diff --git a/litellm/llms/anthropic.py b/litellm/llms/anthropic.py index 44a1b128a..5e0887901 100644 --- a/litellm/llms/anthropic.py +++ b/litellm/llms/anthropic.py @@ -2,11 +2,17 @@ import os, types import json from enum import Enum import requests -import time +import time, uuid from typing import Callable, Optional -from litellm.utils import ModelResponse, Usage +from litellm.utils import ModelResponse, Usage, map_finish_reason import litellm -from .prompt_templates.factory import prompt_factory, custom_prompt +from .prompt_templates.factory import ( + prompt_factory, + custom_prompt, + construct_tool_use_system_prompt, + extract_between_tags, + parse_xml_params, +) import httpx @@ -41,6 +47,7 @@ class AnthropicConfig: top_p: Optional[int] = None top_k: Optional[int] = None metadata: Optional[dict] = None + system: Optional[str] = None def __init__( self, @@ -50,6 +57,7 @@ class AnthropicConfig: top_p: Optional[int] = None, top_k: Optional[int] = None, metadata: Optional[dict] = None, + system: Optional[str] = None, ) -> None: locals_ = locals() for key, value in locals_.items(): @@ -108,6 +116,7 @@ def completion( headers={}, ): headers = validate_environment(api_key, headers) + _is_function_call = False if model in custom_prompt_dict: # check if the model has a registered custom prompt model_prompt_details = custom_prompt_dict[model] @@ -118,38 +127,19 @@ def completion( messages=messages, ) else: - prompt = prompt_factory( + # Separate system prompt from rest of message + system_prompt_idx: Optional[int] = None + for idx, message in enumerate(messages): + if message["role"] == "system": + optional_params["system"] = message["content"] + system_prompt_idx = idx + break + if system_prompt_idx is not None: + messages.pop(system_prompt_idx) + # Format rest of message according to anthropic guidelines + messages = prompt_factory( model=model, messages=messages, custom_llm_provider="anthropic" ) - """ - format messages for anthropic - 1. Anthropic supports roles like "user" and "assistant", (here litellm translates system-> assistant) - 2. The first message always needs to be of role "user" - 3. Each message must alternate between "user" and "assistant" (this is not addressed as now by litellm) - 4. final assistant content cannot end with trailing whitespace (anthropic raises an error otherwise) - """ - # 1. Anthropic only supports roles like "user" and "assistant" - for idx, message in enumerate(messages): - if message["role"] == "system": - message["role"] = "assistant" - - # if this is the final assistant message, remove trailing whitespace - # TODO: only do this if it's the final assistant message - if message["role"] == "assistant": - message["content"] = message["content"].strip() - - # 2. The first message always needs to be of role "user" - if len(messages) > 0: - if messages[0]["role"] != "user": - # find the index of the first user message - for i, message in enumerate(messages): - if message["role"] == "user": - break - - # remove the user message at existing position and add it to the front - messages.pop(i) - # move the first user message to the front - messages = [message] + messages ## Load Config config = litellm.AnthropicConfig.get_config() @@ -159,6 +149,17 @@ def completion( ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in optional_params[k] = v + ## Handle Tool Calling + if "tools" in optional_params: + _is_function_call = True + tool_calling_system_prompt = construct_tool_use_system_prompt( + tools=optional_params["tools"] + ) + optional_params["system"] = ( + optional_params.get("system", "\n") + tool_calling_system_prompt + ) # add the anthropic tool calling prompt to the system prompt + optional_params.pop("tools") + data = { "model": model, "messages": messages, @@ -167,7 +168,7 @@ def completion( ## LOGGING logging_obj.pre_call( - input=prompt, + input=messages, api_key=api_key, additional_args={ "complete_input_dict": data, @@ -225,8 +226,33 @@ def completion( ) else: text_content = completion_response["content"][0].get("text", None) - model_response.choices[0].message.content = text_content # type: ignore - model_response.choices[0].finish_reason = completion_response["stop_reason"] + ## TOOL CALLING - OUTPUT PARSE + if text_content is not None and "invoke" in text_content: + function_name = extract_between_tags("tool_name", text_content)[0] + function_arguments_str = extract_between_tags("invoke", text_content)[ + 0 + ].strip() + function_arguments_str = f"{function_arguments_str}" + function_arguments = parse_xml_params(function_arguments_str) + _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 + else: + model_response.choices[0].message.content = text_content # type: ignore + model_response.choices[0].finish_reason = map_finish_reason( + completion_response["stop_reason"] + ) ## CALCULATING USAGE prompt_tokens = completion_response["usage"]["input_tokens"] diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py index 103eb5977..dec87a61c 100644 --- a/litellm/llms/prompt_templates/factory.py +++ b/litellm/llms/prompt_templates/factory.py @@ -1,8 +1,9 @@ from enum import Enum import requests, traceback -import json +import json, re, xml.etree.ElementTree as ET from jinja2 import Template, exceptions, Environment, meta from typing import Optional, Any +import imghdr, base64 def default_pt(messages): @@ -390,7 +391,7 @@ def format_prompt_togetherai(messages, prompt_format, chat_template): return prompt -### +### ANTHROPIC ### def anthropic_pt( @@ -424,6 +425,184 @@ def anthropic_pt( return prompt +def construct_format_parameters_prompt(parameters: dict): + parameter_str = "\n" + for k, v in parameters.items(): + parameter_str += f"<{k}>" + parameter_str += f"{v}" + parameter_str += f"" + parameter_str += "\n" + return parameter_str + + +def construct_format_tool_for_claude_prompt(name, description, parameters): + constructed_prompt = ( + "\n" + f"{name}\n" + "\n" + f"{description}\n" + "\n" + "\n" + f"{construct_format_parameters_prompt(parameters)}\n" + "\n" + "" + ) + return constructed_prompt + + +def construct_tool_use_system_prompt( + tools, +): # from https://github.com/anthropics/anthropic-cookbook/blob/main/function_calling/function_calling.ipynb + tool_str_list = [] + for tool in tools: + tool_str = construct_format_tool_for_claude_prompt( + tool["function"]["name"], + tool["function"].get("description", ""), + tool["function"].get("parameters", {}), + ) + tool_str_list.append(tool_str) + tool_use_system_prompt = ( + "In this environment you have access to a set of tools you can use to answer the user's question.\n" + "\n" + "You may call them like this:\n" + "\n" + "\n" + "$TOOL_NAME\n" + "\n" + "<$PARAMETER_NAME>$PARAMETER_VALUE\n" + "...\n" + "\n" + "\n" + "\n" + "\n" + "Here are the tools available:\n" + "\n" + "\n".join([tool_str for tool_str in tool_str_list]) + "\n" + ) + return tool_use_system_prompt + + +def convert_to_anthropic_image_obj(openai_image_url: str): + """ + Input: + "image_url": "data:image/jpeg;base64,{base64_image}", + + Return: + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": {base64_image}, + } + """ + # Extract the base64 image data + base64_data = openai_image_url.split("data:image/")[1].split(";base64,")[1] + + # Infer image format from the URL + image_format = openai_image_url.split("data:image/")[1].split(";base64,")[0] + + return { + "type": "base64", + "media_type": f"image/{image_format}", + "data": base64_data, + } + + +def anthropic_messages_pt(messages: list): + """ + format messages for anthropic + 1. Anthropic supports roles like "user" and "assistant", (here litellm translates system-> assistant) + 2. The first message always needs to be of role "user" + 3. Each message must alternate between "user" and "assistant" (this is not addressed as now by litellm) + 4. final assistant content cannot end with trailing whitespace (anthropic raises an error otherwise) + 5. System messages are a separate param to the Messages API (used for tool calling) + """ + ## Ensure final assistant message has no trailing whitespace + last_assistant_message_idx: Optional[int] = None + # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, add a blank 'user' or 'assistant' message to ensure compatibility + new_messages = [] + if len(messages) == 1: + # check if the message is a user message + if messages[0]["role"] == "assistant": + new_messages.append({"role": "user", "content": ""}) + + # check if content is a list (vision) + if isinstance(messages[0]["content"], list): # vision input + new_content = [] + for m in messages[0]["content"]: + if m.get("type", "") == "image_url": + new_content.append( + { + "type": "image", + "source": convert_to_anthropic_image_obj( + m["image_url"]["url"] + ), + } + ) + elif m.get("type", "") == "text": + new_content.append({"type": "text", "text": m["text"]}) + new_messages.append({"role": messages[0]["role"], "content": new_content}) # type: ignore + else: + new_messages.append(messages[0]) + + return new_messages + + for i in range(len(messages) - 1): # type: ignore + if i == 0 and messages[i]["role"] == "assistant": + new_messages.append({"role": "user", "content": ""}) + if isinstance(messages[i]["content"], list): # vision input + new_content = [] + for m in messages[i]["content"]: + if m.get("type", "") == "image_url": + new_content.append( + { + "type": "image", + "source": convert_to_anthropic_image_obj( + m["image_url"]["url"] + ), + } + ) + elif m.get("type", "") == "text": + new_content.append({"type": "text", "content": m["text"]}) + new_messages.append({"role": messages[i]["role"], "content": new_content}) # type: ignore + else: + new_messages.append(messages[i]) + + if messages[i]["role"] == messages[i + 1]["role"]: + if messages[i]["role"] == "user": + new_messages.append({"role": "assistant", "content": ""}) + else: + new_messages.append({"role": "user", "content": ""}) + + if messages[i]["role"] == "assistant": + last_assistant_message_idx = i + + if last_assistant_message_idx is not None: + new_messages[last_assistant_message_idx]["content"] = new_messages[ + last_assistant_message_idx + ][ + "content" + ].strip() # no trailing whitespace for final assistant message + + return new_messages + + +def extract_between_tags(tag: str, string: str, strip: bool = False) -> list[str]: + ext_list = re.findall(f"<{tag}>(.+?)", string, re.DOTALL) + if strip: + ext_list = [e.strip() for e in ext_list] + return ext_list + + +def parse_xml_params(xml_content): + root = ET.fromstring(xml_content) + params = {} + for child in root.findall(".//parameters/*"): + params[child.tag] = child.text + return params + + +### + + def amazon_titan_pt( messages: list, ): # format - https://github.com/BerriAI/litellm/issues/1896 @@ -650,10 +829,9 @@ def prompt_factory( if custom_llm_provider == "ollama": return ollama_pt(model=model, messages=messages) elif custom_llm_provider == "anthropic": - if any(_ in model for _ in ["claude-2.1", "claude-v2:1"]): - return claude_2_1_pt(messages=messages) - else: + if model == "claude-instant-1" or model == "claude-2": return anthropic_pt(messages=messages) + return anthropic_messages_pt(messages=messages) elif custom_llm_provider == "together_ai": prompt_format, chat_template = get_model_info(token=api_key, model=model) return format_prompt_togetherai( diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index 064bb2163..eb5cf4286 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

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dependencies=[Depends(user_api_key_auth)], +) +async def model_metrics( + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +): + global prisma_client + if prisma_client is None: + raise ProxyException( + message="Prisma Client is not initialized", + type="internal_error", + param="None", + code=status.HTTP_500_INTERNAL_SERVER_ERROR, + ) + + sql_query = """ + SELECT + CASE WHEN api_base = '' THEN model ELSE CONCAT(model, '-', api_base) END AS combined_model_api_base, + COUNT(*) AS num_requests, + AVG(EXTRACT(epoch FROM ("endTime" - "startTime"))) AS avg_latency_seconds + FROM + "LiteLLM_SpendLogs" + WHERE + "startTime" >= NOW() - INTERVAL '10000 hours' + GROUP BY + CASE WHEN api_base = '' THEN model ELSE CONCAT(model, '-', api_base) END + ORDER BY + num_requests DESC + LIMIT 50; + """ + + db_response = await prisma_client.db.query_raw(query=sql_query) + response: List[dict] = [] + if response is not None: + # loop through all models + for model_data in db_response: + model = model_data.get("combined_model_api_base", "") + num_requests = model_data.get("num_requests", 0) + avg_latency_seconds = model_data.get("avg_latency_seconds", 0) + response.append( + { + "model": model, + "num_requests": num_requests, + "avg_latency_seconds": avg_latency_seconds, + } + ) + return response + + @router.get( "/model/info", description="Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)", diff --git a/litellm/tests/test_add_function_to_prompt.py b/litellm/tests/test_add_function_to_prompt.py index 93b09cd8c..d703ce849 100644 --- a/litellm/tests/test_add_function_to_prompt.py +++ b/litellm/tests/test_add_function_to_prompt.py @@ -41,10 +41,11 @@ def test_function_call_non_openai_model(): pass -test_function_call_non_openai_model() +# test_function_call_non_openai_model() ## case 2: add_function_to_prompt set +@pytest.mark.skip(reason="Anthropic now supports tool calling") def test_function_call_non_openai_model_litellm_mod_set(): litellm.add_function_to_prompt = True litellm.set_verbose = True diff --git a/litellm/tests/test_amazing_vertex_completion.py b/litellm/tests/test_amazing_vertex_completion.py index 35d66907c..8ed15db65 100644 --- a/litellm/tests/test_amazing_vertex_completion.py +++ b/litellm/tests/test_amazing_vertex_completion.py @@ -351,7 +351,7 @@ def test_gemini_pro_vision_base64(): load_vertex_ai_credentials() litellm.set_verbose = True litellm.num_retries = 3 - image_path = "cached_logo.jpg" + image_path = "../proxy/cached_logo.jpg" # Getting the base64 string base64_image = encode_image(image_path) resp = litellm.completion( diff --git a/litellm/tests/test_completion.py b/litellm/tests/test_completion.py index 13a08689c..dd605d8ee 100644 --- a/litellm/tests/test_completion.py +++ b/litellm/tests/test_completion.py @@ -100,6 +100,47 @@ def test_completion_claude_3(): pytest.fail(f"Error occurred: {e}") +def test_completion_claude_3_function_call(): + litellm.set_verbose = True + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } + ] + messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] + try: + # test without max tokens + response = completion( + model="anthropic/claude-3-opus-20240229", + messages=messages, + tools=tools, + tool_choice="auto", + ) + # Add any assertions, here to check response args + print(response) + assert isinstance(response.choices[0].message.tool_calls[0].function.name, str) + assert isinstance( + response.choices[0].message.tool_calls[0].function.arguments, str + ) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + def test_completion_claude_3_stream(): litellm.set_verbose = False messages = [{"role": "user", "content": "Hello, world"}] @@ -119,6 +160,51 @@ def test_completion_claude_3_stream(): pytest.fail(f"Error occurred: {e}") +def encode_image(image_path): + import base64 + + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode("utf-8") + + +@pytest.mark.skip( + reason="we already test claude-3, this is just another way to pass images" +) +def test_completion_claude_3_base64(): + try: + litellm.set_verbose = True + litellm.num_retries = 3 + image_path = "../proxy/cached_logo.jpg" + # Getting the base64 string + base64_image = encode_image(image_path) + resp = litellm.completion( + model="anthropic/claude-3-opus-20240229", + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": "Whats in this image?"}, + { + "type": "image_url", + "image_url": { + "url": "data:image/jpeg;base64," + base64_image + }, + }, + ], + } + ], + ) + print(f"\nResponse: {resp}") + + prompt_tokens = resp.usage.prompt_tokens + raise Exception("it worked!") + except Exception as e: + if "500 Internal error encountered.'" in str(e): + pass + else: + pytest.fail(f"An exception occurred - {str(e)}") + + def test_completion_mistral_api(): try: litellm.set_verbose = True diff --git a/litellm/utils.py b/litellm/utils.py index 4b9b0c8a4..0b601521f 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -200,6 +200,10 @@ def map_finish_reason( return "content_filter" elif finish_reason == "STOP": # vertex ai return "stop" + elif finish_reason == "end_turn" or finish_reason == "stop_sequence": # anthropic + return "stop" + elif finish_reason == "max_tokens": # anthropic + return "length" return finish_reason @@ -4106,6 +4110,7 @@ def get_optional_params( and custom_llm_provider != "anyscale" and custom_llm_provider != "together_ai" and custom_llm_provider != "mistral" + and custom_llm_provider != "anthropic" ): if custom_llm_provider == "ollama" or custom_llm_provider == "ollama_chat": # ollama actually supports json output @@ -4186,7 +4191,15 @@ def get_optional_params( ## raise exception if provider doesn't support passed in param if custom_llm_provider == "anthropic": ## check if unsupported param passed in - supported_params = ["stream", "stop", "temperature", "top_p", "max_tokens"] + supported_params = [ + "stream", + "stop", + "temperature", + "top_p", + "max_tokens", + "tools", + "tool_choice", + ] _check_valid_arg(supported_params=supported_params) # handle anthropic params if stream: @@ -4205,6 +4218,9 @@ def get_optional_params( optional_params["max_tokens_to_sample"] = max_tokens else: optional_params["max_tokens"] = max_tokens + optional_params["max_tokens"] = max_tokens + if tools is not None: + optional_params["tools"] = tools elif custom_llm_provider == "cohere": ## check if unsupported param passed in supported_params = [ diff --git 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} from "./networking"; +import { modelInfoCall, userGetRequesedtModelsCall, modelMetricsCall } from "./networking"; +import { BarChart } from "@tremor/react"; import { Badge, BadgeDelta, Button } from "@tremor/react"; import RequestAccess from "./request_model_access"; import { Typography } from "antd"; @@ -30,6 +31,7 @@ const ModelDashboard: React.FC = ({ userID, }) => { const [modelData, setModelData] = useState({ data: [] }); + const [modelMetrics, setModelMetrics] = useState([]); const [pendingRequests, setPendingRequests] = useState([]); useEffect(() => { @@ -47,6 +49,15 @@ const ModelDashboard: React.FC = ({ console.log("Model data response:", modelDataResponse.data); setModelData(modelDataResponse); + const modelMetricsResponse = await modelMetricsCall( + accessToken, + userID, + userRole + ); + + console.log("Model metrics response:", modelMetricsResponse); + setModelMetrics(modelMetricsResponse); + // if userRole is Admin, show the pending requests if (userRole === "Admin" && accessToken) { const user_requests = await userGetRequesedtModelsCall(accessToken); @@ -75,8 +86,7 @@ const ModelDashboard: React.FC = ({ // loop through model data and edit each row for (let i = 0; i < modelData.data.length; i++) { let curr_model = modelData.data[i]; - let litellm_model_name = curr_model?.litellm_params?.model; - + let litellm_model_name = curr_model?.litellm_params?.mode let model_info = curr_model?.model_info; let defaultProvider = "openai"; @@ -109,6 +119,7 @@ const ModelDashboard: React.FC = ({ modelData.data[i].input_cost = input_cost; modelData.data[i].output_cost = output_cost; modelData.data[i].max_tokens = max_tokens; + modelData.data[i].api_base = curr_model?.litellm_params?.api_base; all_models_on_proxy.push(curr_model.model_name); @@ -141,6 +152,14 @@ const ModelDashboard: React.FC = ({ Provider + { + userRole === "Admin" && ( + + API Base + + ) + } + Access @@ -162,6 +181,11 @@ const ModelDashboard: React.FC = ({ {model.model_name} {model.provider} + { + userRole === "Admin" && ( + {model.api_base} + ) + } {model.user_access ? ( @@ -183,7 +207,18 @@ const ModelDashboard: React.FC = ({ - {userRole === "Admin" && + + Model Statistics (Number Requests, Latency) + + + {/* {userRole === "Admin" && pendingRequests && pendingRequests.length > 0 ? ( @@ -229,7 +264,7 @@ const ModelDashboard: React.FC = ({ - ) : null} + ) : null} */} ); diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx index 5993df34c..01cc61f3b 100644 --- a/ui/litellm-dashboard/src/components/networking.tsx +++ b/ui/litellm-dashboard/src/components/networking.tsx @@ -242,6 +242,41 @@ export const modelInfoCall = async ( } }; + +export const modelMetricsCall = async ( + accessToken: String, + userID: String, + userRole: String +) => { + /** + * Get all models on proxy + */ + try { + let url = proxyBaseUrl ? `${proxyBaseUrl}/model/metrics` : `/model/metrics`; + // message.info("Requesting model data"); + const response = await fetch(url, { + method: "GET", + headers: { + Authorization: `Bearer ${accessToken}`, + "Content-Type": "application/json", + }, + }); + + if (!response.ok) { + const errorData = await response.text(); + message.error(errorData); + throw new Error("Network response was not ok"); + } + const data = await response.json(); + // message.info("Received model data"); + return data; + // Handle success - you might want to update some state or UI based on the created key + } catch (error) { + console.error("Failed to create key:", error); + throw error; + } +}; + export const modelAvailableCall = async ( accessToken: String, userID: String,