You can access the response as a dictionary or as a class object, just as OpenAI allows you. . path string: The route to be added to the LiteLLM Proxy Server. Supported Models - ALL Groq Models Supported! We support ALL Groq models, just set groq/ as a prefix when sending completion requests Multi-Modal LLM using Azure OpenAI GPT-4V model for image reasoning. create ( model="gpt-4-1106-preview", messages= [ {"role Checking if a model supports vision . 3. yaml Aug 1, 2023 · To prevent these errors and improve model performance, when calling gpt-4-visual-preview or gpt-3. Logging / Caching. In the response headers, you should see the returned api base. num_retries: use tenacity retries. Setup SSO/Auth for UI Step 1: Set upperbounds for keys LiteLLM does 3 things really well: Consistent I/O: It removes the need for multiple if/else statements. Model Name. application/json' \--header 'Authorization: Bearer sk-eVHmb25YS32mCwZt9Aa_Ng' \ I'm encountering an issue integrating litellm with vllm in proxy mode. completions. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs) - guinmoon/litellm-yandexgpt Aug 9, 2023 · LiteLLM simplifies calling LLM providers, with a drop-in replacement for the OpenAI ChatCompletion’s endpoint. Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. The input must not exceed the max input tokens for the model (8192 tokens for text Nov 6, 2023 · To use the new JSON mode in the OpenAI API with Python, you would modify your API call to specify the response_format parameter with the value { type: "json_object" }. My objective is to configure litellm to receive OpenAI-compatible requests and then forward them to a local vllm instance. Instructor helps you manage validation context info. Spec. quality: string (optional) The quality of the image that will be generated. Dec 27, 2023 · Optional LiteLLM Fields. However, you can also set a custom prompt template on your proxy in the config. hd creates images with finer details Quick Start - LiteLLM Proxy + Config. 📄️ Batching Completion() LiteLLM allows you to: 📄️ Mock Completion() Responses - Save Testing Costs 💰 How to use LiteLLM. LiteLLM Replicate - Llama 2 13B LlamaCPP 🦙 x 🦙 Rap Battle Llama API OpenAI JSON Mode vs. # Example dummy function hard coded to return the same weather. Now, when you generate keys for this team-id. Use this config. Once the functions are executed, send the model the information for each function call and its response. The model name you show an end-user might be different from the one you pass to LiteLLM - e. See more details about the config here. Jul 27, 2023 · Library to easily interface with LLM API providers ChatOllama. APIError: MistralException - mistral does not support parameters: {'response_format': {'type': 'json Dec 13, 2023 · gpt-4, api. output_parse_pii = True, to enable this. The response_format parameter is being set to a LiteLLM by default checks if a model has a prompt template and applies it (e. You can find the Dockerfile to build litellm proxy here. The Feature Mistral API now supports json mode, with the usual response_format syntax. This is how you tell the API that you want the response in JSON mode. Conversational task: Here's all the models that use this format. When JSON mode is enabled, the model is constrained to only generate strings that parse into valid JSON. Here you can enter: Model Name: The name of the model as you want it to appear in the models list. Embedding models take text as input, and return a long list of numbers used to capture the semantics of the text. Configure Proxy If you need to: save API keys ; set litellm params (e. Notifications You must be signed in to change notification settings; [Feature]: Support responseMimeType for Gemini (aka JSON mode completion_cost: This returns the overall cost (in USD) for a given LLM API Call. Expected Flow: User Input: "hello world, my name is Jane Doe. Add eu models to model-group. 5-turbo-1106", response_format={ "type": "json_object" }, messages=[. For azure models, litellm can automatically infer the region (no need to set it). Test it! Make a simple chat completions call to the proxy. Function Calling for Data Extraction OpenLLM OpenRouter Completion with 'bad-model': got exception Unable to map your input to a model. Below is an example of using function calling with LiteLLM and Ollama. completion(), allowing you to process multiple prompts efficiently in a single API call. Warning: Make sure to not use set_verbose in production. This allows the model to generate a new response considering the effects of the function calls. 2 participants. : tuned models) can be called using the deployment/<deployment_id> format (where <deployment_id> is the ID of the deployed model in your deployment space). Query health endpoint: Oct 10, 2023 · LiteLLM. LiteLLM python SDK - Python Client to call 100+ LLMs, load balance, cost tracking. It logs API keys, which might end up in log files. Models that have been deployed to a deployment space (e. health_check_interval: 300 # frequency of background health checks. 82 # Sometimes a little more code -> a much better experience! 83 # Display mode actually runs interpreter. 5 while calling gpt-3. litellm_settings: output_parse_pii: true. longer_context_model_fallback_dict: Dictionary which has a mapping for those models which have larger equivalents. api_key, api_base, api_version etc can be passed directly to litellm. LiteLLM Supports the following methods for detecting prompt injection attacks. We've implemented an __anext__() function in the streaming object returned. model='text-embedding-ada-002'. Save key in your environment. service_name="bedrock-runtime", region_name="us-east-1", Go to the Settings > Models > Manage LiteLLM Models. Here's the exact json output and type you can expect from all litellm completion calls for all models. 📄️ Batching Completion() LiteLLM allows you to: 📄️ Mock Completion() Responses - Save Testing Costs 💰 There's 2 ways to do local debugging - litellm. liteLLM detects it using this arg. Patching¶ Mar 11, 2024 · No milestone. All requests made with these keys will log data to their team-specific logging. import os from openai import OpenAI client = OpenAI () OpenAI. second_response = litellm. The next big challenge was adding new LLM APIs. By default we provide a free $10 community-key to try all providers supported on LiteLLM. Notifications You must be signed in to change notification settings; Fork 1. Check your input - {'model': 'bad-model' completion call gpt-3. Enterprise Features Features here are behind a commercial license in our /enterprise folder. You can refer to the official docs here. When adding a new model, your JSON payload should conform to the following structure: model_name: The name of the new model (required). LiteLLM is loaded in the same way as the previous example, however the DolphinCoder model is used as it is better at constructing Supported Models - ALL Groq Models Supported! We support ALL Groq models, just set groq/ as a prefix when sending completion requests LiteLLM supports the following types of Huggingface models: Text-generation-interface: Here's all the models that use this format. 7. Usage . In 'Simple' mode, you will only see the option to enter a Model. Defaults to openai/dall-e-2. environ['OPENAI_API_KEY'] = "" # litellm reads OPENAI_API_KEY from . you already wrote for OpenAI). Call all LLM APIs using the OpenAI format. 65 # If stream=False, pull from the stream. OpenAI’s API now features a JSON mode, streamlining response structuring and enhancing integration capabilities. " How to use LiteLLM. 5-turbo-1106, as stated in the official OpenAI documentation:. Function Call. Jan 18, 2024 · OpenAI’s json-mode facilitates JSON output generation, adapting to various structures. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs) - apinfo1/litellm_original Here's the exact json output you can expect from a litellm completion call: . client(. See Decryption Code. Helper utils. Development. set_verbose=True'. 📄️ ⚡ Best Practices for Production. Store your proxy keys in AWS Secret Manager. But before we proceed, let's first explore the concept of patching. Also, see my GitHub repository with full code for the tutorial. n: int (optional) The number of images to generate. json in the current directory. Important notes: Instructor makes it easy to get structured data like JSON from LLMs like GPT-3. 5 Pro, it throws 500s on the majority of requests. Example Usage - JSON Mode To use ollama JSON Mode pass format="json" to litellm. Create a Config for LiteLLM Proxy Example config Getting Started. Original Answer. basic usage . Reliable: Extensively tested with 50+ cases and used in our production environment. It combines token_counter and cost_per_token to return the cost for that query (counting both cost of input and output). 1. LLM APIs can be unstable, completion() with fallbacks ensures you'll always get a response from your calls Async Streaming . Ollama allows you to run open-source large language models, such as Llama 2, locally. OpenAI Proxy Usage. import os. yaml add: general_settings: background_health_checks: True # enable background health checks. export ANTHROPIC_API_KEY="your-api-key". As a practical example, I’ve developed GuardRail, an open-source project utilizing this mode, showcasing how JSON-formatted outputs can significantly improve system interactions and For replicate models ensure to add a replicate/ prefix to the model arg. It would be nice if I could use Gemini (AI Studio) instead of Vertex AI only for the requests that use response_format. get_max_tokens: This returns the maximum number of tokens allowed for the given model. LiteLLM provides a unified interface to call 100+ LLMs using the same Input/Output format, including OpenAI, Huggingface, Anthropic, vLLM, Cohere, and even custom LLM API server. LiteLLM simplifies LLM API calls by mapping them all to the OpenAI ChatCompletion format. Nov 21, 2023 · Below is complete code which you can copy and paste into a notebook; notice how the model is instructed in the system message to generate a JSON document. Multi-Modal LLM using Google's Gemini model for image understanding and build Retrieval Augmented Generation with LlamaIndex. env and sends the request. Here is a demo Logging Observability - Log LLM Input/Output () LiteLLM exposes pre defined callbacks to send data to Lunary, Langfuse, Helicone, Promptlayer, Traceloop, Slack Useful when using an AWS credentials profile, SSO session, assumed role session, or if environment variables are not available for auth. For all cases, the exception returned inherits from the original OpenAI Exception but contains 3 additional attributes: Send multiple completion calls to 1 model. model: string (optional) The model to use for image generation. 1k; Unit tests for json mode (in supported models) #3061. 5-turbo-16k on the backend. hd creates images with finer details This tutorial demonstrates how to employ the completion() function with model fallbacks to ensure reliability. completion - see here or set as litellm. model="gpt-3. Look at LangChain's Output Parsers if you want a quick answer. Required OS Variables. The input must not exceed the max input tokens for the model (8192 tokens for text Nov 20, 2023 · GPT-4 Turbo’s JSON mode, activated by setting response_format to { type: "json_object" }, ensures valid JSON output. This mode was recently added and in order to use it you need to pass a response_format={ “type Embeddings are used in LlamaIndex to represent your documents using a sophisticated numerical representation. yaml: Step 1: Save your prompt template in a config. import json. bedrock = boto3. model_info: An optional dictionary to provide additional information about the model. Multimodal Structured Outputs: GPT-4o vs. My number is: 034453334". 🔗 📖 All Endpoints (Swagger)📄️ Enterprise Features - SSO, Audit Logs, Guardrails. See Code. No branches or pull requests. All logs are saved to a file called api_logs. Here's how to call Anthropic with the LiteLLM Proxy Server. Turn on/off logging and caching for a specific team id. 📄️ 🎉 Demo App. That's a great question and LangChain provides an easy solution. Here maybe text-bison is the API name but why categorise the provider by mode as well For replicate models ensure to add a replicate/ prefix to the model arg. Here's an example of using it with openai. litellm_params: A dictionary containing parameters specific to the Litellm setup (required). import litellm. Open-source LLMS are gaining popularity, and with the release of Ollama's OpenAI compatibility layer, it has become possible to obtain structured outputs using JSON schema. Important notes: When using JSON mode, always instruct the model to produce JSON via some message in the conversation, for example via your system message. if a huggingface model has a saved chat template in it's tokenizer_config. 'content': " I'm doing well, thank you for asking. For all cases, the exception returned inherits from the original OpenAI Exception but contains 3 additional attributes: On accessing the LiteLLM UI, you will be prompted to enter your username, password. Making it easy for you to add new models to your system in minutes (using the same exception-handling, token logic, etc. It removes the complexity of direct API calls by centralizing interactions with these APIs through a single endpoint. Multi-Modal LLM using DashScope qwen-vl model for image reasoning. , text-bison with litellm_provider: vertex_ai-text-models and mode: completion. Start server. Instructor helps you manage validation context Expected Behavior . input: string or array - Input text to embed, encoded as a string or array of tokens. 5-turbo, you can set response_format to { type: "json_object" } to enable JSON mode. Replace 'your-api-key' with your actual OpenAI API key. yaml The config allows you to create a model list and set api_base, max_tokens (all litellm params). You can also self-host the LiteLLM Proxy as it is open-source. good first issue Good for newcomers help wanted Extra attention is 📄️ 🐳 Docker, Deploying LiteLLM Proxy. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs) 2. BerriAI / litellm Public. 5, GPT-4, GPT-4-Vision, and open-source models including Mistral/Mixtral, Anyscale, Ollama, and llama-cpp-python. supports_vision(model="")-> returns True if model supports vision and False if not Here's how to use it: in the config. completion() with fallbacks: switch between models/keys/api bases in case of errors. Set Verbose This is good for getting print statements for everything litellm is doing. LiteLLM Proxy (opens in a new tab) (GitHub (opens in a new tab)) which standardizes 100+ model provider APIs on the OpenAI API schema. See a detailed walthrough of parallel function calling with litellm here. You can use litellm through either: OpenAI proxy Server - Server to call 100+ LLMs, load balance, cost tracking across projects. Send a /key/generate request with max_budget=200; Key will be created with max_budget=100 since 100 is the upper bound; Default /key/generate params . This example goes over how to use LangChain to interact with an Ollama-run Llama LiteLLM + Ollama with MD_JSON mode #321. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. Based on this currency conversion notebook. 30. When JSON mode is enabled, the model is constrained to only generate strings that parse into valid JSON object. g. In the batch_completion method, you provide a list of messages where each sub-list of messages is passed to litellm. getenv ('OPENAI_API_KEY') completion = client. Apr 30, 2024 · Motivation, pitch. Save AWS Credentials in your environment Call all LLM APIs using the OpenAI format. AWS Secret Manager . Here's what an example response looks like. 2️⃣ Easily add new LLM APIS - LiteLLM UI. Use this, if you need to control the default max_budget or any key/generate param per key. Just set litellm. This is not supported to date in litellm==1. I am Claude, an AI assistant created by Anthropic. It stands out for its simplicity, transparency, and user-centric design, built on top of Pydantic. This feature addresses previous challenges of generating JSON, such as model: string - ID of the model to use. api_key = os. chat (display=False, stream=True) from within the terminal_interface. replicate/llama-2-70b-chat. Taking LiteLLM as the bridge, many LLMs can be onboarded to TaskWeaver. Step 3 - Second litellm. These embedding models have been trained to represent text this way, and help enable many applications, including search! Function calling (aka Tool calling) is a feature of OpenAI's API that AutoGen and LiteLLM support. For additional configuration options, click on the 'Simple' toggle to switch to 'Advanced' mode. Jump to code. 5-turbo model: string - ID of the model to use. yaml. # set openai api key. Open Mar 19, 2024 · I don’t think the OpenAI pages on LiteLLM specify JSON mode in the docs, but I think one can infer that JSON mode can be toggled on by cross-referencing the OpenAI Async Streaming . By the end of this blog post, you will learn how to effectively utilize instructor with Ollama. Must be between 1 and 10. Add eu models to a model group. Displaying GPT-3. Here we use the OpenAI Proxy Server provided by LiteLLM to make configuration. Use litellm. os. f) {mode}_{model_name}: e. For presidio 'replace' operations, LiteLLM can check the LLM response and replace the masked token with the user-submitted values. If you don't include an explicit instruction to generate JSON, the model may Instructor makes it easy to get structured data like JSON from LLMs like GPT-3. Example: This config would send langfuse logs to 2 different langfuse projects, based on the team id. Multi-Modal LLM using OpenAI GPT-4V model for image reasoning; Multi-Modal LLM using Google’s Gemini model for image understanding and build Retrieval Augmented Generation with LlamaIndex Additionally, I think we could consolidate more details in this dataset and perhaps restructure it completely after the first steps have been taken. Outputs will not be saved. For dall-e-3, only n=1 is supported. Example - Make /spend/calculate a publicly available route (by default /spend/calculate on LiteLLM Proxy requires authentication) Usage - Define public routes Step 1 - set allowed public routes on config. A common way to use Chat Completions is to instruct the model to always return JSON in some format that makes sense for your use case, by providing a system message. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs) - BerriAI/litellm Nov 8, 2023 · I just released a new YouTube tutorial on how to get a response in JSON format. TL; Mitigating Prompt hacking with JSON Mode in Autogen; Using RetrieveChat for Retrieve Augmented Code Generation and Question Answering; Agent Tracking with AgentOps; AgentOptimizer: An Agentic Way to Train Your LLM Agent; Task Solving with Code Generation, Execution and Debugging; Assistants with Azure Cognitive Search and Azure Identity Call all LLM APIs using the OpenAI format. Observable: Integrations with Sentry, Posthog, Helicone, etc. completion(. Azure OpenAI API Keys, Params . LiteLLM supports the following functions for reliability: litellm. To get a license, get in touch with us here. It optimizes setup and configuration details, including GPU usage. It is the See a detailed walthrough of parallel function calling with litellm here. Proxy Usage . When activated the model will only generate responses using the JSON format. Usage - Models in deployment spaces . ruv December 13, 2023, 3:51pm 1. Below are examples on how to call replicate LLMs using liteLLM. from litellm import completion. LiteLLM by default checks if a model has a prompt template and applies it (e. Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. This enables async iteration over the streaming object. api_key params see here Nov 7, 2023 · You can get the JSON response back only if using gpt-4-1106-preview or gpt-3. drop unmapped params, set fallback models, etc. Info: If you need to debug this error, use `litellm. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs) - BerriAI/litellm BerriAI / litellm Public. completion() from litellm import completion response = completion (model = "ollama Nov 2, 2023 · OpenAI announced today a new “JSON Mode” at the DevDay Keynote. pass_through_endpoints list: A collection of endpoint configurations for request forwarding. This notebook is open with private outputs. json). $ litellm /path/to/config. 2. You can disable this in Notebook settings. ) set model-specific params (max tokens, temperature, api base, prompt template) The model name you show an end-user might be different from the one you pass to LiteLLM - e. ; target string: The URL to which requests for this path should be forwarded. jxnl opened this issue Jan 4, 2024 · 3 comments Labels. completion () call. Start the proxy. set_verbose=True and by passing in a custom function completion(logger_fn=<your_local_function>). Right now, Vertex AI (not LiteLLM) is pretty broken when using JSON mode with Gemini 1. Non TGI/Conversational-task LLMs; Usage You need to tell LiteLLM when you're calling Huggingface. chat. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly "stuck" request. Notice the response format set to json_object, and the seed parameter is set. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM. Create a client from session credentials: import boto3. wr cx bw lo nb oh fm ut ra mq