diff --git a/.circleci/config.yml b/.circleci/config.yml index fbf3cb867..b996dc312 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -811,7 +811,8 @@ jobs: - run: python ./tests/code_coverage_tests/router_code_coverage.py - run: python ./tests/code_coverage_tests/test_router_strategy_async.py - run: python ./tests/code_coverage_tests/litellm_logging_code_coverage.py - # - run: python ./tests/documentation_tests/test_env_keys.py + - run: python ./tests/documentation_tests/test_env_keys.py + - run: python ./tests/documentation_tests/test_router_settings.py - run: python ./tests/documentation_tests/test_api_docs.py - run: python ./tests/code_coverage_tests/ensure_async_clients_test.py - run: helm lint ./deploy/charts/litellm-helm @@ -1407,7 +1408,7 @@ jobs: command: | docker run -d \ -p 4000:4000 \ - -e DATABASE_URL=$PROXY_DATABASE_URL \ + -e DATABASE_URL=$PROXY_DATABASE_URL_2 \ -e LITELLM_MASTER_KEY="sk-1234" \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ -e UI_USERNAME="admin" \ diff --git a/docs/my-website/docs/moderation.md b/docs/my-website/docs/moderation.md new file mode 100644 index 000000000..6dd092fb5 --- /dev/null +++ b/docs/my-website/docs/moderation.md @@ -0,0 +1,135 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Moderation + + +### Usage + + + +```python +from litellm import moderation + +response = moderation( + input="hello from litellm", + model="text-moderation-stable" +) +``` + + + + +For `/moderations` endpoint, there is **no need to specify `model` in the request or on the litellm config.yaml** + +Start litellm proxy server + +``` +litellm +``` + + + + + +```python +from openai import OpenAI + +# set base_url to your proxy server +# set api_key to send to proxy server +client = OpenAI(api_key="", base_url="http://0.0.0.0:4000") + +response = client.moderations.create( + input="hello from litellm", + model="text-moderation-stable" # optional, defaults to `omni-moderation-latest` +) + +print(response) +``` + + + + +```shell +curl --location 'http://0.0.0.0:4000/moderations' \ + --header 'Content-Type: application/json' \ + --header 'Authorization: Bearer sk-1234' \ + --data '{"input": "Sample text goes here", "model": "text-moderation-stable"}' +``` + + + + + + +## Input Params +LiteLLM accepts and translates the [OpenAI Moderation params](https://platform.openai.com/docs/api-reference/moderations) across all supported providers. + +### Required Fields + +- `input`: *string or array* - Input (or inputs) to classify. Can be a single string, an array of strings, or an array of multi-modal input objects similar to other models. + - If string: A string of text to classify for moderation + - If array of strings: An array of strings to classify for moderation + - If array of objects: An array of multi-modal inputs to the moderation model, where each object can be: + - An object describing an image to classify with: + - `type`: *string, required* - Always `image_url` + - `image_url`: *object, required* - Contains either an image URL or a data URL for a base64 encoded image + - An object describing text to classify with: + - `type`: *string, required* - Always `text` + - `text`: *string, required* - A string of text to classify + +### Optional Fields + +- `model`: *string (optional)* - The moderation model to use. Defaults to `omni-moderation-latest`. + +## Output Format +Here's the exact json output and type you can expect from all moderation calls: + +[**LiteLLM follows OpenAI's output format**](https://platform.openai.com/docs/api-reference/moderations/object) + + +```python +{ + "id": "modr-AB8CjOTu2jiq12hp1AQPfeqFWaORR", + "model": "text-moderation-007", + "results": [ + { + "flagged": true, + "categories": { + "sexual": false, + "hate": false, + "harassment": true, + "self-harm": false, + "sexual/minors": false, + "hate/threatening": false, + "violence/graphic": false, + "self-harm/intent": false, + "self-harm/instructions": false, + "harassment/threatening": true, + "violence": true + }, + "category_scores": { + "sexual": 0.000011726012417057063, + "hate": 0.22706663608551025, + "harassment": 0.5215635299682617, + "self-harm": 2.227119921371923e-6, + "sexual/minors": 7.107352217872176e-8, + "hate/threatening": 0.023547329008579254, + "violence/graphic": 0.00003391829886822961, + "self-harm/intent": 1.646940972932498e-6, + "self-harm/instructions": 1.1198755256458526e-9, + "harassment/threatening": 0.5694745779037476, + "violence": 0.9971134662628174 + } + } + ] +} + +``` + + +## **Supported Providers** + +| Provider | +|-------------| +| OpenAI | diff --git a/docs/my-website/docs/observability/argilla.md b/docs/my-website/docs/observability/argilla.md index 8d20b9daa..dad28ce90 100644 --- a/docs/my-website/docs/observability/argilla.md +++ b/docs/my-website/docs/observability/argilla.md @@ -4,24 +4,63 @@ import TabItem from '@theme/TabItem'; # Argilla -Argilla is a tool for annotating datasets. +Argilla is a collaborative annotation tool for AI engineers and domain experts who need to build high-quality datasets for their projects. +## Getting Started -## Usage +To log the data to Argilla, first you need to deploy the Argilla server. If you have not deployed the Argilla server, please follow the instructions [here](https://docs.argilla.io/latest/getting_started/quickstart/). + +Next, you will need to configure and create the Argilla dataset. + +```python +import argilla as rg + +client = rg.Argilla(api_url="", api_key="") + +settings = rg.Settings( + guidelines="These are some guidelines.", + fields=[ + rg.ChatField( + name="user_input", + ), + rg.TextField( + name="llm_output", + ), + ], + questions=[ + rg.RatingQuestion( + name="rating", + values=[1, 2, 3, 4, 5, 6, 7], + ), + ], +) + +dataset = rg.Dataset( + name="my_first_dataset", + settings=settings, +) + +dataset.create() +``` + +For further configuration, please refer to the [Argilla documentation](https://docs.argilla.io/latest/how_to_guides/dataset/). + + +## Usage ```python -from litellm import completion +import os import litellm -import os +from litellm import completion # add env vars os.environ["ARGILLA_API_KEY"]="argilla.apikey" os.environ["ARGILLA_BASE_URL"]="http://localhost:6900" -os.environ["ARGILLA_DATASET_NAME"]="my_second_dataset" +os.environ["ARGILLA_DATASET_NAME"]="my_first_dataset" os.environ["OPENAI_API_KEY"]="sk-proj-..." litellm.callbacks = ["argilla"] diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 91deba958..c762a0716 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -279,7 +279,31 @@ router_settings: | retry_policy | object | Specifies the number of retries for different types of exceptions. [More information here](reliability) | | allowed_fails | integer | The number of failures allowed before cooling down a model. [More information here](reliability) | | allowed_fails_policy | object | Specifies the number of allowed failures for different error types before cooling down a deployment. [More information here](reliability) | - +| default_max_parallel_requests | Optional[int] | The default maximum number of parallel requests for a deployment. | +| default_priority | (Optional[int]) | The default priority for a request. Only for '.scheduler_acompletion()'. Default is None. | +| polling_interval | (Optional[float]) | frequency of polling queue. Only for '.scheduler_acompletion()'. Default is 3ms. | +| max_fallbacks | Optional[int] | The maximum number of fallbacks to try before exiting the call. Defaults to 5. | +| default_litellm_params | Optional[dict] | The default litellm parameters to add to all requests (e.g. `temperature`, `max_tokens`). | +| timeout | Optional[float] | The default timeout for a request. | +| debug_level | Literal["DEBUG", "INFO"] | The debug level for the logging library in the router. Defaults to "INFO". | +| client_ttl | int | Time-to-live for cached clients in seconds. Defaults to 3600. | +| cache_kwargs | dict | Additional keyword arguments for the cache initialization. | +| routing_strategy_args | dict | Additional keyword arguments for the routing strategy - e.g. lowest latency routing default ttl | +| model_group_alias | dict | Model group alias mapping. E.g. `{"claude-3-haiku": "claude-3-haiku-20240229"}` | +| num_retries | int | Number of retries for a request. Defaults to 3. | +| default_fallbacks | Optional[List[str]] | Fallbacks to try if no model group-specific fallbacks are defined. | +| caching_groups | Optional[List[tuple]] | List of model groups for caching across model groups. Defaults to None. - e.g. caching_groups=[("openai-gpt-3.5-turbo", "azure-gpt-3.5-turbo")]| +| alerting_config | AlertingConfig | [SDK-only arg] Slack alerting configuration. Defaults to None. [Further Docs](../routing.md#alerting-) | +| assistants_config | AssistantsConfig | Set on proxy via `assistant_settings`. [Further docs](../assistants.md) | +| set_verbose | boolean | [DEPRECATED PARAM - see debug docs](./debugging.md) If true, sets the logging level to verbose. | +| retry_after | int | Time to wait before retrying a request in seconds. Defaults to 0. If `x-retry-after` is received from LLM API, this value is overridden. | +| provider_budget_config | ProviderBudgetConfig | Provider budget configuration. Use this to set llm_provider budget limits. example $100/day to OpenAI, $100/day to Azure, etc. Defaults to None. [Further Docs](./provider_budget_routing.md) | +| enable_pre_call_checks | boolean | If true, checks if a call is within the model's context window before making the call. [More information here](reliability) | +| model_group_retry_policy | Dict[str, RetryPolicy] | [SDK-only arg] Set retry policy for model groups. | +| context_window_fallbacks | List[Dict[str, List[str]]] | Fallback models for context window violations. | +| redis_url | str | URL for Redis server. **Known performance issue with Redis URL.** | +| cache_responses | boolean | Flag to enable caching LLM Responses, if cache set under `router_settings`. If true, caches responses. Defaults to False. | +| router_general_settings | RouterGeneralSettings | [SDK-Only] Router general settings - contains optimizations like 'async_only_mode'. [Docs](../routing.md#router-general-settings) | ### environment variables - Reference @@ -335,6 +359,8 @@ router_settings: | DD_SITE | Site URL for Datadog (e.g., datadoghq.com) | DD_SOURCE | Source identifier for Datadog logs | DD_ENV | Environment identifier for Datadog logs. Only supported for `datadog_llm_observability` callback +| DD_SERVICE | Service identifier for Datadog logs. Defaults to "litellm-server" +| DD_VERSION | Version identifier for Datadog logs. Defaults to "unknown" | DEBUG_OTEL | Enable debug mode for OpenTelemetry | DIRECT_URL | Direct URL for service endpoint | DISABLE_ADMIN_UI | Toggle to disable the admin UI diff --git a/docs/my-website/docs/proxy/configs.md b/docs/my-website/docs/proxy/configs.md index ccb9872d6..7876c9dec 100644 --- a/docs/my-website/docs/proxy/configs.md +++ b/docs/my-website/docs/proxy/configs.md @@ -357,77 +357,6 @@ curl --location 'http://0.0.0.0:4000/v1/model/info' \ --data '' ``` - -### Provider specific wildcard routing -**Proxy all models from a provider** - -Use this if you want to **proxy all models from a specific provider without defining them on the config.yaml** - -**Step 1** - define provider specific routing on config.yaml -```yaml -model_list: - # provider specific wildcard routing - - model_name: "anthropic/*" - litellm_params: - model: "anthropic/*" - api_key: os.environ/ANTHROPIC_API_KEY - - model_name: "groq/*" - litellm_params: - model: "groq/*" - api_key: os.environ/GROQ_API_KEY - - model_name: "fo::*:static::*" # all requests matching this pattern will be routed to this deployment, example: model="fo::hi::static::hi" will be routed to deployment: "openai/fo::*:static::*" - litellm_params: - model: "openai/fo::*:static::*" - api_key: os.environ/OPENAI_API_KEY -``` - -Step 2 - Run litellm proxy - -```shell -$ litellm --config /path/to/config.yaml -``` - -Step 3 Test it - -Test with `anthropic/` - all models with `anthropic/` prefix will get routed to `anthropic/*` -```shell -curl http://localhost:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer sk-1234" \ - -d '{ - "model": "anthropic/claude-3-sonnet-20240229", - "messages": [ - {"role": "user", "content": "Hello, Claude!"} - ] - }' -``` - -Test with `groq/` - all models with `groq/` prefix will get routed to `groq/*` -```shell -curl http://localhost:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer sk-1234" \ - -d '{ - "model": "groq/llama3-8b-8192", - "messages": [ - {"role": "user", "content": "Hello, Claude!"} - ] - }' -``` - -Test with `fo::*::static::*` - all requests matching this pattern will be routed to `openai/fo::*:static::*` -```shell -curl http://localhost:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer sk-1234" \ - -d '{ - "model": "fo::hi::static::hi", - "messages": [ - {"role": "user", "content": "Hello, Claude!"} - ] - }' -``` - ### Load Balancing :::info diff --git a/docs/my-website/docs/proxy/db_info.md b/docs/my-website/docs/proxy/db_info.md index 6e6a48bd1..8429f6360 100644 --- a/docs/my-website/docs/proxy/db_info.md +++ b/docs/my-website/docs/proxy/db_info.md @@ -50,18 +50,22 @@ You can see the full DB Schema [here](https://github.com/BerriAI/litellm/blob/ma | LiteLLM_ErrorLogs | Captures failed requests and errors. Stores exception details and request information. Helps with debugging and monitoring. | **Medium - on errors only** | | LiteLLM_AuditLog | Tracks changes to system configuration. Records who made changes and what was modified. Maintains history of updates to teams, users, and models. | **Off by default**, **High - when enabled** | -## How to Disable `LiteLLM_SpendLogs` +## Disable `LiteLLM_SpendLogs` & `LiteLLM_ErrorLogs` -You can disable spend_logs by setting `disable_spend_logs` to `True` on the `general_settings` section of your proxy_config.yaml file. +You can disable spend_logs and error_logs by setting `disable_spend_logs` and `disable_error_logs` to `True` on the `general_settings` section of your proxy_config.yaml file. ```yaml general_settings: - disable_spend_logs: True + disable_spend_logs: True # Disable writing spend logs to DB + disable_error_logs: True # Disable writing error logs to DB ``` +### What is the impact of disabling these logs? -### What is the impact of disabling `LiteLLM_SpendLogs`? - +When disabling spend logs (`disable_spend_logs: True`): - You **will not** be able to view Usage on the LiteLLM UI - You **will** continue seeing cost metrics on s3, Prometheus, Langfuse (any other Logging integration you are using) +When disabling error logs (`disable_error_logs: True`): +- You **will not** be able to view Errors on the LiteLLM UI +- You **will** continue seeing error logs in your application logs and any other logging integrations you are using diff --git a/docs/my-website/docs/proxy/prod.md b/docs/my-website/docs/proxy/prod.md index 32a6fceee..9dacedaab 100644 --- a/docs/my-website/docs/proxy/prod.md +++ b/docs/my-website/docs/proxy/prod.md @@ -23,6 +23,7 @@ general_settings: # OPTIONAL Best Practices disable_spend_logs: True # turn off writing each transaction to the db. We recommend doing this is you don't need to see Usage on the LiteLLM UI and are tracking metrics via Prometheus + disable_error_logs: True # turn off writing LLM Exceptions to DB allow_requests_on_db_unavailable: True # Only USE when running LiteLLM on your VPC. Allow requests to still be processed even if the DB is unavailable. We recommend doing this if you're running LiteLLM on VPC that cannot be accessed from the public internet. litellm_settings: @@ -102,17 +103,22 @@ general_settings: allow_requests_on_db_unavailable: True ``` -## 6. Disable spend_logs if you're not using the LiteLLM UI +## 6. Disable spend_logs & error_logs if not using the LiteLLM UI -By default LiteLLM will write every request to the `LiteLLM_SpendLogs` table. This is used for viewing Usage on the LiteLLM UI. +By default, LiteLLM writes several types of logs to the database: +- Every LLM API request to the `LiteLLM_SpendLogs` table +- LLM Exceptions to the `LiteLLM_LogsErrors` table -If you're not viewing Usage on the LiteLLM UI (most users use Prometheus when this is disabled), you can disable spend_logs by setting `disable_spend_logs` to `True`. +If you're not viewing these logs on the LiteLLM UI (most users use Prometheus for monitoring), you can disable them by setting the following flags to `True`: ```yaml general_settings: - disable_spend_logs: True + disable_spend_logs: True # Disable writing spend logs to DB + disable_error_logs: True # Disable writing error logs to DB ``` +[More information about what the Database is used for here](db_info) + ## 7. Use Helm PreSync Hook for Database Migrations [BETA] To ensure only one service manages database migrations, use our [Helm PreSync hook for Database Migrations](https://github.com/BerriAI/litellm/blob/main/deploy/charts/litellm-helm/templates/migrations-job.yaml). This ensures migrations are handled during `helm upgrade` or `helm install`, while LiteLLM pods explicitly disable migrations. diff --git a/docs/my-website/docs/proxy/prometheus.md b/docs/my-website/docs/proxy/prometheus.md index 58dc3dae3..f19101b36 100644 --- a/docs/my-website/docs/proxy/prometheus.md +++ b/docs/my-website/docs/proxy/prometheus.md @@ -192,3 +192,13 @@ Here is a screenshot of the metrics you can monitor with the LiteLLM Grafana Das |----------------------|--------------------------------------| | `litellm_llm_api_failed_requests_metric` | **deprecated** use `litellm_proxy_failed_requests_metric` | | `litellm_requests_metric` | **deprecated** use `litellm_proxy_total_requests_metric` | + + +## FAQ + +### What are `_created` vs. `_total` metrics? + +- `_created` metrics are metrics that are created when the proxy starts +- `_total` metrics are metrics that are incremented for each request + +You should consume the `_total` metrics for your counting purposes \ No newline at end of file diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md index 702cafa7f..87fad7437 100644 --- a/docs/my-website/docs/routing.md +++ b/docs/my-website/docs/routing.md @@ -1891,3 +1891,22 @@ router = Router( debug_level="DEBUG" # defaults to INFO ) ``` + +## Router General Settings + +### Usage + +```python +router = Router(model_list=..., router_general_settings=RouterGeneralSettings(async_only_mode=True)) +``` + +### Spec +```python +class RouterGeneralSettings(BaseModel): + async_only_mode: bool = Field( + default=False + ) # this will only initialize async clients. Good for memory utils + pass_through_all_models: bool = Field( + default=False + ) # if passed a model not llm_router model list, pass through the request to litellm.acompletion/embedding +``` \ No newline at end of file diff --git a/docs/my-website/docs/text_completion.md b/docs/my-website/docs/text_completion.md new file mode 100644 index 000000000..8be40dfdc --- /dev/null +++ b/docs/my-website/docs/text_completion.md @@ -0,0 +1,174 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Text Completion + +### Usage + + + +```python +from litellm import text_completion + +response = text_completion( + model="gpt-3.5-turbo-instruct", + prompt="Say this is a test", + max_tokens=7 +) +``` + + + + +1. Define models on config.yaml + +```yaml +model_list: + - model_name: gpt-3.5-turbo-instruct + litellm_params: + model: text-completion-openai/gpt-3.5-turbo-instruct # The `text-completion-openai/` prefix will call openai.completions.create + api_key: os.environ/OPENAI_API_KEY + - model_name: text-davinci-003 + litellm_params: + model: text-completion-openai/text-davinci-003 + api_key: os.environ/OPENAI_API_KEY +``` + +2. Start litellm proxy server + +``` +litellm --config config.yaml +``` + + + + +```python +from openai import OpenAI + +# set base_url to your proxy server +# set api_key to send to proxy server +client = OpenAI(api_key="", base_url="http://0.0.0.0:4000") + +response = client.completions.create( + model="gpt-3.5-turbo-instruct", + prompt="Say this is a test", + max_tokens=7 +) + +print(response) +``` + + + + +```shell +curl --location 'http://0.0.0.0:4000/completions' \ + --header 'Content-Type: application/json' \ + --header 'Authorization: Bearer sk-1234' \ + --data '{ + "model": "gpt-3.5-turbo-instruct", + "prompt": "Say this is a test", + "max_tokens": 7 + }' +``` + + + + + + +## Input Params + +LiteLLM accepts and translates the [OpenAI Text Completion params](https://platform.openai.com/docs/api-reference/completions) across all supported providers. + +### Required Fields + +- `model`: *string* - ID of the model to use +- `prompt`: *string or array* - The prompt(s) to generate completions for + +### Optional Fields + +- `best_of`: *integer* - Generates best_of completions server-side and returns the "best" one +- `echo`: *boolean* - Echo back the prompt in addition to the completion. +- `frequency_penalty`: *number* - Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency. +- `logit_bias`: *map* - Modify the likelihood of specified tokens appearing in the completion +- `logprobs`: *integer* - Include the log probabilities on the logprobs most likely tokens. Max value of 5 +- `max_tokens`: *integer* - The maximum number of tokens to generate. +- `n`: *integer* - How many completions to generate for each prompt. +- `presence_penalty`: *number* - Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far. +- `seed`: *integer* - If specified, system will attempt to make deterministic samples +- `stop`: *string or array* - Up to 4 sequences where the API will stop generating tokens +- `stream`: *boolean* - Whether to stream back partial progress. Defaults to false +- `suffix`: *string* - The suffix that comes after a completion of inserted text +- `temperature`: *number* - What sampling temperature to use, between 0 and 2. +- `top_p`: *number* - An alternative to sampling with temperature, called nucleus sampling. +- `user`: *string* - A unique identifier representing your end-user + +## Output Format +Here's the exact JSON output format you can expect from completion calls: + + +[**Follows OpenAI's output format**](https://platform.openai.com/docs/api-reference/completions/object) + + + + + +```python +{ + "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7", + "object": "text_completion", + "created": 1589478378, + "model": "gpt-3.5-turbo-instruct", + "system_fingerprint": "fp_44709d6fcb", + "choices": [ + { + "text": "\n\nThis is indeed a test", + "index": 0, + "logprobs": null, + "finish_reason": "length" + } + ], + "usage": { + "prompt_tokens": 5, + "completion_tokens": 7, + "total_tokens": 12 + } +} + +``` + + + +```python +{ + "id": "cmpl-7iA7iJjj8V2zOkCGvWF2hAkDWBQZe", + "object": "text_completion", + "created": 1690759702, + "choices": [ + { + "text": "This", + "index": 0, + "logprobs": null, + "finish_reason": null + } + ], + "model": "gpt-3.5-turbo-instruct" + "system_fingerprint": "fp_44709d6fcb", +} + +``` + + + + + +## **Supported Providers** + +| Provider | Link to Usage | +|-------------|--------------------| +| OpenAI | [Usage](../docs/providers/text_completion_openai) | +| Azure OpenAI| [Usage](../docs/providers/azure) | + + diff --git a/docs/my-website/docs/wildcard_routing.md b/docs/my-website/docs/wildcard_routing.md new file mode 100644 index 000000000..80926d73e --- /dev/null +++ b/docs/my-website/docs/wildcard_routing.md @@ -0,0 +1,140 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Provider specific Wildcard routing + +**Proxy all models from a provider** + +Use this if you want to **proxy all models from a specific provider without defining them on the config.yaml** + +## Step 1. Define provider specific routing + + + + +```python +from litellm import Router + +router = Router( + model_list=[ + { + "model_name": "anthropic/*", + "litellm_params": { + "model": "anthropic/*", + "api_key": os.environ["ANTHROPIC_API_KEY"] + } + }, + { + "model_name": "groq/*", + "litellm_params": { + "model": "groq/*", + "api_key": os.environ["GROQ_API_KEY"] + } + }, + { + "model_name": "fo::*:static::*", # all requests matching this pattern will be routed to this deployment, example: model="fo::hi::static::hi" will be routed to deployment: "openai/fo::*:static::*" + "litellm_params": { + "model": "openai/fo::*:static::*", + "api_key": os.environ["OPENAI_API_KEY"] + } + } + ] +) +``` + + + + +**Step 1** - define provider specific routing on config.yaml +```yaml +model_list: + # provider specific wildcard routing + - model_name: "anthropic/*" + litellm_params: + model: "anthropic/*" + api_key: os.environ/ANTHROPIC_API_KEY + - model_name: "groq/*" + litellm_params: + model: "groq/*" + api_key: os.environ/GROQ_API_KEY + - model_name: "fo::*:static::*" # all requests matching this pattern will be routed to this deployment, example: model="fo::hi::static::hi" will be routed to deployment: "openai/fo::*:static::*" + litellm_params: + model: "openai/fo::*:static::*" + api_key: os.environ/OPENAI_API_KEY +``` + + + +## [PROXY-Only] Step 2 - Run litellm proxy + +```shell +$ litellm --config /path/to/config.yaml +``` + +## Step 3 - Test it + + + + +```python +from litellm import Router + +router = Router(model_list=...) + +# Test with `anthropic/` - all models with `anthropic/` prefix will get routed to `anthropic/*` +resp = completion(model="anthropic/claude-3-sonnet-20240229", messages=[{"role": "user", "content": "Hello, Claude!"}]) +print(resp) + +# Test with `groq/` - all models with `groq/` prefix will get routed to `groq/*` +resp = completion(model="groq/llama3-8b-8192", messages=[{"role": "user", "content": "Hello, Groq!"}]) +print(resp) + +# Test with `fo::*::static::*` - all requests matching this pattern will be routed to `openai/fo::*:static::*` +resp = completion(model="fo::hi::static::hi", messages=[{"role": "user", "content": "Hello, Claude!"}]) +print(resp) +``` + + + + +Test with `anthropic/` - all models with `anthropic/` prefix will get routed to `anthropic/*` +```bash +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "anthropic/claude-3-sonnet-20240229", + "messages": [ + {"role": "user", "content": "Hello, Claude!"} + ] + }' +``` + +Test with `groq/` - all models with `groq/` prefix will get routed to `groq/*` +```shell +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "groq/llama3-8b-8192", + "messages": [ + {"role": "user", "content": "Hello, Claude!"} + ] + }' +``` + +Test with `fo::*::static::*` - all requests matching this pattern will be routed to `openai/fo::*:static::*` +```shell +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "fo::hi::static::hi", + "messages": [ + {"role": "user", "content": "Hello, Claude!"} + ] + }' +``` + + + diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 1deb0dd75..e6a028d83 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -246,6 +246,7 @@ const sidebars = { "completion/usage", ], }, + "text_completion", "embedding/supported_embedding", "image_generation", { @@ -261,6 +262,7 @@ const sidebars = { "batches", "realtime", "fine_tuning", + "moderation", { type: "link", label: "Use LiteLLM Proxy with Vertex, Bedrock SDK", @@ -277,7 +279,7 @@ const sidebars = { description: "Learn how to load balance, route, and set fallbacks for your LLM requests", slug: "/routing-load-balancing", }, - items: ["routing", "scheduler", "proxy/load_balancing", "proxy/reliability", "proxy/tag_routing", "proxy/provider_budget_routing", "proxy/team_based_routing", "proxy/customer_routing"], + items: ["routing", "scheduler", "proxy/load_balancing", "proxy/reliability", "proxy/tag_routing", "proxy/provider_budget_routing", "proxy/team_based_routing", "proxy/customer_routing", "wildcard_routing"], }, { type: "category", diff --git a/enterprise/utils.py b/enterprise/utils.py index f0af1d676..cc97661d7 100644 --- a/enterprise/utils.py +++ b/enterprise/utils.py @@ -2,7 +2,9 @@ from typing import Optional, List from litellm._logging import verbose_logger from litellm.proxy.proxy_server import PrismaClient, HTTPException +from litellm.llms.custom_httpx.http_handler import HTTPHandler import collections +import httpx from datetime import datetime @@ -114,7 +116,6 @@ async def ui_get_spend_by_tags( def _forecast_daily_cost(data: list): - import requests # type: ignore from datetime import datetime, timedelta if len(data) == 0: @@ -136,17 +137,17 @@ def _forecast_daily_cost(data: list): print("last entry date", last_entry_date) - # Assuming today_date is a datetime object - today_date = datetime.now() - # Calculate the last day of the month last_day_of_todays_month = datetime( today_date.year, today_date.month % 12 + 1, 1 ) - timedelta(days=1) + print("last day of todays month", last_day_of_todays_month) # Calculate the remaining days in the month remaining_days = (last_day_of_todays_month - last_entry_date).days + print("remaining days", remaining_days) + current_spend_this_month = 0 series = {} for entry in data: @@ -176,13 +177,19 @@ def _forecast_daily_cost(data: list): "Content-Type": "application/json", } - response = requests.post( - url="https://trend-api-production.up.railway.app/forecast", - json=payload, - headers=headers, - ) - # check the status code - response.raise_for_status() + client = HTTPHandler() + + try: + response = client.post( + url="https://trend-api-production.up.railway.app/forecast", + json=payload, + headers=headers, + ) + except httpx.HTTPStatusError as e: + raise HTTPException( + status_code=500, + detail={"error": f"Error getting forecast: {e.response.text}"}, + ) json_response = response.json() forecast_data = json_response["forecast"] @@ -206,13 +213,3 @@ def _forecast_daily_cost(data: list): f"Predicted Spend for { today_month } 2024, ${total_predicted_spend}" ) return {"response": response_data, "predicted_spend": predicted_spend} - - # print(f"Date: {entry['date']}, Spend: {entry['spend']}, Response: {response.text}") - - -# _forecast_daily_cost( -# [ -# {"date": "2022-01-01", "spend": 100}, - -# ] -# ) diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 42d9a38d6..482c2bc10 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -279,11 +279,11 @@ class DataDogLogger(CustomBatchLogger): verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload) dd_payload = DatadogPayload( - ddsource=os.getenv("DD_SOURCE", "litellm"), - ddtags="", - hostname="", + ddsource=self._get_datadog_source(), + ddtags=self._get_datadog_tags(), + hostname=self._get_datadog_hostname(), message=json_payload, - service="litellm-server", + service=self._get_datadog_service(), status=status, ) return dd_payload @@ -387,11 +387,11 @@ class DataDogLogger(CustomBatchLogger): json_payload = json.dumps(_exception_payload) verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload) dd_payload = DatadogPayload( - ddsource=os.getenv("DD_SOURCE", "litellm"), - ddtags="", - hostname="", + ddsource=self._get_datadog_source(), + ddtags=self._get_datadog_tags(), + hostname=self._get_datadog_hostname(), message=json_payload, - service="litellm-server", + service=self._get_datadog_service(), status=DataDogStatus.ERROR, ) @@ -473,11 +473,31 @@ class DataDogLogger(CustomBatchLogger): verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload) dd_payload = DatadogPayload( - ddsource=os.getenv("DD_SOURCE", "litellm"), - ddtags="", - hostname="", + ddsource=self._get_datadog_source(), + ddtags=self._get_datadog_tags(), + hostname=self._get_datadog_hostname(), message=json_payload, - service="litellm-server", + service=self._get_datadog_service(), status=DataDogStatus.INFO, ) return dd_payload + + @staticmethod + def _get_datadog_tags(): + return f"env:{os.getenv('DD_ENV', 'unknown')},service:{os.getenv('DD_SERVICE', 'litellm')},version:{os.getenv('DD_VERSION', 'unknown')}" + + @staticmethod + def _get_datadog_source(): + return os.getenv("DD_SOURCE", "litellm") + + @staticmethod + def _get_datadog_service(): + return os.getenv("DD_SERVICE", "litellm-server") + + @staticmethod + def _get_datadog_hostname(): + return "" + + @staticmethod + def _get_datadog_env(): + return os.getenv("DD_ENV", "unknown") diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index 6c08758dd..23ee97a47 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -458,7 +458,7 @@ class AmazonConverseConfig: """ Abbreviations of regions AWS Bedrock supports for cross region inference """ - return ["us", "eu"] + return ["us", "eu", "apac"] def _get_base_model(self, model: str) -> str: """ diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index f5c4f694d..f4d20f8fb 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -28,6 +28,62 @@ headers = { _DEFAULT_TIMEOUT = httpx.Timeout(timeout=5.0, connect=5.0) _DEFAULT_TTL_FOR_HTTPX_CLIENTS = 3600 # 1 hour, re-use the same httpx client for 1 hour +import re + + +def mask_sensitive_info(error_message): + # Find the start of the key parameter + if isinstance(error_message, str): + key_index = error_message.find("key=") + else: + return error_message + + # If key is found + if key_index != -1: + # Find the end of the key parameter (next & or end of string) + next_param = error_message.find("&", key_index) + + if next_param == -1: + # If no more parameters, mask until the end of the string + masked_message = error_message[: key_index + 4] + "[REDACTED_API_KEY]" + else: + # Replace the key with redacted value, keeping other parameters + masked_message = ( + error_message[: key_index + 4] + + "[REDACTED_API_KEY]" + + error_message[next_param:] + ) + + return masked_message + + return error_message + + +class MaskedHTTPStatusError(httpx.HTTPStatusError): + def __init__( + self, original_error, message: Optional[str] = None, text: Optional[str] = None + ): + # Create a new error with the masked URL + masked_url = mask_sensitive_info(str(original_error.request.url)) + # Create a new error that looks like the original, but with a masked URL + + super().__init__( + message=original_error.message, + request=httpx.Request( + method=original_error.request.method, + url=masked_url, + headers=original_error.request.headers, + content=original_error.request.content, + ), + response=httpx.Response( + status_code=original_error.response.status_code, + content=original_error.response.content, + headers=original_error.response.headers, + ), + ) + self.message = message + self.text = text + class AsyncHTTPHandler: def __init__( @@ -155,13 +211,16 @@ class AsyncHTTPHandler: headers=headers, ) except httpx.HTTPStatusError as e: - setattr(e, "status_code", e.response.status_code) + if stream is True: setattr(e, "message", await e.response.aread()) setattr(e, "text", await e.response.aread()) else: - setattr(e, "message", e.response.text) - setattr(e, "text", e.response.text) + setattr(e, "message", mask_sensitive_info(e.response.text)) + setattr(e, "text", mask_sensitive_info(e.response.text)) + + setattr(e, "status_code", e.response.status_code) + raise e except Exception as e: raise e @@ -399,11 +458,17 @@ class HTTPHandler: llm_provider="litellm-httpx-handler", ) except httpx.HTTPStatusError as e: - setattr(e, "status_code", e.response.status_code) + if stream is True: - setattr(e, "message", e.response.read()) + setattr(e, "message", mask_sensitive_info(e.response.read())) + setattr(e, "text", mask_sensitive_info(e.response.read())) else: - setattr(e, "message", e.response.text) + error_text = mask_sensitive_info(e.response.text) + setattr(e, "message", error_text) + setattr(e, "text", error_text) + + setattr(e, "status_code", e.response.status_code) + raise e except Exception as e: raise e diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py index cb79a81b7..bfd35ca47 100644 --- a/litellm/llms/prompt_templates/factory.py +++ b/litellm/llms/prompt_templates/factory.py @@ -1159,15 +1159,44 @@ def convert_to_anthropic_tool_result( ] } """ - content_str: str = "" + anthropic_content: Union[ + str, + List[Union[AnthropicMessagesToolResultContent, AnthropicMessagesImageParam]], + ] = "" if isinstance(message["content"], str): - content_str = message["content"] + anthropic_content = message["content"] elif isinstance(message["content"], List): content_list = message["content"] + anthropic_content_list: List[ + Union[AnthropicMessagesToolResultContent, AnthropicMessagesImageParam] + ] = [] for content in content_list: if content["type"] == "text": - content_str += content["text"] + anthropic_content_list.append( + AnthropicMessagesToolResultContent( + type="text", + text=content["text"], + ) + ) + elif content["type"] == "image_url": + if isinstance(content["image_url"], str): + image_chunk = convert_to_anthropic_image_obj(content["image_url"]) + else: + image_chunk = convert_to_anthropic_image_obj( + content["image_url"]["url"] + ) + anthropic_content_list.append( + AnthropicMessagesImageParam( + type="image", + source=AnthropicContentParamSource( + type="base64", + media_type=image_chunk["media_type"], + data=image_chunk["data"], + ), + ) + ) + anthropic_content = anthropic_content_list anthropic_tool_result: Optional[AnthropicMessagesToolResultParam] = None ## PROMPT CACHING CHECK ## cache_control = message.get("cache_control", None) @@ -1178,14 +1207,14 @@ def convert_to_anthropic_tool_result( # 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 = AnthropicMessagesToolResultParam( - type="tool_result", tool_use_id=tool_call_id, content=content_str + type="tool_result", tool_use_id=tool_call_id, content=anthropic_content ) if message["role"] == "function": function_message: ChatCompletionFunctionMessage = message tool_call_id = function_message.get("tool_call_id") or str(uuid.uuid4()) anthropic_tool_result = AnthropicMessagesToolResultParam( - type="tool_result", tool_use_id=tool_call_id, content=content_str + type="tool_result", tool_use_id=tool_call_id, content=anthropic_content ) if anthropic_tool_result is None: diff --git a/litellm/llms/vertex_ai_and_google_ai_studio/gemini/transformation.py b/litellm/llms/vertex_ai_and_google_ai_studio/gemini/transformation.py index 4b5b7281b..c9fe6e3f4 100644 --- a/litellm/llms/vertex_ai_and_google_ai_studio/gemini/transformation.py +++ b/litellm/llms/vertex_ai_and_google_ai_studio/gemini/transformation.py @@ -107,6 +107,10 @@ def _get_image_mime_type_from_url(url: str) -> Optional[str]: return "image/png" elif url.endswith(".webp"): return "image/webp" + elif url.endswith(".mp4"): + return "video/mp4" + elif url.endswith(".pdf"): + return "application/pdf" return None diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index b0d0e7d37..ac22871bc 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -3383,6 +3383,8 @@ "supports_vision": true, "supports_response_schema": true, "supports_prompt_caching": true, + "tpm": 4000000, + "rpm": 2000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-1.5-flash-001": { @@ -3406,6 +3408,8 @@ "supports_vision": true, "supports_response_schema": true, "supports_prompt_caching": true, + "tpm": 4000000, + "rpm": 2000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-1.5-flash": { @@ -3428,6 +3432,8 @@ "supports_function_calling": true, "supports_vision": true, "supports_response_schema": true, + "tpm": 4000000, + "rpm": 2000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-1.5-flash-latest": { @@ -3450,6 +3456,32 @@ "supports_function_calling": true, "supports_vision": true, "supports_response_schema": true, + "tpm": 4000000, + "rpm": 2000, + "source": "https://ai.google.dev/pricing" + }, + "gemini/gemini-1.5-flash-8b": { + "max_tokens": 8192, + "max_input_tokens": 1048576, + "max_output_tokens": 8192, + "max_images_per_prompt": 3000, + "max_videos_per_prompt": 10, + "max_video_length": 1, + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_pdf_size_mb": 30, + "input_cost_per_token": 0, + "input_cost_per_token_above_128k_tokens": 0, + "output_cost_per_token": 0, + "output_cost_per_token_above_128k_tokens": 0, + "litellm_provider": "gemini", + "mode": "chat", + "supports_system_messages": true, + "supports_function_calling": true, + "supports_vision": true, + "supports_response_schema": true, + "tpm": 4000000, + "rpm": 4000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-1.5-flash-8b-exp-0924": { @@ -3472,6 +3504,8 @@ "supports_function_calling": true, "supports_vision": true, "supports_response_schema": true, + "tpm": 4000000, + "rpm": 4000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-exp-1114": { @@ -3494,7 +3528,12 @@ "supports_function_calling": true, "supports_vision": true, "supports_response_schema": true, - "source": "https://ai.google.dev/pricing" + "tpm": 4000000, + "rpm": 1000, + "source": "https://ai.google.dev/pricing", + "metadata": { + "notes": "Rate limits not documented for gemini-exp-1114. Assuming same as gemini-1.5-pro." + } }, "gemini/gemini-1.5-flash-exp-0827": { "max_tokens": 8192, @@ -3516,6 +3555,8 @@ "supports_function_calling": true, "supports_vision": true, "supports_response_schema": true, + "tpm": 4000000, + "rpm": 2000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-1.5-flash-8b-exp-0827": { @@ -3537,6 +3578,9 @@ "supports_system_messages": true, "supports_function_calling": true, "supports_vision": true, + "supports_response_schema": true, + "tpm": 4000000, + "rpm": 4000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-pro": { @@ -3550,7 +3594,10 @@ "litellm_provider": "gemini", "mode": "chat", "supports_function_calling": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + "rpd": 30000, + "tpm": 120000, + "rpm": 360, + "source": "https://ai.google.dev/gemini-api/docs/models/gemini" }, "gemini/gemini-1.5-pro": { "max_tokens": 8192, @@ -3567,6 +3614,8 @@ "supports_vision": true, "supports_tool_choice": true, "supports_response_schema": true, + "tpm": 4000000, + "rpm": 1000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-1.5-pro-002": { @@ -3585,6 +3634,8 @@ "supports_tool_choice": true, "supports_response_schema": true, "supports_prompt_caching": true, + "tpm": 4000000, + "rpm": 1000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-1.5-pro-001": { @@ -3603,6 +3654,8 @@ "supports_tool_choice": true, "supports_response_schema": true, "supports_prompt_caching": true, + "tpm": 4000000, + "rpm": 1000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-1.5-pro-exp-0801": { @@ -3620,6 +3673,8 @@ "supports_vision": true, "supports_tool_choice": true, "supports_response_schema": true, + "tpm": 4000000, + "rpm": 1000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-1.5-pro-exp-0827": { @@ -3637,6 +3692,8 @@ "supports_vision": true, "supports_tool_choice": true, "supports_response_schema": true, + "tpm": 4000000, + "rpm": 1000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-1.5-pro-latest": { @@ -3654,6 +3711,8 @@ "supports_vision": true, "supports_tool_choice": true, "supports_response_schema": true, + "tpm": 4000000, + "rpm": 1000, "source": "https://ai.google.dev/pricing" }, "gemini/gemini-pro-vision": { @@ -3668,6 +3727,9 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, + "rpd": 30000, + "tpm": 120000, + "rpm": 360, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" }, "gemini/gemini-gemma-2-27b-it": { diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html deleted file mode 100644 index 223f5d80e..000000000 --- a/litellm/proxy/_experimental/out/404.html +++ /dev/null @@ -1 +0,0 @@ -404: This page could not be found.LiteLLM Dashboard

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