/tool calling
+ chunk_data = chunk_data.replace("data:", "")
+ chunk_data = chunk_data.strip()
+ if len(chunk_data) == 0:
+ return {
+ "text": "",
+ "is_finished": is_finished,
+ "finish_reason": finish_reason,
+ }
+ chunk_data_dict = json.loads(chunk_data)
+ str_line = litellm.ModelResponse(**chunk_data_dict, stream=True)
+
+ if len(str_line.choices) > 0:
+ if (
+ str_line.choices[0].delta is not None # type: ignore
+ and str_line.choices[0].delta.content is not None # type: ignore
+ ):
+ text = str_line.choices[0].delta.content # type: ignore
+ else: # function/tool calling chunk - when content is None. in this case we just return the original chunk from openai
+ original_chunk = str_line
+ if str_line.choices[0].finish_reason:
+ is_finished = True
+ finish_reason = str_line.choices[0].finish_reason
+ if finish_reason == "content_filter":
+ if hasattr(str_line.choices[0], "content_filter_result"):
+ error_message = json.dumps(
+ str_line.choices[0].content_filter_result # type: ignore
+ )
+ else:
+ error_message = "Azure Response={}".format(
+ str(dict(str_line))
+ )
+ raise litellm.AzureOpenAIError(
+ status_code=400, message=error_message
+ )
+
+ # checking for logprobs
+ if (
+ hasattr(str_line.choices[0], "logprobs")
+ and str_line.choices[0].logprobs is not None
+ ):
+ logprobs = str_line.choices[0].logprobs
+ else:
+ logprobs = None
+
+ usage = getattr(str_line, "usage", None)
+
+ return GenericStreamingChunk(
+ text=text,
+ is_finished=is_finished,
+ finish_reason=finish_reason,
+ logprobs=logprobs,
+ original_chunk=original_chunk,
+ usage=usage,
+ )
+ except Exception as e:
+ raise e
+
+
+class DatabricksEmbeddingConfig:
+ """
+ Reference: https://learn.microsoft.com/en-us/azure/databricks/machine-learning/foundation-models/api-reference#--embedding-task
+ """
+
+ instruction: Optional[str] = (
+ None # An optional instruction to pass to the embedding model. BGE Authors recommend 'Represent this sentence for searching relevant passages:' for retrieval queries
+ )
+
+ def __init__(self, instruction: Optional[str] = None) -> None:
+ locals_ = locals()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params(
+ self,
+ ): # no optional openai embedding params supported
+ return []
+
+ def map_openai_params(self, non_default_params: dict, optional_params: dict):
+ return optional_params
+
+
+class DatabricksChatCompletion(BaseLLM):
+ def __init__(self) -> None:
+ super().__init__()
+
+ # makes headers for API call
+
+ def _validate_environment(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ endpoint_type: Literal["chat_completions", "embeddings"],
+ ) -> Tuple[str, dict]:
+ if api_key is None:
+ raise DatabricksError(
+ status_code=400,
+ message="Missing Databricks API Key - A call is being made to Databricks but no key is set either in the environment variables (DATABRICKS_API_KEY) or via params",
+ )
+
+ if api_base is None:
+ raise DatabricksError(
+ status_code=400,
+ message="Missing Databricks API Base - A call is being made to Databricks but no api base is set either in the environment variables (DATABRICKS_API_BASE) or via params",
+ )
+
+ headers = {
+ "Authorization": "Bearer {}".format(api_key),
+ "Content-Type": "application/json",
+ }
+
+ if endpoint_type == "chat_completions":
+ api_base = "{}/chat/completions".format(api_base)
+ elif endpoint_type == "embeddings":
+ api_base = "{}/embeddings".format(api_base)
+ return api_base, headers
+
+ def process_response(
+ self,
+ model: str,
+ response: Union[requests.Response, httpx.Response],
+ model_response: ModelResponse,
+ stream: bool,
+ logging_obj: litellm.utils.Logging,
+ optional_params: dict,
+ api_key: str,
+ data: Union[dict, str],
+ messages: List,
+ print_verbose,
+ encoding,
+ ) -> ModelResponse:
+ ## LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key=api_key,
+ original_response=response.text,
+ additional_args={"complete_input_dict": data},
+ )
+ print_verbose(f"raw model_response: {response.text}")
+ ## RESPONSE OBJECT
+ try:
+ completion_response = response.json()
+ except:
+ raise DatabricksError(
+ message=response.text, status_code=response.status_code
+ )
+ if "error" in completion_response:
+ raise DatabricksError(
+ message=str(completion_response["error"]),
+ status_code=response.status_code,
+ )
+ else:
+ text_content = ""
+ tool_calls = []
+ for content in completion_response["content"]:
+ if content["type"] == "text":
+ text_content += content["text"]
+ ## TOOL CALLING
+ elif content["type"] == "tool_use":
+ tool_calls.append(
+ {
+ "id": content["id"],
+ "type": "function",
+ "function": {
+ "name": content["name"],
+ "arguments": json.dumps(content["input"]),
+ },
+ }
+ )
+
+ _message = litellm.Message(
+ tool_calls=tool_calls,
+ content=text_content or None,
+ )
+ model_response.choices[0].message = _message # type: ignore
+ model_response._hidden_params["original_response"] = completion_response[
+ "content"
+ ] # allow user to access raw anthropic tool calling response
+
+ model_response.choices[0].finish_reason = map_finish_reason(
+ completion_response["stop_reason"]
+ )
+
+ ## CALCULATING USAGE
+ prompt_tokens = completion_response["usage"]["input_tokens"]
+ completion_tokens = completion_response["usage"]["output_tokens"]
+ total_tokens = prompt_tokens + completion_tokens
+
+ model_response["created"] = int(time.time())
+ model_response["model"] = model
+ usage = Usage(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=total_tokens,
+ )
+ setattr(model_response, "usage", usage) # type: ignore
+ return model_response
+
+ async def acompletion_stream_function(
+ self,
+ model: str,
+ messages: list,
+ api_base: str,
+ custom_prompt_dict: dict,
+ model_response: ModelResponse,
+ print_verbose: Callable,
+ encoding,
+ api_key,
+ logging_obj,
+ stream,
+ data: dict,
+ optional_params=None,
+ litellm_params=None,
+ logger_fn=None,
+ headers={},
+ ):
+ self.async_handler = AsyncHTTPHandler(
+ timeout=httpx.Timeout(timeout=600.0, connect=5.0)
+ )
+ data["stream"] = True
+ try:
+ response = await self.async_handler.post(
+ api_base, headers=headers, data=json.dumps(data), stream=True
+ )
+ response.raise_for_status()
+
+ completion_stream = response.aiter_lines()
+ except httpx.HTTPStatusError as e:
+ raise DatabricksError(
+ status_code=e.response.status_code, message=response.text
+ )
+ except httpx.TimeoutException as e:
+ raise DatabricksError(status_code=408, message="Timeout error occurred.")
+ except Exception as e:
+ raise DatabricksError(status_code=500, message=str(e))
+
+ streamwrapper = CustomStreamWrapper(
+ completion_stream=completion_stream,
+ model=model,
+ custom_llm_provider="databricks",
+ logging_obj=logging_obj,
+ )
+ return streamwrapper
+
+ async def acompletion_function(
+ self,
+ model: str,
+ messages: list,
+ api_base: str,
+ custom_prompt_dict: dict,
+ model_response: ModelResponse,
+ print_verbose: Callable,
+ encoding,
+ api_key,
+ logging_obj,
+ stream,
+ data: dict,
+ optional_params: dict,
+ litellm_params=None,
+ logger_fn=None,
+ headers={},
+ timeout: Optional[Union[float, httpx.Timeout]] = None,
+ ) -> ModelResponse:
+ if timeout is None:
+ timeout = httpx.Timeout(timeout=600.0, connect=5.0)
+
+ self.async_handler = AsyncHTTPHandler(timeout=timeout)
+
+ try:
+ response = await self.async_handler.post(
+ api_base, headers=headers, data=json.dumps(data)
+ )
+ response.raise_for_status()
+
+ response_json = response.json()
+ except httpx.HTTPStatusError as e:
+ raise DatabricksError(
+ status_code=e.response.status_code,
+ message=response.text if response else str(e),
+ )
+ except httpx.TimeoutException as e:
+ raise DatabricksError(status_code=408, message="Timeout error occurred.")
+ except Exception as e:
+ raise DatabricksError(status_code=500, message=str(e))
+
+ return ModelResponse(**response_json)
+
+ def completion(
+ self,
+ model: str,
+ messages: list,
+ api_base: str,
+ custom_prompt_dict: dict,
+ model_response: ModelResponse,
+ print_verbose: Callable,
+ encoding,
+ api_key,
+ logging_obj,
+ optional_params: dict,
+ acompletion=None,
+ litellm_params=None,
+ logger_fn=None,
+ headers={},
+ timeout: Optional[Union[float, httpx.Timeout]] = None,
+ client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+ ):
+ api_base, headers = self._validate_environment(
+ api_base=api_base, api_key=api_key, endpoint_type="chat_completions"
+ )
+ ## Load Config
+ config = litellm.DatabricksConfig().get_config()
+ for k, v in config.items():
+ if (
+ k not in optional_params
+ ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+ optional_params[k] = v
+
+ stream = optional_params.pop("stream", None)
+
+ data = {
+ "model": model,
+ "messages": messages,
+ **optional_params,
+ }
+
+ ## LOGGING
+ logging_obj.pre_call(
+ input=messages,
+ api_key=api_key,
+ additional_args={
+ "complete_input_dict": data,
+ "api_base": api_base,
+ "headers": headers,
+ },
+ )
+ if acompletion == True:
+ if (
+ stream is not None and stream == True
+ ): # if function call - fake the streaming (need complete blocks for output parsing in openai format)
+ print_verbose("makes async anthropic streaming POST request")
+ data["stream"] = stream
+ return self.acompletion_stream_function(
+ model=model,
+ messages=messages,
+ data=data,
+ api_base=api_base,
+ custom_prompt_dict=custom_prompt_dict,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ encoding=encoding,
+ api_key=api_key,
+ logging_obj=logging_obj,
+ optional_params=optional_params,
+ stream=stream,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ headers=headers,
+ )
+ else:
+ return self.acompletion_function(
+ model=model,
+ messages=messages,
+ data=data,
+ api_base=api_base,
+ custom_prompt_dict=custom_prompt_dict,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ encoding=encoding,
+ api_key=api_key,
+ logging_obj=logging_obj,
+ optional_params=optional_params,
+ stream=stream,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ headers=headers,
+ timeout=timeout,
+ )
+ else:
+ if client is None or isinstance(client, AsyncHTTPHandler):
+ self.client = HTTPHandler(timeout=timeout) # type: ignore
+ else:
+ self.client = client
+ ## COMPLETION CALL
+ if (
+ stream is not None and stream == True
+ ): # if function call - fake the streaming (need complete blocks for output parsing in openai format)
+ print_verbose("makes dbrx streaming POST request")
+ data["stream"] = stream
+ try:
+ response = self.client.post(
+ api_base, headers=headers, data=json.dumps(data), stream=stream
+ )
+ response.raise_for_status()
+ completion_stream = response.iter_lines()
+ except httpx.HTTPStatusError as e:
+ raise DatabricksError(
+ status_code=e.response.status_code, message=response.text
+ )
+ except httpx.TimeoutException as e:
+ raise DatabricksError(
+ status_code=408, message="Timeout error occurred."
+ )
+ except Exception as e:
+ raise DatabricksError(status_code=408, message=str(e))
+
+ streaming_response = CustomStreamWrapper(
+ completion_stream=completion_stream,
+ model=model,
+ custom_llm_provider="databricks",
+ logging_obj=logging_obj,
+ )
+ return streaming_response
+
+ else:
+ try:
+ response = self.client.post(
+ api_base, headers=headers, data=json.dumps(data)
+ )
+ response.raise_for_status()
+
+ response_json = response.json()
+ except httpx.HTTPStatusError as e:
+ raise DatabricksError(
+ status_code=e.response.status_code, message=response.text
+ )
+ except httpx.TimeoutException as e:
+ raise DatabricksError(
+ status_code=408, message="Timeout error occurred."
+ )
+ except Exception as e:
+ raise DatabricksError(status_code=500, message=str(e))
+
+ return ModelResponse(**response_json)
+
+ async def aembedding(
+ self,
+ input: list,
+ data: dict,
+ model_response: ModelResponse,
+ timeout: float,
+ api_key: str,
+ api_base: str,
+ logging_obj,
+ headers: dict,
+ client=None,
+ ) -> EmbeddingResponse:
+ response = None
+ try:
+ if client is None or isinstance(client, AsyncHTTPHandler):
+ self.async_client = AsyncHTTPHandler(timeout=timeout) # type: ignore
+ else:
+ self.async_client = client
+
+ try:
+ response = await self.async_client.post(
+ api_base,
+ headers=headers,
+ data=json.dumps(data),
+ ) # type: ignore
+
+ response.raise_for_status()
+
+ response_json = response.json()
+ except httpx.HTTPStatusError as e:
+ raise DatabricksError(
+ status_code=e.response.status_code,
+ message=response.text if response else str(e),
+ )
+ except httpx.TimeoutException as e:
+ raise DatabricksError(
+ status_code=408, message="Timeout error occurred."
+ )
+ except Exception as e:
+ raise DatabricksError(status_code=500, message=str(e))
+
+ ## LOGGING
+ logging_obj.post_call(
+ input=input,
+ api_key=api_key,
+ additional_args={"complete_input_dict": data},
+ original_response=response_json,
+ )
+ return EmbeddingResponse(**response_json)
+ except Exception as e:
+ ## LOGGING
+ logging_obj.post_call(
+ input=input,
+ api_key=api_key,
+ original_response=str(e),
+ )
+ raise e
+
+ def embedding(
+ self,
+ model: str,
+ input: list,
+ timeout: float,
+ logging_obj,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ optional_params: dict,
+ model_response: Optional[litellm.utils.EmbeddingResponse] = None,
+ client=None,
+ aembedding=None,
+ ) -> EmbeddingResponse:
+ api_base, headers = self._validate_environment(
+ api_base=api_base, api_key=api_key, endpoint_type="embeddings"
+ )
+ model = model
+ data = {"model": model, "input": input, **optional_params}
+
+ ## LOGGING
+ logging_obj.pre_call(
+ input=input,
+ api_key=api_key,
+ additional_args={"complete_input_dict": data, "api_base": api_base},
+ )
+
+ if aembedding == True:
+ return self.aembedding(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, headers=headers) # type: ignore
+ if client is None or isinstance(client, AsyncHTTPHandler):
+ self.client = HTTPHandler(timeout=timeout) # type: ignore
+ else:
+ self.client = client
+
+ ## EMBEDDING CALL
+ try:
+ response = self.client.post(
+ api_base,
+ headers=headers,
+ data=json.dumps(data),
+ ) # type: ignore
+
+ response.raise_for_status() # type: ignore
+
+ response_json = response.json() # type: ignore
+ except httpx.HTTPStatusError as e:
+ raise DatabricksError(
+ status_code=e.response.status_code,
+ message=response.text if response else str(e),
+ )
+ except httpx.TimeoutException as e:
+ raise DatabricksError(status_code=408, message="Timeout error occurred.")
+ except Exception as e:
+ raise DatabricksError(status_code=500, message=str(e))
+
+ ## LOGGING
+ logging_obj.post_call(
+ input=input,
+ api_key=api_key,
+ additional_args={"complete_input_dict": data},
+ original_response=response_json,
+ )
+
+ return litellm.EmbeddingResponse(**response_json)
diff --git a/litellm/llms/gemini.py b/litellm/llms/gemini.py
index 60220fd29..a55b39aef 100644
--- a/litellm/llms/gemini.py
+++ b/litellm/llms/gemini.py
@@ -260,7 +260,7 @@ def completion(
message_obj = Message(content=item.content.parts[0].text)
else:
message_obj = Message(content=None)
- choice_obj = Choices(index=idx + 1, message=message_obj)
+ choice_obj = Choices(index=idx, message=message_obj)
choices_list.append(choice_obj)
model_response["choices"] = choices_list
except Exception as e:
@@ -352,7 +352,7 @@ async def async_completion(
message_obj = Message(content=item.content.parts[0].text)
else:
message_obj = Message(content=None)
- choice_obj = Choices(index=idx + 1, message=message_obj)
+ choice_obj = Choices(index=idx, message=message_obj)
choices_list.append(choice_obj)
model_response["choices"] = choices_list
except Exception as e:
diff --git a/litellm/llms/ollama.py b/litellm/llms/ollama.py
index 9c9b5e898..283878056 100644
--- a/litellm/llms/ollama.py
+++ b/litellm/llms/ollama.py
@@ -45,6 +45,8 @@ class OllamaConfig:
- `temperature` (float): The temperature of the model. Increasing the temperature will make the model answer more creatively. Default: 0.8. Example usage: temperature 0.7
+ - `seed` (int): Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. Example usage: seed 42
+
- `stop` (string[]): Sets the stop sequences to use. Example usage: stop "AI assistant:"
- `tfs_z` (float): Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. Default: 1. Example usage: tfs_z 1
@@ -69,6 +71,7 @@ class OllamaConfig:
repeat_last_n: Optional[int] = None
repeat_penalty: Optional[float] = None
temperature: Optional[float] = None
+ seed: Optional[int] = None
stop: Optional[list] = (
None # stop is a list based on this - https://github.com/ollama/ollama/pull/442
)
@@ -90,6 +93,7 @@ class OllamaConfig:
repeat_last_n: Optional[int] = None,
repeat_penalty: Optional[float] = None,
temperature: Optional[float] = None,
+ seed: Optional[int] = None,
stop: Optional[list] = None,
tfs_z: Optional[float] = None,
num_predict: Optional[int] = None,
@@ -120,6 +124,44 @@ class OllamaConfig:
)
and v is not None
}
+ def get_supported_openai_params(
+ self,
+ ):
+ return [
+ "max_tokens",
+ "stream",
+ "top_p",
+ "temperature",
+ "seed",
+ "frequency_penalty",
+ "stop",
+ "response_format",
+ ]
+
+# ollama wants plain base64 jpeg/png files as images. strip any leading dataURI
+# and convert to jpeg if necessary.
+def _convert_image(image):
+ import base64, io
+ try:
+ from PIL import Image
+ except:
+ raise Exception(
+ "ollama image conversion failed please run `pip install Pillow`"
+ )
+
+ orig = image
+ if image.startswith("data:"):
+ image = image.split(",")[-1]
+ try:
+ image_data = Image.open(io.BytesIO(base64.b64decode(image)))
+ if image_data.format in ["JPEG", "PNG"]:
+ return image
+ except:
+ return orig
+ jpeg_image = io.BytesIO()
+ image_data.convert("RGB").save(jpeg_image, "JPEG")
+ jpeg_image.seek(0)
+ return base64.b64encode(jpeg_image.getvalue()).decode("utf-8")
# ollama implementation
@@ -158,7 +200,7 @@ def get_ollama_response(
if format is not None:
data["format"] = format
if images is not None:
- data["images"] = images
+ data["images"] = [_convert_image(image) for image in images]
## LOGGING
logging_obj.pre_call(
diff --git a/litellm/llms/ollama_chat.py b/litellm/llms/ollama_chat.py
index d1ff4953f..a05807722 100644
--- a/litellm/llms/ollama_chat.py
+++ b/litellm/llms/ollama_chat.py
@@ -45,6 +45,8 @@ class OllamaChatConfig:
- `temperature` (float): The temperature of the model. Increasing the temperature will make the model answer more creatively. Default: 0.8. Example usage: temperature 0.7
+ - `seed` (int): Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. Example usage: seed 42
+
- `stop` (string[]): Sets the stop sequences to use. Example usage: stop "AI assistant:"
- `tfs_z` (float): Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. Default: 1. Example usage: tfs_z 1
@@ -69,6 +71,7 @@ class OllamaChatConfig:
repeat_last_n: Optional[int] = None
repeat_penalty: Optional[float] = None
temperature: Optional[float] = None
+ seed: Optional[int] = None
stop: Optional[list] = (
None # stop is a list based on this - https://github.com/ollama/ollama/pull/442
)
@@ -90,6 +93,7 @@ class OllamaChatConfig:
repeat_last_n: Optional[int] = None,
repeat_penalty: Optional[float] = None,
temperature: Optional[float] = None,
+ seed: Optional[int] = None,
stop: Optional[list] = None,
tfs_z: Optional[float] = None,
num_predict: Optional[int] = None,
@@ -130,6 +134,7 @@ class OllamaChatConfig:
"stream",
"top_p",
"temperature",
+ "seed",
"frequency_penalty",
"stop",
"tools",
@@ -146,6 +151,8 @@ class OllamaChatConfig:
optional_params["stream"] = value
if param == "temperature":
optional_params["temperature"] = value
+ if param == "seed":
+ optional_params["seed"] = value
if param == "top_p":
optional_params["top_p"] = value
if param == "frequency_penalty":
diff --git a/litellm/llms/openai.py b/litellm/llms/openai.py
index 7acbdfae0..e68a50347 100644
--- a/litellm/llms/openai.py
+++ b/litellm/llms/openai.py
@@ -21,11 +21,12 @@ from litellm.utils import (
TranscriptionResponse,
TextCompletionResponse,
)
-from typing import Callable, Optional
+from typing import Callable, Optional, Coroutine
import litellm
from .prompt_templates.factory import prompt_factory, custom_prompt
from openai import OpenAI, AsyncOpenAI
from ..types.llms.openai import *
+import openai
class OpenAIError(Exception):
@@ -96,7 +97,7 @@ class MistralConfig:
safe_prompt: Optional[bool] = None,
response_format: Optional[dict] = None,
) -> None:
- locals_ = locals()
+ locals_ = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@@ -157,6 +158,102 @@ class MistralConfig:
)
if param == "seed":
optional_params["extra_body"] = {"random_seed": value}
+ if param == "response_format":
+ optional_params["response_format"] = value
+ return optional_params
+
+
+class DeepInfraConfig:
+ """
+ Reference: https://deepinfra.com/docs/advanced/openai_api
+
+ The class `DeepInfra` provides configuration for the DeepInfra's Chat Completions API interface. Below are the parameters:
+ """
+
+ frequency_penalty: Optional[int] = None
+ function_call: Optional[Union[str, dict]] = None
+ functions: Optional[list] = None
+ logit_bias: Optional[dict] = None
+ max_tokens: Optional[int] = None
+ n: Optional[int] = None
+ presence_penalty: Optional[int] = None
+ stop: Optional[Union[str, list]] = None
+ temperature: Optional[int] = None
+ top_p: Optional[int] = None
+ response_format: Optional[dict] = None
+ tools: Optional[list] = None
+ tool_choice: Optional[Union[str, dict]] = None
+
+ def __init__(
+ self,
+ frequency_penalty: Optional[int] = None,
+ function_call: Optional[Union[str, dict]] = None,
+ functions: Optional[list] = None,
+ logit_bias: Optional[dict] = None,
+ max_tokens: Optional[int] = None,
+ n: Optional[int] = None,
+ presence_penalty: Optional[int] = None,
+ stop: Optional[Union[str, list]] = None,
+ temperature: Optional[int] = None,
+ top_p: Optional[int] = None,
+ response_format: Optional[dict] = None,
+ tools: Optional[list] = None,
+ tool_choice: Optional[Union[str, dict]] = None,
+ ) -> None:
+ locals_ = locals().copy()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params(self):
+ return [
+ "stream",
+ "frequency_penalty",
+ "function_call",
+ "functions",
+ "logit_bias",
+ "max_tokens",
+ "n",
+ "presence_penalty",
+ "stop",
+ "temperature",
+ "top_p",
+ "response_format",
+ "tools",
+ "tool_choice",
+ ]
+
+ def map_openai_params(
+ self, non_default_params: dict, optional_params: dict, model: str
+ ):
+ supported_openai_params = self.get_supported_openai_params()
+ for param, value in non_default_params.items():
+ if (
+ param == "temperature"
+ and value == 0
+ and model == "mistralai/Mistral-7B-Instruct-v0.1"
+ ): # this model does no support temperature == 0
+ value = 0.0001 # close to 0
+ if param in supported_openai_params:
+ optional_params[param] = value
return optional_params
@@ -197,6 +294,7 @@ class OpenAIConfig:
stop: Optional[Union[str, list]] = None
temperature: Optional[int] = None
top_p: Optional[int] = None
+ response_format: Optional[dict] = None
def __init__(
self,
@@ -210,8 +308,9 @@ class OpenAIConfig:
stop: Optional[Union[str, list]] = None,
temperature: Optional[int] = None,
top_p: Optional[int] = None,
+ response_format: Optional[dict] = None,
) -> None:
- locals_ = locals()
+ locals_ = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@@ -234,6 +333,52 @@ class OpenAIConfig:
and v is not None
}
+ def get_supported_openai_params(self, model: str) -> list:
+ base_params = [
+ "frequency_penalty",
+ "logit_bias",
+ "logprobs",
+ "top_logprobs",
+ "max_tokens",
+ "n",
+ "presence_penalty",
+ "seed",
+ "stop",
+ "stream",
+ "stream_options",
+ "temperature",
+ "top_p",
+ "tools",
+ "tool_choice",
+ "function_call",
+ "functions",
+ "max_retries",
+ "extra_headers",
+ ] # works across all models
+
+ model_specific_params = []
+ if (
+ model != "gpt-3.5-turbo-16k" and model != "gpt-4"
+ ): # gpt-4 does not support 'response_format'
+ model_specific_params.append("response_format")
+
+ if (
+ model in litellm.open_ai_chat_completion_models
+ ) or model in litellm.open_ai_text_completion_models:
+ model_specific_params.append(
+ "user"
+ ) # user is not a param supported by all openai-compatible endpoints - e.g. azure ai
+ return base_params + model_specific_params
+
+ def map_openai_params(
+ self, non_default_params: dict, optional_params: dict, model: str
+ ) -> dict:
+ supported_openai_params = self.get_supported_openai_params(model)
+ for param, value in non_default_params.items():
+ if param in supported_openai_params:
+ optional_params[param] = value
+ return optional_params
+
class OpenAITextCompletionConfig:
"""
@@ -294,7 +439,7 @@ class OpenAITextCompletionConfig:
temperature: Optional[float] = None,
top_p: Optional[float] = None,
) -> None:
- locals_ = locals()
+ locals_ = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@@ -363,6 +508,7 @@ class OpenAIChatCompletion(BaseLLM):
self,
model_response: ModelResponse,
timeout: Union[float, httpx.Timeout],
+ optional_params: dict,
model: Optional[str] = None,
messages: Optional[list] = None,
print_verbose: Optional[Callable] = None,
@@ -370,7 +516,6 @@ class OpenAIChatCompletion(BaseLLM):
api_base: Optional[str] = None,
acompletion: bool = False,
logging_obj=None,
- optional_params=None,
litellm_params=None,
logger_fn=None,
headers: Optional[dict] = None,
@@ -754,10 +899,10 @@ class OpenAIChatCompletion(BaseLLM):
model: str,
input: list,
timeout: float,
+ logging_obj,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
model_response: Optional[litellm.utils.EmbeddingResponse] = None,
- logging_obj=None,
optional_params=None,
client=None,
aembedding=None,
@@ -946,8 +1091,8 @@ class OpenAIChatCompletion(BaseLLM):
model_response: TranscriptionResponse,
timeout: float,
max_retries: int,
- api_key: Optional[str] = None,
- api_base: Optional[str] = None,
+ api_key: Optional[str],
+ api_base: Optional[str],
client=None,
logging_obj=None,
atranscription: bool = False,
@@ -1003,7 +1148,6 @@ class OpenAIChatCompletion(BaseLLM):
max_retries=None,
logging_obj=None,
):
- response = None
try:
if client is None:
openai_aclient = AsyncOpenAI(
@@ -1037,6 +1181,95 @@ class OpenAIChatCompletion(BaseLLM):
)
raise e
+ def audio_speech(
+ self,
+ model: str,
+ input: str,
+ voice: str,
+ optional_params: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ organization: Optional[str],
+ project: Optional[str],
+ max_retries: int,
+ timeout: Union[float, httpx.Timeout],
+ aspeech: Optional[bool] = None,
+ client=None,
+ ) -> HttpxBinaryResponseContent:
+
+ if aspeech is not None and aspeech == True:
+ return self.async_audio_speech(
+ model=model,
+ input=input,
+ voice=voice,
+ optional_params=optional_params,
+ api_key=api_key,
+ api_base=api_base,
+ organization=organization,
+ project=project,
+ max_retries=max_retries,
+ timeout=timeout,
+ client=client,
+ ) # type: ignore
+
+ if client is None:
+ openai_client = OpenAI(
+ api_key=api_key,
+ base_url=api_base,
+ organization=organization,
+ project=project,
+ http_client=litellm.client_session,
+ timeout=timeout,
+ max_retries=max_retries,
+ )
+ else:
+ openai_client = client
+
+ response = openai_client.audio.speech.create(
+ model=model,
+ voice=voice, # type: ignore
+ input=input,
+ **optional_params,
+ )
+ return response
+
+ async def async_audio_speech(
+ self,
+ model: str,
+ input: str,
+ voice: str,
+ optional_params: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ organization: Optional[str],
+ project: Optional[str],
+ max_retries: int,
+ timeout: Union[float, httpx.Timeout],
+ client=None,
+ ) -> HttpxBinaryResponseContent:
+
+ if client is None:
+ openai_client = AsyncOpenAI(
+ api_key=api_key,
+ base_url=api_base,
+ organization=organization,
+ project=project,
+ http_client=litellm.aclient_session,
+ timeout=timeout,
+ max_retries=max_retries,
+ )
+ else:
+ openai_client = client
+
+ response = await openai_client.audio.speech.create(
+ model=model,
+ voice=voice, # type: ignore
+ input=input,
+ **optional_params,
+ )
+
+ return response
+
async def ahealth_check(
self,
model: Optional[str],
@@ -1358,6 +1591,322 @@ class OpenAITextCompletion(BaseLLM):
yield transformed_chunk
+class OpenAIFilesAPI(BaseLLM):
+ """
+ OpenAI methods to support for batches
+ - create_file()
+ - retrieve_file()
+ - list_files()
+ - delete_file()
+ - file_content()
+ - update_file()
+ """
+
+ def __init__(self) -> None:
+ super().__init__()
+
+ def get_openai_client(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ _is_async: bool = False,
+ ) -> Optional[Union[OpenAI, AsyncOpenAI]]:
+ received_args = locals()
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = None
+ if client is None:
+ data = {}
+ for k, v in received_args.items():
+ if k == "self" or k == "client" or k == "_is_async":
+ pass
+ elif k == "api_base" and v is not None:
+ data["base_url"] = v
+ elif v is not None:
+ data[k] = v
+ if _is_async is True:
+ openai_client = AsyncOpenAI(**data)
+ else:
+ openai_client = OpenAI(**data) # type: ignore
+ else:
+ openai_client = client
+
+ return openai_client
+
+ async def acreate_file(
+ self,
+ create_file_data: CreateFileRequest,
+ openai_client: AsyncOpenAI,
+ ) -> FileObject:
+ response = await openai_client.files.create(**create_file_data)
+ return response
+
+ def create_file(
+ self,
+ _is_async: bool,
+ create_file_data: CreateFileRequest,
+ api_base: str,
+ api_key: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ ) -> Union[FileObject, Coroutine[Any, Any, FileObject]]:
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+
+ if _is_async is True:
+ if not isinstance(openai_client, AsyncOpenAI):
+ raise ValueError(
+ "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+ )
+ return self.acreate_file( # type: ignore
+ create_file_data=create_file_data, openai_client=openai_client
+ )
+ response = openai_client.files.create(**create_file_data)
+ return response
+
+ async def afile_content(
+ self,
+ file_content_request: FileContentRequest,
+ openai_client: AsyncOpenAI,
+ ) -> HttpxBinaryResponseContent:
+ response = await openai_client.files.content(**file_content_request)
+ return response
+
+ def file_content(
+ self,
+ _is_async: bool,
+ file_content_request: FileContentRequest,
+ api_base: str,
+ api_key: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ ) -> Union[
+ HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent]
+ ]:
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+
+ if _is_async is True:
+ if not isinstance(openai_client, AsyncOpenAI):
+ raise ValueError(
+ "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+ )
+ return self.afile_content( # type: ignore
+ file_content_request=file_content_request,
+ openai_client=openai_client,
+ )
+ response = openai_client.files.content(**file_content_request)
+
+ return response
+
+
+class OpenAIBatchesAPI(BaseLLM):
+ """
+ OpenAI methods to support for batches
+ - create_batch()
+ - retrieve_batch()
+ - cancel_batch()
+ - list_batch()
+ """
+
+ def __init__(self) -> None:
+ super().__init__()
+
+ def get_openai_client(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ _is_async: bool = False,
+ ) -> Optional[Union[OpenAI, AsyncOpenAI]]:
+ received_args = locals()
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = None
+ if client is None:
+ data = {}
+ for k, v in received_args.items():
+ if k == "self" or k == "client" or k == "_is_async":
+ pass
+ elif k == "api_base" and v is not None:
+ data["base_url"] = v
+ elif v is not None:
+ data[k] = v
+ if _is_async is True:
+ openai_client = AsyncOpenAI(**data)
+ else:
+ openai_client = OpenAI(**data) # type: ignore
+ else:
+ openai_client = client
+
+ return openai_client
+
+ async def acreate_batch(
+ self,
+ create_batch_data: CreateBatchRequest,
+ openai_client: AsyncOpenAI,
+ ) -> Batch:
+ response = await openai_client.batches.create(**create_batch_data)
+ return response
+
+ def create_batch(
+ self,
+ _is_async: bool,
+ create_batch_data: CreateBatchRequest,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ ) -> Union[Batch, Coroutine[Any, Any, Batch]]:
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+
+ if _is_async is True:
+ if not isinstance(openai_client, AsyncOpenAI):
+ raise ValueError(
+ "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+ )
+ return self.acreate_batch( # type: ignore
+ create_batch_data=create_batch_data, openai_client=openai_client
+ )
+ response = openai_client.batches.create(**create_batch_data)
+ return response
+
+ async def aretrieve_batch(
+ self,
+ retrieve_batch_data: RetrieveBatchRequest,
+ openai_client: AsyncOpenAI,
+ ) -> Batch:
+ response = await openai_client.batches.retrieve(**retrieve_batch_data)
+ return response
+
+ def retrieve_batch(
+ self,
+ _is_async: bool,
+ retrieve_batch_data: RetrieveBatchRequest,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[OpenAI] = None,
+ ):
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+
+ if _is_async is True:
+ if not isinstance(openai_client, AsyncOpenAI):
+ raise ValueError(
+ "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+ )
+ return self.aretrieve_batch( # type: ignore
+ retrieve_batch_data=retrieve_batch_data, openai_client=openai_client
+ )
+ response = openai_client.batches.retrieve(**retrieve_batch_data)
+ return response
+
+ def cancel_batch(
+ self,
+ _is_async: bool,
+ cancel_batch_data: CancelBatchRequest,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[OpenAI] = None,
+ ):
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+ response = openai_client.batches.cancel(**cancel_batch_data)
+ return response
+
+ # def list_batch(
+ # self,
+ # list_batch_data: ListBatchRequest,
+ # api_key: Optional[str],
+ # api_base: Optional[str],
+ # timeout: Union[float, httpx.Timeout],
+ # max_retries: Optional[int],
+ # organization: Optional[str],
+ # client: Optional[OpenAI] = None,
+ # ):
+ # openai_client: OpenAI = self.get_openai_client(
+ # api_key=api_key,
+ # api_base=api_base,
+ # timeout=timeout,
+ # max_retries=max_retries,
+ # organization=organization,
+ # client=client,
+ # )
+ # response = openai_client.batches.list(**list_batch_data)
+ # return response
+
+
class OpenAIAssistantsAPI(BaseLLM):
def __init__(self) -> None:
super().__init__()
diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py
index cf593369c..41ecb486c 100644
--- a/litellm/llms/prompt_templates/factory.py
+++ b/litellm/llms/prompt_templates/factory.py
@@ -12,6 +12,7 @@ from typing import (
Sequence,
)
import litellm
+import litellm.types
from litellm.types.completion import (
ChatCompletionUserMessageParam,
ChatCompletionSystemMessageParam,
@@ -20,9 +21,12 @@ from litellm.types.completion import (
ChatCompletionMessageToolCallParam,
ChatCompletionToolMessageParam,
)
+import litellm.types.llms
from litellm.types.llms.anthropic import *
import uuid
+import litellm.types.llms.vertex_ai
+
def default_pt(messages):
return " ".join(message["content"] for message in messages)
@@ -111,6 +115,26 @@ def llama_2_chat_pt(messages):
return prompt
+def convert_to_ollama_image(openai_image_url: str):
+ try:
+ if openai_image_url.startswith("http"):
+ openai_image_url = convert_url_to_base64(url=openai_image_url)
+
+ if openai_image_url.startswith("data:image/"):
+ # Extract the base64 image data
+ base64_data = openai_image_url.split("data:image/")[1].split(";base64,")[1]
+ else:
+ base64_data = openai_image_url
+
+ return base64_data
+ except Exception as e:
+ if "Error: Unable to fetch image from URL" in str(e):
+ raise e
+ raise Exception(
+ """Image url not in expected format. Example Expected input - "image_url": "data:image/jpeg;base64,{base64_image}". """
+ )
+
+
def ollama_pt(
model, messages
): # https://github.com/ollama/ollama/blob/af4cf55884ac54b9e637cd71dadfe9b7a5685877/docs/modelfile.md#template
@@ -143,8 +167,10 @@ def ollama_pt(
if element["type"] == "text":
prompt += element["text"]
elif element["type"] == "image_url":
- image_url = element["image_url"]["url"]
- images.append(image_url)
+ base64_image = convert_to_ollama_image(
+ element["image_url"]["url"]
+ )
+ images.append(base64_image)
return {"prompt": prompt, "images": images}
else:
prompt = "".join(
@@ -841,6 +867,175 @@ def anthropic_messages_pt_xml(messages: list):
# ------------------------------------------------------------------------------
+def infer_protocol_value(
+ value: Any,
+) -> Literal[
+ "string_value",
+ "number_value",
+ "bool_value",
+ "struct_value",
+ "list_value",
+ "null_value",
+ "unknown",
+]:
+ if value is None:
+ return "null_value"
+ if isinstance(value, int) or isinstance(value, float):
+ return "number_value"
+ if isinstance(value, str):
+ return "string_value"
+ if isinstance(value, bool):
+ return "bool_value"
+ if isinstance(value, dict):
+ return "struct_value"
+ if isinstance(value, list):
+ return "list_value"
+
+ return "unknown"
+
+
+def convert_to_gemini_tool_call_invoke(
+ tool_calls: list,
+) -> List[litellm.types.llms.vertex_ai.PartType]:
+ """
+ OpenAI tool invokes:
+ {
+ "role": "assistant",
+ "content": null,
+ "tool_calls": [
+ {
+ "id": "call_abc123",
+ "type": "function",
+ "function": {
+ "name": "get_current_weather",
+ "arguments": "{\n\"location\": \"Boston, MA\"\n}"
+ }
+ }
+ ]
+ },
+ """
+ """
+ Gemini tool call invokes: - https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling#submit-api-output
+ content {
+ role: "model"
+ parts [
+ {
+ function_call {
+ name: "get_current_weather"
+ args {
+ fields {
+ key: "unit"
+ value {
+ string_value: "fahrenheit"
+ }
+ }
+ fields {
+ key: "predicted_temperature"
+ value {
+ number_value: 45
+ }
+ }
+ fields {
+ key: "location"
+ value {
+ string_value: "Boston, MA"
+ }
+ }
+ }
+ },
+ {
+ function_call {
+ name: "get_current_weather"
+ args {
+ fields {
+ key: "location"
+ value {
+ string_value: "San Francisco"
+ }
+ }
+ }
+ }
+ }
+ ]
+ }
+ """
+
+ """
+ - json.load the arguments
+ - iterate through arguments -> create a FunctionCallArgs for each field
+ """
+ try:
+ _parts_list: List[litellm.types.llms.vertex_ai.PartType] = []
+ for tool in tool_calls:
+ if "function" in tool:
+ name = tool["function"].get("name", "")
+ arguments = tool["function"].get("arguments", "")
+ arguments_dict = json.loads(arguments)
+ for k, v in arguments_dict.items():
+ inferred_protocol_value = infer_protocol_value(value=v)
+ _field = litellm.types.llms.vertex_ai.Field(
+ key=k, value={inferred_protocol_value: v}
+ )
+ _fields = litellm.types.llms.vertex_ai.FunctionCallArgs(
+ fields=_field
+ )
+ function_call = litellm.types.llms.vertex_ai.FunctionCall(
+ name=name,
+ args=_fields,
+ )
+ _parts_list.append(
+ litellm.types.llms.vertex_ai.PartType(function_call=function_call)
+ )
+ return _parts_list
+ except Exception as e:
+ raise Exception(
+ "Unable to convert openai tool calls={} to gemini tool calls. Received error={}".format(
+ tool_calls, str(e)
+ )
+ )
+
+
+def convert_to_gemini_tool_call_result(
+ message: dict,
+) -> litellm.types.llms.vertex_ai.PartType:
+ """
+ OpenAI message with a tool result looks like:
+ {
+ "tool_call_id": "tool_1",
+ "role": "tool",
+ "name": "get_current_weather",
+ "content": "function result goes here",
+ },
+
+ OpenAI message with a function call result looks like:
+ {
+ "role": "function",
+ "name": "get_current_weather",
+ "content": "function result goes here",
+ }
+ """
+ content = message.get("content", "")
+ name = message.get("name", "")
+
+ # We can't determine from openai message format whether it's a successful or
+ # error call result so default to the successful result template
+ inferred_content_value = infer_protocol_value(value=content)
+
+ _field = litellm.types.llms.vertex_ai.Field(
+ key="content", value={inferred_content_value: content}
+ )
+
+ _function_call_args = litellm.types.llms.vertex_ai.FunctionCallArgs(fields=_field)
+
+ _function_response = litellm.types.llms.vertex_ai.FunctionResponse(
+ name=name, response=_function_call_args
+ )
+
+ _part = litellm.types.llms.vertex_ai.PartType(function_response=_function_response)
+
+ return _part
+
+
def convert_to_anthropic_tool_result(message: dict) -> dict:
"""
OpenAI message with a tool result looks like:
@@ -1328,6 +1523,7 @@ def _gemini_vision_convert_messages(messages: list):
# Case 1: Image from URL
image = _load_image_from_url(img)
processed_images.append(image)
+
else:
try:
from PIL import Image
@@ -1335,8 +1531,23 @@ def _gemini_vision_convert_messages(messages: list):
raise Exception(
"gemini image conversion failed please run `pip install Pillow`"
)
- # Case 2: Image filepath (e.g. temp.jpeg) given
- image = Image.open(img)
+
+ if "base64" in img:
+ # Case 2: Base64 image data
+ import base64
+ import io
+
+ # Extract the base64 image data
+ base64_data = img.split("base64,")[1]
+
+ # Decode the base64 image data
+ image_data = base64.b64decode(base64_data)
+
+ # Load the image from the decoded data
+ image = Image.open(io.BytesIO(image_data))
+ else:
+ # Case 3: Image filepath (e.g. temp.jpeg) given
+ image = Image.open(img)
processed_images.append(image)
content = [prompt] + processed_images
return content
@@ -1513,7 +1724,7 @@ def prompt_factory(
elif custom_llm_provider == "clarifai":
if "claude" in model:
return anthropic_pt(messages=messages)
-
+
elif custom_llm_provider == "perplexity":
for message in messages:
message.pop("name", None)
diff --git a/litellm/llms/replicate.py b/litellm/llms/replicate.py
index c29728134..386d24f59 100644
--- a/litellm/llms/replicate.py
+++ b/litellm/llms/replicate.py
@@ -2,11 +2,12 @@ import os, types
import json
import requests # type: ignore
import time
-from typing import Callable, Optional
-from litellm.utils import ModelResponse, Usage
-import litellm
+from typing import Callable, Optional, Union, Tuple, Any
+from litellm.utils import ModelResponse, Usage, CustomStreamWrapper
+import litellm, asyncio
import httpx # type: ignore
from .prompt_templates.factory import prompt_factory, custom_prompt
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
class ReplicateError(Exception):
@@ -145,6 +146,65 @@ def start_prediction(
)
+async def async_start_prediction(
+ version_id,
+ input_data,
+ api_token,
+ api_base,
+ logging_obj,
+ print_verbose,
+ http_handler: AsyncHTTPHandler,
+) -> str:
+ base_url = api_base
+ if "deployments" in version_id:
+ print_verbose("\nLiteLLM: Request to custom replicate deployment")
+ version_id = version_id.replace("deployments/", "")
+ base_url = f"https://api.replicate.com/v1/deployments/{version_id}"
+ print_verbose(f"Deployment base URL: {base_url}\n")
+ else: # assume it's a model
+ base_url = f"https://api.replicate.com/v1/models/{version_id}"
+ headers = {
+ "Authorization": f"Token {api_token}",
+ "Content-Type": "application/json",
+ }
+
+ initial_prediction_data = {
+ "input": input_data,
+ }
+
+ if ":" in version_id and len(version_id) > 64:
+ model_parts = version_id.split(":")
+ if (
+ len(model_parts) > 1 and len(model_parts[1]) == 64
+ ): ## checks if model name has a 64 digit code - e.g. "meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3"
+ initial_prediction_data["version"] = model_parts[1]
+
+ ## LOGGING
+ logging_obj.pre_call(
+ input=input_data["prompt"],
+ api_key="",
+ additional_args={
+ "complete_input_dict": initial_prediction_data,
+ "headers": headers,
+ "api_base": base_url,
+ },
+ )
+
+ response = await http_handler.post(
+ url="{}/predictions".format(base_url),
+ data=json.dumps(initial_prediction_data),
+ headers=headers,
+ )
+
+ if response.status_code == 201:
+ response_data = response.json()
+ return response_data.get("urls", {}).get("get")
+ else:
+ raise ReplicateError(
+ response.status_code, f"Failed to start prediction {response.text}"
+ )
+
+
# Function to handle prediction response (non-streaming)
def handle_prediction_response(prediction_url, api_token, print_verbose):
output_string = ""
@@ -178,6 +238,40 @@ def handle_prediction_response(prediction_url, api_token, print_verbose):
return output_string, logs
+async def async_handle_prediction_response(
+ prediction_url, api_token, print_verbose, http_handler: AsyncHTTPHandler
+) -> Tuple[str, Any]:
+ output_string = ""
+ headers = {
+ "Authorization": f"Token {api_token}",
+ "Content-Type": "application/json",
+ }
+
+ status = ""
+ logs = ""
+ while True and (status not in ["succeeded", "failed", "canceled"]):
+ print_verbose(f"replicate: polling endpoint: {prediction_url}")
+ await asyncio.sleep(0.5)
+ response = await http_handler.get(prediction_url, headers=headers)
+ if response.status_code == 200:
+ response_data = response.json()
+ if "output" in response_data:
+ output_string = "".join(response_data["output"])
+ print_verbose(f"Non-streamed output:{output_string}")
+ status = response_data.get("status", None)
+ logs = response_data.get("logs", "")
+ if status == "failed":
+ replicate_error = response_data.get("error", "")
+ raise ReplicateError(
+ status_code=400,
+ message=f"Error: {replicate_error}, \nReplicate logs:{logs}",
+ )
+ else:
+ # this can fail temporarily but it does not mean the replicate request failed, replicate request fails when status=="failed"
+ print_verbose("Replicate: Failed to fetch prediction status and output.")
+ return output_string, logs
+
+
# Function to handle prediction response (streaming)
def handle_prediction_response_streaming(prediction_url, api_token, print_verbose):
previous_output = ""
@@ -214,6 +308,45 @@ def handle_prediction_response_streaming(prediction_url, api_token, print_verbos
)
+# Function to handle prediction response (streaming)
+async def async_handle_prediction_response_streaming(
+ prediction_url, api_token, print_verbose
+):
+ http_handler = AsyncHTTPHandler(concurrent_limit=1)
+ previous_output = ""
+ output_string = ""
+
+ headers = {
+ "Authorization": f"Token {api_token}",
+ "Content-Type": "application/json",
+ }
+ status = ""
+ while True and (status not in ["succeeded", "failed", "canceled"]):
+ await asyncio.sleep(0.5) # prevent being rate limited by replicate
+ print_verbose(f"replicate: polling endpoint: {prediction_url}")
+ response = await http_handler.get(prediction_url, headers=headers)
+ if response.status_code == 200:
+ response_data = response.json()
+ status = response_data["status"]
+ if "output" in response_data:
+ output_string = "".join(response_data["output"])
+ new_output = output_string[len(previous_output) :]
+ print_verbose(f"New chunk: {new_output}")
+ yield {"output": new_output, "status": status}
+ previous_output = output_string
+ status = response_data["status"]
+ if status == "failed":
+ replicate_error = response_data.get("error", "")
+ raise ReplicateError(
+ status_code=400, message=f"Error: {replicate_error}"
+ )
+ else:
+ # this can fail temporarily but it does not mean the replicate request failed, replicate request fails when status=="failed"
+ print_verbose(
+ f"Replicate: Failed to fetch prediction status and output.{response.status_code}{response.text}"
+ )
+
+
# Function to extract version ID from model string
def model_to_version_id(model):
if ":" in model:
@@ -222,6 +355,39 @@ def model_to_version_id(model):
return model
+def process_response(
+ model_response: ModelResponse,
+ result: str,
+ model: str,
+ encoding: Any,
+ prompt: str,
+) -> ModelResponse:
+ if len(result) == 0: # edge case, where result from replicate is empty
+ result = " "
+
+ ## Building RESPONSE OBJECT
+ if len(result) > 1:
+ model_response["choices"][0]["message"]["content"] = result
+
+ # Calculate usage
+ prompt_tokens = len(encoding.encode(prompt, disallowed_special=()))
+ completion_tokens = len(
+ encoding.encode(
+ model_response["choices"][0]["message"].get("content", ""),
+ disallowed_special=(),
+ )
+ )
+ model_response["model"] = "replicate/" + model
+ usage = Usage(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=prompt_tokens + completion_tokens,
+ )
+ setattr(model_response, "usage", usage)
+
+ return model_response
+
+
# Main function for prediction completion
def completion(
model: str,
@@ -229,14 +395,15 @@ def completion(
api_base: str,
model_response: ModelResponse,
print_verbose: Callable,
+ optional_params: dict,
logging_obj,
api_key,
encoding,
custom_prompt_dict={},
- optional_params=None,
litellm_params=None,
logger_fn=None,
-):
+ acompletion=None,
+) -> Union[ModelResponse, CustomStreamWrapper]:
# Start a prediction and get the prediction URL
version_id = model_to_version_id(model)
## Load Config
@@ -274,6 +441,12 @@ def completion(
else:
prompt = prompt_factory(model=model, messages=messages)
+ if prompt is None or not isinstance(prompt, str):
+ raise ReplicateError(
+ status_code=400,
+ message="LiteLLM Error - prompt is not a string - {}".format(prompt),
+ )
+
# If system prompt is supported, and a system prompt is provided, use it
if system_prompt is not None:
input_data = {
@@ -285,6 +458,20 @@ def completion(
else:
input_data = {"prompt": prompt, **optional_params}
+ if acompletion is not None and acompletion == True:
+ return async_completion(
+ model_response=model_response,
+ model=model,
+ prompt=prompt,
+ encoding=encoding,
+ optional_params=optional_params,
+ version_id=version_id,
+ input_data=input_data,
+ api_key=api_key,
+ api_base=api_base,
+ logging_obj=logging_obj,
+ print_verbose=print_verbose,
+ ) # type: ignore
## COMPLETION CALL
## Replicate Compeltion calls have 2 steps
## Step1: Start Prediction: gets a prediction url
@@ -293,6 +480,7 @@ def completion(
model_response["created"] = int(
time.time()
) # for pricing this must remain right before calling api
+
prediction_url = start_prediction(
version_id,
input_data,
@@ -306,9 +494,10 @@ def completion(
# Handle the prediction response (streaming or non-streaming)
if "stream" in optional_params and optional_params["stream"] == True:
print_verbose("streaming request")
- return handle_prediction_response_streaming(
+ _response = handle_prediction_response_streaming(
prediction_url, api_key, print_verbose
)
+ return CustomStreamWrapper(_response, model, logging_obj=logging_obj, custom_llm_provider="replicate") # type: ignore
else:
result, logs = handle_prediction_response(
prediction_url, api_key, print_verbose
@@ -328,29 +517,56 @@ def completion(
print_verbose(f"raw model_response: {result}")
- if len(result) == 0: # edge case, where result from replicate is empty
- result = " "
-
- ## Building RESPONSE OBJECT
- if len(result) > 1:
- model_response["choices"][0]["message"]["content"] = result
-
- # Calculate usage
- prompt_tokens = len(encoding.encode(prompt, disallowed_special=()))
- completion_tokens = len(
- encoding.encode(
- model_response["choices"][0]["message"].get("content", ""),
- disallowed_special=(),
- )
+ return process_response(
+ model_response=model_response,
+ result=result,
+ model=model,
+ encoding=encoding,
+ prompt=prompt,
)
- model_response["model"] = "replicate/" + model
- usage = Usage(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
+
+
+async def async_completion(
+ model_response: ModelResponse,
+ model: str,
+ prompt: str,
+ encoding,
+ optional_params: dict,
+ version_id,
+ input_data,
+ api_key,
+ api_base,
+ logging_obj,
+ print_verbose,
+) -> Union[ModelResponse, CustomStreamWrapper]:
+ http_handler = AsyncHTTPHandler(concurrent_limit=1)
+ prediction_url = await async_start_prediction(
+ version_id,
+ input_data,
+ api_key,
+ api_base,
+ logging_obj=logging_obj,
+ print_verbose=print_verbose,
+ http_handler=http_handler,
+ )
+
+ if "stream" in optional_params and optional_params["stream"] == True:
+ _response = async_handle_prediction_response_streaming(
+ prediction_url, api_key, print_verbose
)
- setattr(model_response, "usage", usage)
- return model_response
+ return CustomStreamWrapper(_response, model, logging_obj=logging_obj, custom_llm_provider="replicate") # type: ignore
+
+ result, logs = await async_handle_prediction_response(
+ prediction_url, api_key, print_verbose, http_handler=http_handler
+ )
+
+ return process_response(
+ model_response=model_response,
+ result=result,
+ model=model,
+ encoding=encoding,
+ prompt=prompt,
+ )
# # Example usage:
diff --git a/litellm/llms/vertex_ai.py b/litellm/llms/vertex_ai.py
index 84fec734f..dc185aef9 100644
--- a/litellm/llms/vertex_ai.py
+++ b/litellm/llms/vertex_ai.py
@@ -3,10 +3,15 @@ import json
from enum import Enum
import requests # type: ignore
import time
-from typing import Callable, Optional, Union, List
+from typing import Callable, Optional, Union, List, Literal
from litellm.utils import ModelResponse, Usage, CustomStreamWrapper, map_finish_reason
import litellm, uuid
import httpx, inspect # type: ignore
+from litellm.types.llms.vertex_ai import *
+from litellm.llms.prompt_templates.factory import (
+ convert_to_gemini_tool_call_result,
+ convert_to_gemini_tool_call_invoke,
+)
class VertexAIError(Exception):
@@ -283,6 +288,139 @@ def _load_image_from_url(image_url: str):
return Image.from_bytes(data=image_bytes)
+def _convert_gemini_role(role: str) -> Literal["user", "model"]:
+ if role == "user":
+ return "user"
+ else:
+ return "model"
+
+
+def _process_gemini_image(image_url: str) -> PartType:
+ try:
+ if "gs://" in image_url:
+ # Case 1: Images with Cloud Storage URIs
+ # The supported MIME types for images include image/png and image/jpeg.
+ part_mime = "image/png" if "png" in image_url else "image/jpeg"
+ _file_data = FileDataType(mime_type=part_mime, file_uri=image_url)
+ return PartType(file_data=_file_data)
+ elif "https:/" in image_url:
+ # Case 2: Images with direct links
+ image = _load_image_from_url(image_url)
+ _blob = BlobType(data=image.data, mime_type=image._mime_type)
+ return PartType(inline_data=_blob)
+ elif ".mp4" in image_url and "gs://" in image_url:
+ # Case 3: Videos with Cloud Storage URIs
+ part_mime = "video/mp4"
+ _file_data = FileDataType(mime_type=part_mime, file_uri=image_url)
+ return PartType(file_data=_file_data)
+ elif "base64" in image_url:
+ # Case 4: Images with base64 encoding
+ import base64, re
+
+ # base 64 is passed as data:image/jpeg;base64,
+ image_metadata, img_without_base_64 = image_url.split(",")
+
+ # read mime_type from img_without_base_64=data:image/jpeg;base64
+ # Extract MIME type using regular expression
+ mime_type_match = re.match(r"data:(.*?);base64", image_metadata)
+
+ if mime_type_match:
+ mime_type = mime_type_match.group(1)
+ else:
+ mime_type = "image/jpeg"
+ decoded_img = base64.b64decode(img_without_base_64)
+ _blob = BlobType(data=decoded_img, mime_type=mime_type)
+ return PartType(inline_data=_blob)
+ raise Exception("Invalid image received - {}".format(image_url))
+ except Exception as e:
+ raise e
+
+
+def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]:
+ """
+ Converts given messages from OpenAI format to Gemini format
+
+ - Parts must be iterable
+ - Roles must alternate b/w 'user' and 'model' (same as anthropic -> merge consecutive roles)
+ - Please ensure that function response turn comes immediately after a function call turn
+ """
+ user_message_types = {"user", "system"}
+ contents: List[ContentType] = []
+
+ msg_i = 0
+ while msg_i < len(messages):
+ user_content: List[PartType] = []
+ init_msg_i = msg_i
+ ## MERGE CONSECUTIVE USER CONTENT ##
+ while msg_i < len(messages) and messages[msg_i]["role"] in user_message_types:
+ if isinstance(messages[msg_i]["content"], list):
+ _parts: List[PartType] = []
+ for element in messages[msg_i]["content"]:
+ if isinstance(element, dict):
+ if element["type"] == "text":
+ _part = PartType(text=element["text"])
+ _parts.append(_part)
+ elif element["type"] == "image_url":
+ image_url = element["image_url"]["url"]
+ _part = _process_gemini_image(image_url=image_url)
+ _parts.append(_part) # type: ignore
+ user_content.extend(_parts)
+ else:
+ _part = PartType(text=messages[msg_i]["content"])
+ user_content.append(_part)
+
+ msg_i += 1
+
+ if user_content:
+ contents.append(ContentType(role="user", parts=user_content))
+ assistant_content = []
+ ## MERGE CONSECUTIVE ASSISTANT CONTENT ##
+ while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
+ if isinstance(messages[msg_i]["content"], list):
+ _parts = []
+ for element in messages[msg_i]["content"]:
+ if isinstance(element, dict):
+ if element["type"] == "text":
+ _part = PartType(text=element["text"])
+ _parts.append(_part)
+ elif element["type"] == "image_url":
+ image_url = element["image_url"]["url"]
+ _part = _process_gemini_image(image_url=image_url)
+ _parts.append(_part) # type: ignore
+ assistant_content.extend(_parts)
+ elif messages[msg_i].get(
+ "tool_calls", []
+ ): # support assistant tool invoke convertion
+ assistant_content.extend(
+ convert_to_gemini_tool_call_invoke(messages[msg_i]["tool_calls"])
+ )
+ else:
+ assistant_text = (
+ messages[msg_i].get("content") or ""
+ ) # either string or none
+ if assistant_text:
+ assistant_content.append(PartType(text=assistant_text))
+
+ msg_i += 1
+
+ if assistant_content:
+ contents.append(ContentType(role="model", parts=assistant_content))
+
+ ## APPEND TOOL CALL MESSAGES ##
+ if msg_i < len(messages) and messages[msg_i]["role"] == "tool":
+ _part = convert_to_gemini_tool_call_result(messages[msg_i])
+ contents.append(ContentType(parts=[_part])) # type: ignore
+ msg_i += 1
+ if msg_i == init_msg_i: # prevent infinite loops
+ raise Exception(
+ "Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format(
+ messages[msg_i]
+ )
+ )
+
+ return contents
+
+
def _gemini_vision_convert_messages(messages: list):
"""
Converts given messages for GPT-4 Vision to Gemini format.
@@ -396,10 +534,10 @@ def completion(
print_verbose: Callable,
encoding,
logging_obj,
+ optional_params: dict,
vertex_project=None,
vertex_location=None,
vertex_credentials=None,
- optional_params=None,
litellm_params=None,
logger_fn=None,
acompletion: bool = False,
@@ -556,6 +694,7 @@ def completion(
"model_response": model_response,
"encoding": encoding,
"messages": messages,
+ "request_str": request_str,
"print_verbose": print_verbose,
"client_options": client_options,
"instances": instances,
@@ -574,11 +713,9 @@ def completion(
print_verbose("\nMaking VertexAI Gemini Pro / Pro Vision Call")
print_verbose(f"\nProcessing input messages = {messages}")
tools = optional_params.pop("tools", None)
- prompt, images = _gemini_vision_convert_messages(messages=messages)
- content = [prompt] + images
+ content = _gemini_convert_messages_with_history(messages=messages)
stream = optional_params.pop("stream", False)
if stream == True:
-
request_str += f"response = llm_model.generate_content({content}, generation_config=GenerationConfig(**{optional_params}), safety_settings={safety_settings}, stream={stream})\n"
logging_obj.pre_call(
input=prompt,
@@ -589,7 +726,7 @@ def completion(
},
)
- model_response = llm_model.generate_content(
+ _model_response = llm_model.generate_content(
contents=content,
generation_config=optional_params,
safety_settings=safety_settings,
@@ -597,7 +734,7 @@ def completion(
tools=tools,
)
- return model_response
+ return _model_response
request_str += f"response = llm_model.generate_content({content})\n"
## LOGGING
@@ -850,12 +987,12 @@ async def async_completion(
mode: str,
prompt: str,
model: str,
+ messages: list,
model_response: ModelResponse,
- logging_obj=None,
- request_str=None,
+ request_str: str,
+ print_verbose: Callable,
+ logging_obj,
encoding=None,
- messages=None,
- print_verbose=None,
client_options=None,
instances=None,
vertex_project=None,
@@ -875,8 +1012,7 @@ async def async_completion(
tools = optional_params.pop("tools", None)
stream = optional_params.pop("stream", False)
- prompt, images = _gemini_vision_convert_messages(messages=messages)
- content = [prompt] + images
+ content = _gemini_convert_messages_with_history(messages=messages)
request_str += f"response = llm_model.generate_content({content})\n"
## LOGGING
@@ -1076,11 +1212,11 @@ async def async_streaming(
prompt: str,
model: str,
model_response: ModelResponse,
- logging_obj=None,
- request_str=None,
+ messages: list,
+ print_verbose: Callable,
+ logging_obj,
+ request_str: str,
encoding=None,
- messages=None,
- print_verbose=None,
client_options=None,
instances=None,
vertex_project=None,
@@ -1097,8 +1233,8 @@ async def async_streaming(
print_verbose("\nMaking VertexAI Gemini Pro Vision Call")
print_verbose(f"\nProcessing input messages = {messages}")
- prompt, images = _gemini_vision_convert_messages(messages=messages)
- content = [prompt] + images
+ content = _gemini_convert_messages_with_history(messages=messages)
+
request_str += f"response = llm_model.generate_content({content}, generation_config=GenerationConfig(**{optional_params}), stream={stream})\n"
logging_obj.pre_call(
input=prompt,
diff --git a/litellm/llms/vertex_ai_anthropic.py b/litellm/llms/vertex_ai_anthropic.py
index 3bdcf4fd6..065294280 100644
--- a/litellm/llms/vertex_ai_anthropic.py
+++ b/litellm/llms/vertex_ai_anthropic.py
@@ -35,7 +35,7 @@ class VertexAIError(Exception):
class VertexAIAnthropicConfig:
"""
- Reference: https://docs.anthropic.com/claude/reference/messages_post
+ Reference:https://docs.anthropic.com/claude/reference/messages_post
Note that the API for Claude on Vertex differs from the Anthropic API documentation in the following ways:
diff --git a/litellm/llms/vertex_httpx.py b/litellm/llms/vertex_httpx.py
new file mode 100644
index 000000000..b8c698c90
--- /dev/null
+++ b/litellm/llms/vertex_httpx.py
@@ -0,0 +1,224 @@
+import os, types
+import json
+from enum import Enum
+import requests # type: ignore
+import time
+from typing import Callable, Optional, Union, List, Any, Tuple
+from litellm.utils import ModelResponse, Usage, CustomStreamWrapper, map_finish_reason
+import litellm, uuid
+import httpx, inspect # type: ignore
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from .base import BaseLLM
+
+
+class VertexAIError(Exception):
+ def __init__(self, status_code, message):
+ self.status_code = status_code
+ self.message = message
+ self.request = httpx.Request(
+ method="POST", url=" https://cloud.google.com/vertex-ai/"
+ )
+ self.response = httpx.Response(status_code=status_code, request=self.request)
+ super().__init__(
+ self.message
+ ) # Call the base class constructor with the parameters it needs
+
+
+class VertexLLM(BaseLLM):
+ def __init__(self) -> None:
+ super().__init__()
+ self.access_token: Optional[str] = None
+ self.refresh_token: Optional[str] = None
+ self._credentials: Optional[Any] = None
+ self.project_id: Optional[str] = None
+ self.async_handler: Optional[AsyncHTTPHandler] = None
+
+ def load_auth(self) -> Tuple[Any, str]:
+ from google.auth.transport.requests import Request # type: ignore[import-untyped]
+ from google.auth.credentials import Credentials # type: ignore[import-untyped]
+ import google.auth as google_auth
+
+ credentials, project_id = google_auth.default(
+ scopes=["https://www.googleapis.com/auth/cloud-platform"],
+ )
+
+ credentials.refresh(Request())
+
+ if not project_id:
+ raise ValueError("Could not resolve project_id")
+
+ if not isinstance(project_id, str):
+ raise TypeError(
+ f"Expected project_id to be a str but got {type(project_id)}"
+ )
+
+ return credentials, project_id
+
+ def refresh_auth(self, credentials: Any) -> None:
+ from google.auth.transport.requests import Request # type: ignore[import-untyped]
+
+ credentials.refresh(Request())
+
+ def _prepare_request(self, request: httpx.Request) -> None:
+ access_token = self._ensure_access_token()
+
+ if request.headers.get("Authorization"):
+ # already authenticated, nothing for us to do
+ return
+
+ request.headers["Authorization"] = f"Bearer {access_token}"
+
+ def _ensure_access_token(self) -> str:
+ if self.access_token is not None:
+ return self.access_token
+
+ if not self._credentials:
+ self._credentials, project_id = self.load_auth()
+ if not self.project_id:
+ self.project_id = project_id
+ else:
+ self.refresh_auth(self._credentials)
+
+ if not self._credentials.token:
+ raise RuntimeError("Could not resolve API token from the environment")
+
+ assert isinstance(self._credentials.token, str)
+ return self._credentials.token
+
+ def image_generation(
+ self,
+ prompt: str,
+ vertex_project: str,
+ vertex_location: str,
+ model: Optional[
+ str
+ ] = "imagegeneration", # vertex ai uses imagegeneration as the default model
+ client: Optional[AsyncHTTPHandler] = None,
+ optional_params: Optional[dict] = None,
+ timeout: Optional[int] = None,
+ logging_obj=None,
+ model_response=None,
+ aimg_generation=False,
+ ):
+ if aimg_generation == True:
+ response = self.aimage_generation(
+ prompt=prompt,
+ vertex_project=vertex_project,
+ vertex_location=vertex_location,
+ model=model,
+ client=client,
+ optional_params=optional_params,
+ timeout=timeout,
+ logging_obj=logging_obj,
+ model_response=model_response,
+ )
+ return response
+
+ async def aimage_generation(
+ self,
+ prompt: str,
+ vertex_project: str,
+ vertex_location: str,
+ model_response: litellm.ImageResponse,
+ model: Optional[
+ str
+ ] = "imagegeneration", # vertex ai uses imagegeneration as the default model
+ client: Optional[AsyncHTTPHandler] = None,
+ optional_params: Optional[dict] = None,
+ timeout: Optional[int] = None,
+ logging_obj=None,
+ ):
+ response = None
+ if client is None:
+ _params = {}
+ if timeout is not None:
+ if isinstance(timeout, float) or isinstance(timeout, int):
+ _httpx_timeout = httpx.Timeout(timeout)
+ _params["timeout"] = _httpx_timeout
+ else:
+ _params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
+
+ self.async_handler = AsyncHTTPHandler(**_params) # type: ignore
+ else:
+ self.async_handler = client # type: ignore
+
+ # make POST request to
+ # https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagegeneration:predict
+ url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:predict"
+
+ """
+ Docs link: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/imagegeneration?project=adroit-crow-413218
+ curl -X POST \
+ -H "Authorization: Bearer $(gcloud auth print-access-token)" \
+ -H "Content-Type: application/json; charset=utf-8" \
+ -d {
+ "instances": [
+ {
+ "prompt": "a cat"
+ }
+ ],
+ "parameters": {
+ "sampleCount": 1
+ }
+ } \
+ "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagegeneration:predict"
+ """
+ auth_header = self._ensure_access_token()
+ optional_params = optional_params or {
+ "sampleCount": 1
+ } # default optional params
+
+ request_data = {
+ "instances": [{"prompt": prompt}],
+ "parameters": optional_params,
+ }
+
+ request_str = f"\n curl -X POST \\\n -H \"Authorization: Bearer {auth_header[:10] + 'XXXXXXXXXX'}\" \\\n -H \"Content-Type: application/json; charset=utf-8\" \\\n -d {request_data} \\\n \"{url}\""
+ logging_obj.pre_call(
+ input=prompt,
+ api_key=None,
+ additional_args={
+ "complete_input_dict": optional_params,
+ "request_str": request_str,
+ },
+ )
+
+ response = await self.async_handler.post(
+ url=url,
+ headers={
+ "Content-Type": "application/json; charset=utf-8",
+ "Authorization": f"Bearer {auth_header}",
+ },
+ data=json.dumps(request_data),
+ )
+
+ if response.status_code != 200:
+ raise Exception(f"Error: {response.status_code} {response.text}")
+ """
+ Vertex AI Image generation response example:
+ {
+ "predictions": [
+ {
+ "bytesBase64Encoded": "BASE64_IMG_BYTES",
+ "mimeType": "image/png"
+ },
+ {
+ "mimeType": "image/png",
+ "bytesBase64Encoded": "BASE64_IMG_BYTES"
+ }
+ ]
+ }
+ """
+
+ _json_response = response.json()
+ _predictions = _json_response["predictions"]
+
+ _response_data: List[litellm.ImageObject] = []
+ for _prediction in _predictions:
+ _bytes_base64_encoded = _prediction["bytesBase64Encoded"]
+ image_object = litellm.ImageObject(b64_json=_bytes_base64_encoded)
+ _response_data.append(image_object)
+
+ model_response.data = _response_data
+
+ return model_response
diff --git a/litellm/main.py b/litellm/main.py
index 3429cab4d..525a39d68 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -14,7 +14,6 @@ from functools import partial
import dotenv, traceback, random, asyncio, time, contextvars
from copy import deepcopy
import httpx
-
import litellm
from ._logging import verbose_logger
from litellm import ( # type: ignore
@@ -73,12 +72,14 @@ from .llms import (
)
from .llms.openai import OpenAIChatCompletion, OpenAITextCompletion
from .llms.azure import AzureChatCompletion
+from .llms.databricks import DatabricksChatCompletion
from .llms.azure_text import AzureTextCompletion
from .llms.anthropic import AnthropicChatCompletion
from .llms.anthropic_text import AnthropicTextCompletion
from .llms.huggingface_restapi import Huggingface
from .llms.predibase import PredibaseChatCompletion
from .llms.bedrock_httpx import BedrockLLM
+from .llms.vertex_httpx import VertexLLM
from .llms.triton import TritonChatCompletion
from .llms.prompt_templates.factory import (
prompt_factory,
@@ -90,6 +91,7 @@ import tiktoken
from concurrent.futures import ThreadPoolExecutor
from typing import Callable, List, Optional, Dict, Union, Mapping
from .caching import enable_cache, disable_cache, update_cache
+from .types.llms.openai import HttpxBinaryResponseContent
encoding = tiktoken.get_encoding("cl100k_base")
from litellm.utils import (
@@ -110,6 +112,7 @@ from litellm.utils import (
####### ENVIRONMENT VARIABLES ###################
openai_chat_completions = OpenAIChatCompletion()
openai_text_completions = OpenAITextCompletion()
+databricks_chat_completions = DatabricksChatCompletion()
anthropic_chat_completions = AnthropicChatCompletion()
anthropic_text_completions = AnthropicTextCompletion()
azure_chat_completions = AzureChatCompletion()
@@ -118,6 +121,7 @@ huggingface = Huggingface()
predibase_chat_completions = PredibaseChatCompletion()
triton_chat_completions = TritonChatCompletion()
bedrock_chat_completion = BedrockLLM()
+vertex_chat_completion = VertexLLM()
####### COMPLETION ENDPOINTS ################
@@ -290,6 +294,7 @@ async def acompletion(
"api_version": api_version,
"api_key": api_key,
"model_list": model_list,
+ "extra_headers": extra_headers,
"acompletion": True, # assuming this is a required parameter
}
if custom_llm_provider is None:
@@ -320,12 +325,14 @@ async def acompletion(
or custom_llm_provider == "huggingface"
or custom_llm_provider == "ollama"
or custom_llm_provider == "ollama_chat"
+ or custom_llm_provider == "replicate"
or custom_llm_provider == "vertex_ai"
or custom_llm_provider == "gemini"
or custom_llm_provider == "sagemaker"
or custom_llm_provider == "anthropic"
or custom_llm_provider == "predibase"
- or (custom_llm_provider == "bedrock" and "cohere" in model)
+ or custom_llm_provider == "bedrock"
+ or custom_llm_provider == "databricks"
or custom_llm_provider in litellm.openai_compatible_providers
): # currently implemented aiohttp calls for just azure, openai, hf, ollama, vertex ai soon all.
init_response = await loop.run_in_executor(None, func_with_context)
@@ -367,6 +374,8 @@ async def acompletion(
async def _async_streaming(response, model, custom_llm_provider, args):
try:
print_verbose(f"received response in _async_streaming: {response}")
+ if asyncio.iscoroutine(response):
+ response = await response
async for line in response:
print_verbose(f"line in async streaming: {line}")
yield line
@@ -412,6 +421,8 @@ def mock_completion(
api_key="mock-key",
)
if isinstance(mock_response, Exception):
+ if isinstance(mock_response, openai.APIError):
+ raise mock_response
raise litellm.APIError(
status_code=500, # type: ignore
message=str(mock_response),
@@ -455,7 +466,9 @@ def mock_completion(
return model_response
- except:
+ except Exception as e:
+ if isinstance(e, openai.APIError):
+ raise e
traceback.print_exc()
raise Exception("Mock completion response failed")
@@ -481,7 +494,7 @@ def completion(
response_format: Optional[dict] = None,
seed: Optional[int] = None,
tools: Optional[List] = None,
- tool_choice: Optional[str] = None,
+ tool_choice: Optional[Union[str, dict]] = None,
logprobs: Optional[bool] = None,
top_logprobs: Optional[int] = None,
deployment_id=None,
@@ -552,7 +565,7 @@ def completion(
model_info = kwargs.get("model_info", None)
proxy_server_request = kwargs.get("proxy_server_request", None)
fallbacks = kwargs.get("fallbacks", None)
- headers = kwargs.get("headers", None)
+ headers = kwargs.get("headers", None) or extra_headers
num_retries = kwargs.get("num_retries", None) ## deprecated
max_retries = kwargs.get("max_retries", None)
context_window_fallback_dict = kwargs.get("context_window_fallback_dict", None)
@@ -667,6 +680,7 @@ def completion(
"region_name",
"allowed_model_region",
"model_config",
+ "fastest_response",
]
default_params = openai_params + litellm_params
@@ -674,20 +688,6 @@ def completion(
k: v for k, v in kwargs.items() if k not in default_params
} # model-specific params - pass them straight to the model/provider
- ### TIMEOUT LOGIC ###
- timeout = timeout or kwargs.get("request_timeout", 600) or 600
- # set timeout for 10 minutes by default
-
- if (
- timeout is not None
- and isinstance(timeout, httpx.Timeout)
- and supports_httpx_timeout(custom_llm_provider) == False
- ):
- read_timeout = timeout.read or 600
- timeout = read_timeout # default 10 min timeout
- elif timeout is not None and not isinstance(timeout, httpx.Timeout):
- timeout = float(timeout) # type: ignore
-
try:
if base_url is not None:
api_base = base_url
@@ -727,6 +727,16 @@ def completion(
"aws_region_name", None
) # support region-based pricing for bedrock
+ ### TIMEOUT LOGIC ###
+ timeout = timeout or kwargs.get("request_timeout", 600) or 600
+ # set timeout for 10 minutes by default
+ if isinstance(timeout, httpx.Timeout) and not supports_httpx_timeout(
+ custom_llm_provider
+ ):
+ timeout = timeout.read or 600 # default 10 min timeout
+ elif not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+
### REGISTER CUSTOM MODEL PRICING -- IF GIVEN ###
if input_cost_per_token is not None and output_cost_per_token is not None:
litellm.register_model(
@@ -860,6 +870,7 @@ def completion(
user=user,
optional_params=optional_params,
litellm_params=litellm_params,
+ custom_llm_provider=custom_llm_provider,
)
if mock_response:
return mock_completion(
@@ -1192,7 +1203,7 @@ def completion(
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
- model_response = replicate.completion(
+ model_response = replicate.completion( # type: ignore
model=model,
messages=messages,
api_base=api_base,
@@ -1205,12 +1216,10 @@ def completion(
api_key=replicate_key,
logging_obj=logging,
custom_prompt_dict=custom_prompt_dict,
+ acompletion=acompletion,
)
- if "stream" in optional_params and optional_params["stream"] == True:
- # don't try to access stream object,
- model_response = CustomStreamWrapper(model_response, model, logging_obj=logging, custom_llm_provider="replicate") # type: ignore
- if optional_params.get("stream", False) or acompletion == True:
+ if optional_params.get("stream", False) == True:
## LOGGING
logging.post_call(
input=messages,
@@ -1616,6 +1625,61 @@ def completion(
)
return response
response = model_response
+ elif custom_llm_provider == "databricks":
+ api_base = (
+ api_base # for databricks we check in get_llm_provider and pass in the api base from there
+ or litellm.api_base
+ or os.getenv("DATABRICKS_API_BASE")
+ )
+
+ # set API KEY
+ api_key = (
+ api_key
+ or litellm.api_key # for databricks we check in get_llm_provider and pass in the api key from there
+ or litellm.databricks_key
+ or get_secret("DATABRICKS_API_KEY")
+ )
+
+ headers = headers or litellm.headers
+
+ ## COMPLETION CALL
+ try:
+ response = databricks_chat_completions.completion(
+ model=model,
+ messages=messages,
+ headers=headers,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ api_key=api_key,
+ api_base=api_base,
+ acompletion=acompletion,
+ logging_obj=logging,
+ optional_params=optional_params,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ timeout=timeout, # type: ignore
+ custom_prompt_dict=custom_prompt_dict,
+ client=client, # pass AsyncOpenAI, OpenAI client
+ encoding=encoding,
+ )
+ except Exception as e:
+ ## LOGGING - log the original exception returned
+ logging.post_call(
+ input=messages,
+ api_key=api_key,
+ original_response=str(e),
+ additional_args={"headers": headers},
+ )
+ raise e
+
+ if optional_params.get("stream", False):
+ ## LOGGING
+ logging.post_call(
+ input=messages,
+ api_key=api_key,
+ original_response=response,
+ additional_args={"headers": headers},
+ )
elif custom_llm_provider == "openrouter":
api_base = api_base or litellm.api_base or "https://openrouter.ai/api/v1"
@@ -1984,23 +2048,9 @@ def completion(
# boto3 reads keys from .env
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
- if "cohere" in model:
- response = bedrock_chat_completion.completion(
- model=model,
- messages=messages,
- custom_prompt_dict=litellm.custom_prompt_dict,
- model_response=model_response,
- print_verbose=print_verbose,
- optional_params=optional_params,
- litellm_params=litellm_params,
- logger_fn=logger_fn,
- encoding=encoding,
- logging_obj=logging,
- extra_headers=extra_headers,
- timeout=timeout,
- acompletion=acompletion,
- )
- else:
+ if (
+ "aws_bedrock_client" in optional_params
+ ): # use old bedrock flow for aws_bedrock_client users.
response = bedrock.completion(
model=model,
messages=messages,
@@ -2036,7 +2086,23 @@ def completion(
custom_llm_provider="bedrock",
logging_obj=logging,
)
-
+ else:
+ response = bedrock_chat_completion.completion(
+ model=model,
+ messages=messages,
+ custom_prompt_dict=custom_prompt_dict,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ optional_params=optional_params,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ encoding=encoding,
+ logging_obj=logging,
+ extra_headers=extra_headers,
+ timeout=timeout,
+ acompletion=acompletion,
+ client=client,
+ )
if optional_params.get("stream", False):
## LOGGING
logging.post_call(
@@ -2477,6 +2543,7 @@ def batch_completion(
list: A list of completion results.
"""
args = locals()
+
batch_messages = messages
completions = []
model = model
@@ -2530,7 +2597,15 @@ def batch_completion(
completions.append(future)
# Retrieve the results from the futures
- results = [future.result() for future in completions]
+ # results = [future.result() for future in completions]
+ # return exceptions if any
+ results = []
+ for future in completions:
+ try:
+ results.append(future.result())
+ except Exception as exc:
+ results.append(exc)
+
return results
@@ -2669,7 +2744,7 @@ def batch_completion_models_all_responses(*args, **kwargs):
### EMBEDDING ENDPOINTS ####################
@client
-async def aembedding(*args, **kwargs):
+async def aembedding(*args, **kwargs) -> EmbeddingResponse:
"""
Asynchronously calls the `embedding` function with the given arguments and keyword arguments.
@@ -2714,12 +2789,13 @@ async def aembedding(*args, **kwargs):
or custom_llm_provider == "fireworks_ai"
or custom_llm_provider == "ollama"
or custom_llm_provider == "vertex_ai"
+ or custom_llm_provider == "databricks"
): # currently implemented aiohttp calls for just azure and openai, soon all.
# Await normally
init_response = await loop.run_in_executor(None, func_with_context)
- if isinstance(init_response, dict) or isinstance(
- init_response, ModelResponse
- ): ## CACHING SCENARIO
+ if isinstance(init_response, dict):
+ response = EmbeddingResponse(**init_response)
+ elif isinstance(init_response, EmbeddingResponse): ## CACHING SCENARIO
response = init_response
elif asyncio.iscoroutine(init_response):
response = await init_response
@@ -2759,7 +2835,7 @@ def embedding(
litellm_logging_obj=None,
logger_fn=None,
**kwargs,
-):
+) -> EmbeddingResponse:
"""
Embedding function that calls an API to generate embeddings for the given input.
@@ -2907,7 +2983,7 @@ def embedding(
)
try:
response = None
- logging = litellm_logging_obj
+ logging: Logging = litellm_logging_obj # type: ignore
logging.update_environment_variables(
model=model,
user=user,
@@ -2997,6 +3073,32 @@ def embedding(
client=client,
aembedding=aembedding,
)
+ elif custom_llm_provider == "databricks":
+ api_base = (
+ api_base or litellm.api_base or get_secret("DATABRICKS_API_BASE")
+ ) # type: ignore
+
+ # set API KEY
+ api_key = (
+ api_key
+ or litellm.api_key
+ or litellm.databricks_key
+ or get_secret("DATABRICKS_API_KEY")
+ ) # type: ignore
+
+ ## EMBEDDING CALL
+ response = databricks_chat_completions.embedding(
+ model=model,
+ input=input,
+ api_base=api_base,
+ api_key=api_key,
+ logging_obj=logging,
+ timeout=timeout,
+ model_response=EmbeddingResponse(),
+ optional_params=optional_params,
+ client=client,
+ aembedding=aembedding,
+ )
elif custom_llm_provider == "cohere":
cohere_key = (
api_key
@@ -3856,6 +3958,36 @@ def image_generation(
model_response=model_response,
aimg_generation=aimg_generation,
)
+ elif custom_llm_provider == "vertex_ai":
+ vertex_ai_project = (
+ optional_params.pop("vertex_project", None)
+ or optional_params.pop("vertex_ai_project", None)
+ or litellm.vertex_project
+ or get_secret("VERTEXAI_PROJECT")
+ )
+ vertex_ai_location = (
+ optional_params.pop("vertex_location", None)
+ or optional_params.pop("vertex_ai_location", None)
+ or litellm.vertex_location
+ or get_secret("VERTEXAI_LOCATION")
+ )
+ vertex_credentials = (
+ optional_params.pop("vertex_credentials", None)
+ or optional_params.pop("vertex_ai_credentials", None)
+ or get_secret("VERTEXAI_CREDENTIALS")
+ )
+ model_response = vertex_chat_completion.image_generation(
+ model=model,
+ prompt=prompt,
+ timeout=timeout,
+ logging_obj=litellm_logging_obj,
+ optional_params=optional_params,
+ model_response=model_response,
+ vertex_project=vertex_ai_project,
+ vertex_location=vertex_ai_location,
+ aimg_generation=aimg_generation,
+ )
+
return model_response
except Exception as e:
## Map to OpenAI Exception
@@ -3999,6 +4131,24 @@ def transcription(
max_retries=max_retries,
)
elif custom_llm_provider == "openai":
+ api_base = (
+ api_base
+ or litellm.api_base
+ or get_secret("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ ) # type: ignore
+ openai.organization = (
+ litellm.organization
+ or get_secret("OPENAI_ORGANIZATION")
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ api_key
+ or litellm.api_key
+ or litellm.openai_key
+ or get_secret("OPENAI_API_KEY")
+ ) # type: ignore
response = openai_chat_completions.audio_transcriptions(
model=model,
audio_file=file,
@@ -4008,6 +4158,139 @@ def transcription(
timeout=timeout,
logging_obj=litellm_logging_obj,
max_retries=max_retries,
+ api_base=api_base,
+ api_key=api_key,
+ )
+ return response
+
+
+@client
+async def aspeech(*args, **kwargs) -> HttpxBinaryResponseContent:
+ """
+ Calls openai tts endpoints.
+ """
+ loop = asyncio.get_event_loop()
+ model = args[0] if len(args) > 0 else kwargs["model"]
+ ### PASS ARGS TO Image Generation ###
+ kwargs["aspeech"] = True
+ custom_llm_provider = kwargs.get("custom_llm_provider", None)
+ try:
+ # Use a partial function to pass your keyword arguments
+ func = partial(speech, *args, **kwargs)
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+
+ _, custom_llm_provider, _, _ = get_llm_provider(
+ model=model, api_base=kwargs.get("api_base", None)
+ )
+
+ # Await normally
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ # Call the synchronous function using run_in_executor
+ response = await loop.run_in_executor(None, func_with_context)
+ return response # type: ignore
+ except Exception as e:
+ custom_llm_provider = custom_llm_provider or "openai"
+ raise exception_type(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs=args,
+ extra_kwargs=kwargs,
+ )
+
+
+@client
+def speech(
+ model: str,
+ input: str,
+ voice: str,
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ organization: Optional[str] = None,
+ project: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ metadata: Optional[dict] = None,
+ timeout: Optional[Union[float, httpx.Timeout]] = None,
+ response_format: Optional[str] = None,
+ speed: Optional[int] = None,
+ client=None,
+ headers: Optional[dict] = None,
+ custom_llm_provider: Optional[str] = None,
+ aspeech: Optional[bool] = None,
+ **kwargs,
+) -> HttpxBinaryResponseContent:
+
+ model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore
+
+ optional_params = {}
+ if response_format is not None:
+ optional_params["response_format"] = response_format
+ if speed is not None:
+ optional_params["speed"] = speed # type: ignore
+
+ if timeout is None:
+ timeout = litellm.request_timeout
+
+ if max_retries is None:
+ max_retries = litellm.num_retries or openai.DEFAULT_MAX_RETRIES
+ response: Optional[HttpxBinaryResponseContent] = None
+ if custom_llm_provider == "openai":
+ api_base = (
+ api_base # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ or litellm.api_base
+ or get_secret("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ ) # type: ignore
+ # set API KEY
+ api_key = (
+ api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or get_secret("OPENAI_API_KEY")
+ ) # type: ignore
+
+ organization = (
+ organization
+ or litellm.organization
+ or get_secret("OPENAI_ORGANIZATION")
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ ) # type: ignore
+
+ project = (
+ project
+ or litellm.project
+ or get_secret("OPENAI_PROJECT")
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ ) # type: ignore
+
+ headers = headers or litellm.headers
+
+ response = openai_chat_completions.audio_speech(
+ model=model,
+ input=input,
+ voice=voice,
+ optional_params=optional_params,
+ api_key=api_key,
+ api_base=api_base,
+ organization=organization,
+ project=project,
+ max_retries=max_retries,
+ timeout=timeout,
+ client=client, # pass AsyncOpenAI, OpenAI client
+ aspeech=aspeech,
+ )
+
+ if response is None:
+ raise Exception(
+ "Unable to map the custom llm provider={} to a known provider={}.".format(
+ custom_llm_provider, litellm.provider_list
+ )
)
return response
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index ff9194578..f090c5a3f 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -234,6 +234,24 @@
"litellm_provider": "openai",
"mode": "chat"
},
+ "ft:davinci-002": {
+ "max_tokens": 16384,
+ "max_input_tokens": 16384,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.000002,
+ "output_cost_per_token": 0.000002,
+ "litellm_provider": "text-completion-openai",
+ "mode": "completion"
+ },
+ "ft:babbage-002": {
+ "max_tokens": 16384,
+ "max_input_tokens": 16384,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.0000004,
+ "output_cost_per_token": 0.0000004,
+ "litellm_provider": "text-completion-openai",
+ "mode": "completion"
+ },
"text-embedding-3-large": {
"max_tokens": 8191,
"max_input_tokens": 8191,
@@ -500,8 +518,8 @@
"max_tokens": 4096,
"max_input_tokens": 4097,
"max_output_tokens": 4096,
- "input_cost_per_token": 0.0000015,
- "output_cost_per_token": 0.000002,
+ "input_cost_per_token": 0.0000005,
+ "output_cost_per_token": 0.0000015,
"litellm_provider": "azure",
"mode": "chat",
"supports_function_calling": true
@@ -1247,13 +1265,19 @@
"max_tokens": 4096,
"max_input_tokens": 200000,
"max_output_tokens": 4096,
- "input_cost_per_token": 0.0000015,
- "output_cost_per_token": 0.0000075,
+ "input_cost_per_token": 0.000015,
+ "output_cost_per_token": 0.000075,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
"supports_vision": true
},
+ "vertex_ai/imagegeneration@006": {
+ "cost_per_image": 0.020,
+ "litellm_provider": "vertex_ai-image-models",
+ "mode": "image_generation",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
+ },
"textembedding-gecko": {
"max_tokens": 3072,
"max_input_tokens": 3072,
@@ -1385,6 +1409,24 @@
"mode": "completion",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
},
+ "gemini/gemini-1.5-flash-latest": {
+ "max_tokens": 8192,
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 8192,
+ "max_images_per_prompt": 3000,
+ "max_videos_per_prompt": 10,
+ "max_video_length": 1,
+ "max_audio_length_hours": 8.4,
+ "max_audio_per_prompt": 1,
+ "max_pdf_size_mb": 30,
+ "input_cost_per_token": 0,
+ "output_cost_per_token": 0,
+ "litellm_provider": "vertex_ai-language-models",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
"gemini/gemini-pro": {
"max_tokens": 8192,
"max_input_tokens": 32760,
@@ -1563,36 +1605,36 @@
"mode": "chat"
},
"replicate/meta/llama-3-70b": {
- "max_tokens": 4096,
- "max_input_tokens": 4096,
- "max_output_tokens": 4096,
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
"input_cost_per_token": 0.00000065,
"output_cost_per_token": 0.00000275,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-3-70b-instruct": {
- "max_tokens": 4096,
- "max_input_tokens": 4096,
- "max_output_tokens": 4096,
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
"input_cost_per_token": 0.00000065,
"output_cost_per_token": 0.00000275,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-3-8b": {
- "max_tokens": 4096,
- "max_input_tokens": 4096,
- "max_output_tokens": 4096,
+ "max_tokens": 8086,
+ "max_input_tokens": 8086,
+ "max_output_tokens": 8086,
"input_cost_per_token": 0.00000005,
"output_cost_per_token": 0.00000025,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-3-8b-instruct": {
- "max_tokens": 4096,
- "max_input_tokens": 4096,
- "max_output_tokens": 4096,
+ "max_tokens": 8086,
+ "max_input_tokens": 8086,
+ "max_output_tokens": 8086,
"input_cost_per_token": 0.00000005,
"output_cost_per_token": 0.00000025,
"litellm_provider": "replicate",
@@ -1856,7 +1898,7 @@
"mode": "chat"
},
"openrouter/meta-llama/codellama-34b-instruct": {
- "max_tokens": 8096,
+ "max_tokens": 8192,
"input_cost_per_token": 0.0000005,
"output_cost_per_token": 0.0000005,
"litellm_provider": "openrouter",
@@ -3348,9 +3390,10 @@
"output_cost_per_token": 0.00000015,
"litellm_provider": "anyscale",
"mode": "chat",
- "supports_function_calling": true
+ "supports_function_calling": true,
+ "source": "https://docs.anyscale.com/preview/endpoints/text-generation/supported-models/mistralai-Mistral-7B-Instruct-v0.1"
},
- "anyscale/Mixtral-8x7B-Instruct-v0.1": {
+ "anyscale/mistralai/Mixtral-8x7B-Instruct-v0.1": {
"max_tokens": 16384,
"max_input_tokens": 16384,
"max_output_tokens": 16384,
@@ -3358,7 +3401,19 @@
"output_cost_per_token": 0.00000015,
"litellm_provider": "anyscale",
"mode": "chat",
- "supports_function_calling": true
+ "supports_function_calling": true,
+ "source": "https://docs.anyscale.com/preview/endpoints/text-generation/supported-models/mistralai-Mixtral-8x7B-Instruct-v0.1"
+ },
+ "anyscale/mistralai/Mixtral-8x22B-Instruct-v0.1": {
+ "max_tokens": 65536,
+ "max_input_tokens": 65536,
+ "max_output_tokens": 65536,
+ "input_cost_per_token": 0.00000090,
+ "output_cost_per_token": 0.00000090,
+ "litellm_provider": "anyscale",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "source": "https://docs.anyscale.com/preview/endpoints/text-generation/supported-models/mistralai-Mixtral-8x22B-Instruct-v0.1"
},
"anyscale/HuggingFaceH4/zephyr-7b-beta": {
"max_tokens": 16384,
@@ -3369,6 +3424,16 @@
"litellm_provider": "anyscale",
"mode": "chat"
},
+ "anyscale/google/gemma-7b-it": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.00000015,
+ "output_cost_per_token": 0.00000015,
+ "litellm_provider": "anyscale",
+ "mode": "chat",
+ "source": "https://docs.anyscale.com/preview/endpoints/text-generation/supported-models/google-gemma-7b-it"
+ },
"anyscale/meta-llama/Llama-2-7b-chat-hf": {
"max_tokens": 4096,
"max_input_tokens": 4096,
@@ -3405,6 +3470,36 @@
"litellm_provider": "anyscale",
"mode": "chat"
},
+ "anyscale/codellama/CodeLlama-70b-Instruct-hf": {
+ "max_tokens": 4096,
+ "max_input_tokens": 4096,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.000001,
+ "output_cost_per_token": 0.000001,
+ "litellm_provider": "anyscale",
+ "mode": "chat",
+ "source" : "https://docs.anyscale.com/preview/endpoints/text-generation/supported-models/codellama-CodeLlama-70b-Instruct-hf"
+ },
+ "anyscale/meta-llama/Meta-Llama-3-8B-Instruct": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.00000015,
+ "output_cost_per_token": 0.00000015,
+ "litellm_provider": "anyscale",
+ "mode": "chat",
+ "source": "https://docs.anyscale.com/preview/endpoints/text-generation/supported-models/meta-llama-Meta-Llama-3-8B-Instruct"
+ },
+ "anyscale/meta-llama/Meta-Llama-3-70B-Instruct": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.00000100,
+ "output_cost_per_token": 0.00000100,
+ "litellm_provider": "anyscale",
+ "mode": "chat",
+ "source" : "https://docs.anyscale.com/preview/endpoints/text-generation/supported-models/meta-llama-Meta-Llama-3-70B-Instruct"
+ },
"cloudflare/@cf/meta/llama-2-7b-chat-fp16": {
"max_tokens": 3072,
"max_input_tokens": 3072,
@@ -3496,6 +3591,76 @@
"output_cost_per_token": 0.000000,
"litellm_provider": "voyage",
"mode": "embedding"
- }
+ },
+ "databricks/databricks-dbrx-instruct": {
+ "max_tokens": 32768,
+ "max_input_tokens": 32768,
+ "max_output_tokens": 32768,
+ "input_cost_per_token": 0.00000075,
+ "output_cost_per_token": 0.00000225,
+ "litellm_provider": "databricks",
+ "mode": "chat",
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+ },
+ "databricks/databricks-meta-llama-3-70b-instruct": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.000001,
+ "output_cost_per_token": 0.000003,
+ "litellm_provider": "databricks",
+ "mode": "chat",
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+ },
+ "databricks/databricks-llama-2-70b-chat": {
+ "max_tokens": 4096,
+ "max_input_tokens": 4096,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.0000005,
+ "output_cost_per_token": 0.0000015,
+ "litellm_provider": "databricks",
+ "mode": "chat",
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+ },
+ "databricks/databricks-mixtral-8x7b-instruct": {
+ "max_tokens": 4096,
+ "max_input_tokens": 4096,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.0000005,
+ "output_cost_per_token": 0.000001,
+ "litellm_provider": "databricks",
+ "mode": "chat",
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+ },
+ "databricks/databricks-mpt-30b-instruct": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.000001,
+ "output_cost_per_token": 0.000001,
+ "litellm_provider": "databricks",
+ "mode": "chat",
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+ },
+ "databricks/databricks-mpt-7b-instruct": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.0000005,
+ "output_cost_per_token": 0.0000005,
+ "litellm_provider": "databricks",
+ "mode": "chat",
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+ },
+ "databricks/databricks-bge-large-en": {
+ "max_tokens": 512,
+ "max_input_tokens": 512,
+ "output_vector_size": 1024,
+ "input_cost_per_token": 0.0000001,
+ "output_cost_per_token": 0.0,
+ "litellm_provider": "databricks",
+ "mode": "embedding",
+ "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+ }
}
diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html
index b0b75d094..a53f906ff 100644
--- a/litellm/proxy/_experimental/out/404.html
+++ b/litellm/proxy/_experimental/out/404.html
@@ -1 +1 @@
-404: This page could not be found. LiteLLM Dashboard 404
This page could not be found. 404: This page could not be found. LiteLLM Dashboard 404
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new file mode 100644
index 000000000..d40aa7e63
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r=n(26483),a=n(12925);function o(e){var 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t(e){e.languages.less=e.languages.extend("css",{comment:[/\/\*[\s\S]*?\*\//,{pattern:/(^|[^\\])\/\/.*/,lookbehind:!0}],atrule:{pattern:/@[\w-](?:\((?:[^(){}]|\([^(){}]*\))*\)|[^(){};\s]|\s+(?!\s))*?(?=\s*\{)/,inside:{punctuation:/[:()]/}},selector:{pattern:/(?:@\{[\w-]+\}|[^{};\s@])(?:@\{[\w-]+\}|\((?:[^(){}]|\([^(){}]*\))*\)|[^(){};@\s]|\s+(?!\s))*?(?=\s*\{)/,inside:{variable:/@+[\w-]+/}},property:/(?:@\{[\w-]+\}|[\w-])+(?:\+_?)?(?=\s*:)/,operator:/[+\-*\/]/}),e.languages.insertBefore("less","property",{variable:[{pattern:/@[\w-]+\s*:/,inside:{punctuation:/:/}},/@@?[\w-]+/],"mixin-usage":{pattern:/([{;]\s*)[.#](?!\d)[\w-].*?(?=[(;])/,lookbehind:!0,alias:"function"}})}e.exports=t,t.displayName="less",t.aliases=[]},72968:function(e,t,n){var r=n(30876);function a(e){e.register(r),function(e){for(var t=/\((?:[^();"#\\]|\\[\s\S]|;.*(?!.)|"(?:[^"\\]|\\.)*"|#(?:\{(?:(?!#\})[\s\S])*#\}|[^{])|)*\)/.source,n=0;n<5;n++)t=t.replace(//g,function(){return t});t=t.replace(//g,/[^\s\S]/.source);var 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