diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 0000000000..51c5789717 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,47 @@ + + + +## Title + + + +## Relevant issues + + + +## Type + + + + +🆕 New Feature +🐛 Bug Fix +🧹 Refactoring +📖 Documentation +💻 Development Environment +🚄 Infrastructure +✅ Test + +## Changes + + + +## Testing + + + +## Notes + + + + + +## Pre-Submission Checklist (optional but appreciated): + +- [ ] I have included relevant documentation updates (stored in /docs/my-website) + +## OS Tests (optional but appreciated): + +- [ ] Tested on Windows +- [ ] Tested on MacOS +- [ ] Tested on Linux diff --git a/README.md b/README.md index 38a1669357..9344c0f223 100644 --- a/README.md +++ b/README.md @@ -248,7 +248,7 @@ Step 2: Navigate into the project, and install dependencies: ``` cd litellm -poetry install +poetry install -E extra_proxy -E proxy ``` Step 3: Test your change: diff --git a/docs/my-website/docs/completion/input.md b/docs/my-website/docs/completion/input.md index fd55946108..11ca131210 100644 --- a/docs/my-website/docs/completion/input.md +++ b/docs/my-website/docs/completion/input.md @@ -84,7 +84,7 @@ def completion( n: Optional[int] = None, stream: Optional[bool] = None, stop=None, - max_tokens: Optional[float] = None, + max_tokens: Optional[int] = None, presence_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, logit_bias: Optional[dict] = None, diff --git a/docs/my-website/docs/completion/token_usage.md b/docs/my-website/docs/completion/token_usage.md index 626973c57b..807ccfd91e 100644 --- a/docs/my-website/docs/completion/token_usage.md +++ b/docs/my-website/docs/completion/token_usage.md @@ -1,7 +1,7 @@ # Completion Token Usage & Cost By default LiteLLM returns token usage in all completion requests ([See here](https://litellm.readthedocs.io/en/latest/output/)) -However, we also expose 5 helper functions + **[NEW]** an API to calculate token usage across providers: +However, we also expose some helper functions + **[NEW]** an API to calculate token usage across providers: - `encode`: This encodes the text passed in, using the model-specific tokenizer. [**Jump to code**](#1-encode) @@ -9,17 +9,19 @@ However, we also expose 5 helper functions + **[NEW]** an API to calculate token - `token_counter`: This returns the number of tokens for a given input - it uses the tokenizer based on the model, and defaults to tiktoken if no model-specific tokenizer is available. [**Jump to code**](#3-token_counter) -- `cost_per_token`: This returns the cost (in USD) for prompt (input) and completion (output) tokens. Uses the live list from `api.litellm.ai`. [**Jump to code**](#4-cost_per_token) +- `create_pretrained_tokenizer` and `create_tokenizer`: LiteLLM provides default tokenizer support for OpenAI, Cohere, Anthropic, Llama2, and Llama3 models. If you are using a different model, you can create a custom tokenizer and pass it as `custom_tokenizer` to the `encode`, `decode`, and `token_counter` methods. [**Jump to code**](#4-create_pretrained_tokenizer-and-create_tokenizer) -- `completion_cost`: This returns the overall cost (in USD) for a given LLM API Call. It combines `token_counter` and `cost_per_token` to return the cost for that query (counting both cost of input and output). [**Jump to code**](#5-completion_cost) +- `cost_per_token`: This returns the cost (in USD) for prompt (input) and completion (output) tokens. Uses the live list from `api.litellm.ai`. [**Jump to code**](#5-cost_per_token) -- `get_max_tokens`: This returns the maximum number of tokens allowed for the given model. [**Jump to code**](#6-get_max_tokens) +- `completion_cost`: This returns the overall cost (in USD) for a given LLM API Call. It combines `token_counter` and `cost_per_token` to return the cost for that query (counting both cost of input and output). [**Jump to code**](#6-completion_cost) -- `model_cost`: This returns a dictionary for all models, with their max_tokens, input_cost_per_token and output_cost_per_token. It uses the `api.litellm.ai` call shown below. [**Jump to code**](#7-model_cost) +- `get_max_tokens`: This returns the maximum number of tokens allowed for the given model. [**Jump to code**](#7-get_max_tokens) -- `register_model`: This registers new / overrides existing models (and their pricing details) in the model cost dictionary. [**Jump to code**](#8-register_model) +- `model_cost`: This returns a dictionary for all models, with their max_tokens, input_cost_per_token and output_cost_per_token. It uses the `api.litellm.ai` call shown below. [**Jump to code**](#8-model_cost) -- `api.litellm.ai`: Live token + price count across [all supported models](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). [**Jump to code**](#9-apilitellmai) +- `register_model`: This registers new / overrides existing models (and their pricing details) in the model cost dictionary. [**Jump to code**](#9-register_model) + +- `api.litellm.ai`: Live token + price count across [all supported models](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). [**Jump to code**](#10-apilitellmai) 📣 This is a community maintained list. Contributions are welcome! ❤️ @@ -60,7 +62,24 @@ messages = [{"user": "role", "content": "Hey, how's it going"}] print(token_counter(model="gpt-3.5-turbo", messages=messages)) ``` -### 4. `cost_per_token` +### 4. `create_pretrained_tokenizer` and `create_tokenizer` + +```python +from litellm import create_pretrained_tokenizer, create_tokenizer + +# get tokenizer from huggingface repo +custom_tokenizer_1 = create_pretrained_tokenizer("Xenova/llama-3-tokenizer") + +# use tokenizer from json file +with open("tokenizer.json") as f: + json_data = json.load(f) + +json_str = json.dumps(json_data) + +custom_tokenizer_2 = create_tokenizer(json_str) +``` + +### 5. `cost_per_token` ```python from litellm import cost_per_token @@ -72,7 +91,7 @@ prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar = cost_per_toke print(prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar) ``` -### 5. `completion_cost` +### 6. `completion_cost` * Input: Accepts a `litellm.completion()` response **OR** prompt + completion strings * Output: Returns a `float` of cost for the `completion` call @@ -99,7 +118,7 @@ cost = completion_cost(model="bedrock/anthropic.claude-v2", prompt="Hey!", compl formatted_string = f"${float(cost):.10f}" print(formatted_string) ``` -### 6. `get_max_tokens` +### 7. `get_max_tokens` Input: Accepts a model name - e.g., gpt-3.5-turbo (to get a complete list, call litellm.model_list). Output: Returns the maximum number of tokens allowed for the given model @@ -112,7 +131,7 @@ model = "gpt-3.5-turbo" print(get_max_tokens(model)) # Output: 4097 ``` -### 7. `model_cost` +### 8. `model_cost` * Output: Returns a dict object containing the max_tokens, input_cost_per_token, output_cost_per_token for all models on [community-maintained list](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json) @@ -122,7 +141,7 @@ from litellm import model_cost print(model_cost) # {'gpt-3.5-turbo': {'max_tokens': 4000, 'input_cost_per_token': 1.5e-06, 'output_cost_per_token': 2e-06}, ...} ``` -### 8. `register_model` +### 9. `register_model` * Input: Provide EITHER a model cost dictionary or a url to a hosted json blob * Output: Returns updated model_cost dictionary + updates litellm.model_cost with model details. @@ -157,5 +176,3 @@ export LITELLM_LOCAL_MODEL_COST_MAP="True" ``` Note: this means you will need to upgrade to get updated pricing, and newer models. - - diff --git a/docs/my-website/docs/exception_mapping.md b/docs/my-website/docs/exception_mapping.md index db17fb093e..2345e9f838 100644 --- a/docs/my-website/docs/exception_mapping.md +++ b/docs/my-website/docs/exception_mapping.md @@ -13,7 +13,7 @@ LiteLLM maps exceptions across all providers to their OpenAI counterparts. | >=500 | InternalServerError | | N/A | ContextWindowExceededError| | 400 | ContentPolicyViolationError| -| N/A | APIConnectionError | +| 500 | APIConnectionError | Base case we return APIConnectionError @@ -74,6 +74,28 @@ except Exception as e: ``` +## Usage - Should you retry exception? + +``` +import litellm +import openai + +try: + response = litellm.completion( + model="gpt-4", + messages=[ + { + "role": "user", + "content": "hello, write a 20 pageg essay" + } + ], + timeout=0.01, # this will raise a timeout exception + ) +except openai.APITimeoutError as e: + should_retry = litellm._should_retry(e.status_code) + print(f"should_retry: {should_retry}") +``` + ## Details To see how it's implemented - [check out the code](https://github.com/BerriAI/litellm/blob/a42c197e5a6de56ea576c73715e6c7c6b19fa249/litellm/utils.py#L1217) @@ -86,21 +108,34 @@ To see how it's implemented - [check out the code](https://github.com/BerriAI/li Base case - we return the original exception. -| | ContextWindowExceededError | AuthenticationError | InvalidRequestError | RateLimitError | ServiceUnavailableError | -|---------------|----------------------------|---------------------|---------------------|---------------|-------------------------| -| Anthropic | ✅ | ✅ | ✅ | ✅ | | -| OpenAI | ✅ | ✅ |✅ |✅ |✅| -| Azure OpenAI | ✅ | ✅ |✅ |✅ |✅| -| Replicate | ✅ | ✅ | ✅ | ✅ | ✅ | -| Cohere | ✅ | ✅ | ✅ | ✅ | ✅ | -| Huggingface | ✅ | ✅ | ✅ | ✅ | | -| Openrouter | ✅ | ✅ | ✅ | ✅ | | -| AI21 | ✅ | ✅ | ✅ | ✅ | | -| VertexAI | | |✅ | | | -| Bedrock | | |✅ | | | -| Sagemaker | | |✅ | | | -| TogetherAI | ✅ | ✅ | ✅ | ✅ | | -| AlephAlpha | ✅ | ✅ | ✅ | ✅ | ✅ | +| custom_llm_provider | Timeout | ContextWindowExceededError | BadRequestError | NotFoundError | ContentPolicyViolationError | AuthenticationError | APIError | RateLimitError | ServiceUnavailableError | PermissionDeniedError | UnprocessableEntityError | +|----------------------------|---------|----------------------------|------------------|---------------|-----------------------------|---------------------|----------|----------------|-------------------------|-----------------------|-------------------------| +| openai | ✓ | ✓ | ✓ | | ✓ | ✓ | | | | | | +| text-completion-openai | ✓ | ✓ | ✓ | | ✓ | ✓ | | | | | | +| custom_openai | ✓ | ✓ | ✓ | | ✓ | ✓ | | | | | | +| openai_compatible_providers| ✓ | ✓ | ✓ | | ✓ | ✓ | | | | | | +| anthropic | ✓ | ✓ | ✓ | ✓ | | ✓ | | | ✓ | ✓ | | +| replicate | ✓ | ✓ | ✓ | ✓ | | ✓ | | ✓ | ✓ | | | +| bedrock | ✓ | ✓ | ✓ | ✓ | | ✓ | | ✓ | ✓ | ✓ | | +| sagemaker | | ✓ | ✓ | | | | | | | | | +| vertex_ai | ✓ | | ✓ | | | | ✓ | | | | ✓ | +| palm | ✓ | ✓ | | | | | ✓ | | | | | +| gemini | ✓ | ✓ | | | | | ✓ | | | | | +| cloudflare | | | ✓ | | | ✓ | | | | | | +| cohere | | ✓ | ✓ | | | ✓ | | | ✓ | | | +| cohere_chat | | ✓ | ✓ | | | ✓ | | | ✓ | | | +| huggingface | ✓ | ✓ | ✓ | | | ✓ | | ✓ | ✓ | | | +| ai21 | ✓ | ✓ | ✓ | ✓ | | ✓ | | ✓ | | | | +| nlp_cloud | ✓ | ✓ | ✓ | | | ✓ | ✓ | ✓ | ✓ | | | +| together_ai | ✓ | ✓ | ✓ | | | ✓ | | | | | | +| aleph_alpha | | | ✓ | | | ✓ | | | | | | +| ollama | ✓ | | ✓ | | | | | | ✓ | | | +| ollama_chat | ✓ | | ✓ | | | | | | ✓ | | | +| vllm | | | | | | ✓ | ✓ | | | | | +| azure | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | | ✓ | | | + +- "✓" indicates that the specified `custom_llm_provider` can raise the corresponding exception. +- Empty cells indicate the lack of association or that the provider does not raise that particular exception type as indicated by the function. > For a deeper understanding of these exceptions, you can check out [this](https://github.com/BerriAI/litellm/blob/d7e58d13bf9ba9edbab2ab2f096f3de7547f35fa/litellm/utils.py#L1544) implementation for additional insights. diff --git a/docs/my-website/docs/observability/greenscale_integration.md b/docs/my-website/docs/observability/greenscale_integration.md index 8fc2b7ea31..0dd673226e 100644 --- a/docs/my-website/docs/observability/greenscale_integration.md +++ b/docs/my-website/docs/observability/greenscale_integration.md @@ -1,4 +1,4 @@ -# Greenscale Tutorial +# Greenscale - Track LLM Spend and Responsible Usage [Greenscale](https://greenscale.ai/) is a production monitoring platform for your LLM-powered app that provides you granular key insights into your GenAI spending and responsible usage. Greenscale only captures metadata to minimize the exposure risk of personally identifiable information (PII). diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md index 590ffc423b..147c12e65a 100644 --- a/docs/my-website/docs/providers/bedrock.md +++ b/docs/my-website/docs/providers/bedrock.md @@ -535,7 +535,8 @@ print(response) | Model Name | Function Call | |----------------------|---------------------------------------------| -| Titan Embeddings - G1 | `embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)` | +| Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | +| Titan Embeddings - V1 | `embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)` | | Cohere Embeddings - English | `embedding(model="bedrock/cohere.embed-english-v3", input=input)` | | Cohere Embeddings - Multilingual | `embedding(model="bedrock/cohere.embed-multilingual-v3", input=input)` | diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md index 1c3b4f81c2..932ea4f577 100644 --- a/docs/my-website/docs/proxy/logging.md +++ b/docs/my-website/docs/proxy/logging.md @@ -914,39 +914,72 @@ Test Request litellm --test ``` -## Logging Proxy Input/Output Traceloop (OpenTelemetry) +## Logging Proxy Input/Output in OpenTelemetry format using Traceloop's OpenLLMetry -Traceloop allows you to log LLM Input/Output in the OpenTelemetry format +[OpenLLMetry](https://github.com/traceloop/openllmetry) _(built and maintained by Traceloop)_ is a set of extensions +built on top of [OpenTelemetry](https://opentelemetry.io/) that gives you complete observability over your LLM +application. Because it uses OpenTelemetry under the +hood, [it can be connected to various observability solutions](https://www.traceloop.com/docs/openllmetry/integrations/introduction) +like: -We will use the `--config` to set `litellm.success_callback = ["traceloop"]` this will log all successfull LLM calls to traceloop +* [Traceloop](https://www.traceloop.com/docs/openllmetry/integrations/traceloop) +* [Axiom](https://www.traceloop.com/docs/openllmetry/integrations/axiom) +* [Azure Application Insights](https://www.traceloop.com/docs/openllmetry/integrations/azure) +* [Datadog](https://www.traceloop.com/docs/openllmetry/integrations/datadog) +* [Dynatrace](https://www.traceloop.com/docs/openllmetry/integrations/dynatrace) +* [Grafana Tempo](https://www.traceloop.com/docs/openllmetry/integrations/grafana) +* [Honeycomb](https://www.traceloop.com/docs/openllmetry/integrations/honeycomb) +* [HyperDX](https://www.traceloop.com/docs/openllmetry/integrations/hyperdx) +* [Instana](https://www.traceloop.com/docs/openllmetry/integrations/instana) +* [New Relic](https://www.traceloop.com/docs/openllmetry/integrations/newrelic) +* [OpenTelemetry Collector](https://www.traceloop.com/docs/openllmetry/integrations/otel-collector) +* [Service Now Cloud Observability](https://www.traceloop.com/docs/openllmetry/integrations/service-now) +* [Sentry](https://www.traceloop.com/docs/openllmetry/integrations/sentry) +* [SigNoz](https://www.traceloop.com/docs/openllmetry/integrations/signoz) +* [Splunk](https://www.traceloop.com/docs/openllmetry/integrations/splunk) -**Step 1** Install traceloop-sdk and set Traceloop API key +We will use the `--config` to set `litellm.success_callback = ["traceloop"]` to achieve this, steps are listed below. + +**Step 1:** Install the SDK ```shell -pip install traceloop-sdk -U +pip install traceloop-sdk ``` -Traceloop outputs standard OpenTelemetry data that can be connected to your observability stack. Send standard OpenTelemetry from LiteLLM Proxy to [Traceloop](https://www.traceloop.com/docs/openllmetry/integrations/traceloop), [Dynatrace](https://www.traceloop.com/docs/openllmetry/integrations/dynatrace), [Datadog](https://www.traceloop.com/docs/openllmetry/integrations/datadog) -, [New Relic](https://www.traceloop.com/docs/openllmetry/integrations/newrelic), [Honeycomb](https://www.traceloop.com/docs/openllmetry/integrations/honeycomb), [Grafana Tempo](https://www.traceloop.com/docs/openllmetry/integrations/grafana), [Splunk](https://www.traceloop.com/docs/openllmetry/integrations/splunk), [OpenTelemetry Collector](https://www.traceloop.com/docs/openllmetry/integrations/otel-collector) +**Step 2:** Configure Environment Variable for trace exporting + +You will need to configure where to export your traces. Environment variables will control this, example: For Traceloop +you should use `TRACELOOP_API_KEY`, whereas for Datadog you use `TRACELOOP_BASE_URL`. For more +visit [the Integrations Catalog](https://www.traceloop.com/docs/openllmetry/integrations/introduction). + +If you are using Datadog as the observability solutions then you can set `TRACELOOP_BASE_URL` as: + +```shell +TRACELOOP_BASE_URL=http://:4318 +``` + +**Step 3**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` -**Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` ```yaml model_list: - - model_name: gpt-3.5-turbo + - model_name: gpt-3.5-turbo litellm_params: model: gpt-3.5-turbo + api_key: my-fake-key # replace api_key with actual key litellm_settings: - success_callback: ["traceloop"] + success_callback: [ "traceloop" ] ``` -**Step 3**: Start the proxy, make a test request +**Step 4**: Start the proxy, make a test request Start proxy + ```shell litellm --config config.yaml --debug ``` Test Request + ``` curl --location 'http://0.0.0.0:4000/chat/completions' \ --header 'Content-Type: application/json' \ @@ -1003,4 +1036,4 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ } ] }' -``` \ No newline at end of file +``` diff --git a/docs/my-website/docs/proxy/prod.md b/docs/my-website/docs/proxy/prod.md index 980bba5426..32cd916c98 100644 --- a/docs/my-website/docs/proxy/prod.md +++ b/docs/my-website/docs/proxy/prod.md @@ -3,34 +3,38 @@ import TabItem from '@theme/TabItem'; # ⚡ Best Practices for Production -Expected Performance in Production +## 1. Use this config.yaml +Use this config.yaml in production (with your own LLMs) -1 LiteLLM Uvicorn Worker on Kubernetes - -| Description | Value | -|--------------|-------| -| Avg latency | `50ms` | -| Median latency | `51ms` | -| `/chat/completions` Requests/second | `35` | -| `/chat/completions` Requests/minute | `2100` | -| `/chat/completions` Requests/hour | `126K` | - - -## 1. Switch off Debug Logging - -Remove `set_verbose: True` from your config.yaml ```yaml +model_list: + - model_name: fake-openai-endpoint + litellm_params: + model: openai/fake + api_key: fake-key + api_base: https://exampleopenaiendpoint-production.up.railway.app/ + +general_settings: + master_key: sk-1234 # enter your own master key, ensure it starts with 'sk-' + alerting: ["slack"] # Setup slack alerting - get alerts on LLM exceptions, Budget Alerts, Slow LLM Responses + proxy_batch_write_at: 60 # Batch write spend updates every 60s + litellm_settings: - set_verbose: True + set_verbose: False # Switch off Debug Logging, ensure your logs do not have any debugging on ``` -You should only see the following level of details in logs on the proxy server +Set slack webhook url in your env ```shell -# INFO: 192.168.2.205:11774 - "POST /chat/completions HTTP/1.1" 200 OK -# INFO: 192.168.2.205:34717 - "POST /chat/completions HTTP/1.1" 200 OK -# INFO: 192.168.2.205:29734 - "POST /chat/completions HTTP/1.1" 200 OK +export SLACK_WEBHOOK_URL="https://hooks.slack.com/services/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" ``` +:::info + +Need Help or want dedicated support ? Talk to a founder [here]: (https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat) + +::: + + ## 2. On Kubernetes - Use 1 Uvicorn worker [Suggested CMD] Use this Docker `CMD`. This will start the proxy with 1 Uvicorn Async Worker @@ -40,21 +44,12 @@ Use this Docker `CMD`. This will start the proxy with 1 Uvicorn Async Worker CMD ["--port", "4000", "--config", "./proxy_server_config.yaml"] ``` -## 3. Batch write spend updates every 60s -The default proxy batch write is 10s. This is to make it easy to see spend when debugging locally. +## 3. Use Redis 'port','host', 'password'. NOT 'redis_url' -In production, we recommend using a longer interval period of 60s. This reduces the number of connections used to make DB writes. +If you decide to use Redis, DO NOT use 'redis_url'. We recommend usig redis port, host, and password params. -```yaml -general_settings: - master_key: sk-1234 - proxy_batch_write_at: 60 # 👈 Frequency of batch writing logs to server (in seconds) -``` - -## 4. use Redis 'port','host', 'password'. NOT 'redis_url' - -When connecting to Redis use redis port, host, and password params. Not 'redis_url'. We've seen a 80 RPS difference between these 2 approaches when using the async redis client. +`redis_url`is 80 RPS slower This is still something we're investigating. Keep track of it [here](https://github.com/BerriAI/litellm/issues/3188) @@ -69,103 +64,31 @@ router_settings: redis_password: os.environ/REDIS_PASSWORD ``` -## 5. Switch off resetting budgets +## Extras +### Expected Performance in Production -Add this to your config.yaml. (Only spend per Key, User and Team will be tracked - spend per API Call will not be written to the LiteLLM Database) -```yaml -general_settings: - disable_reset_budget: true -``` +1 LiteLLM Uvicorn Worker on Kubernetes -## 6. Move spend logs to separate server (BETA) - -Writing each spend log to the db can slow down your proxy. In testing we saw a 70% improvement in median response time, by moving writing spend logs to a separate server. - -👉 [LiteLLM Spend Logs Server](https://github.com/BerriAI/litellm/tree/main/litellm-js/spend-logs) +| Description | Value | +|--------------|-------| +| Avg latency | `50ms` | +| Median latency | `51ms` | +| `/chat/completions` Requests/second | `35` | +| `/chat/completions` Requests/minute | `2100` | +| `/chat/completions` Requests/hour | `126K` | -**Spend Logs** -This is a log of the key, tokens, model, and latency for each call on the proxy. +### Verifying Debugging logs are off -[**Full Payload**](https://github.com/BerriAI/litellm/blob/8c9623a6bc4ad9da0a2dac64249a60ed8da719e8/litellm/proxy/utils.py#L1769) - - -**1. Start the spend logs server** - -```bash -docker run -p 3000:3000 \ - -e DATABASE_URL="postgres://.." \ - ghcr.io/berriai/litellm-spend_logs:main-latest - -# RUNNING on http://0.0.0.0:3000 -``` - -**2. Connect to proxy** - - -Example litellm_config.yaml - -```yaml -model_list: -- model_name: fake-openai-endpoint - litellm_params: - model: openai/my-fake-model - api_key: my-fake-key - api_base: https://exampleopenaiendpoint-production.up.railway.app/ - -general_settings: - master_key: sk-1234 - proxy_batch_write_at: 5 # 👈 Frequency of batch writing logs to server (in seconds) -``` - -Add `SPEND_LOGS_URL` as an environment variable when starting the proxy - -```bash -docker run \ - -v $(pwd)/litellm_config.yaml:/app/config.yaml \ - -e DATABASE_URL="postgresql://.." \ - -e SPEND_LOGS_URL="http://host.docker.internal:3000" \ # 👈 KEY CHANGE - -p 4000:4000 \ - ghcr.io/berriai/litellm:main-latest \ - --config /app/config.yaml --detailed_debug - -# Running on http://0.0.0.0:4000 -``` - -**3. Test Proxy!** - - -```bash -curl --location 'http://0.0.0.0:4000/v1/chat/completions' \ ---header 'Content-Type: application/json' \ ---header 'Authorization: Bearer sk-1234' \ ---data '{ - "model": "fake-openai-endpoint", - "messages": [ - {"role": "system", "content": "Be helpful"}, - {"role": "user", "content": "What do you know?"} - ] -}' -``` - -In your LiteLLM Spend Logs Server, you should see - -**Expected Response** - -``` -Received and stored 1 logs. Total logs in memory: 1 -... -Flushed 1 log to the DB. +You should only see the following level of details in logs on the proxy server +```shell +# INFO: 192.168.2.205:11774 - "POST /chat/completions HTTP/1.1" 200 OK +# INFO: 192.168.2.205:34717 - "POST /chat/completions HTTP/1.1" 200 OK +# INFO: 192.168.2.205:29734 - "POST /chat/completions HTTP/1.1" 200 OK ``` -### Machine Specification - -A t2.micro should be sufficient to handle 1k logs / minute on this server. - -This consumes at max 120MB, and <0.1 vCPU. - -## Machine Specifications to Deploy LiteLLM +### Machine Specifications to Deploy LiteLLM | Service | Spec | CPUs | Memory | Architecture | Version| | --- | --- | --- | --- | --- | --- | @@ -173,7 +96,7 @@ This consumes at max 120MB, and <0.1 vCPU. | Redis Cache | - | - | - | - | 7.0+ Redis Engine| -## Reference Kubernetes Deployment YAML +### Reference Kubernetes Deployment YAML Reference Kubernetes `deployment.yaml` that was load tested by us diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md index 0aa7901c05..4fa166548f 100644 --- a/docs/my-website/docs/routing.md +++ b/docs/my-website/docs/routing.md @@ -616,6 +616,57 @@ response = router.completion(model="gpt-3.5-turbo", messages=messages) print(f"response: {response}") ``` +#### Retries based on Error Type + +Use `RetryPolicy` if you want to set a `num_retries` based on the Exception receieved + +Example: +- 4 retries for `ContentPolicyViolationError` +- 0 retries for `RateLimitErrors` + +Example Usage + +```python +from litellm.router import RetryPolicy +retry_policy = RetryPolicy( + ContentPolicyViolationErrorRetries=3, # run 3 retries for ContentPolicyViolationErrors + AuthenticationErrorRetries=0, # run 0 retries for AuthenticationErrorRetries + BadRequestErrorRetries=1, + TimeoutErrorRetries=2, + RateLimitErrorRetries=3, +) + +router = litellm.Router( + model_list=[ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + }, + { + "model_name": "bad-model", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + }, + ], + retry_policy=retry_policy, +) + +response = await router.acompletion( + model=model, + messages=messages, +) +``` + + ### Fallbacks If a call fails after num_retries, fall back to another model group. diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index ef2ddb57e5..f5777d6e7b 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -178,6 +178,7 @@ const sidebars = { "observability/traceloop_integration", "observability/athina_integration", "observability/lunary_integration", + "observability/greenscale_integration", "observability/helicone_integration", "observability/supabase_integration", `observability/telemetry`, diff --git a/litellm-js/spend-logs/package-lock.json b/litellm-js/spend-logs/package-lock.json index ef8cb1da0a..cb4b599d30 100644 --- a/litellm-js/spend-logs/package-lock.json +++ b/litellm-js/spend-logs/package-lock.json @@ -5,7 +5,7 @@ "packages": { "": { "dependencies": { - "@hono/node-server": "^1.9.0", + "@hono/node-server": "^1.10.1", "hono": "^4.2.7" }, "devDependencies": { @@ -382,9 +382,9 @@ } }, "node_modules/@hono/node-server": { - "version": "1.9.0", - "resolved": "https://registry.npmjs.org/@hono/node-server/-/node-server-1.9.0.tgz", - "integrity": "sha512-oJjk7WXBlENeHhWiMqSyxPIZ3Kmf5ZYxqdlcSIXyN8Rn50bNJsPl99G4POBS03Jxh56FdfRJ0SEnC8mAVIiavQ==", + "version": "1.10.1", + "resolved": "https://registry.npmjs.org/@hono/node-server/-/node-server-1.10.1.tgz", + "integrity": "sha512-5BKW25JH5PQKPDkTcIgv3yNUPtOAbnnjFFgWvIxxAY/B/ZNeYjjWoAeDmqhIiCgOAJ3Tauuw+0G+VainhuZRYQ==", "engines": { "node": ">=18.14.1" } diff --git a/litellm-js/spend-logs/package.json b/litellm-js/spend-logs/package.json index 92839a01b7..d9543220b2 100644 --- a/litellm-js/spend-logs/package.json +++ b/litellm-js/spend-logs/package.json @@ -3,7 +3,7 @@ "dev": "tsx watch src/index.ts" }, "dependencies": { - "@hono/node-server": "^1.9.0", + "@hono/node-server": "^1.10.1", "hono": "^4.2.7" }, "devDependencies": { diff --git a/litellm/__init__.py b/litellm/__init__.py index 5cc4d2316e..5e745668bd 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -542,7 +542,11 @@ models_by_provider: dict = { "together_ai": together_ai_models, "baseten": baseten_models, "openrouter": openrouter_models, - "vertex_ai": vertex_chat_models + vertex_text_models, + "vertex_ai": vertex_chat_models + + vertex_text_models + + vertex_anthropic_models + + vertex_vision_models + + vertex_language_models, "ai21": ai21_models, "bedrock": bedrock_models, "petals": petals_models, @@ -601,7 +605,6 @@ all_embedding_models = ( ####### IMAGE GENERATION MODELS ################### openai_image_generation_models = ["dall-e-2", "dall-e-3"] - from .timeout import timeout from .utils import ( client, @@ -609,6 +612,8 @@ from .utils import ( get_optional_params, modify_integration, token_counter, + create_pretrained_tokenizer, + create_tokenizer, cost_per_token, completion_cost, supports_function_calling, @@ -632,6 +637,7 @@ from .utils import ( get_secret, get_supported_openai_params, get_api_base, + get_first_chars_messages, ) from .llms.huggingface_restapi import HuggingfaceConfig from .llms.anthropic import AnthropicConfig @@ -688,3 +694,4 @@ from .exceptions import ( from .budget_manager import BudgetManager from .proxy.proxy_cli import run_server from .router import Router +from .assistants.main import * diff --git a/litellm/assistants/main.py b/litellm/assistants/main.py new file mode 100644 index 0000000000..25d2433d7b --- /dev/null +++ b/litellm/assistants/main.py @@ -0,0 +1,495 @@ +# What is this? +## Main file for assistants API logic +from typing import Iterable +import os +import litellm +from openai import OpenAI +from litellm import client +from litellm.utils import supports_httpx_timeout +from ..llms.openai import OpenAIAssistantsAPI +from ..types.llms.openai import * +from ..types.router import * + +####### ENVIRONMENT VARIABLES ################### +openai_assistants_api = OpenAIAssistantsAPI() + +### ASSISTANTS ### + + +def get_assistants( + custom_llm_provider: Literal["openai"], + client: Optional[OpenAI] = None, + **kwargs, +) -> SyncCursorPage[Assistant]: + optional_params = GenericLiteLLMParams(**kwargs) + + ### 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 + + response: Optional[SyncCursorPage[Assistant]] = None + if custom_llm_provider == "openai": + api_base = ( + optional_params.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 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") + ) + response = openai_assistants_api.get_assistants( + api_base=api_base, + api_key=api_key, + timeout=timeout, + max_retries=optional_params.max_retries, + organization=organization, + client=client, + ) + else: + raise litellm.exceptions.BadRequestError( + message="LiteLLM doesn't support {} for 'get_assistants'. 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 + + +### THREADS ### + + +def create_thread( + custom_llm_provider: Literal["openai"], + messages: Optional[Iterable[OpenAICreateThreadParamsMessage]] = None, + metadata: Optional[dict] = None, + tool_resources: Optional[OpenAICreateThreadParamsToolResources] = None, + client: Optional[OpenAI] = None, + **kwargs, +) -> Thread: + """ + - get the llm provider + - if openai - route it there + - pass through relevant params + + ``` + from litellm import create_thread + + create_thread( + custom_llm_provider="openai", + ### OPTIONAL ### + messages = { + "role": "user", + "content": "Hello, what is AI?" + }, + { + "role": "user", + "content": "How does AI work? Explain it in simple terms." + }] + ) + ``` + """ + optional_params = GenericLiteLLMParams(**kwargs) + + ### 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 + + response: Optional[Thread] = None + if custom_llm_provider == "openai": + api_base = ( + optional_params.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 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") + ) + response = openai_assistants_api.create_thread( + messages=messages, + metadata=metadata, + api_base=api_base, + api_key=api_key, + timeout=timeout, + max_retries=optional_params.max_retries, + organization=organization, + client=client, + ) + else: + raise litellm.exceptions.BadRequestError( + message="LiteLLM doesn't support {} for 'create_thread'. 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 + + +def get_thread( + custom_llm_provider: Literal["openai"], + thread_id: str, + client: Optional[OpenAI] = None, + **kwargs, +) -> Thread: + """Get the thread object, given a thread_id""" + optional_params = GenericLiteLLMParams(**kwargs) + + ### 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 + + response: Optional[Thread] = None + if custom_llm_provider == "openai": + api_base = ( + optional_params.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 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") + ) + response = openai_assistants_api.get_thread( + thread_id=thread_id, + api_base=api_base, + api_key=api_key, + timeout=timeout, + max_retries=optional_params.max_retries, + organization=organization, + client=client, + ) + else: + raise litellm.exceptions.BadRequestError( + message="LiteLLM doesn't support {} for 'get_thread'. 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 + + +### MESSAGES ### + + +def add_message( + custom_llm_provider: Literal["openai"], + thread_id: str, + role: Literal["user", "assistant"], + content: str, + attachments: Optional[List[Attachment]] = None, + metadata: Optional[dict] = None, + client: Optional[OpenAI] = None, + **kwargs, +) -> OpenAIMessage: + ### COMMON OBJECTS ### + message_data = MessageData( + role=role, content=content, attachments=attachments, metadata=metadata + ) + optional_params = GenericLiteLLMParams(**kwargs) + + ### 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 + + response: Optional[OpenAIMessage] = None + if custom_llm_provider == "openai": + api_base = ( + optional_params.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 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") + ) + response = openai_assistants_api.add_message( + thread_id=thread_id, + message_data=message_data, + api_base=api_base, + api_key=api_key, + timeout=timeout, + max_retries=optional_params.max_retries, + organization=organization, + client=client, + ) + else: + raise litellm.exceptions.BadRequestError( + message="LiteLLM doesn't support {} for 'create_thread'. 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 + + +def get_messages( + custom_llm_provider: Literal["openai"], + thread_id: str, + client: Optional[OpenAI] = None, + **kwargs, +) -> SyncCursorPage[OpenAIMessage]: + optional_params = GenericLiteLLMParams(**kwargs) + + ### 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 + + response: Optional[SyncCursorPage[OpenAIMessage]] = None + if custom_llm_provider == "openai": + api_base = ( + optional_params.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 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") + ) + response = openai_assistants_api.get_messages( + thread_id=thread_id, + api_base=api_base, + api_key=api_key, + timeout=timeout, + max_retries=optional_params.max_retries, + organization=organization, + client=client, + ) + else: + raise litellm.exceptions.BadRequestError( + message="LiteLLM doesn't support {} for 'get_messages'. 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 + + +### RUNS ### + + +def run_thread( + custom_llm_provider: Literal["openai"], + thread_id: str, + assistant_id: str, + additional_instructions: Optional[str] = None, + instructions: Optional[str] = None, + metadata: Optional[dict] = None, + model: Optional[str] = None, + stream: Optional[bool] = None, + tools: Optional[Iterable[AssistantToolParam]] = None, + client: Optional[OpenAI] = None, + **kwargs, +) -> Run: + """Run a given thread + assistant.""" + optional_params = GenericLiteLLMParams(**kwargs) + + ### 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 + + response: Optional[Run] = None + if custom_llm_provider == "openai": + api_base = ( + optional_params.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 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") + ) + response = openai_assistants_api.run_thread( + thread_id=thread_id, + assistant_id=assistant_id, + additional_instructions=additional_instructions, + instructions=instructions, + metadata=metadata, + model=model, + stream=stream, + tools=tools, + api_base=api_base, + api_key=api_key, + timeout=timeout, + max_retries=optional_params.max_retries, + organization=organization, + client=client, + ) + else: + raise litellm.exceptions.BadRequestError( + message="LiteLLM doesn't support {} for 'run_thread'. 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 diff --git a/litellm/caching.py b/litellm/caching.py index 86e3ef40d1..d7cf033705 100644 --- a/litellm/caching.py +++ b/litellm/caching.py @@ -177,11 +177,18 @@ class RedisCache(BaseCache): try: # asyncio.get_running_loop().create_task(self.ping()) result = asyncio.get_running_loop().create_task(self.ping()) - except Exception: - pass + except Exception as e: + verbose_logger.error( + "Error connecting to Async Redis client", extra={"error": str(e)} + ) ### SYNC HEALTH PING ### - self.redis_client.ping() + try: + self.redis_client.ping() + except Exception as e: + verbose_logger.error( + "Error connecting to Sync Redis client", extra={"error": str(e)} + ) def init_async_client(self): from ._redis import get_redis_async_client diff --git a/litellm/integrations/openmeter.py b/litellm/integrations/openmeter.py index 2ed551c8d8..237a40eb8d 100644 --- a/litellm/integrations/openmeter.py +++ b/litellm/integrations/openmeter.py @@ -38,7 +38,7 @@ class OpenMeterLogger(CustomLogger): in the environment """ missing_keys = [] - if litellm.get_secret("OPENMETER_API_KEY", None) is None: + if os.getenv("OPENMETER_API_KEY", None) is None: missing_keys.append("OPENMETER_API_KEY") if len(missing_keys) > 0: @@ -60,47 +60,56 @@ class OpenMeterLogger(CustomLogger): "total_tokens": response_obj["usage"].get("total_tokens"), } + subject = kwargs.get("user", None), # end-user passed in via 'user' param + if not subject: + raise Exception("OpenMeter: user is required") + return { "specversion": "1.0", "type": os.getenv("OPENMETER_EVENT_TYPE", "litellm_tokens"), "id": call_id, "time": dt, - "subject": kwargs.get("user", ""), # end-user passed in via 'user' param + "subject": subject, "source": "litellm-proxy", "data": {"model": model, "cost": cost, **usage}, } def log_success_event(self, kwargs, response_obj, start_time, end_time): - _url = litellm.get_secret( - "OPENMETER_API_ENDPOINT", default_value="https://openmeter.cloud" - ) + _url = os.getenv("OPENMETER_API_ENDPOINT", "https://openmeter.cloud") if _url.endswith("/"): _url += "api/v1/events" else: _url += "/api/v1/events" - api_key = litellm.get_secret("OPENMETER_API_KEY") + api_key = os.getenv("OPENMETER_API_KEY") _data = self._common_logic(kwargs=kwargs, response_obj=response_obj) - self.sync_http_handler.post( - url=_url, - data=_data, - headers={ - "Content-Type": "application/cloudevents+json", - "Authorization": "Bearer {}".format(api_key), - }, - ) + _headers = { + "Content-Type": "application/cloudevents+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): - _url = litellm.get_secret( - "OPENMETER_API_ENDPOINT", default_value="https://openmeter.cloud" - ) + _url = os.getenv("OPENMETER_API_ENDPOINT", "https://openmeter.cloud") if _url.endswith("/"): _url += "api/v1/events" else: _url += "/api/v1/events" - api_key = litellm.get_secret("OPENMETER_API_KEY") + api_key = os.getenv("OPENMETER_API_KEY") _data = self._common_logic(kwargs=kwargs, response_obj=response_obj) _headers = { @@ -117,7 +126,6 @@ class OpenMeterLogger(CustomLogger): response.raise_for_status() except Exception as e: - print(f"\nAn Exception Occurred - {str(e)}") if hasattr(response, "text"): - print(f"\nError Message: {response.text}") + litellm.print_verbose(f"\nError Message: {response.text}") raise e diff --git a/litellm/integrations/slack_alerting.py b/litellm/integrations/slack_alerting.py index 8f8ce712e9..a9aba2f1c6 100644 --- a/litellm/integrations/slack_alerting.py +++ b/litellm/integrations/slack_alerting.py @@ -48,19 +48,6 @@ class SlackAlerting: self.internal_usage_cache = DualCache() self.async_http_handler = AsyncHTTPHandler() self.alert_to_webhook_url = alert_to_webhook_url - self.langfuse_logger = None - - try: - from litellm.integrations.langfuse import LangFuseLogger - - self.langfuse_logger = LangFuseLogger( - os.getenv("LANGFUSE_PUBLIC_KEY"), - os.getenv("LANGFUSE_SECRET_KEY"), - flush_interval=1, - ) - except: - pass - pass def update_values( @@ -110,62 +97,8 @@ class SlackAlerting: start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, ): - import uuid - - # For now: do nothing as we're debugging why this is not working as expected - if request_data is not None: - trace_id = request_data.get("metadata", {}).get( - "trace_id", None - ) # get langfuse trace id - if trace_id is None: - trace_id = "litellm-alert-trace-" + str(uuid.uuid4()) - request_data["metadata"]["trace_id"] = trace_id - elif kwargs is not None: - _litellm_params = kwargs.get("litellm_params", {}) - trace_id = _litellm_params.get("metadata", {}).get( - "trace_id", None - ) # get langfuse trace id - if trace_id is None: - trace_id = "litellm-alert-trace-" + str(uuid.uuid4()) - _litellm_params["metadata"]["trace_id"] = trace_id - - # Log hanging request as an error on langfuse - if type == "hanging_request": - if self.langfuse_logger is not None: - _logging_kwargs = copy.deepcopy(request_data) - if _logging_kwargs is None: - _logging_kwargs = {} - _logging_kwargs["litellm_params"] = {} - request_data = request_data or {} - _logging_kwargs["litellm_params"]["metadata"] = request_data.get( - "metadata", {} - ) - # log to langfuse in a separate thread - import threading - - threading.Thread( - target=self.langfuse_logger.log_event, - args=( - _logging_kwargs, - None, - start_time, - end_time, - None, - print, - "ERROR", - "Requests is hanging", - ), - ).start() - - _langfuse_host = os.environ.get("LANGFUSE_HOST", "https://cloud.langfuse.com") - _langfuse_project_id = os.environ.get("LANGFUSE_PROJECT_ID") - - # langfuse urls look like: https://us.cloud.langfuse.com/project/************/traces/litellm-alert-trace-ididi9dk-09292-************ - - _langfuse_url = ( - f"{_langfuse_host}/project/{_langfuse_project_id}/traces/{trace_id}" - ) - request_info += f"\n🪢 Langfuse Trace: {_langfuse_url}" + # do nothing for now + pass return request_info def _response_taking_too_long_callback( @@ -242,10 +175,6 @@ class SlackAlerting: request_info = f"\nRequest Model: `{model}`\nAPI Base: `{api_base}`\nMessages: `{messages}`" slow_message = f"`Responses are slow - {round(time_difference_float,2)}s response time > Alerting threshold: {self.alerting_threshold}s`" if time_difference_float > self.alerting_threshold: - if "langfuse" in litellm.success_callback: - request_info = self._add_langfuse_trace_id_to_alert( - request_info=request_info, kwargs=kwargs, type="slow_response" - ) # add deployment latencies to alert if ( kwargs is not None diff --git a/litellm/llms/anthropic.py b/litellm/llms/anthropic.py index 24d889b0f4..3c130aafcb 100644 --- a/litellm/llms/anthropic.py +++ b/litellm/llms/anthropic.py @@ -84,6 +84,51 @@ class AnthropicConfig: and v is not None } + def get_supported_openai_params(self): + return [ + "stream", + "stop", + "temperature", + "top_p", + "max_tokens", + "tools", + "tool_choice", + ] + + 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 == "tools": + optional_params["tools"] = value + if param == "stream" and value == True: + optional_params["stream"] = value + if param == "stop": + if isinstance(value, str): + if ( + value == "\n" + ) and litellm.drop_params == True: # anthropic doesn't allow whitespace characters as stop-sequences + continue + value = [value] + elif isinstance(value, list): + new_v = [] + for v in value: + if ( + v == "\n" + ) and litellm.drop_params == True: # anthropic doesn't allow whitespace characters as stop-sequences + continue + new_v.append(v) + if len(new_v) > 0: + value = new_v + else: + continue + optional_params["stop_sequences"] = value + if param == "temperature": + optional_params["temperature"] = value + if param == "top_p": + optional_params["top_p"] = value + return optional_params + # makes headers for API call def validate_environment(api_key, user_headers): diff --git a/litellm/llms/azure.py b/litellm/llms/azure.py index 0fe5c4e7e5..e7af9d43b6 100644 --- a/litellm/llms/azure.py +++ b/litellm/llms/azure.py @@ -151,7 +151,7 @@ class AzureChatCompletion(BaseLLM): api_type: str, azure_ad_token: str, print_verbose: Callable, - timeout, + timeout: Union[float, httpx.Timeout], logging_obj, optional_params, litellm_params, diff --git a/litellm/llms/bedrock.py b/litellm/llms/bedrock.py index 991c4656a8..2f26ae4a9a 100644 --- a/litellm/llms/bedrock.py +++ b/litellm/llms/bedrock.py @@ -4,7 +4,13 @@ from enum import Enum import time, uuid from typing import Callable, Optional, Any, Union, List import litellm -from litellm.utils import ModelResponse, get_secret, Usage, ImageResponse +from litellm.utils import ( + ModelResponse, + get_secret, + Usage, + ImageResponse, + map_finish_reason, +) from .prompt_templates.factory import ( prompt_factory, custom_prompt, @@ -545,7 +551,7 @@ def init_bedrock_client( aws_profile_name: Optional[str] = None, aws_role_name: Optional[str] = None, extra_headers: Optional[dict] = None, - timeout: Optional[int] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, ): # check for custom AWS_REGION_NAME and use it if not passed to init_bedrock_client litellm_aws_region_name = get_secret("AWS_REGION_NAME", None) @@ -603,7 +609,14 @@ def init_bedrock_client( import boto3 - config = boto3.session.Config(connect_timeout=timeout, read_timeout=timeout) + if isinstance(timeout, float): + config = boto3.session.Config(connect_timeout=timeout, read_timeout=timeout) + elif isinstance(timeout, httpx.Timeout): + config = boto3.session.Config( + connect_timeout=timeout.connect, read_timeout=timeout.read + ) + else: + config = boto3.session.Config() ### CHECK STS ### if aws_role_name is not None and aws_session_name is not None: @@ -1058,7 +1071,9 @@ def completion( logging_obj=logging_obj, ) - model_response["finish_reason"] = response_body["stop_reason"] + model_response["finish_reason"] = map_finish_reason( + response_body["stop_reason"] + ) _usage = litellm.Usage( prompt_tokens=response_body["usage"]["input_tokens"], completion_tokens=response_body["usage"]["output_tokens"], diff --git a/litellm/llms/openai.py b/litellm/llms/openai.py index f68ab235e6..f007507c9b 100644 --- a/litellm/llms/openai.py +++ b/litellm/llms/openai.py @@ -1,4 +1,13 @@ -from typing import Optional, Union, Any, BinaryIO +from typing import ( + Optional, + Union, + Any, + BinaryIO, + Literal, + Iterable, +) +from typing_extensions import override +from pydantic import BaseModel import types, time, json, traceback import httpx from .base import BaseLLM @@ -17,6 +26,7 @@ import aiohttp, requests import litellm from .prompt_templates.factory import prompt_factory, custom_prompt from openai import OpenAI, AsyncOpenAI +from ..types.llms.openai import * class OpenAIError(Exception): @@ -246,7 +256,7 @@ class OpenAIChatCompletion(BaseLLM): def completion( self, model_response: ModelResponse, - timeout: float, + timeout: Union[float, httpx.Timeout], model: Optional[str] = None, messages: Optional[list] = None, print_verbose: Optional[Callable] = None, @@ -271,9 +281,12 @@ class OpenAIChatCompletion(BaseLLM): if model is None or messages is None: raise OpenAIError(status_code=422, message=f"Missing model or messages") - if not isinstance(timeout, float): + if not isinstance(timeout, float) and not isinstance( + timeout, httpx.Timeout + ): raise OpenAIError( - status_code=422, message=f"Timeout needs to be a float" + status_code=422, + message=f"Timeout needs to be a float or httpx.Timeout", ) if custom_llm_provider != "openai": @@ -425,7 +438,7 @@ class OpenAIChatCompletion(BaseLLM): self, data: dict, model_response: ModelResponse, - timeout: float, + timeout: Union[float, httpx.Timeout], api_key: Optional[str] = None, api_base: Optional[str] = None, organization: Optional[str] = None, @@ -480,7 +493,7 @@ class OpenAIChatCompletion(BaseLLM): def streaming( self, logging_obj, - timeout: float, + timeout: Union[float, httpx.Timeout], data: dict, model: str, api_key: Optional[str] = None, @@ -524,7 +537,7 @@ class OpenAIChatCompletion(BaseLLM): async def async_streaming( self, logging_obj, - timeout: float, + timeout: Union[float, httpx.Timeout], data: dict, model: str, api_key: Optional[str] = None, @@ -1233,3 +1246,223 @@ class OpenAITextCompletion(BaseLLM): async for transformed_chunk in streamwrapper: yield transformed_chunk + + +class OpenAIAssistantsAPI(BaseLLM): + 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[OpenAI] = None, + ) -> OpenAI: + received_args = locals() + if client is None: + data = {} + for k, v in received_args.items(): + if k == "self" or k == "client": + pass + elif k == "api_base" and v is not None: + data["base_url"] = v + elif v is not None: + data[k] = v + openai_client = OpenAI(**data) # type: ignore + else: + openai_client = client + + return openai_client + + ### ASSISTANTS ### + + def get_assistants( + self, + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[OpenAI], + ) -> SyncCursorPage[Assistant]: + openai_client = 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.beta.assistants.list() + + return response + + ### MESSAGES ### + + def add_message( + self, + thread_id: str, + message_data: MessageData, + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[OpenAI] = None, + ) -> OpenAIMessage: + + openai_client = self.get_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, + ) + + thread_message: OpenAIMessage = openai_client.beta.threads.messages.create( + thread_id, **message_data + ) + + response_obj: Optional[OpenAIMessage] = None + if getattr(thread_message, "status", None) is None: + thread_message.status = "completed" + response_obj = OpenAIMessage(**thread_message.dict()) + else: + response_obj = OpenAIMessage(**thread_message.dict()) + return response_obj + + def get_messages( + self, + thread_id: str, + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[OpenAI] = None, + ) -> SyncCursorPage[OpenAIMessage]: + openai_client = 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.beta.threads.messages.list(thread_id=thread_id) + + return response + + ### THREADS ### + + def create_thread( + self, + metadata: Optional[dict], + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[OpenAI], + messages: Optional[Iterable[OpenAICreateThreadParamsMessage]], + ) -> Thread: + """ + Here's an example: + ``` + from litellm.llms.openai import OpenAIAssistantsAPI, MessageData + + # create thread + message: MessageData = {"role": "user", "content": "Hey, how's it going?"} + openai_api.create_thread(messages=[message]) + ``` + """ + openai_client = self.get_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, + ) + + data = {} + if messages is not None: + data["messages"] = messages # type: ignore + if metadata is not None: + data["metadata"] = metadata # type: ignore + + message_thread = openai_client.beta.threads.create(**data) # type: ignore + + return Thread(**message_thread.dict()) + + def get_thread( + self, + thread_id: str, + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[OpenAI], + ) -> Thread: + openai_client = 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.beta.threads.retrieve(thread_id=thread_id) + + return Thread(**response.dict()) + + def delete_thread(self): + pass + + ### RUNS ### + + def run_thread( + self, + thread_id: str, + assistant_id: str, + additional_instructions: Optional[str], + instructions: Optional[str], + metadata: Optional[object], + model: Optional[str], + stream: Optional[bool], + tools: Optional[Iterable[AssistantToolParam]], + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[OpenAI], + ) -> Run: + openai_client = 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.beta.threads.runs.create_and_poll( + thread_id=thread_id, + assistant_id=assistant_id, + additional_instructions=additional_instructions, + instructions=instructions, + metadata=metadata, + model=model, + tools=tools, + ) + + return response diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py index e1fa354c63..0820303685 100644 --- a/litellm/llms/prompt_templates/factory.py +++ b/litellm/llms/prompt_templates/factory.py @@ -12,6 +12,16 @@ from typing import ( Sequence, ) import litellm +from litellm.types.completion import ( + ChatCompletionUserMessageParam, + ChatCompletionSystemMessageParam, + ChatCompletionMessageParam, + ChatCompletionFunctionMessageParam, + ChatCompletionMessageToolCallParam, + ChatCompletionToolMessageParam, +) +from litellm.types.llms.anthropic import * +import uuid def default_pt(messages): @@ -22,6 +32,41 @@ def prompt_injection_detection_default_pt(): return """Detect if a prompt is safe to run. Return 'UNSAFE' if not.""" +def map_system_message_pt(messages: list) -> list: + """ + Convert 'system' message to 'user' message if provider doesn't support 'system' role. + + Enabled via `completion(...,supports_system_message=False)` + + If next message is a user message or assistant message -> merge system prompt into it + + if next message is system -> append a user message instead of the system message + """ + + new_messages = [] + for i, m in enumerate(messages): + if m["role"] == "system": + if i < len(messages) - 1: # Not the last message + next_m = messages[i + 1] + next_role = next_m["role"] + if ( + next_role == "user" or next_role == "assistant" + ): # Next message is a user or assistant message + # Merge system prompt into the next message + next_m["content"] = m["content"] + " " + next_m["content"] + elif next_role == "system": # Next message is a system message + # Append a user message instead of the system message + new_message = {"role": "user", "content": m["content"]} + new_messages.append(new_message) + else: # Last message + new_message = {"role": "user", "content": m["content"]} + new_messages.append(new_message) + else: # Not a system message + new_messages.append(m) + + return new_messages + + # alpaca prompt template - for models like mythomax, etc. def alpaca_pt(messages): prompt = custom_prompt( @@ -805,6 +850,13 @@ def convert_to_anthropic_tool_result(message: dict) -> dict: "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", + } """ """ @@ -821,18 +873,42 @@ def convert_to_anthropic_tool_result(message: dict) -> dict: ] } """ - tool_call_id = message.get("tool_call_id") - content = message.get("content") + if message["role"] == "tool": + tool_call_id = message.get("tool_call_id") + content = message.get("content") - # We can't determine from openai message format whether it's a successful or - # error call result so default to the successful result template - anthropic_tool_result = { - "type": "tool_result", - "tool_use_id": tool_call_id, - "content": content, - } + # We can't determine from openai message format whether it's a successful or + # error call result so default to the successful result template + anthropic_tool_result = { + "type": "tool_result", + "tool_use_id": tool_call_id, + "content": content, + } + return anthropic_tool_result + elif message["role"] == "function": + content = message.get("content") + anthropic_tool_result = { + "type": "tool_result", + "tool_use_id": str(uuid.uuid4()), + "content": content, + } + return anthropic_tool_result + return {} - return anthropic_tool_result + +def convert_function_to_anthropic_tool_invoke(function_call): + try: + anthropic_tool_invoke = [ + { + "type": "tool_use", + "id": str(uuid.uuid4()), + "name": get_attribute_or_key(function_call, "name"), + "input": json.loads(get_attribute_or_key(function_call, "arguments")), + } + ] + return anthropic_tool_invoke + except Exception as e: + raise e def convert_to_anthropic_tool_invoke(tool_calls: list) -> list: @@ -895,7 +971,7 @@ def convert_to_anthropic_tool_invoke(tool_calls: list) -> list: def anthropic_messages_pt(messages: list): """ format messages for anthropic - 1. Anthropic supports roles like "user" and "assistant", (here litellm translates system-> assistant) + 1. Anthropic supports roles like "user" and "assistant" (system prompt sent separately) 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) @@ -903,12 +979,14 @@ def anthropic_messages_pt(messages: list): 6. Ensure we only accept role, content. (message.name is not supported) """ # add role=tool support to allow function call result/error submission - user_message_types = {"user", "tool"} + user_message_types = {"user", "tool", "function"} # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, merge them. new_messages = [] msg_i = 0 + tool_use_param = False while msg_i < len(messages): user_content = [] + 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): @@ -924,7 +1002,10 @@ def anthropic_messages_pt(messages: list): ) elif m.get("type", "") == "text": user_content.append({"type": "text", "text": m["text"]}) - elif messages[msg_i]["role"] == "tool": + elif ( + messages[msg_i]["role"] == "tool" + or messages[msg_i]["role"] == "function" + ): # OpenAI's tool message content will always be a string user_content.append(convert_to_anthropic_tool_result(messages[msg_i])) else: @@ -953,11 +1034,24 @@ def anthropic_messages_pt(messages: list): convert_to_anthropic_tool_invoke(messages[msg_i]["tool_calls"]) ) + if messages[msg_i].get("function_call"): + assistant_content.extend( + convert_function_to_anthropic_tool_invoke( + messages[msg_i]["function_call"] + ) + ) + msg_i += 1 if assistant_content: new_messages.append({"role": "assistant", "content": assistant_content}) + 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] + ) + ) if not new_messages or new_messages[0]["role"] != "user": if litellm.modify_params: new_messages.insert( @@ -969,11 +1063,14 @@ def anthropic_messages_pt(messages: list): ) if new_messages[-1]["role"] == "assistant": - for content in new_messages[-1]["content"]: - if isinstance(content, dict) and content["type"] == "text": - content["text"] = content[ - "text" - ].rstrip() # no trailing whitespace for final assistant message + if isinstance(new_messages[-1]["content"], str): + new_messages[-1]["content"] = new_messages[-1]["content"].rstrip() + elif isinstance(new_messages[-1]["content"], list): + for content in new_messages[-1]["content"]: + if isinstance(content, dict) and content["type"] == "text": + content["text"] = content[ + "text" + ].rstrip() # no trailing whitespace for final assistant message return new_messages diff --git a/litellm/main.py b/litellm/main.py index a0920da295..de35dbfd08 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -12,9 +12,9 @@ from typing import Any, Literal, Union, BinaryIO 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 client, @@ -34,9 +34,12 @@ from litellm.utils import ( async_mock_completion_streaming_obj, convert_to_model_response_object, token_counter, + create_pretrained_tokenizer, + create_tokenizer, Usage, get_optional_params_embeddings, get_optional_params_image_gen, + supports_httpx_timeout, ) from .llms import ( anthropic_text, @@ -75,6 +78,7 @@ from .llms.prompt_templates.factory import ( prompt_factory, custom_prompt, function_call_prompt, + map_system_message_pt, ) import tiktoken from concurrent.futures import ThreadPoolExecutor @@ -448,7 +452,7 @@ def completion( model: str, # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create messages: List = [], - timeout: Optional[Union[float, int]] = None, + timeout: Optional[Union[float, str, httpx.Timeout]] = None, temperature: Optional[float] = None, top_p: Optional[float] = None, n: Optional[int] = None, @@ -551,6 +555,7 @@ def completion( eos_token = kwargs.get("eos_token", None) preset_cache_key = kwargs.get("preset_cache_key", None) hf_model_name = kwargs.get("hf_model_name", None) + supports_system_message = kwargs.get("supports_system_message", None) ### TEXT COMPLETION CALLS ### text_completion = kwargs.get("text_completion", False) atext_completion = kwargs.get("atext_completion", False) @@ -616,6 +621,7 @@ def completion( "model_list", "num_retries", "context_window_fallback_dict", + "retry_policy", "roles", "final_prompt_value", "bos_token", @@ -641,16 +647,27 @@ def completion( "no-log", "base_model", "stream_timeout", + "supports_system_message", ] default_params = openai_params + litellm_params non_default_params = { k: v for k, v in kwargs.items() if k not in default_params } # model-specific params - pass them straight to the model/provider - if timeout is None: - timeout = ( - kwargs.get("request_timeout", None) or 600 - ) # set timeout for 10 minutes by default - timeout = float(timeout) + + ### 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 @@ -745,6 +762,13 @@ def completion( custom_prompt_dict[model]["bos_token"] = bos_token if eos_token: custom_prompt_dict[model]["eos_token"] = eos_token + + if ( + supports_system_message is not None + and isinstance(supports_system_message, bool) + and supports_system_message == False + ): + messages = map_system_message_pt(messages=messages) model_api_key = get_api_key( llm_provider=custom_llm_provider, dynamic_api_key=api_key ) # get the api key from the environment if required for the model @@ -871,7 +895,7 @@ def completion( logger_fn=logger_fn, logging_obj=logging, acompletion=acompletion, - timeout=timeout, + timeout=timeout, # type: ignore client=client, # pass AsyncAzureOpenAI, AzureOpenAI client ) @@ -1012,7 +1036,7 @@ def completion( optional_params=optional_params, litellm_params=litellm_params, logger_fn=logger_fn, - timeout=timeout, + timeout=timeout, # type: ignore custom_prompt_dict=custom_prompt_dict, client=client, # pass AsyncOpenAI, OpenAI client organization=organization, @@ -1097,7 +1121,7 @@ def completion( optional_params=optional_params, litellm_params=litellm_params, logger_fn=logger_fn, - timeout=timeout, + timeout=timeout, # type: ignore ) if ( @@ -1471,7 +1495,7 @@ def completion( acompletion=acompletion, logging_obj=logging, custom_prompt_dict=custom_prompt_dict, - timeout=timeout, + timeout=timeout, # type: ignore ) if ( "stream" in optional_params @@ -1564,7 +1588,7 @@ def completion( logger_fn=logger_fn, logging_obj=logging, acompletion=acompletion, - timeout=timeout, + timeout=timeout, # type: ignore ) ## LOGGING logging.post_call( @@ -1892,7 +1916,7 @@ def completion( logger_fn=logger_fn, encoding=encoding, logging_obj=logging, - timeout=timeout, + timeout=timeout, # type: ignore ) if ( "stream" in optional_params @@ -2273,7 +2297,7 @@ def batch_completion( n: Optional[int] = None, stream: Optional[bool] = None, stop=None, - max_tokens: Optional[float] = None, + max_tokens: Optional[int] = None, presence_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, logit_bias: Optional[dict] = None, @@ -2666,6 +2690,7 @@ def embedding( "model_list", "num_retries", "context_window_fallback_dict", + "retry_policy", "roles", "final_prompt_value", "bos_token", @@ -3535,6 +3560,7 @@ def image_generation( "model_list", "num_retries", "context_window_fallback_dict", + "retry_policy", "roles", "final_prompt_value", "bos_token", diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index ce6f9b800f..c7d5aae2d0 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -338,6 +338,18 @@ "output_cost_per_second": 0.0001, "litellm_provider": "azure" }, + "azure/gpt-4-turbo-2024-04-09": { + "max_tokens": 4096, + "max_input_tokens": 128000, + "max_output_tokens": 4096, + "input_cost_per_token": 0.00001, + "output_cost_per_token": 0.00003, + "litellm_provider": "azure", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true + }, "azure/gpt-4-0125-preview": { "max_tokens": 4096, "max_input_tokens": 128000, @@ -813,6 +825,7 @@ "litellm_provider": "anthropic", "mode": "chat", "supports_function_calling": true, + "supports_vision": true, "tool_use_system_prompt_tokens": 264 }, "claude-3-opus-20240229": { @@ -824,6 +837,7 @@ "litellm_provider": "anthropic", "mode": "chat", "supports_function_calling": true, + "supports_vision": true, "tool_use_system_prompt_tokens": 395 }, "claude-3-sonnet-20240229": { @@ -835,6 +849,7 @@ "litellm_provider": "anthropic", "mode": "chat", "supports_function_calling": true, + "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, "text-bison": { @@ -1142,7 +1157,8 @@ "output_cost_per_token": 0.000015, "litellm_provider": "vertex_ai-anthropic_models", "mode": "chat", - "supports_function_calling": true + "supports_function_calling": true, + "supports_vision": true }, "vertex_ai/claude-3-haiku@20240307": { "max_tokens": 4096, @@ -1152,7 +1168,8 @@ "output_cost_per_token": 0.00000125, "litellm_provider": "vertex_ai-anthropic_models", "mode": "chat", - "supports_function_calling": true + "supports_function_calling": true, + "supports_vision": true }, "vertex_ai/claude-3-opus@20240229": { "max_tokens": 4096, @@ -1162,7 +1179,8 @@ "output_cost_per_token": 0.0000075, "litellm_provider": "vertex_ai-anthropic_models", "mode": "chat", - "supports_function_calling": true + "supports_function_calling": true, + "supports_vision": true }, "textembedding-gecko": { "max_tokens": 3072, @@ -1581,6 +1599,7 @@ "litellm_provider": "openrouter", "mode": "chat", "supports_function_calling": true, + "supports_vision": true, "tool_use_system_prompt_tokens": 395 }, "openrouter/google/palm-2-chat-bison": { @@ -1813,6 +1832,15 @@ "litellm_provider": "bedrock", "mode": "embedding" }, + "amazon.titan-embed-text-v2:0": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "output_vector_size": 1024, + "input_cost_per_token": 0.0000002, + "output_cost_per_token": 0.0, + "litellm_provider": "bedrock", + "mode": "embedding" + }, "mistral.mistral-7b-instruct-v0:2": { "max_tokens": 8191, "max_input_tokens": 32000, @@ -1929,7 +1957,8 @@ "output_cost_per_token": 0.000015, "litellm_provider": "bedrock", "mode": "chat", - "supports_function_calling": true + "supports_function_calling": true, + "supports_vision": true }, "anthropic.claude-3-haiku-20240307-v1:0": { "max_tokens": 4096, @@ -1939,7 +1968,8 @@ "output_cost_per_token": 0.00000125, "litellm_provider": "bedrock", "mode": "chat", - "supports_function_calling": true + "supports_function_calling": true, + "supports_vision": true }, "anthropic.claude-3-opus-20240229-v1:0": { "max_tokens": 4096, @@ -1949,7 +1979,8 @@ "output_cost_per_token": 0.000075, "litellm_provider": "bedrock", "mode": "chat", - "supports_function_calling": true + "supports_function_calling": true, + "supports_vision": true }, "anthropic.claude-v1": { "max_tokens": 8191, diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index ae30e10a82..2a1c15e384 100644 --- a/litellm/proxy/_experimental/out/404.html +++ b/litellm/proxy/_experimental/out/404.html @@ -1,5 +1 @@ -<<<<<<< HEAD -404: This page could not be found.LiteLLM Dashboard

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->>>>>>> 73a7b4f4 (refactor(main.py): trigger new build) +404: This page could not be found.LiteLLM Dashboard

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