diff --git a/.circleci/config.yml b/.circleci/config.yml index 18bfeedb52..5f4628d26d 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -344,4 +344,4 @@ workflows: filters: branches: only: - - main + - main \ No newline at end of file diff --git a/.gitignore b/.gitignore index 8d99ae8af8..69061d62d3 100644 --- a/.gitignore +++ b/.gitignore @@ -59,3 +59,4 @@ myenv/* litellm/proxy/_experimental/out/404/index.html litellm/proxy/_experimental/out/model_hub/index.html litellm/proxy/_experimental/out/onboarding/index.html +litellm/tests/log.txt diff --git a/docs/my-website/docs/observability/custom_callback.md b/docs/my-website/docs/observability/custom_callback.md index 3168222273..373b4a96c0 100644 --- a/docs/my-website/docs/observability/custom_callback.md +++ b/docs/my-website/docs/observability/custom_callback.md @@ -38,7 +38,7 @@ class MyCustomHandler(CustomLogger): print(f"On Async Success") async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): - print(f"On Async Success") + print(f"On Async Failure") customHandler = MyCustomHandler() diff --git a/docs/my-website/docs/projects/llmcord.py (Discord LLM Chatbot).md b/docs/my-website/docs/projects/llmcord.py (Discord LLM Chatbot).md new file mode 100644 index 0000000000..f8acb9383c --- /dev/null +++ b/docs/my-website/docs/projects/llmcord.py (Discord LLM Chatbot).md @@ -0,0 +1,3 @@ +llmcord.py lets you and your friends chat with LLMs directly in your Discord server. It works with practically any LLM, remote or locally hosted. + +Github: https://github.com/jakobdylanc/discord-llm-chatbot diff --git a/docs/my-website/docs/proxy/alerting.md b/docs/my-website/docs/proxy/alerting.md index 3ef676bbd6..402de410c9 100644 --- a/docs/my-website/docs/proxy/alerting.md +++ b/docs/my-website/docs/proxy/alerting.md @@ -62,6 +62,23 @@ curl -X GET 'http://localhost:4000/health/services?service=slack' \ -H 'Authorization: Bearer sk-1234' ``` +## Advanced - Redacting Messages from Alerts + +By default alerts show the `messages/input` passed to the LLM. If you want to redact this from slack alerting set the following setting on your config + + +```shell +general_settings: + alerting: ["slack"] + alert_types: ["spend_reports"] + +litellm_settings: + redact_messages_in_exceptions: True +``` + + + + ## Advanced - Opting into specific alert types Set `alert_types` if you want to Opt into only specific alert types diff --git a/docs/my-website/docs/proxy/enterprise.md b/docs/my-website/docs/proxy/enterprise.md index 2b984b3e78..48cab60932 100644 --- a/docs/my-website/docs/proxy/enterprise.md +++ b/docs/my-website/docs/proxy/enterprise.md @@ -14,6 +14,7 @@ Features: - ✅ [SSO for Admin UI](./ui.md#✨-enterprise-features) - ✅ [Audit Logs](#audit-logs) - ✅ [Tracking Spend for Custom Tags](#tracking-spend-for-custom-tags) +- ✅ [Enforce Required Params for LLM Requests (ex. Reject requests missing ["metadata"]["generation_name"])](#enforce-required-params-for-llm-requests) - ✅ [Content Moderation with LLM Guard, LlamaGuard, Google Text Moderations](#content-moderation) - ✅ [Prompt Injection Detection (with LakeraAI API)](#prompt-injection-detection---lakeraai) - ✅ [Custom Branding + Routes on Swagger Docs](#swagger-docs---custom-routes--branding) @@ -204,6 +205,109 @@ curl -X GET "http://0.0.0.0:4000/spend/tags" \ ``` +## Enforce Required Params for LLM Requests +Use this when you want to enforce all requests to include certain params. Example you need all requests to include the `user` and `["metadata]["generation_name"]` params. + +**Step 1** Define all Params you want to enforce on config.yaml + +This means `["user"]` and `["metadata]["generation_name"]` are required in all LLM Requests to LiteLLM + +```yaml +general_settings: + master_key: sk-1234 + enforced_params: + - user + - metadata.generation_name +``` + +Start LiteLLM Proxy + +**Step 2 Verify if this works** + + + + + +```shell +curl --location 'http://localhost:4000/chat/completions' \ + --header 'Authorization: Bearer sk-5fmYeaUEbAMpwBNT-QpxyA' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "hi" + } + ] +}' +``` + +Expected Response + +```shell +{"error":{"message":"Authentication Error, BadRequest please pass param=user in request body. This is a required param","type":"auth_error","param":"None","code":401}}% +``` + + + + + +```shell +curl --location 'http://localhost:4000/chat/completions' \ + --header 'Authorization: Bearer sk-5fmYeaUEbAMpwBNT-QpxyA' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-3.5-turbo", + "user": "gm", + "messages": [ + { + "role": "user", + "content": "hi" + } + ], + "metadata": {} +}' +``` + +Expected Response + +```shell +{"error":{"message":"Authentication Error, BadRequest please pass param=[metadata][generation_name] in request body. This is a required param","type":"auth_error","param":"None","code":401}}% +``` + + + + + +```shell +curl --location 'http://localhost:4000/chat/completions' \ + --header 'Authorization: Bearer sk-5fmYeaUEbAMpwBNT-QpxyA' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-3.5-turbo", + "user": "gm", + "messages": [ + { + "role": "user", + "content": "hi" + } + ], + "metadata": {"generation_name": "prod-app"} +}' +``` + +Expected Response + +```shell +{"id":"chatcmpl-9XALnHqkCBMBKrOx7Abg0hURHqYtY","choices":[{"finish_reason":"stop","index":0,"message":{"content":"Hello! How can I assist you today?","role":"assistant"}}],"created":1717691639,"model":"gpt-3.5-turbo-0125","object":"chat.completion","system_fingerprint":null,"usage":{"completion_tokens":9,"prompt_tokens":8,"total_tokens":17}}% +``` + + + + + + diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md index fd57545ca9..a55e42d558 100644 --- a/docs/my-website/docs/proxy/logging.md +++ b/docs/my-website/docs/proxy/logging.md @@ -313,6 +313,18 @@ You will see `raw_request` in your Langfuse Metadata. This is the RAW CURL comma ## Logging Proxy Input/Output in OpenTelemetry format + +:::info + +[Optional] Customize OTEL Service Name and OTEL TRACER NAME by setting the following variables in your environment + +```shell +OTEL_TRACER_NAME= # default="litellm" +OTEL_SERVICE_NAME=` # default="litellm" +``` + +::: + diff --git a/docs/my-website/docs/scheduler.md b/docs/my-website/docs/scheduler.md index 486549a08c..e7943c4591 100644 --- a/docs/my-website/docs/scheduler.md +++ b/docs/my-website/docs/scheduler.md @@ -100,4 +100,76 @@ print(response) ``` - \ No newline at end of file + + +## Advanced - Redis Caching + +Use redis caching to do request prioritization across multiple instances of LiteLLM. + +### SDK +```python +from litellm import Router + +router = Router( + model_list=[ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo", + "mock_response": "Hello world this is Macintosh!", # fakes the LLM API call + "rpm": 1, + }, + }, + ], + ### REDIS PARAMS ### + redis_host=os.environ["REDIS_HOST"], + redis_password=os.environ["REDIS_PASSWORD"], + redis_port=os.environ["REDIS_PORT"], +) + +try: + _response = await router.schedule_acompletion( # 👈 ADDS TO QUEUE + POLLS + MAKES CALL + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hey!"}], + priority=0, # 👈 LOWER IS BETTER + ) +except Exception as e: + print("didn't make request") +``` + +### PROXY + +```yaml +model_list: + - model_name: gpt-3.5-turbo-fake-model + litellm_params: + model: gpt-3.5-turbo + mock_response: "hello world!" + api_key: my-good-key + +router_settings: + redis_host; os.environ/REDIS_HOST + redis_password: os.environ/REDIS_PASSWORD + redis_port: os.environ/REDIS_PORT +``` + +```bash +$ litellm --config /path/to/config.yaml + +# RUNNING on http://0.0.0.0:4000s +``` + +```bash +curl -X POST 'http://localhost:4000/queue/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-D '{ + "model": "gpt-3.5-turbo-fake-model", + "messages": [ + { + "role": "user", + "content": "what is the meaning of the universe? 1234" + }], + "priority": 0 👈 SET VALUE HERE +}' +``` \ No newline at end of file diff --git a/docs/my-website/docs/secret.md b/docs/my-website/docs/secret.md index 2b945837ad..08c2e89d1f 100644 --- a/docs/my-website/docs/secret.md +++ b/docs/my-website/docs/secret.md @@ -1,11 +1,31 @@ # Secret Manager LiteLLM supports reading secrets from Azure Key Vault and Infisical +- AWS Key Managemenet Service +- AWS Secret Manager - [Azure Key Vault](#azure-key-vault) - Google Key Management Service - [Infisical Secret Manager](#infisical-secret-manager) - [.env Files](#env-files) +## AWS Key Management Service + +Use AWS KMS to storing a hashed copy of your Proxy Master Key in the environment. + +```bash +export LITELLM_MASTER_KEY="djZ9xjVaZ..." # 👈 ENCRYPTED KEY +export AWS_REGION_NAME="us-west-2" +``` + +```yaml +general_settings: + key_management_system: "aws_kms" + key_management_settings: + hosted_keys: ["LITELLM_MASTER_KEY"] # 👈 WHICH KEYS ARE STORED ON KMS +``` + +[**See Decryption Code**](https://github.com/BerriAI/litellm/blob/a2da2a8f168d45648b61279d4795d647d94f90c9/litellm/utils.py#L10182) + ## AWS Secret Manager Store your proxy keys in AWS Secret Manager. diff --git a/docs/my-website/package-lock.json b/docs/my-website/package-lock.json index 0738c4d09f..48b5419862 100644 --- a/docs/my-website/package-lock.json +++ b/docs/my-website/package-lock.json @@ -5975,9 +5975,9 @@ } }, "node_modules/caniuse-lite": { - "version": "1.0.30001519", - "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001519.tgz", - "integrity": "sha512-0QHgqR+Jv4bxHMp8kZ1Kn8CH55OikjKJ6JmKkZYP1F3D7w+lnFXF70nG5eNfsZS89jadi5Ywy5UCSKLAglIRkg==", + "version": "1.0.30001629", + "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001629.tgz", + "integrity": "sha512-c3dl911slnQhmxUIT4HhYzT7wnBK/XYpGnYLOj4nJBaRiw52Ibe7YxlDaAeRECvA786zCuExhxIUJ2K7nHMrBw==", "funding": [ { "type": "opencollective", diff --git a/docs/my-website/yarn.lock b/docs/my-website/yarn.lock index 3acc481539..e96d90e26f 100644 --- a/docs/my-website/yarn.lock +++ b/docs/my-website/yarn.lock @@ -84,7 +84,7 @@ "@algolia/requester-common" "4.19.1" "@algolia/transporter" "4.19.1" -"@algolia/client-search@4.19.1": +"@algolia/client-search@>= 4.9.1 < 6", "@algolia/client-search@4.19.1": version "4.19.1" resolved "https://registry.npmjs.org/@algolia/client-search/-/client-search-4.19.1.tgz" integrity sha512-mBecfMFS4N+yK/p0ZbK53vrZbL6OtWMk8YmnOv1i0LXx4pelY8TFhqKoTit3NPVPwoSNN0vdSN9dTu1xr1XOVw== @@ -146,13 +146,6 @@ "@jridgewell/gen-mapping" "^0.3.0" "@jridgewell/trace-mapping" "^0.3.9" -"@babel/code-frame@7.10.4", "@babel/code-frame@^7.5.5": - version "7.10.4" - resolved "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.10.4.tgz" - integrity sha512-vG6SvB6oYEhvgisZNFRmRCUkLz11c7rp+tbNTynGqc6mS1d5ATd/sGyV6W0KZZnXRKMTzZDRgQT3Ou9jhpAfUg== - dependencies: - "@babel/highlight" "^7.10.4" - "@babel/code-frame@^7.0.0", "@babel/code-frame@^7.10.4", "@babel/code-frame@^7.16.0", "@babel/code-frame@^7.22.10", "@babel/code-frame@^7.22.13", "@babel/code-frame@^7.8.3": version "7.22.13" resolved "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.22.13.tgz" @@ -161,11 +154,39 @@ "@babel/highlight" "^7.22.13" chalk "^2.4.2" +"@babel/code-frame@^7.5.5", "@babel/code-frame@7.10.4": + version "7.10.4" + resolved "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.10.4.tgz" + integrity sha512-vG6SvB6oYEhvgisZNFRmRCUkLz11c7rp+tbNTynGqc6mS1d5ATd/sGyV6W0KZZnXRKMTzZDRgQT3Ou9jhpAfUg== + dependencies: + "@babel/highlight" "^7.10.4" + "@babel/compat-data@^7.22.5", "@babel/compat-data@^7.22.6", "@babel/compat-data@^7.22.9": version "7.22.9" resolved "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.22.9.tgz" integrity 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json5 "^2.2.2" + semver "^6.3.1" + "@babel/core@7.12.9": version "7.12.9" resolved "https://registry.npmjs.org/@babel/core/-/core-7.12.9.tgz" @@ -188,27 +209,6 @@ semver "^5.4.1" source-map "^0.5.0" -"@babel/core@^7.12.3", "@babel/core@^7.18.6", "@babel/core@^7.19.6": - version "7.22.10" - resolved "https://registry.npmjs.org/@babel/core/-/core-7.22.10.tgz" - integrity sha512-fTmqbbUBAwCcre6zPzNngvsI0aNrPZe77AeqvDxWM9Nm+04RrJ3CAmGHA9f7lJQY6ZMhRztNemy4uslDxTX4Qw== - dependencies: - "@ampproject/remapping" "^2.2.0" - "@babel/code-frame" "^7.22.10" - "@babel/generator" "^7.22.10" - "@babel/helper-compilation-targets" "^7.22.10" - "@babel/helper-module-transforms" "^7.22.9" - "@babel/helpers" "^7.22.10" - "@babel/parser" "^7.22.10" - "@babel/template" "^7.22.5" - "@babel/traverse" "^7.22.10" - "@babel/types" "^7.22.10" - convert-source-map "^1.7.0" - debug "^4.1.0" - gensync "^1.0.0-beta.2" - json5 "^2.2.2" - semver "^6.3.1" - "@babel/generator@^7.12.5", "@babel/generator@^7.18.7", "@babel/generator@^7.22.10", "@babel/generator@^7.23.3": version "7.23.3" resolved "https://registry.npmjs.org/@babel/generator/-/generator-7.23.3.tgz" @@ -331,16 +331,16 @@ dependencies: "@babel/types" "^7.22.5" -"@babel/helper-plugin-utils@7.10.4": - version "7.10.4" - resolved "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.10.4.tgz" - integrity sha512-O4KCvQA6lLiMU9l2eawBPMf1xPP8xPfB3iEQw150hOVTqj/rfXz0ThTb4HEzqQfs2Bmo5Ay8BzxfzVtBrr9dVg== - "@babel/helper-plugin-utils@^7.0.0", "@babel/helper-plugin-utils@^7.10.4", "@babel/helper-plugin-utils@^7.12.13", "@babel/helper-plugin-utils@^7.14.5", "@babel/helper-plugin-utils@^7.18.6", "@babel/helper-plugin-utils@^7.22.5", "@babel/helper-plugin-utils@^7.8.0", "@babel/helper-plugin-utils@^7.8.3": version "7.22.5" resolved "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.22.5.tgz" integrity sha512-uLls06UVKgFG9QD4OeFYLEGteMIAa5kpTPcFL28yuCIIzsf6ZyKZMllKVOCZFhiZ5ptnwX4mtKdWCBE/uT4amg== +"@babel/helper-plugin-utils@7.10.4": + version "7.10.4" + resolved "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.10.4.tgz" + integrity sha512-O4KCvQA6lLiMU9l2eawBPMf1xPP8xPfB3iEQw150hOVTqj/rfXz0ThTb4HEzqQfs2Bmo5Ay8BzxfzVtBrr9dVg== + "@babel/helper-remap-async-to-generator@^7.22.5", "@babel/helper-remap-async-to-generator@^7.22.9": version "7.22.9" resolved "https://registry.npmjs.org/@babel/helper-remap-async-to-generator/-/helper-remap-async-to-generator-7.22.9.tgz" @@ -451,7 +451,7 @@ "@babel/helper-create-class-features-plugin" "^7.18.6" "@babel/helper-plugin-utils" "^7.18.6" -"@babel/plugin-proposal-object-rest-spread@7.12.1", "@babel/plugin-proposal-object-rest-spread@^7.12.1": +"@babel/plugin-proposal-object-rest-spread@^7.12.1", "@babel/plugin-proposal-object-rest-spread@7.12.1": version "7.12.1" resolved "https://registry.npmjs.org/@babel/plugin-proposal-object-rest-spread/-/plugin-proposal-object-rest-spread-7.12.1.tgz" integrity sha512-s6SowJIjzlhx8o7lsFx5zmY4At6CTtDvgNQDdPzkBQucle58A6b/TTeEBYtyDgmcXjUTM+vE8YOGHZzzbc/ioA== @@ -528,13 +528,6 @@ dependencies: "@babel/helper-plugin-utils" "^7.8.0" -"@babel/plugin-syntax-jsx@7.12.1": - version "7.12.1" - resolved "https://registry.npmjs.org/@babel/plugin-syntax-jsx/-/plugin-syntax-jsx-7.12.1.tgz" - integrity sha512-1yRi7yAtB0ETgxdY9ti/p2TivUxJkTdhu/ZbF9MshVGqOx1TdB3b7xCXs49Fupgg50N45KcAsRP/ZqWjs9SRjg== - dependencies: - "@babel/helper-plugin-utils" "^7.10.4" - "@babel/plugin-syntax-jsx@^7.22.5": version "7.22.5" resolved "https://registry.npmjs.org/@babel/plugin-syntax-jsx/-/plugin-syntax-jsx-7.22.5.tgz" @@ -542,6 +535,13 @@ dependencies: "@babel/helper-plugin-utils" "^7.22.5" +"@babel/plugin-syntax-jsx@7.12.1": + version "7.12.1" + resolved "https://registry.npmjs.org/@babel/plugin-syntax-jsx/-/plugin-syntax-jsx-7.12.1.tgz" + integrity sha512-1yRi7yAtB0ETgxdY9ti/p2TivUxJkTdhu/ZbF9MshVGqOx1TdB3b7xCXs49Fupgg50N45KcAsRP/ZqWjs9SRjg== + dependencies: + "@babel/helper-plugin-utils" "^7.10.4" + "@babel/plugin-syntax-logical-assignment-operators@^7.10.4": version "7.10.4" resolved "https://registry.npmjs.org/@babel/plugin-syntax-logical-assignment-operators/-/plugin-syntax-logical-assignment-operators-7.10.4.tgz" @@ -563,7 +563,7 @@ dependencies: "@babel/helper-plugin-utils" "^7.10.4" -"@babel/plugin-syntax-object-rest-spread@7.8.3", "@babel/plugin-syntax-object-rest-spread@^7.8.0", "@babel/plugin-syntax-object-rest-spread@^7.8.3": +"@babel/plugin-syntax-object-rest-spread@^7.8.0", "@babel/plugin-syntax-object-rest-spread@^7.8.3", "@babel/plugin-syntax-object-rest-spread@7.8.3": version "7.8.3" resolved "https://registry.npmjs.org/@babel/plugin-syntax-object-rest-spread/-/plugin-syntax-object-rest-spread-7.8.3.tgz" integrity sha512-XoqMijGZb9y3y2XskN+P1wUGiVwWZ5JmoDRwx5+3GmEplNyVM2s2Dg8ILFQm8rWM48orGy5YpI5Bl8U1y7ydlA== @@ -1279,7 +1279,7 @@ "@docsearch/css" "3.5.1" algoliasearch "^4.0.0" -"@docusaurus/core@2.4.1": +"@docusaurus/core@^2.0.0-alpha.60 || ^2.0.0", "@docusaurus/core@2.4.1": version "2.4.1" resolved "https://registry.npmjs.org/@docusaurus/core/-/core-2.4.1.tgz" integrity sha512-SNsY7PshK3Ri7vtsLXVeAJGS50nJN3RgF836zkyUfAD01Fq+sAk5EwWgLw+nnm5KVNGDu7PRR2kRGDsWvqpo0g== @@ -1502,7 +1502,7 @@ "@docusaurus/utils-validation" "2.4.1" tslib "^2.4.0" -"@docusaurus/plugin-google-gtag@2.4.1", "@docusaurus/plugin-google-gtag@^2.4.1": +"@docusaurus/plugin-google-gtag@^2.4.1", "@docusaurus/plugin-google-gtag@2.4.1": version "2.4.1" resolved "https://registry.npmjs.org/@docusaurus/plugin-google-gtag/-/plugin-google-gtag-2.4.1.tgz" integrity sha512-mKIefK+2kGTQBYvloNEKtDmnRD7bxHLsBcxgnbt4oZwzi2nxCGjPX6+9SQO2KCN5HZbNrYmGo5GJfMgoRvy6uA== @@ -1573,7 +1573,7 @@ "@docusaurus/theme-search-algolia" "2.4.1" "@docusaurus/types" "2.4.1" -"@docusaurus/react-loadable@5.5.2", "react-loadable@npm:@docusaurus/react-loadable@5.5.2": +"@docusaurus/react-loadable@5.5.2": version "5.5.2" resolved "https://registry.npmjs.org/@docusaurus/react-loadable/-/react-loadable-5.5.2.tgz" integrity sha512-A3dYjdBGuy0IGT+wyLIGIKLRE+sAk1iNk0f1HjNDysO7u8lhL4N3VEm+FAubmJbAztn94F7MxBTPmnixbiyFdQ== @@ -1671,7 +1671,7 @@ fs-extra "^10.1.0" tslib "^2.4.0" -"@docusaurus/types@2.4.1": +"@docusaurus/types@*", "@docusaurus/types@2.4.1": version "2.4.1" resolved "https://registry.npmjs.org/@docusaurus/types/-/types-2.4.1.tgz" integrity sha512-0R+cbhpMkhbRXX138UOc/2XZFF8hiZa6ooZAEEJFp5scytzCw4tC1gChMFXrpa3d2tYE6AX8IrOEpSonLmfQuQ== @@ -1857,16 +1857,16 @@ "@nodelib/fs.stat" "2.0.5" run-parallel "^1.1.9" -"@nodelib/fs.stat@2.0.5", "@nodelib/fs.stat@^2.0.2": - version "2.0.5" - resolved "https://registry.npmjs.org/@nodelib/fs.stat/-/fs.stat-2.0.5.tgz" - integrity sha512-RkhPPp2zrqDAQA/2jNhnztcPAlv64XdhIp7a7454A5ovI7Bukxgt7MX7udwAu3zg1DcpPU0rz3VV1SeaqvY4+A== - "@nodelib/fs.stat@^1.1.2": 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-ajv@^8.0.0, ajv@^8.9.0: +ajv@^8.0.0: + version "8.12.0" + resolved "https://registry.npmjs.org/ajv/-/ajv-8.12.0.tgz" + integrity sha512-sRu1kpcO9yLtYxBKvqfTeh9KzZEwO3STyX1HT+4CaDzC6HpTGYhIhPIzj9XuKU7KYDwnaeh5hcOwjy1QuJzBPA== + dependencies: + fast-deep-equal "^3.1.1" + json-schema-traverse "^1.0.0" + require-from-string "^2.0.2" + uri-js "^4.2.2" + +ajv@^8.8.2, ajv@^8.9.0: version "8.12.0" resolved "https://registry.npmjs.org/ajv/-/ajv-8.12.0.tgz" integrity sha512-sRu1kpcO9yLtYxBKvqfTeh9KzZEwO3STyX1HT+4CaDzC6HpTGYhIhPIzj9XuKU7KYDwnaeh5hcOwjy1QuJzBPA== @@ -2567,7 +2577,7 @@ algoliasearch-helper@^3.10.0: dependencies: "@algolia/events" "^4.0.1" -algoliasearch@^4.0.0, algoliasearch@^4.13.1: +algoliasearch@^4.0.0, algoliasearch@^4.13.1, "algoliasearch@>= 3.1 < 6", "algoliasearch@>= 4.9.1 < 6": version "4.19.1" resolved "https://registry.npmjs.org/algoliasearch/-/algoliasearch-4.19.1.tgz" integrity 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sha512-PCVAQswWemu6UdxsDFFX/+gVeYqKAod3D3UVm91jHwynguOwAvYPhx8nNlM++NqRcK6CxxpUafjmhIdKiHibqg== + array-union@^1.0.1: version "1.0.2" resolved "https://registry.npmjs.org/array-union/-/array-union-1.0.2.tgz" @@ -2829,7 +2839,7 @@ asn1@~0.2.3: dependencies: safer-buffer "~2.1.0" -assert-plus@1.0.0, assert-plus@^1.0.0: +assert-plus@^1.0.0, assert-plus@1.0.0: version "1.0.0" resolved "https://registry.npmjs.org/assert-plus/-/assert-plus-1.0.0.tgz" integrity sha512-NfJ4UzBCcQGLDlQq7nHxH+tv3kyZ0hHQqF5BO6J7tNJeP5do1llPr8dZ8zHonfhAu0PHAdMkSo+8o0wxg9lZWw== @@ -3005,16 +3015,6 @@ balanced-match@^1.0.0: resolved "https://registry.npmjs.org/balanced-match/-/balanced-match-1.0.2.tgz" integrity sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw== -base16@^1.0.0: - version "1.0.0" - resolved "https://registry.npmjs.org/base16/-/base16-1.0.0.tgz" - integrity sha512-pNdYkNPiJUnEhnfXV56+sQy8+AaPcG3POZAUnwr4EeqCUZFz4u2PePbo3e5Gj4ziYPCWGUZT9RHisvJKnwFuBQ== - 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resolved "https://registry.npmjs.org/filename-reserved-regex/-/filename-reserved-regex-2.0.0.tgz" @@ -5594,16 +5678,16 @@ filenamify@^2.0.0: strip-outer "^1.0.0" trim-repeated "^1.0.0" -filesize@6.1.0: - version "6.1.0" - resolved "https://registry.npmjs.org/filesize/-/filesize-6.1.0.tgz" - integrity sha512-LpCHtPQ3sFx67z+uh2HnSyWSLLu5Jxo21795uRDuar/EOuYWXib5EmPaGIBuSnRqH2IODiKA2k5re/K9OnN/Yg== - filesize@^8.0.6: version "8.0.7" resolved "https://registry.npmjs.org/filesize/-/filesize-8.0.7.tgz" integrity sha512-pjmC+bkIF8XI7fWaH8KxHcZL3DPybs1roSKP4rKDvy20tAWwIObE4+JIseG2byfGKhud5ZnM4YSGKBz7Sh0ndQ== +filesize@6.1.0: + version "6.1.0" + resolved "https://registry.npmjs.org/filesize/-/filesize-6.1.0.tgz" + integrity sha512-LpCHtPQ3sFx67z+uh2HnSyWSLLu5Jxo21795uRDuar/EOuYWXib5EmPaGIBuSnRqH2IODiKA2k5re/K9OnN/Yg== + fill-range@^2.1.0: version "2.2.4" resolved "https://registry.npmjs.org/fill-range/-/fill-range-2.2.4.tgz" @@ -5663,14 +5747,6 @@ find-cache-dir@^3.3.1: make-dir "^3.0.2" pkg-dir 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+ prop-types "^15.6.2" + react-router-config@^5.1.1: version "5.1.1" resolved "https://registry.npmjs.org/react-router-config/-/react-router-config-5.1.1.tgz" @@ -10114,7 +10334,7 @@ react-router-dom@^5.3.3: tiny-invariant "^1.0.2" tiny-warning "^1.0.0" -react-router@5.3.4, react-router@^5.3.3: +react-router@^5.3.3, react-router@>=5, react-router@5.3.4: version "5.3.4" resolved "https://registry.npmjs.org/react-router/-/react-router-5.3.4.tgz" integrity sha512-Ys9K+ppnJah3QuaRiLxk+jDWOR1MekYQrlytiXxC1RyfbdsZkS5pvKAzCCr031xHixZwpnsYNT5xysdFHQaYsA== @@ -10148,7 +10368,7 @@ react-textarea-autosize@^8.3.2: use-composed-ref "^1.3.0" use-latest "^1.2.1" -react-waypoint@^10.3.0: +react-waypoint@^10.3.0, react-waypoint@>=9.0.2: version "10.3.0" resolved "https://registry.npmjs.org/react-waypoint/-/react-waypoint-10.3.0.tgz" integrity sha512-iF1y2c1BsoXuEGz08NoahaLFIGI9gTUAAOKip96HUmylRT6DUtpgoBPjk/Y8dfcFVmfVDvUzWjNXpZyKTOV0SQ== @@ -10158,7 +10378,15 @@ react-waypoint@^10.3.0: prop-types 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resolved "https://registry.npmjs.org/webpack-dev-middleware/-/webpack-dev-middleware-5.3.4.tgz" integrity sha512-BVdTqhhs+0IfoeAf7EoH5WE+exCmqGerHfDM0IL096Px60Tq2Mn9MAbnaGUe6HiMa41KMCYF19gyzZmBcq/o4Q== dependencies: colorette "^2.0.10" @@ -12465,7 +12881,7 @@ webpack-sources@^3.2.2, webpack-sources@^3.2.3: resolved "https://registry.npmjs.org/webpack-sources/-/webpack-sources-3.2.3.tgz" integrity sha512-/DyMEOrDgLKKIG0fmvtz+4dUX/3Ghozwgm6iPp8KRhvn+eQf9+Q7GWxVNMk3+uCPWfdXYC4ExGBckIXdFEfH1w== -webpack@^5.73.0: +"webpack@^4.0.0 || ^5.0.0", "webpack@^4.37.0 || ^5.0.0", webpack@^5.0.0, webpack@^5.1.0, webpack@^5.20.0, webpack@^5.73.0, "webpack@>= 4", webpack@>=2, "webpack@>=4.41.1 || 5.x", "webpack@3 || 4 || 5": version "5.88.2" resolved "https://registry.npmjs.org/webpack/-/webpack-5.88.2.tgz" integrity sha512-JmcgNZ1iKj+aiR0OvTYtWQqJwq37Pf683dY9bVORwVbUrDhLhdn/PlO2sHsFHPkj7sHNQF3JwaAkp49V+Sq1tQ== @@ -12505,7 +12921,7 @@ webpackbar@^5.0.2: pretty-time "^1.1.0" std-env "^3.0.1" -websocket-driver@>=0.5.1, websocket-driver@^0.7.4: +websocket-driver@^0.7.4, websocket-driver@>=0.5.1: version "0.7.4" resolved "https://registry.npmjs.org/websocket-driver/-/websocket-driver-0.7.4.tgz" integrity sha512-b17KeDIQVjvb0ssuSDF2cYXSg2iztliJ4B9WdsuB6J952qCPKmnVq4DyW5motImXHDC1cBT/1UezrJVsKw5zjg== @@ -12558,7 +12974,14 @@ which-typed-array@^1.1.11, which-typed-array@^1.1.13: gopd "^1.0.1" has-tostringtag "^1.0.0" -which@^1.2.9, which@^1.3.1: +which@^1.2.9: + version "1.3.1" + resolved "https://registry.npmjs.org/which/-/which-1.3.1.tgz" + integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ== + dependencies: + isexe "^2.0.0" + +which@^1.3.1: version "1.3.1" resolved "https://registry.npmjs.org/which/-/which-1.3.1.tgz" integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ== diff --git a/enterprise/enterprise_callbacks/example_logging_api.py b/enterprise/enterprise_callbacks/example_logging_api.py index 57ea99a674..c3d3f5e63f 100644 --- a/enterprise/enterprise_callbacks/example_logging_api.py +++ b/enterprise/enterprise_callbacks/example_logging_api.py @@ -18,10 +18,6 @@ async def log_event(request: Request): return {"message": "Request received successfully"} except Exception as e: - print(f"Error processing request: {str(e)}") - import traceback - - traceback.print_exc() raise HTTPException(status_code=500, detail="Internal Server Error") diff --git a/enterprise/enterprise_callbacks/generic_api_callback.py b/enterprise/enterprise_callbacks/generic_api_callback.py index cf1d22e8f8..ba189b149c 100644 --- a/enterprise/enterprise_callbacks/generic_api_callback.py +++ b/enterprise/enterprise_callbacks/generic_api_callback.py @@ -120,6 +120,5 @@ class GenericAPILogger: ) return response except Exception as e: - traceback.print_exc() - verbose_logger.debug(f"Generic - {str(e)}\n{traceback.format_exc()}") + verbose_logger.error(f"Generic - {str(e)}\n{traceback.format_exc()}") pass diff --git a/enterprise/enterprise_hooks/banned_keywords.py b/enterprise/enterprise_hooks/banned_keywords.py index acd390d798..4cf68b2fd9 100644 --- a/enterprise/enterprise_hooks/banned_keywords.py +++ b/enterprise/enterprise_hooks/banned_keywords.py @@ -82,7 +82,7 @@ class _ENTERPRISE_BannedKeywords(CustomLogger): except HTTPException as e: raise e except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error(traceback.format_exc()) async def async_post_call_success_hook( self, diff --git a/enterprise/enterprise_hooks/blocked_user_list.py b/enterprise/enterprise_hooks/blocked_user_list.py index cbc14d2c2b..8e642a026f 100644 --- a/enterprise/enterprise_hooks/blocked_user_list.py +++ b/enterprise/enterprise_hooks/blocked_user_list.py @@ -118,4 +118,4 @@ class _ENTERPRISE_BlockedUserList(CustomLogger): except HTTPException as e: raise e except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error(traceback.format_exc()) diff --git a/enterprise/enterprise_hooks/llm_guard.py b/enterprise/enterprise_hooks/llm_guard.py index 3a15ca52b9..9db10cf79c 100644 --- a/enterprise/enterprise_hooks/llm_guard.py +++ b/enterprise/enterprise_hooks/llm_guard.py @@ -92,7 +92,7 @@ class _ENTERPRISE_LLMGuard(CustomLogger): }, ) except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error(traceback.format_exc()) raise e def should_proceed(self, user_api_key_dict: UserAPIKeyAuth, data: dict) -> bool: diff --git a/litellm/__init__.py b/litellm/__init__.py index 9fb614396e..b6e6d97dc8 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -5,7 +5,7 @@ warnings.filterwarnings("ignore", message=".*conflict with protected namespace.* ### INIT VARIABLES ### import threading, requests, os from typing import Callable, List, Optional, Dict, Union, Any, Literal -from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.caching import Cache from litellm._logging import ( set_verbose, @@ -60,6 +60,7 @@ _async_failure_callback: List[Callable] = ( pre_call_rules: List[Callable] = [] post_call_rules: List[Callable] = [] turn_off_message_logging: Optional[bool] = False +redact_messages_in_exceptions: Optional[bool] = False store_audit_logs = False # Enterprise feature, allow users to see audit logs ## end of callbacks ############# @@ -233,6 +234,7 @@ max_end_user_budget: Optional[float] = None #### RELIABILITY #### request_timeout: float = 6000 module_level_aclient = AsyncHTTPHandler(timeout=request_timeout) +module_level_client = HTTPHandler(timeout=request_timeout) num_retries: Optional[int] = None # per model endpoint default_fallbacks: Optional[List] = None fallbacks: Optional[List] = None @@ -766,7 +768,7 @@ from .llms.sagemaker import SagemakerConfig from .llms.ollama import OllamaConfig from .llms.ollama_chat import OllamaChatConfig from .llms.maritalk import MaritTalkConfig -from .llms.bedrock_httpx import AmazonCohereChatConfig +from .llms.bedrock_httpx import AmazonCohereChatConfig, AmazonConverseConfig from .llms.bedrock import ( AmazonTitanConfig, AmazonAI21Config, @@ -808,6 +810,7 @@ from .exceptions import ( APIConnectionError, APIResponseValidationError, UnprocessableEntityError, + InternalServerError, LITELLM_EXCEPTION_TYPES, ) from .budget_manager import BudgetManager diff --git a/litellm/_logging.py b/litellm/_logging.py index 1ff6e45ddb..ab7a08f976 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -1,5 +1,6 @@ import logging, os, json from logging import Formatter +import traceback set_verbose = False json_logs = bool(os.getenv("JSON_LOGS", False)) diff --git a/litellm/caching.py b/litellm/caching.py index c8c1736d86..d1f3387ee4 100644 --- a/litellm/caching.py +++ b/litellm/caching.py @@ -253,7 +253,6 @@ class RedisCache(BaseCache): str(e), value, ) - traceback.print_exc() raise e async def async_scan_iter(self, pattern: str, count: int = 100) -> list: @@ -313,7 +312,6 @@ class RedisCache(BaseCache): str(e), value, ) - traceback.print_exc() key = self.check_and_fix_namespace(key=key) async with _redis_client as redis_client: @@ -352,7 +350,6 @@ class RedisCache(BaseCache): str(e), value, ) - traceback.print_exc() async def async_set_cache_pipeline(self, cache_list, ttl=None): """ @@ -413,7 +410,6 @@ class RedisCache(BaseCache): str(e), cache_value, ) - traceback.print_exc() async def batch_cache_write(self, key, value, **kwargs): print_verbose( @@ -458,7 +454,6 @@ class RedisCache(BaseCache): str(e), value, ) - traceback.print_exc() raise e async def flush_cache_buffer(self): @@ -495,8 +490,9 @@ class RedisCache(BaseCache): return self._get_cache_logic(cached_response=cached_response) except Exception as e: # NON blocking - notify users Redis is throwing an exception - traceback.print_exc() - logging.debug("LiteLLM Caching: get() - Got exception from REDIS: ", e) + verbose_logger.error( + "LiteLLM Caching: get() - Got exception from REDIS: ", e + ) def batch_get_cache(self, key_list) -> dict: """ @@ -646,10 +642,9 @@ class RedisCache(BaseCache): error=e, call_type="sync_ping", ) - print_verbose( + verbose_logger.error( f"LiteLLM Redis Cache PING: - Got exception from REDIS : {str(e)}" ) - traceback.print_exc() raise e async def ping(self) -> bool: @@ -683,10 +678,9 @@ class RedisCache(BaseCache): call_type="async_ping", ) ) - print_verbose( + verbose_logger.error( f"LiteLLM Redis Cache PING: - Got exception from REDIS : {str(e)}" ) - traceback.print_exc() raise e async def delete_cache_keys(self, keys): @@ -1138,22 +1132,23 @@ class S3Cache(BaseCache): cached_response = ast.literal_eval(cached_response) if type(cached_response) is not dict: cached_response = dict(cached_response) - print_verbose( + verbose_logger.debug( f"Got S3 Cache: key: {key}, cached_response {cached_response}. Type Response {type(cached_response)}" ) return cached_response except botocore.exceptions.ClientError as e: if e.response["Error"]["Code"] == "NoSuchKey": - print_verbose( + verbose_logger.error( f"S3 Cache: The specified key '{key}' does not exist in the S3 bucket." ) return None except Exception as e: # NON blocking - notify users S3 is throwing an exception - traceback.print_exc() - print_verbose(f"S3 Caching: get_cache() - Got exception from S3: {e}") + verbose_logger.error( + f"S3 Caching: get_cache() - Got exception from S3: {e}" + ) async def async_get_cache(self, key, **kwargs): return self.get_cache(key=key, **kwargs) @@ -1234,8 +1229,7 @@ class DualCache(BaseCache): return result except Exception as e: - print_verbose(f"LiteLLM Cache: Excepton async add_cache: {str(e)}") - traceback.print_exc() + verbose_logger.error(f"LiteLLM Cache: Excepton async add_cache: {str(e)}") raise e def get_cache(self, key, local_only: bool = False, **kwargs): @@ -1262,7 +1256,7 @@ class DualCache(BaseCache): print_verbose(f"get cache: cache result: {result}") return result except Exception as e: - traceback.print_exc() + verbose_logger.error(traceback.format_exc()) def batch_get_cache(self, keys: list, local_only: bool = False, **kwargs): try: @@ -1295,7 +1289,7 @@ class DualCache(BaseCache): print_verbose(f"async batch get cache: cache result: {result}") return result except Exception as e: - traceback.print_exc() + verbose_logger.error(traceback.format_exc()) async def async_get_cache(self, key, local_only: bool = False, **kwargs): # Try to fetch from in-memory cache first @@ -1328,7 +1322,7 @@ class DualCache(BaseCache): print_verbose(f"get cache: cache result: {result}") return result except Exception as e: - traceback.print_exc() + verbose_logger.error(traceback.format_exc()) async def async_batch_get_cache( self, keys: list, local_only: bool = False, **kwargs @@ -1368,7 +1362,7 @@ class DualCache(BaseCache): return result except Exception as e: - traceback.print_exc() + verbose_logger.error(traceback.format_exc()) async def async_set_cache(self, key, value, local_only: bool = False, **kwargs): print_verbose( @@ -1381,8 +1375,8 @@ class DualCache(BaseCache): if self.redis_cache is not None and local_only == False: await self.redis_cache.async_set_cache(key, value, **kwargs) except Exception as e: - print_verbose(f"LiteLLM Cache: Excepton async add_cache: {str(e)}") - traceback.print_exc() + verbose_logger.error(f"LiteLLM Cache: Excepton async add_cache: {str(e)}") + verbose_logger.debug(traceback.format_exc()) async def async_batch_set_cache( self, cache_list: list, local_only: bool = False, **kwargs @@ -1404,8 +1398,8 @@ class DualCache(BaseCache): cache_list=cache_list, ttl=kwargs.get("ttl", None) ) except Exception as e: - print_verbose(f"LiteLLM Cache: Excepton async add_cache: {str(e)}") - traceback.print_exc() + verbose_logger.error(f"LiteLLM Cache: Excepton async add_cache: {str(e)}") + verbose_logger.debug(traceback.format_exc()) async def async_increment_cache( self, key, value: float, local_only: bool = False, **kwargs @@ -1429,8 +1423,8 @@ class DualCache(BaseCache): return result except Exception as e: - print_verbose(f"LiteLLM Cache: Excepton async add_cache: {str(e)}") - traceback.print_exc() + verbose_logger.error(f"LiteLLM Cache: Excepton async add_cache: {str(e)}") + verbose_logger.debug(traceback.format_exc()) raise e def flush_cache(self): @@ -1846,8 +1840,8 @@ class Cache: ) self.cache.set_cache(cache_key, cached_data, **kwargs) except Exception as e: - print_verbose(f"LiteLLM Cache: Excepton add_cache: {str(e)}") - traceback.print_exc() + verbose_logger.error(f"LiteLLM Cache: Excepton add_cache: {str(e)}") + verbose_logger.debug(traceback.format_exc()) pass async def async_add_cache(self, result, *args, **kwargs): @@ -1864,8 +1858,8 @@ class Cache: ) await self.cache.async_set_cache(cache_key, cached_data, **kwargs) except Exception as e: - print_verbose(f"LiteLLM Cache: Excepton add_cache: {str(e)}") - traceback.print_exc() + verbose_logger.error(f"LiteLLM Cache: Excepton add_cache: {str(e)}") + verbose_logger.debug(traceback.format_exc()) async def async_add_cache_pipeline(self, result, *args, **kwargs): """ @@ -1897,8 +1891,8 @@ class Cache: ) await asyncio.gather(*tasks) except Exception as e: - print_verbose(f"LiteLLM Cache: Excepton add_cache: {str(e)}") - traceback.print_exc() + verbose_logger.error(f"LiteLLM Cache: Excepton add_cache: {str(e)}") + verbose_logger.debug(traceback.format_exc()) async def batch_cache_write(self, result, *args, **kwargs): cache_key, cached_data, kwargs = self._add_cache_logic( diff --git a/litellm/exceptions.py b/litellm/exceptions.py index f84cf31668..484e843b6d 100644 --- a/litellm/exceptions.py +++ b/litellm/exceptions.py @@ -638,6 +638,7 @@ LITELLM_EXCEPTION_TYPES = [ APIConnectionError, APIResponseValidationError, OpenAIError, + InternalServerError, ] diff --git a/litellm/integrations/aispend.py b/litellm/integrations/aispend.py index 2fe8ea0dfa..ca284e62e5 100644 --- a/litellm/integrations/aispend.py +++ b/litellm/integrations/aispend.py @@ -169,6 +169,5 @@ class AISpendLogger: print_verbose(f"AISpend Logging - final data object: {data}") except: - # traceback.print_exc() print_verbose(f"AISpend Logging Error - {traceback.format_exc()}") pass diff --git a/litellm/integrations/berrispend.py b/litellm/integrations/berrispend.py index 7d30b706c8..d428fb54d8 100644 --- a/litellm/integrations/berrispend.py +++ b/litellm/integrations/berrispend.py @@ -178,6 +178,5 @@ class BerriSpendLogger: print_verbose(f"BerriSpend Logging - final data object: {data}") response = requests.post(url, headers=headers, json=data) except: - # traceback.print_exc() print_verbose(f"BerriSpend Logging Error - {traceback.format_exc()}") pass diff --git a/litellm/integrations/clickhouse.py b/litellm/integrations/clickhouse.py index 0c38b86267..f8b6b1bbf0 100644 --- a/litellm/integrations/clickhouse.py +++ b/litellm/integrations/clickhouse.py @@ -297,6 +297,5 @@ class ClickhouseLogger: # make request to endpoint with payload verbose_logger.debug(f"Clickhouse Logger - final response = {response}") except Exception as e: - traceback.print_exc() verbose_logger.debug(f"Clickhouse - {str(e)}\n{traceback.format_exc()}") pass diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index e192cdaea7..1d447da1f2 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -115,7 +115,6 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac ) print_verbose(f"Custom Logger - model call details: {kwargs}") except: - traceback.print_exc() print_verbose(f"Custom Logger Error - {traceback.format_exc()}") async def async_log_input_event( @@ -130,7 +129,6 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac ) print_verbose(f"Custom Logger - model call details: {kwargs}") except: - traceback.print_exc() print_verbose(f"Custom Logger Error - {traceback.format_exc()}") def log_event( @@ -146,7 +144,6 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac end_time, ) except: - # traceback.print_exc() print_verbose(f"Custom Logger Error - {traceback.format_exc()}") pass @@ -163,6 +160,5 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac end_time, ) except: - # traceback.print_exc() print_verbose(f"Custom Logger Error - {traceback.format_exc()}") pass diff --git a/litellm/integrations/datadog.py b/litellm/integrations/datadog.py index 6d5e08faff..d835b3d670 100644 --- a/litellm/integrations/datadog.py +++ b/litellm/integrations/datadog.py @@ -134,7 +134,6 @@ class DataDogLogger: f"Datadog Layer Logging - final response object: {response_obj}" ) except Exception as e: - traceback.print_exc() verbose_logger.debug( f"Datadog Layer Error - {str(e)}\n{traceback.format_exc()}" ) diff --git a/litellm/integrations/dynamodb.py b/litellm/integrations/dynamodb.py index 21ccabe4b7..847f930ece 100644 --- a/litellm/integrations/dynamodb.py +++ b/litellm/integrations/dynamodb.py @@ -85,6 +85,5 @@ class DyanmoDBLogger: ) return response except: - traceback.print_exc() print_verbose(f"DynamoDB Layer Error - {traceback.format_exc()}") pass diff --git a/litellm/integrations/helicone.py b/litellm/integrations/helicone.py index 85e73258ea..8ea18a7d5b 100644 --- a/litellm/integrations/helicone.py +++ b/litellm/integrations/helicone.py @@ -112,6 +112,5 @@ class HeliconeLogger: ) print_verbose(f"Helicone Logging - Error {response.text}") except: - # traceback.print_exc() print_verbose(f"Helicone Logging Error - {traceback.format_exc()}") pass diff --git a/litellm/integrations/langfuse.py b/litellm/integrations/langfuse.py index 80d90f38aa..7fe2e9f227 100644 --- a/litellm/integrations/langfuse.py +++ b/litellm/integrations/langfuse.py @@ -220,9 +220,11 @@ class LangFuseLogger: verbose_logger.info(f"Langfuse Layer Logging - logging success") return {"trace_id": trace_id, "generation_id": generation_id} - except: - traceback.print_exc() - verbose_logger.debug(f"Langfuse Layer Error - {traceback.format_exc()}") + except Exception as e: + verbose_logger.error( + "Langfuse Layer Error(): Exception occured - {}".format(str(e)) + ) + verbose_logger.debug(traceback.format_exc()) return {"trace_id": None, "generation_id": None} async def _async_log_event( diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py index 3e25b4ee77..48185afeea 100644 --- a/litellm/integrations/langsmith.py +++ b/litellm/integrations/langsmith.py @@ -44,7 +44,9 @@ class LangsmithLogger: print_verbose( f"Langsmith Logging - project_name: {project_name}, run_name {run_name}" ) - langsmith_base_url = os.getenv("LANGSMITH_BASE_URL", "https://api.smith.langchain.com") + langsmith_base_url = os.getenv( + "LANGSMITH_BASE_URL", "https://api.smith.langchain.com" + ) try: print_verbose( @@ -89,9 +91,7 @@ class LangsmithLogger: } url = f"{langsmith_base_url}/runs" - print_verbose( - f"Langsmith Logging - About to send data to {url} ..." - ) + print_verbose(f"Langsmith Logging - About to send data to {url} ...") response = requests.post( url=url, json=data, @@ -106,6 +106,5 @@ class LangsmithLogger: f"Langsmith Layer Logging - final response object: {response_obj}" ) except: - # traceback.print_exc() print_verbose(f"Langsmith Layer Error - {traceback.format_exc()}") pass diff --git a/litellm/integrations/logfire_logger.py b/litellm/integrations/logfire_logger.py index e27d848fb4..b4ab00820e 100644 --- a/litellm/integrations/logfire_logger.py +++ b/litellm/integrations/logfire_logger.py @@ -171,7 +171,6 @@ class LogfireLogger: f"Logfire Layer Logging - final response object: {response_obj}" ) except Exception as e: - traceback.print_exc() verbose_logger.debug( f"Logfire Layer Error - {str(e)}\n{traceback.format_exc()}" ) diff --git a/litellm/integrations/lunary.py b/litellm/integrations/lunary.py index 2e16e44a14..141ea64884 100644 --- a/litellm/integrations/lunary.py +++ b/litellm/integrations/lunary.py @@ -14,6 +14,7 @@ def parse_usage(usage): "prompt": usage["prompt_tokens"] if "prompt_tokens" in usage else 0, } + def parse_tool_calls(tool_calls): if tool_calls is None: return None @@ -26,13 +27,13 @@ def parse_tool_calls(tool_calls): "function": { "name": tool_call.function.name, "arguments": tool_call.function.arguments, - } + }, } return serialized - + return [clean_tool_call(tool_call) for tool_call in tool_calls] - + def parse_messages(input): @@ -176,6 +177,5 @@ class LunaryLogger: ) except: - # traceback.print_exc() print_verbose(f"Lunary Logging Error - {traceback.format_exc()}") pass diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 3fc50848e8..aa67839141 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -14,8 +14,11 @@ if TYPE_CHECKING: else: Span = Any -LITELLM_TRACER_NAME = "litellm" -LITELLM_RESOURCE = {"service.name": "litellm"} + +LITELLM_TRACER_NAME = os.getenv("OTEL_TRACER_NAME", "litellm") +LITELLM_RESOURCE = { + "service.name": os.getenv("OTEL_SERVICE_NAME", "litellm"), +} @dataclass diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 6fbc6ca4ce..af0d1d310b 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -109,8 +109,8 @@ class PrometheusLogger: end_user_id, user_api_key, model, user_api_team, user_id ).inc() except Exception as e: - traceback.print_exc() - verbose_logger.debug( - f"prometheus Layer Error - {str(e)}\n{traceback.format_exc()}" + verbose_logger.error( + "prometheus Layer Error(): Exception occured - {}".format(str(e)) ) + verbose_logger.debug(traceback.format_exc()) pass diff --git a/litellm/integrations/s3.py b/litellm/integrations/s3.py index d131e44f0e..0796d1048b 100644 --- a/litellm/integrations/s3.py +++ b/litellm/integrations/s3.py @@ -180,6 +180,5 @@ class S3Logger: print_verbose(f"s3 Layer Logging - final response object: {response_obj}") return response except Exception as e: - traceback.print_exc() verbose_logger.debug(f"s3 Layer Error - {str(e)}\n{traceback.format_exc()}") pass diff --git a/litellm/integrations/slack_alerting.py b/litellm/integrations/slack_alerting.py index c98d60f1fd..21415fb6d6 100644 --- a/litellm/integrations/slack_alerting.py +++ b/litellm/integrations/slack_alerting.py @@ -326,8 +326,8 @@ class SlackAlerting(CustomLogger): end_time=end_time, ) ) - if litellm.turn_off_message_logging: - messages = "Message not logged. `litellm.turn_off_message_logging=True`." + if litellm.turn_off_message_logging or litellm.redact_messages_in_exceptions: + messages = "Message not logged. litellm.redact_messages_in_exceptions=True" 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: @@ -567,9 +567,12 @@ class SlackAlerting(CustomLogger): except: messages = "" - if litellm.turn_off_message_logging: + if ( + litellm.turn_off_message_logging + or litellm.redact_messages_in_exceptions + ): messages = ( - "Message not logged. `litellm.turn_off_message_logging=True`." + "Message not logged. litellm.redact_messages_in_exceptions=True" ) request_info = f"\nRequest Model: `{model}`\nMessages: `{messages}`" else: diff --git a/litellm/integrations/supabase.py b/litellm/integrations/supabase.py index 4e6bf517f3..7309342e4c 100644 --- a/litellm/integrations/supabase.py +++ b/litellm/integrations/supabase.py @@ -110,6 +110,5 @@ class Supabase: ) except: - # traceback.print_exc() print_verbose(f"Supabase Logging Error - {traceback.format_exc()}") pass diff --git a/litellm/integrations/weights_biases.py b/litellm/integrations/weights_biases.py index a56233b22f..1ac535c4f2 100644 --- a/litellm/integrations/weights_biases.py +++ b/litellm/integrations/weights_biases.py @@ -217,6 +217,5 @@ class WeightsBiasesLogger: f"W&B Logging Logging - final response object: {response_obj}" ) except: - # traceback.print_exc() print_verbose(f"W&B Logging Layer Error - {traceback.format_exc()}") pass diff --git a/litellm/llms/bedrock_httpx.py b/litellm/llms/bedrock_httpx.py index dbd7e7c695..59945a5857 100644 --- a/litellm/llms/bedrock_httpx.py +++ b/litellm/llms/bedrock_httpx.py @@ -38,6 +38,8 @@ from .prompt_templates.factory import ( extract_between_tags, parse_xml_params, contains_tag, + _bedrock_converse_messages_pt, + _bedrock_tools_pt, ) from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from .base import BaseLLM @@ -45,6 +47,11 @@ import httpx # type: ignore from .bedrock import BedrockError, convert_messages_to_prompt, ModelResponseIterator from litellm.types.llms.bedrock import * import urllib.parse +from litellm.types.llms.openai import ( + ChatCompletionResponseMessage, + ChatCompletionToolCallChunk, + ChatCompletionToolCallFunctionChunk, +) class AmazonCohereChatConfig: @@ -118,6 +125,8 @@ class AmazonCohereChatConfig: "presence_penalty", "seed", "stop", + "tools", + "tool_choice", ] def map_openai_params( @@ -176,6 +185,37 @@ async def make_call( return completion_stream +def make_sync_call( + client: Optional[HTTPHandler], + api_base: str, + headers: dict, + data: str, + model: str, + messages: list, + logging_obj, +): + if client is None: + client = HTTPHandler() # Create a new client if none provided + + response = client.post(api_base, headers=headers, data=data, stream=True) + + if response.status_code != 200: + raise BedrockError(status_code=response.status_code, message=response.read()) + + decoder = AWSEventStreamDecoder(model=model) + completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) + + # LOGGING + logging_obj.post_call( + input=messages, + api_key="", + original_response=completion_stream, # Pass the completion stream for logging + additional_args={"complete_input_dict": data}, + ) + + return completion_stream + + class BedrockLLM(BaseLLM): """ Example call @@ -1000,12 +1040,12 @@ class BedrockLLM(BaseLLM): if isinstance(timeout, float) or isinstance(timeout, int): timeout = httpx.Timeout(timeout) _params["timeout"] = timeout - self.client = AsyncHTTPHandler(**_params) # type: ignore + client = AsyncHTTPHandler(**_params) # type: ignore else: - self.client = client # type: ignore + client = client # type: ignore try: - response = await self.client.post(api_base, headers=headers, data=data) # type: ignore + response = await client.post(api_base, headers=headers, data=data) # type: ignore response.raise_for_status() except httpx.HTTPStatusError as err: error_code = err.response.status_code @@ -1069,6 +1109,745 @@ class BedrockLLM(BaseLLM): return super().embedding(*args, **kwargs) +class AmazonConverseConfig: + """ + Reference - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html + #2 - https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html#conversation-inference-supported-models-features + """ + + maxTokens: Optional[int] + stopSequences: Optional[List[str]] + temperature: Optional[int] + topP: Optional[int] + + def __init__( + self, + maxTokens: Optional[int] = None, + stopSequences: Optional[List[str]] = None, + temperature: Optional[int] = None, + topP: Optional[int] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + def get_supported_openai_params(self, model: str) -> List[str]: + supported_params = [ + "max_tokens", + "stream", + "stream_options", + "stop", + "temperature", + "top_p", + "extra_headers", + ] + + if ( + model.startswith("anthropic") + or model.startswith("mistral") + or model.startswith("cohere") + ): + supported_params.append("tools") + + if model.startswith("anthropic") or model.startswith("mistral"): + # only anthropic and mistral support tool choice config. otherwise (E.g. cohere) will fail the call - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html + supported_params.append("tool_choice") + + return supported_params + + def map_tool_choice_values( + self, model: str, tool_choice: Union[str, dict], drop_params: bool + ) -> Optional[ToolChoiceValuesBlock]: + if tool_choice == "none": + if litellm.drop_params is True or drop_params is True: + return None + else: + raise litellm.utils.UnsupportedParamsError( + message="Bedrock doesn't support tool_choice={}. To drop it from the call, set `litellm.drop_params = True.".format( + tool_choice + ), + status_code=400, + ) + elif tool_choice == "required": + return ToolChoiceValuesBlock(any={}) + elif tool_choice == "auto": + return ToolChoiceValuesBlock(auto={}) + elif isinstance(tool_choice, dict): + # only supported for anthropic + mistral models - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html + specific_tool = SpecificToolChoiceBlock( + name=tool_choice.get("function", {}).get("name", "") + ) + return ToolChoiceValuesBlock(tool=specific_tool) + else: + raise litellm.utils.UnsupportedParamsError( + message="Bedrock doesn't support tool_choice={}. Supported tool_choice values=['auto', 'required', json object]. To drop it from the call, set `litellm.drop_params = True.".format( + tool_choice + ), + status_code=400, + ) + + def get_supported_image_types(self) -> List[str]: + return ["png", "jpeg", "gif", "webp"] + + def map_openai_params( + self, + model: str, + non_default_params: dict, + optional_params: dict, + drop_params: bool, + ) -> dict: + for param, value in non_default_params.items(): + if param == "max_tokens": + optional_params["maxTokens"] = value + if param == "stream": + optional_params["stream"] = value + if param == "stop": + if isinstance(value, str): + value = [value] + optional_params["stop_sequences"] = value + if param == "temperature": + optional_params["temperature"] = value + if param == "top_p": + optional_params["topP"] = value + if param == "tools": + optional_params["tools"] = value + if param == "tool_choice": + _tool_choice_value = self.map_tool_choice_values( + model=model, tool_choice=value, drop_params=drop_params # type: ignore + ) + if _tool_choice_value is not None: + optional_params["tool_choice"] = _tool_choice_value + return optional_params + + +class BedrockConverseLLM(BaseLLM): + def __init__(self) -> None: + super().__init__() + + def process_response( + self, + model: str, + response: Union[requests.Response, httpx.Response], + model_response: ModelResponse, + stream: bool, + logging_obj: Logging, + optional_params: dict, + api_key: str, + data: Union[dict, str], + messages: List, + print_verbose, + encoding, + ) -> Union[ModelResponse, CustomStreamWrapper]: + + ## LOGGING + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + + ## RESPONSE OBJECT + try: + completion_response = ConverseResponseBlock(**response.json()) # type: ignore + except Exception as e: + raise BedrockError( + message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format( + response.text, str(e) + ), + status_code=422, + ) + + """ + Bedrock Response Object has optional message block + + completion_response["output"].get("message", None) + + A message block looks like this (Example 1): + "output": { + "message": { + "role": "assistant", + "content": [ + { + "text": "Is there anything else you'd like to talk about? Perhaps I can help with some economic questions or provide some information about economic concepts?" + } + ] + } + }, + (Example 2): + "output": { + "message": { + "role": "assistant", + "content": [ + { + "toolUse": { + "toolUseId": "tooluse_hbTgdi0CSLq_hM4P8csZJA", + "name": "top_song", + "input": { + "sign": "WZPZ" + } + } + } + ] + } + } + + """ + message: Optional[MessageBlock] = completion_response["output"]["message"] + chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"} + content_str = "" + tools: List[ChatCompletionToolCallChunk] = [] + if message is not None: + for content in message["content"]: + """ + - Content is either a tool response or text + """ + if "text" in content: + content_str += content["text"] + if "toolUse" in content: + _function_chunk = ChatCompletionToolCallFunctionChunk( + name=content["toolUse"]["name"], + arguments=json.dumps(content["toolUse"]["input"]), + ) + _tool_response_chunk = ChatCompletionToolCallChunk( + id=content["toolUse"]["toolUseId"], + type="function", + function=_function_chunk, + ) + tools.append(_tool_response_chunk) + chat_completion_message["content"] = content_str + chat_completion_message["tool_calls"] = tools + + ## CALCULATING USAGE - bedrock returns usage in the headers + input_tokens = completion_response["usage"]["inputTokens"] + output_tokens = completion_response["usage"]["outputTokens"] + total_tokens = completion_response["usage"]["totalTokens"] + + model_response.choices = [ + litellm.Choices( + finish_reason=map_finish_reason(completion_response["stopReason"]), + index=0, + message=litellm.Message(**chat_completion_message), + ) + ] + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=input_tokens, + completion_tokens=output_tokens, + total_tokens=total_tokens, + ) + setattr(model_response, "usage", usage) + + return model_response + + def encode_model_id(self, model_id: str) -> str: + """ + Double encode the model ID to ensure it matches the expected double-encoded format. + Args: + model_id (str): The model ID to encode. + Returns: + str: The double-encoded model ID. + """ + return urllib.parse.quote(model_id, safe="") + + def get_credentials( + self, + aws_access_key_id: Optional[str] = None, + aws_secret_access_key: Optional[str] = None, + aws_region_name: Optional[str] = None, + aws_session_name: Optional[str] = None, + aws_profile_name: Optional[str] = None, + aws_role_name: Optional[str] = None, + aws_web_identity_token: Optional[str] = None, + ): + """ + Return a boto3.Credentials object + """ + import boto3 + + ## CHECK IS 'os.environ/' passed in + params_to_check: List[Optional[str]] = [ + aws_access_key_id, + aws_secret_access_key, + aws_region_name, + aws_session_name, + aws_profile_name, + aws_role_name, + aws_web_identity_token, + ] + + # Iterate over parameters and update if needed + for i, param in enumerate(params_to_check): + if param and param.startswith("os.environ/"): + _v = get_secret(param) + if _v is not None and isinstance(_v, str): + params_to_check[i] = _v + # Assign updated values back to parameters + ( + aws_access_key_id, + aws_secret_access_key, + aws_region_name, + aws_session_name, + aws_profile_name, + aws_role_name, + aws_web_identity_token, + ) = params_to_check + + ### CHECK STS ### + if ( + aws_web_identity_token is not None + and aws_role_name is not None + and aws_session_name is not None + ): + oidc_token = get_secret(aws_web_identity_token) + + if oidc_token is None: + raise BedrockError( + message="OIDC token could not be retrieved from secret manager.", + status_code=401, + ) + + sts_client = boto3.client("sts") + + # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html + # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html + sts_response = sts_client.assume_role_with_web_identity( + RoleArn=aws_role_name, + RoleSessionName=aws_session_name, + WebIdentityToken=oidc_token, + DurationSeconds=3600, + ) + + session = boto3.Session( + aws_access_key_id=sts_response["Credentials"]["AccessKeyId"], + aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"], + aws_session_token=sts_response["Credentials"]["SessionToken"], + region_name=aws_region_name, + ) + + return session.get_credentials() + elif aws_role_name is not None and aws_session_name is not None: + sts_client = boto3.client( + "sts", + aws_access_key_id=aws_access_key_id, # [OPTIONAL] + aws_secret_access_key=aws_secret_access_key, # [OPTIONAL] + ) + + sts_response = sts_client.assume_role( + RoleArn=aws_role_name, RoleSessionName=aws_session_name + ) + + # Extract the credentials from the response and convert to Session Credentials + sts_credentials = sts_response["Credentials"] + from botocore.credentials import Credentials + + credentials = Credentials( + access_key=sts_credentials["AccessKeyId"], + secret_key=sts_credentials["SecretAccessKey"], + token=sts_credentials["SessionToken"], + ) + return credentials + elif aws_profile_name is not None: ### CHECK SESSION ### + # uses auth values from AWS profile usually stored in ~/.aws/credentials + client = boto3.Session(profile_name=aws_profile_name) + + return client.get_credentials() + else: + session = boto3.Session( + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + region_name=aws_region_name, + ) + + return session.get_credentials() + + async def async_streaming( + self, + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + data: str, + timeout: Optional[Union[float, httpx.Timeout]], + encoding, + logging_obj, + stream, + optional_params: dict, + litellm_params=None, + logger_fn=None, + headers={}, + client: Optional[AsyncHTTPHandler] = None, + ) -> CustomStreamWrapper: + streaming_response = CustomStreamWrapper( + completion_stream=None, + make_call=partial( + make_call, + client=client, + api_base=api_base, + headers=headers, + data=data, + model=model, + messages=messages, + logging_obj=logging_obj, + ), + model=model, + custom_llm_provider="bedrock", + logging_obj=logging_obj, + ) + return streaming_response + + async def async_completion( + self, + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + data: str, + timeout: Optional[Union[float, httpx.Timeout]], + encoding, + logging_obj, + stream, + optional_params: dict, + litellm_params=None, + logger_fn=None, + headers={}, + client: Optional[AsyncHTTPHandler] = None, + ) -> Union[ModelResponse, CustomStreamWrapper]: + if client is None: + _params = {} + if timeout is not None: + if isinstance(timeout, float) or isinstance(timeout, int): + timeout = httpx.Timeout(timeout) + _params["timeout"] = timeout + client = AsyncHTTPHandler(**_params) # type: ignore + else: + client = client # type: ignore + + try: + response = await client.post(api_base, headers=headers, data=data) # type: ignore + response.raise_for_status() + except httpx.HTTPStatusError as err: + error_code = err.response.status_code + raise BedrockError(status_code=error_code, message=err.response.text) + except httpx.TimeoutException as e: + raise BedrockError(status_code=408, message="Timeout error occurred.") + + return self.process_response( + model=model, + response=response, + model_response=model_response, + stream=stream if isinstance(stream, bool) else False, + logging_obj=logging_obj, + api_key="", + data=data, + messages=messages, + print_verbose=print_verbose, + optional_params=optional_params, + encoding=encoding, + ) + + def completion( + self, + model: str, + messages: list, + custom_prompt_dict: dict, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + optional_params: dict, + acompletion: bool, + timeout: Optional[Union[float, httpx.Timeout]], + litellm_params=None, + logger_fn=None, + extra_headers: Optional[dict] = None, + client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None, + ): + try: + import boto3 + + from botocore.auth import SigV4Auth + from botocore.awsrequest import AWSRequest + from botocore.credentials import Credentials + except ImportError: + raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") + + ## SETUP ## + stream = optional_params.pop("stream", None) + modelId = optional_params.pop("model_id", None) + if modelId is not None: + modelId = self.encode_model_id(model_id=modelId) + else: + modelId = model + + provider = model.split(".")[0] + + ## CREDENTIALS ## + # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them + aws_secret_access_key = optional_params.pop("aws_secret_access_key", None) + aws_access_key_id = optional_params.pop("aws_access_key_id", None) + aws_region_name = optional_params.pop("aws_region_name", None) + aws_role_name = optional_params.pop("aws_role_name", None) + aws_session_name = optional_params.pop("aws_session_name", None) + aws_profile_name = optional_params.pop("aws_profile_name", None) + aws_bedrock_runtime_endpoint = optional_params.pop( + "aws_bedrock_runtime_endpoint", None + ) # https://bedrock-runtime.{region_name}.amazonaws.com + aws_web_identity_token = optional_params.pop("aws_web_identity_token", None) + + ### SET REGION NAME ### + if aws_region_name is None: + # check env # + litellm_aws_region_name = get_secret("AWS_REGION_NAME", None) + + if litellm_aws_region_name is not None and isinstance( + litellm_aws_region_name, str + ): + aws_region_name = litellm_aws_region_name + + standard_aws_region_name = get_secret("AWS_REGION", None) + if standard_aws_region_name is not None and isinstance( + standard_aws_region_name, str + ): + aws_region_name = standard_aws_region_name + + if aws_region_name is None: + aws_region_name = "us-west-2" + + credentials: Credentials = self.get_credentials( + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + aws_region_name=aws_region_name, + aws_session_name=aws_session_name, + aws_profile_name=aws_profile_name, + aws_role_name=aws_role_name, + aws_web_identity_token=aws_web_identity_token, + ) + + ### SET RUNTIME ENDPOINT ### + endpoint_url = "" + env_aws_bedrock_runtime_endpoint = get_secret("AWS_BEDROCK_RUNTIME_ENDPOINT") + if aws_bedrock_runtime_endpoint is not None and isinstance( + aws_bedrock_runtime_endpoint, str + ): + endpoint_url = aws_bedrock_runtime_endpoint + elif env_aws_bedrock_runtime_endpoint and isinstance( + env_aws_bedrock_runtime_endpoint, str + ): + endpoint_url = env_aws_bedrock_runtime_endpoint + else: + endpoint_url = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com" + + if (stream is not None and stream is True) and provider != "ai21": + endpoint_url = f"{endpoint_url}/model/{modelId}/converse-stream" + else: + endpoint_url = f"{endpoint_url}/model/{modelId}/converse" + + sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name) + + # Separate system prompt from rest of message + system_prompt_indices = [] + system_content_blocks: List[SystemContentBlock] = [] + for idx, message in enumerate(messages): + if message["role"] == "system": + _system_content_block = SystemContentBlock(text=message["content"]) + system_content_blocks.append(_system_content_block) + system_prompt_indices.append(idx) + if len(system_prompt_indices) > 0: + for idx in reversed(system_prompt_indices): + messages.pop(idx) + + inference_params = copy.deepcopy(optional_params) + additional_request_keys = [] + additional_request_params = {} + supported_converse_params = AmazonConverseConfig.__annotations__.keys() + supported_tool_call_params = ["tools", "tool_choice"] + ## TRANSFORMATION ## + # send all model-specific params in 'additional_request_params' + for k, v in inference_params.items(): + if ( + k not in supported_converse_params + and k not in supported_tool_call_params + ): + additional_request_params[k] = v + additional_request_keys.append(k) + for key in additional_request_keys: + inference_params.pop(key, None) + + bedrock_messages: List[MessageBlock] = _bedrock_converse_messages_pt( + messages=messages + ) + bedrock_tools: List[ToolBlock] = _bedrock_tools_pt( + inference_params.pop("tools", []) + ) + bedrock_tool_config: Optional[ToolConfigBlock] = None + if len(bedrock_tools) > 0: + tool_choice_values: ToolChoiceValuesBlock = inference_params.pop( + "tool_choice", None + ) + bedrock_tool_config = ToolConfigBlock( + tools=bedrock_tools, + ) + if tool_choice_values is not None: + bedrock_tool_config["toolChoice"] = tool_choice_values + + _data: RequestObject = { + "messages": bedrock_messages, + "additionalModelRequestFields": additional_request_params, + "system": system_content_blocks, + "inferenceConfig": InferenceConfig(**inference_params), + } + if bedrock_tool_config is not None: + _data["toolConfig"] = bedrock_tool_config + data = json.dumps(_data) + ## COMPLETION CALL + + headers = {"Content-Type": "application/json"} + if extra_headers is not None: + headers = {"Content-Type": "application/json", **extra_headers} + request = AWSRequest( + method="POST", url=endpoint_url, data=data, headers=headers + ) + sigv4.add_auth(request) + prepped = request.prepare() + + ## LOGGING + logging_obj.pre_call( + input=messages, + api_key="", + additional_args={ + "complete_input_dict": data, + "api_base": prepped.url, + "headers": prepped.headers, + }, + ) + + ### ROUTING (ASYNC, STREAMING, SYNC) + if acompletion: + if isinstance(client, HTTPHandler): + client = None + if stream is True and provider != "ai21": + return self.async_streaming( + model=model, + messages=messages, + data=data, + api_base=prepped.url, + model_response=model_response, + print_verbose=print_verbose, + encoding=encoding, + logging_obj=logging_obj, + optional_params=optional_params, + stream=True, + litellm_params=litellm_params, + logger_fn=logger_fn, + headers=prepped.headers, + timeout=timeout, + client=client, + ) # type: ignore + ### ASYNC COMPLETION + return self.async_completion( + model=model, + messages=messages, + data=data, + api_base=prepped.url, + model_response=model_response, + print_verbose=print_verbose, + encoding=encoding, + logging_obj=logging_obj, + optional_params=optional_params, + stream=stream, # type: ignore + litellm_params=litellm_params, + logger_fn=logger_fn, + headers=prepped.headers, + timeout=timeout, + client=client, + ) # type: ignore + + if (stream is not None and stream is True) and provider != "ai21": + + streaming_response = CustomStreamWrapper( + completion_stream=None, + make_call=partial( + make_sync_call, + client=None, + api_base=prepped.url, + headers=prepped.headers, # type: ignore + data=data, + model=model, + messages=messages, + logging_obj=logging_obj, + ), + model=model, + custom_llm_provider="bedrock", + logging_obj=logging_obj, + ) + + ## LOGGING + logging_obj.post_call( + input=messages, + api_key="", + original_response=streaming_response, + additional_args={"complete_input_dict": data}, + ) + return streaming_response + ### COMPLETION + + if client is None or isinstance(client, AsyncHTTPHandler): + _params = {} + if timeout is not None: + if isinstance(timeout, float) or isinstance(timeout, int): + timeout = httpx.Timeout(timeout) + _params["timeout"] = timeout + client = HTTPHandler(**_params) # type: ignore + else: + client = client + try: + response = client.post(url=prepped.url, headers=prepped.headers, data=data) # type: ignore + response.raise_for_status() + except httpx.HTTPStatusError as err: + error_code = err.response.status_code + raise BedrockError(status_code=error_code, message=response.text) + except httpx.TimeoutException: + raise BedrockError(status_code=408, message="Timeout error occurred.") + + return self.process_response( + model=model, + response=response, + model_response=model_response, + stream=stream, + logging_obj=logging_obj, + optional_params=optional_params, + api_key="", + data=data, + messages=messages, + print_verbose=print_verbose, + encoding=encoding, + ) + + def get_response_stream_shape(): from botocore.model import ServiceModel from botocore.loaders import Loader @@ -1086,6 +1865,31 @@ class AWSEventStreamDecoder: self.model = model self.parser = EventStreamJSONParser() + def converse_chunk_parser(self, chunk_data: dict) -> GenericStreamingChunk: + text = "" + tool_str = "" + is_finished = False + finish_reason = "" + usage: Optional[ConverseTokenUsageBlock] = None + if "delta" in chunk_data: + delta_obj = ContentBlockDeltaEvent(**chunk_data["delta"]) + if "text" in delta_obj: + text = delta_obj["text"] + elif "toolUse" in delta_obj: + tool_str = delta_obj["toolUse"]["input"] + elif "stopReason" in chunk_data: + finish_reason = map_finish_reason(chunk_data.get("stopReason", "stop")) + elif "usage" in chunk_data: + usage = ConverseTokenUsageBlock(**chunk_data["usage"]) # type: ignore + response = GenericStreamingChunk( + text=text, + tool_str=tool_str, + is_finished=is_finished, + finish_reason=finish_reason, + usage=usage, + ) + return response + def _chunk_parser(self, chunk_data: dict) -> GenericStreamingChunk: text = "" is_finished = False @@ -1098,19 +1902,8 @@ class AWSEventStreamDecoder: is_finished = True finish_reason = "stop" ######## bedrock.anthropic mappings ############### - elif "completion" in chunk_data: # not claude-3 - text = chunk_data["completion"] # bedrock.anthropic - stop_reason = chunk_data.get("stop_reason", None) - if stop_reason != None: - is_finished = True - finish_reason = stop_reason elif "delta" in chunk_data: - if chunk_data["delta"].get("text", None) is not None: - text = chunk_data["delta"]["text"] - stop_reason = chunk_data["delta"].get("stop_reason", None) - if stop_reason != None: - is_finished = True - finish_reason = stop_reason + return self.converse_chunk_parser(chunk_data=chunk_data) ######## bedrock.mistral mappings ############### elif "outputs" in chunk_data: if ( @@ -1137,11 +1930,11 @@ class AWSEventStreamDecoder: is_finished = True finish_reason = chunk_data["completionReason"] return GenericStreamingChunk( - **{ - "text": text, - "is_finished": is_finished, - "finish_reason": finish_reason, - } + text=text, + is_finished=is_finished, + finish_reason=finish_reason, + tool_str="", + usage=None, ) def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[GenericStreamingChunk]: @@ -1178,9 +1971,14 @@ class AWSEventStreamDecoder: parsed_response = self.parser.parse(response_dict, get_response_stream_shape()) if response_dict["status_code"] != 200: raise ValueError(f"Bad response code, expected 200: {response_dict}") + if "chunk" in parsed_response: + chunk = parsed_response.get("chunk") + if not chunk: + return None + return chunk.get("bytes").decode() # type: ignore[no-any-return] + else: + chunk = response_dict.get("body") + if not chunk: + return None - chunk = parsed_response.get("chunk") - if not chunk: - return None - - return chunk.get("bytes").decode() # type: ignore[no-any-return] + return chunk.decode() # type: ignore[no-any-return] diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index b91aaee2ae..5ec9c79bb2 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -156,12 +156,13 @@ class HTTPHandler: self, url: str, data: Optional[Union[dict, str]] = None, + json: Optional[Union[dict, str]] = None, params: Optional[dict] = None, headers: Optional[dict] = None, stream: bool = False, ): req = self.client.build_request( - "POST", url, data=data, params=params, headers=headers # type: ignore + "POST", url, data=data, json=json, params=params, headers=headers # type: ignore ) response = self.client.send(req, stream=stream) return response diff --git a/litellm/llms/gemini.py b/litellm/llms/gemini.py index a55b39aef9..cfdf39eca2 100644 --- a/litellm/llms/gemini.py +++ b/litellm/llms/gemini.py @@ -1,13 +1,14 @@ -import os, types, traceback, copy, asyncio -import json -from enum import Enum +import types +import traceback +import copy import time from typing import Callable, Optional -from litellm.utils import ModelResponse, get_secret, Choices, Message, Usage +from litellm.utils import ModelResponse, Choices, Message, Usage import litellm -import sys, httpx +import httpx from .prompt_templates.factory import prompt_factory, custom_prompt, get_system_prompt from packaging.version import Version +from litellm import verbose_logger class GeminiError(Exception): @@ -264,7 +265,8 @@ def completion( choices_list.append(choice_obj) model_response["choices"] = choices_list except Exception as e: - traceback.print_exc() + verbose_logger.error("LiteLLM.gemini.py: Exception occured - {}".format(str(e))) + verbose_logger.debug(traceback.format_exc()) raise GeminiError( message=traceback.format_exc(), status_code=response.status_code ) @@ -356,7 +358,8 @@ async def async_completion( choices_list.append(choice_obj) model_response["choices"] = choices_list except Exception as e: - traceback.print_exc() + verbose_logger.error("LiteLLM.gemini.py: Exception occured - {}".format(str(e))) + verbose_logger.debug(traceback.format_exc()) raise GeminiError( message=traceback.format_exc(), status_code=response.status_code ) diff --git a/litellm/llms/ollama.py b/litellm/llms/ollama.py index 76687839d8..e7dd1d5f55 100644 --- a/litellm/llms/ollama.py +++ b/litellm/llms/ollama.py @@ -7,6 +7,7 @@ import litellm from litellm.types.utils import ProviderField import httpx, aiohttp, asyncio # type: ignore from .prompt_templates.factory import prompt_factory, custom_prompt +from litellm import verbose_logger class OllamaError(Exception): @@ -137,6 +138,7 @@ class OllamaConfig: ) ] + def get_supported_openai_params( self, ): @@ -151,10 +153,12 @@ class OllamaConfig: "response_format", ] + # ollama wants plain base64 jpeg/png files as images. strip any leading dataURI # and convert to jpeg if necessary. def _convert_image(image): import base64, io + try: from PIL import Image except: @@ -404,7 +408,13 @@ async def ollama_async_streaming(url, data, model_response, encoding, logging_ob async for transformed_chunk in streamwrapper: yield transformed_chunk except Exception as e: - traceback.print_exc() + verbose_logger.error( + "LiteLLM.ollama.py::ollama_async_streaming(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) + raise e @@ -468,7 +478,12 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj): ) return model_response except Exception as e: - traceback.print_exc() + verbose_logger.error( + "LiteLLM.ollama.py::ollama_acompletion(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) raise e diff --git a/litellm/llms/ollama_chat.py b/litellm/llms/ollama_chat.py index a058077227..a7439bbcc0 100644 --- a/litellm/llms/ollama_chat.py +++ b/litellm/llms/ollama_chat.py @@ -1,11 +1,15 @@ from itertools import chain -import requests, types, time -import json, uuid +import requests +import types +import time +import json +import uuid import traceback from typing import Optional +from litellm import verbose_logger import litellm -import httpx, aiohttp, asyncio -from .prompt_templates.factory import prompt_factory, custom_prompt +import httpx +import aiohttp class OllamaError(Exception): @@ -299,7 +303,10 @@ def get_ollama_response( tool_calls=[ { "id": f"call_{str(uuid.uuid4())}", - "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])}, + "function": { + "name": function_call["name"], + "arguments": json.dumps(function_call["arguments"]), + }, "type": "function", } ], @@ -307,7 +314,9 @@ def get_ollama_response( model_response["choices"][0]["message"] = message model_response["choices"][0]["finish_reason"] = "tool_calls" else: - model_response["choices"][0]["message"]["content"] = response_json["message"]["content"] + model_response["choices"][0]["message"]["content"] = response_json["message"][ + "content" + ] model_response["created"] = int(time.time()) model_response["model"] = "ollama/" + model prompt_tokens = response_json.get("prompt_eval_count", litellm.token_counter(messages=messages)) # type: ignore @@ -361,7 +370,10 @@ def ollama_completion_stream(url, api_key, data, logging_obj): tool_calls=[ { "id": f"call_{str(uuid.uuid4())}", - "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])}, + "function": { + "name": function_call["name"], + "arguments": json.dumps(function_call["arguments"]), + }, "type": "function", } ], @@ -410,9 +422,10 @@ async def ollama_async_streaming( first_chunk_content = first_chunk.choices[0].delta.content or "" response_content = first_chunk_content + "".join( [ - chunk.choices[0].delta.content - async for chunk in streamwrapper - if chunk.choices[0].delta.content] + chunk.choices[0].delta.content + async for chunk in streamwrapper + if chunk.choices[0].delta.content + ] ) function_call = json.loads(response_content) delta = litellm.utils.Delta( @@ -420,7 +433,10 @@ async def ollama_async_streaming( tool_calls=[ { "id": f"call_{str(uuid.uuid4())}", - "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])}, + "function": { + "name": function_call["name"], + "arguments": json.dumps(function_call["arguments"]), + }, "type": "function", } ], @@ -433,7 +449,8 @@ async def ollama_async_streaming( async for transformed_chunk in streamwrapper: yield transformed_chunk except Exception as e: - traceback.print_exc() + verbose_logger.error("LiteLLM.gemini(): Exception occured - {}".format(str(e))) + verbose_logger.debug(traceback.format_exc()) async def ollama_acompletion( @@ -483,7 +500,10 @@ async def ollama_acompletion( tool_calls=[ { "id": f"call_{str(uuid.uuid4())}", - "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])}, + "function": { + "name": function_call["name"], + "arguments": json.dumps(function_call["arguments"]), + }, "type": "function", } ], @@ -491,7 +511,9 @@ async def ollama_acompletion( model_response["choices"][0]["message"] = message model_response["choices"][0]["finish_reason"] = "tool_calls" else: - model_response["choices"][0]["message"]["content"] = response_json["message"]["content"] + model_response["choices"][0]["message"]["content"] = response_json[ + "message" + ]["content"] model_response["created"] = int(time.time()) model_response["model"] = "ollama_chat/" + data["model"] @@ -509,5 +531,9 @@ async def ollama_acompletion( ) return model_response except Exception as e: - traceback.print_exc() + verbose_logger.error( + "LiteLLM.ollama_acompletion(): Exception occured - {}".format(str(e)) + ) + verbose_logger.debug(traceback.format_exc()) + raise e diff --git a/litellm/llms/palm.py b/litellm/llms/palm.py index f15be43db4..4d9953e77a 100644 --- a/litellm/llms/palm.py +++ b/litellm/llms/palm.py @@ -1,11 +1,12 @@ -import os, types, traceback, copy -import json -from enum import Enum +import types +import traceback +import copy import time from typing import Callable, Optional -from litellm.utils import ModelResponse, get_secret, Choices, Message, Usage +from litellm.utils import ModelResponse, Choices, Message, Usage import litellm -import sys, httpx +import httpx +from litellm import verbose_logger class PalmError(Exception): @@ -165,7 +166,10 @@ def completion( choices_list.append(choice_obj) model_response["choices"] = choices_list except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.llms.palm.py::completion(): Exception occured - {}".format(str(e)) + ) + verbose_logger.debug(traceback.format_exc()) raise PalmError( message=traceback.format_exc(), status_code=response.status_code ) diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py index 10f3f16ed4..6bf03b52d4 100644 --- a/litellm/llms/prompt_templates/factory.py +++ b/litellm/llms/prompt_templates/factory.py @@ -3,14 +3,7 @@ import requests, traceback import json, re, xml.etree.ElementTree as ET from jinja2 import Template, exceptions, meta, BaseLoader from jinja2.sandbox import ImmutableSandboxedEnvironment -from typing import ( - Any, - List, - Mapping, - MutableMapping, - Optional, - Sequence, -) +from typing import Any, List, Mapping, MutableMapping, Optional, Sequence, Tuple import litellm import litellm.types from litellm.types.completion import ( @@ -24,7 +17,7 @@ from litellm.types.completion import ( import litellm.types.llms from litellm.types.llms.anthropic import * import uuid - +from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock import litellm.types.llms.vertex_ai @@ -1460,9 +1453,7 @@ def _load_image_from_url(image_url): try: from PIL import Image except: - raise Exception( - "gemini image conversion failed please run `pip install Pillow`" - ) + raise Exception("image conversion failed please run `pip install Pillow`") from io import BytesIO try: @@ -1613,6 +1604,380 @@ def azure_text_pt(messages: list): return prompt +###### AMAZON BEDROCK ####### + +from litellm.types.llms.bedrock import ( + ToolResultContentBlock as BedrockToolResultContentBlock, + ToolResultBlock as BedrockToolResultBlock, + ToolConfigBlock as BedrockToolConfigBlock, + ToolUseBlock as BedrockToolUseBlock, + ImageSourceBlock as BedrockImageSourceBlock, + ImageBlock as BedrockImageBlock, + ContentBlock as BedrockContentBlock, + ToolInputSchemaBlock as BedrockToolInputSchemaBlock, + ToolSpecBlock as BedrockToolSpecBlock, + ToolBlock as BedrockToolBlock, + ToolChoiceValuesBlock as BedrockToolChoiceValuesBlock, +) + + +def get_image_details(image_url) -> Tuple[str, str]: + try: + import base64 + + # Send a GET request to the image URL + response = requests.get(image_url) + response.raise_for_status() # Raise an exception for HTTP errors + + # Check the response's content type to ensure it is an image + content_type = response.headers.get("content-type") + if not content_type or "image" not in content_type: + raise ValueError( + f"URL does not point to a valid image (content-type: {content_type})" + ) + + # Convert the image content to base64 bytes + base64_bytes = base64.b64encode(response.content).decode("utf-8") + + # Get mime-type + mime_type = content_type.split("/")[ + 1 + ] # Extract mime-type from content-type header + + return base64_bytes, mime_type + + except requests.RequestException as e: + raise Exception(f"Request failed: {e}") + except Exception as e: + raise e + + +def _process_bedrock_converse_image_block(image_url: str) -> BedrockImageBlock: + if "base64" in image_url: + # Case 1: Images with base64 encoding + import base64, re + + # base 64 is passed as data:image/jpeg;base64, + image_metadata, img_without_base_64 = image_url.split(",") + + # read mime_type from img_without_base_64=data:image/jpeg;base64 + # Extract MIME type using regular expression + mime_type_match = re.match(r"data:(.*?);base64", image_metadata) + if mime_type_match: + mime_type = mime_type_match.group(1) + image_format = mime_type.split("/")[1] + else: + mime_type = "image/jpeg" + image_format = "jpeg" + _blob = BedrockImageSourceBlock(bytes=img_without_base_64) + supported_image_formats = ( + litellm.AmazonConverseConfig().get_supported_image_types() + ) + if image_format in supported_image_formats: + return BedrockImageBlock(source=_blob, format=image_format) # type: ignore + else: + # Handle the case when the image format is not supported + raise ValueError( + "Unsupported image format: {}. Supported formats: {}".format( + image_format, supported_image_formats + ) + ) + elif "https:/" in image_url: + # Case 2: Images with direct links + image_bytes, image_format = get_image_details(image_url) + _blob = BedrockImageSourceBlock(bytes=image_bytes) + supported_image_formats = ( + litellm.AmazonConverseConfig().get_supported_image_types() + ) + if image_format in supported_image_formats: + return BedrockImageBlock(source=_blob, format=image_format) # type: ignore + else: + # Handle the case when the image format is not supported + raise ValueError( + "Unsupported image format: {}. Supported formats: {}".format( + image_format, supported_image_formats + ) + ) + else: + raise ValueError( + "Unsupported image type. Expected either image url or base64 encoded string - \ + e.g. 'data:image/jpeg;base64,'" + ) + + +def _convert_to_bedrock_tool_call_invoke( + tool_calls: list, +) -> List[BedrockContentBlock]: + """ + OpenAI tool invokes: + { + "role": "assistant", + "content": null, + "tool_calls": [ + { + "id": "call_abc123", + "type": "function", + "function": { + "name": "get_current_weather", + "arguments": "{\n\"location\": \"Boston, MA\"\n}" + } + } + ] + }, + """ + """ + Bedrock tool invokes: + [ + { + "role": "assistant", + "toolUse": { + "input": {"location": "Boston, MA", ..}, + "name": "get_current_weather", + "toolUseId": "call_abc123" + } + } + ] + """ + """ + - json.loads argument + - extract name + - extract id + """ + + try: + _parts_list: List[BedrockContentBlock] = [] + for tool in tool_calls: + if "function" in tool: + id = tool["id"] + name = tool["function"].get("name", "") + arguments = tool["function"].get("arguments", "") + arguments_dict = json.loads(arguments) + bedrock_tool = BedrockToolUseBlock( + input=arguments_dict, name=name, toolUseId=id + ) + bedrock_content_block = BedrockContentBlock(toolUse=bedrock_tool) + _parts_list.append(bedrock_content_block) + return _parts_list + except Exception as e: + raise Exception( + "Unable to convert openai tool calls={} to bedrock tool calls. Received error={}".format( + tool_calls, str(e) + ) + ) + + +def _convert_to_bedrock_tool_call_result( + message: dict, +) -> BedrockMessageBlock: + """ + OpenAI message with a tool result looks like: + { + "tool_call_id": "tool_1", + "role": "tool", + "name": "get_current_weather", + "content": "function result goes here", + }, + + OpenAI message with a function call result looks like: + { + "role": "function", + "name": "get_current_weather", + "content": "function result goes here", + } + """ + """ + Bedrock result looks like this: + { + "role": "user", + "content": [ + { + "toolResult": { + "toolUseId": "tooluse_kZJMlvQmRJ6eAyJE5GIl7Q", + "content": [ + { + "json": { + "song": "Elemental Hotel", + "artist": "8 Storey Hike" + } + } + ] + } + } + ] + } + """ + """ + - + """ + content = message.get("content", "") + name = message.get("name", "") + id = message.get("tool_call_id", str(uuid.uuid4())) + + tool_result_content_block = BedrockToolResultContentBlock(text=content) + tool_result = BedrockToolResultBlock( + content=[tool_result_content_block], + toolUseId=id, + ) + content_block = BedrockContentBlock(toolResult=tool_result) + + return BedrockMessageBlock(role="user", content=[content_block]) + + +def _bedrock_converse_messages_pt(messages: List) -> List[BedrockMessageBlock]: + """ + Converts given messages from OpenAI format to Bedrock format + + - Roles must alternate b/w 'user' and 'model' (same as anthropic -> merge consecutive roles) + - Please ensure that function response turn comes immediately after a function call turn + """ + + contents: List[BedrockMessageBlock] = [] + msg_i = 0 + while msg_i < len(messages): + user_content: List[BedrockContentBlock] = [] + init_msg_i = msg_i + ## MERGE CONSECUTIVE USER CONTENT ## + while msg_i < len(messages) and messages[msg_i]["role"] == "user": + if isinstance(messages[msg_i]["content"], list): + _parts: List[BedrockContentBlock] = [] + for element in messages[msg_i]["content"]: + if isinstance(element, dict): + if element["type"] == "text": + _part = BedrockContentBlock(text=element["text"]) + _parts.append(_part) + elif element["type"] == "image_url": + image_url = element["image_url"]["url"] + _part = _process_bedrock_converse_image_block( # type: ignore + image_url=image_url + ) + _parts.append(BedrockContentBlock(image=_part)) # type: ignore + user_content.extend(_parts) + else: + _part = BedrockContentBlock(text=messages[msg_i]["content"]) + user_content.append(_part) + + msg_i += 1 + + if user_content: + contents.append(BedrockMessageBlock(role="user", content=user_content)) + assistant_content: List[BedrockContentBlock] = [] + ## MERGE CONSECUTIVE ASSISTANT CONTENT ## + while msg_i < len(messages) and messages[msg_i]["role"] == "assistant": + if isinstance(messages[msg_i]["content"], list): + assistants_parts: List[BedrockContentBlock] = [] + for element in messages[msg_i]["content"]: + if isinstance(element, dict): + if element["type"] == "text": + assistants_part = BedrockContentBlock(text=element["text"]) + assistants_parts.append(assistants_part) + elif element["type"] == "image_url": + image_url = element["image_url"]["url"] + assistants_part = _process_bedrock_converse_image_block( # type: ignore + image_url=image_url + ) + assistants_parts.append( + BedrockContentBlock(image=assistants_part) # type: ignore + ) + assistant_content.extend(assistants_parts) + elif messages[msg_i].get( + "tool_calls", [] + ): # support assistant tool invoke convertion + assistant_content.extend( + _convert_to_bedrock_tool_call_invoke(messages[msg_i]["tool_calls"]) + ) + else: + assistant_text = ( + messages[msg_i].get("content") or "" + ) # either string or none + if assistant_text: + assistant_content.append(BedrockContentBlock(text=assistant_text)) + + msg_i += 1 + + if assistant_content: + contents.append( + BedrockMessageBlock(role="assistant", content=assistant_content) + ) + + ## APPEND TOOL CALL MESSAGES ## + if msg_i < len(messages) and messages[msg_i]["role"] == "tool": + tool_call_result = _convert_to_bedrock_tool_call_result(messages[msg_i]) + contents.append(tool_call_result) + msg_i += 1 + if msg_i == init_msg_i: # prevent infinite loops + raise Exception( + "Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format( + messages[msg_i] + ) + ) + + return contents + + +def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]: + """ + OpenAI tools looks like: + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + } + ] + """ + """ + Bedrock toolConfig looks like: + "tools": [ + { + "toolSpec": { + "name": "top_song", + "description": "Get the most popular song played on a radio station.", + "inputSchema": { + "json": { + "type": "object", + "properties": { + "sign": { + "type": "string", + "description": "The call sign for the radio station for which you want the most popular song. Example calls signs are WZPZ, and WKRP." + } + }, + "required": [ + "sign" + ] + } + } + } + } + ] + """ + tool_block_list: List[BedrockToolBlock] = [] + for tool in tools: + parameters = tool.get("function", {}).get("parameters", None) + name = tool.get("function", {}).get("name", "") + description = tool.get("function", {}).get("description", "") + tool_input_schema = BedrockToolInputSchemaBlock(json=parameters) + tool_spec = BedrockToolSpecBlock( + inputSchema=tool_input_schema, name=name, description=description + ) + tool_block = BedrockToolBlock(toolSpec=tool_spec) + tool_block_list.append(tool_block) + + return tool_block_list + + # Function call template def function_call_prompt(messages: list, functions: list): function_prompt = """Produce JSON OUTPUT ONLY! Adhere to this format {"name": "function_name", "arguments":{"argument_name": "argument_value"}} The following functions are available to you:""" diff --git a/litellm/llms/vertex_ai.py b/litellm/llms/vertex_ai.py index dc79e7e4e9..bd9cfaa8d6 100644 --- a/litellm/llms/vertex_ai.py +++ b/litellm/llms/vertex_ai.py @@ -12,6 +12,7 @@ from litellm.llms.prompt_templates.factory import ( convert_to_gemini_tool_call_result, convert_to_gemini_tool_call_invoke, ) +from litellm.types.files import get_file_mime_type_for_file_type, get_file_type_from_extension, is_gemini_1_5_accepted_file_type, is_video_file_type class VertexAIError(Exception): @@ -297,29 +298,31 @@ def _convert_gemini_role(role: str) -> Literal["user", "model"]: def _process_gemini_image(image_url: str) -> PartType: try: - if ".mp4" in image_url and "gs://" in image_url: - # Case 1: Videos with Cloud Storage URIs - part_mime = "video/mp4" - _file_data = FileDataType(mime_type=part_mime, file_uri=image_url) - return PartType(file_data=_file_data) - elif ".pdf" in image_url and "gs://" in image_url: - # Case 2: PDF's with Cloud Storage URIs - part_mime = "application/pdf" - _file_data = FileDataType(mime_type=part_mime, file_uri=image_url) - return PartType(file_data=_file_data) - elif "gs://" in image_url: - # Case 3: Images with Cloud Storage URIs - # The supported MIME types for images include image/png and image/jpeg. - part_mime = "image/png" if "png" in image_url else "image/jpeg" - _file_data = FileDataType(mime_type=part_mime, file_uri=image_url) - return PartType(file_data=_file_data) + # GCS URIs + if "gs://" in image_url: + # Figure out file type + extension_with_dot = os.path.splitext(image_url)[-1] # Ex: ".png" + extension = extension_with_dot[1:] # Ex: "png" + + file_type = get_file_type_from_extension(extension) + + # Validate the file type is supported by Gemini + if not is_gemini_1_5_accepted_file_type(file_type): + raise Exception(f"File type not supported by gemini - {file_type}") + + mime_type = get_file_mime_type_for_file_type(file_type) + file_data = FileDataType(mime_type=mime_type, file_uri=image_url) + + return PartType(file_data=file_data) + + # Direct links elif "https:/" in image_url: - # Case 4: Images with direct links image = _load_image_from_url(image_url) _blob = BlobType(data=image.data, mime_type=image._mime_type) return PartType(inline_data=_blob) + + # Base64 encoding elif "base64" in image_url: - # Case 5: Images with base64 encoding import base64, re # base 64 is passed as data:image/jpeg;base64, @@ -426,112 +429,6 @@ def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]: return contents -def _gemini_vision_convert_messages(messages: list): - """ - Converts given messages for GPT-4 Vision to Gemini format. - - Args: - messages (list): The messages to convert. Each message can be a dictionary with a "content" key. The content can be a string or a list of elements. If it is a string, it will be concatenated to the prompt. If it is a list, each element will be processed based on its type: - - If the element is a dictionary with a "type" key equal to "text", its "text" value will be concatenated to the prompt. - - If the element is a dictionary with a "type" key equal to "image_url", its "image_url" value will be added to the list of images. - - Returns: - tuple: A tuple containing the prompt (a string) and the processed images (a list of objects representing the images). - - Raises: - VertexAIError: If the import of the 'vertexai' module fails, indicating that 'google-cloud-aiplatform' needs to be installed. - Exception: If any other exception occurs during the execution of the function. - - Note: - This function is based on the code from the 'gemini/getting-started/intro_gemini_python.ipynb' notebook in the 'generative-ai' repository on GitHub. - The supported MIME types for images include 'image/png' and 'image/jpeg'. - - Examples: - >>> messages = [ - ... {"content": "Hello, world!"}, - ... {"content": [{"type": "text", "text": "This is a text message."}, {"type": "image_url", "image_url": "example.com/image.png"}]}, - ... ] - >>> _gemini_vision_convert_messages(messages) - ('Hello, world!This is a text message.', [, ]) - """ - try: - import vertexai - except: - raise VertexAIError( - status_code=400, - message="vertexai import failed please run `pip install google-cloud-aiplatform`", - ) - try: - from vertexai.preview.language_models import ( - ChatModel, - CodeChatModel, - InputOutputTextPair, - ) - from vertexai.language_models import TextGenerationModel, CodeGenerationModel - from vertexai.preview.generative_models import ( - GenerativeModel, - Part, - GenerationConfig, - Image, - ) - - # given messages for gpt-4 vision, convert them for gemini - # https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/getting-started/intro_gemini_python.ipynb - prompt = "" - images = [] - for message in messages: - if isinstance(message["content"], str): - prompt += message["content"] - elif isinstance(message["content"], list): - # see https://docs.litellm.ai/docs/providers/openai#openai-vision-models - for element in message["content"]: - if isinstance(element, dict): - if element["type"] == "text": - prompt += element["text"] - elif element["type"] == "image_url": - image_url = element["image_url"]["url"] - images.append(image_url) - # processing images passed to gemini - processed_images = [] - for img in images: - if "gs://" in img: - # Case 1: Images with Cloud Storage URIs - # The supported MIME types for images include image/png and image/jpeg. - part_mime = "image/png" if "png" in img else "image/jpeg" - google_clooud_part = Part.from_uri(img, mime_type=part_mime) - processed_images.append(google_clooud_part) - elif "https:/" in img: - # Case 2: Images with direct links - image = _load_image_from_url(img) - processed_images.append(image) - elif ".mp4" in img and "gs://" in img: - # Case 3: Videos with Cloud Storage URIs - part_mime = "video/mp4" - google_clooud_part = Part.from_uri(img, mime_type=part_mime) - processed_images.append(google_clooud_part) - elif "base64" in img: - # Case 4: Images with base64 encoding - import base64, re - - # base 64 is passed as data:image/jpeg;base64, - image_metadata, img_without_base_64 = img.split(",") - - # read mime_type from img_without_base_64=data:image/jpeg;base64 - # Extract MIME type using regular expression - mime_type_match = re.match(r"data:(.*?);base64", image_metadata) - - if mime_type_match: - mime_type = mime_type_match.group(1) - else: - mime_type = "image/jpeg" - decoded_img = base64.b64decode(img_without_base_64) - processed_image = Part.from_data(data=decoded_img, mime_type=mime_type) - processed_images.append(processed_image) - return prompt, processed_images - except Exception as e: - raise e - - def _get_client_cache_key(model: str, vertex_project: str, vertex_location: str): _cache_key = f"{model}-{vertex_project}-{vertex_location}" return _cache_key @@ -647,9 +544,9 @@ def completion( prompt = " ".join( [ - message["content"] + message.get("content") for message in messages - if isinstance(message["content"], str) + if isinstance(message.get("content", None), str) ] ) diff --git a/litellm/main.py b/litellm/main.py index 596f85f334..2b1712d91b 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -79,7 +79,7 @@ from .llms.anthropic import AnthropicChatCompletion from .llms.anthropic_text import AnthropicTextCompletion from .llms.huggingface_restapi import Huggingface from .llms.predibase import PredibaseChatCompletion -from .llms.bedrock_httpx import BedrockLLM +from .llms.bedrock_httpx import BedrockLLM, BedrockConverseLLM from .llms.vertex_httpx import VertexLLM from .llms.triton import TritonChatCompletion from .llms.prompt_templates.factory import ( @@ -122,6 +122,7 @@ huggingface = Huggingface() predibase_chat_completions = PredibaseChatCompletion() triton_chat_completions = TritonChatCompletion() bedrock_chat_completion = BedrockLLM() +bedrock_converse_chat_completion = BedrockConverseLLM() vertex_chat_completion = VertexLLM() ####### COMPLETION ENDPOINTS ################ @@ -364,7 +365,10 @@ async def acompletion( ) # sets the logging event loop if the user does sync streaming (e.g. on proxy for sagemaker calls) return response except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.acompletion(): Exception occured - {}".format(str(e)) + ) + verbose_logger.debug(traceback.format_exc()) custom_llm_provider = custom_llm_provider or "openai" raise exception_type( model=model, @@ -477,7 +481,10 @@ def mock_completion( except Exception as e: if isinstance(e, openai.APIError): raise e - traceback.print_exc() + verbose_logger.error( + "litellm.mock_completion(): Exception occured - {}".format(str(e)) + ) + verbose_logger.debug(traceback.format_exc()) raise Exception("Mock completion response failed") @@ -2100,22 +2107,40 @@ def completion( logging_obj=logging, ) else: - response = bedrock_chat_completion.completion( - model=model, - messages=messages, - custom_prompt_dict=custom_prompt_dict, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - logging_obj=logging, - extra_headers=extra_headers, - timeout=timeout, - acompletion=acompletion, - client=client, - ) + if model.startswith("anthropic"): + response = bedrock_converse_chat_completion.completion( + model=model, + messages=messages, + custom_prompt_dict=custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + logging_obj=logging, + extra_headers=extra_headers, + timeout=timeout, + acompletion=acompletion, + client=client, + ) + else: + response = bedrock_chat_completion.completion( + model=model, + messages=messages, + custom_prompt_dict=custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + logging_obj=logging, + extra_headers=extra_headers, + timeout=timeout, + acompletion=acompletion, + client=client, + ) if optional_params.get("stream", False): ## LOGGING logging.post_call( @@ -4433,7 +4458,10 @@ async def ahealth_check( response = {} # args like remaining ratelimit etc. return response except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.ahealth_check(): Exception occured - {}".format(str(e)) + ) + verbose_logger.debug(traceback.format_exc()) stack_trace = traceback.format_exc() if isinstance(stack_trace, str): stack_trace = stack_trace[:1000] diff --git a/litellm/proxy/_logging.py b/litellm/proxy/_logging.py index 22cbd88cb7..f453cef395 100644 --- a/litellm/proxy/_logging.py +++ b/litellm/proxy/_logging.py @@ -1,6 +1,7 @@ import json import logging from logging import Formatter +import sys class JsonFormatter(Formatter): diff --git a/litellm/proxy/_super_secret_config.yaml b/litellm/proxy/_super_secret_config.yaml index 72479bd5d5..7fa1bbc19c 100644 --- a/litellm/proxy/_super_secret_config.yaml +++ b/litellm/proxy/_super_secret_config.yaml @@ -56,8 +56,10 @@ router_settings: litellm_settings: success_callback: ["langfuse"] - json_logs: true general_settings: alerting: ["email"] + key_management_system: "aws_kms" + key_management_settings: + hosted_keys: ["LITELLM_MASTER_KEY"] diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 05efc7495d..3bdbda7579 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -955,6 +955,7 @@ class KeyManagementSystem(enum.Enum): AZURE_KEY_VAULT = "azure_key_vault" AWS_SECRET_MANAGER = "aws_secret_manager" LOCAL = "local" + AWS_KMS = "aws_kms" class KeyManagementSettings(LiteLLMBase): diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py index b9813afc91..17f0822e61 100644 --- a/litellm/proxy/auth/auth_checks.py +++ b/litellm/proxy/auth/auth_checks.py @@ -106,6 +106,40 @@ def common_checks( raise Exception( f"'user' param not passed in. 'enforce_user_param'={general_settings['enforce_user_param']}" ) + if general_settings.get("enforced_params", None) is not None: + # Enterprise ONLY Feature + # we already validate if user is premium_user when reading the config + # Add an extra premium_usercheck here too, just incase + from litellm.proxy.proxy_server import premium_user, CommonProxyErrors + + if premium_user is not True: + raise ValueError( + "Trying to use `enforced_params`" + + CommonProxyErrors.not_premium_user.value + ) + + if route in LiteLLMRoutes.openai_routes.value: + # loop through each enforced param + # example enforced_params ['user', 'metadata', 'metadata.generation_name'] + for enforced_param in general_settings["enforced_params"]: + _enforced_params = enforced_param.split(".") + if len(_enforced_params) == 1: + if _enforced_params[0] not in request_body: + raise ValueError( + f"BadRequest please pass param={_enforced_params[0]} in request body. This is a required param" + ) + elif len(_enforced_params) == 2: + # this is a scenario where user requires request['metadata']['generation_name'] to exist + if _enforced_params[0] not in request_body: + raise ValueError( + f"BadRequest please pass param={_enforced_params[0]} in request body. This is a required param" + ) + if _enforced_params[1] not in request_body[_enforced_params[0]]: + raise ValueError( + f"BadRequest please pass param=[{_enforced_params[0]}][{_enforced_params[1]}] in request body. This is a required param" + ) + + pass # 7. [OPTIONAL] If 'litellm.max_budget' is set (>0), is proxy under budget if ( litellm.max_budget > 0 diff --git a/litellm/proxy/hooks/azure_content_safety.py b/litellm/proxy/hooks/azure_content_safety.py index 5b5139f8c7..47ba36a683 100644 --- a/litellm/proxy/hooks/azure_content_safety.py +++ b/litellm/proxy/hooks/azure_content_safety.py @@ -88,7 +88,7 @@ class _PROXY_AzureContentSafety( verbose_proxy_logger.debug( "Error in Azure Content-Safety: %s", traceback.format_exc() ) - traceback.print_exc() + verbose_proxy_logger.debug(traceback.format_exc()) raise result = self._compute_result(response) @@ -123,7 +123,12 @@ class _PROXY_AzureContentSafety( except HTTPException as e: raise e except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.hooks.azure_content_safety.py::async_pre_call_hook(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) async def async_post_call_success_hook( self, diff --git a/litellm/proxy/hooks/batch_redis_get.py b/litellm/proxy/hooks/batch_redis_get.py index 64541c1bff..d506109b81 100644 --- a/litellm/proxy/hooks/batch_redis_get.py +++ b/litellm/proxy/hooks/batch_redis_get.py @@ -94,7 +94,12 @@ class _PROXY_BatchRedisRequests(CustomLogger): except HTTPException as e: raise e except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.hooks.batch_redis_get.py::async_pre_call_hook(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) async def async_get_cache(self, *args, **kwargs): """ diff --git a/litellm/proxy/hooks/cache_control_check.py b/litellm/proxy/hooks/cache_control_check.py index 3160fe97ad..89971a0bf7 100644 --- a/litellm/proxy/hooks/cache_control_check.py +++ b/litellm/proxy/hooks/cache_control_check.py @@ -1,13 +1,13 @@ # What this does? ## Checks if key is allowed to use the cache controls passed in to the completion() call -from typing import Optional import litellm +from litellm import verbose_logger from litellm.caching import DualCache from litellm.proxy._types import UserAPIKeyAuth from litellm.integrations.custom_logger import CustomLogger from fastapi import HTTPException -import json, traceback +import traceback class _PROXY_CacheControlCheck(CustomLogger): @@ -54,4 +54,9 @@ class _PROXY_CacheControlCheck(CustomLogger): except HTTPException as e: raise e except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.proxy.hooks.cache_control_check.py::async_pre_call_hook(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) diff --git a/litellm/proxy/hooks/max_budget_limiter.py b/litellm/proxy/hooks/max_budget_limiter.py index 442cc53e37..c4b328bab0 100644 --- a/litellm/proxy/hooks/max_budget_limiter.py +++ b/litellm/proxy/hooks/max_budget_limiter.py @@ -1,10 +1,10 @@ -from typing import Optional +from litellm import verbose_logger import litellm from litellm.caching import DualCache from litellm.proxy._types import UserAPIKeyAuth from litellm.integrations.custom_logger import CustomLogger from fastapi import HTTPException -import json, traceback +import traceback class _PROXY_MaxBudgetLimiter(CustomLogger): @@ -44,4 +44,9 @@ class _PROXY_MaxBudgetLimiter(CustomLogger): except HTTPException as e: raise e except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.proxy.hooks.max_budget_limiter.py::async_pre_call_hook(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) diff --git a/litellm/proxy/hooks/presidio_pii_masking.py b/litellm/proxy/hooks/presidio_pii_masking.py index 95a6e9c3c8..e64e69c457 100644 --- a/litellm/proxy/hooks/presidio_pii_masking.py +++ b/litellm/proxy/hooks/presidio_pii_masking.py @@ -8,8 +8,8 @@ # Tell us how we can improve! - Krrish & Ishaan -from typing import Optional, Literal, Union -import litellm, traceback, sys, uuid, json +from typing import Optional, Union +import litellm, traceback, uuid, json # noqa: E401 from litellm.caching import DualCache from litellm.proxy._types import UserAPIKeyAuth from litellm.integrations.custom_logger import CustomLogger @@ -21,8 +21,8 @@ from litellm.utils import ( ImageResponse, StreamingChoices, ) -from datetime import datetime -import aiohttp, asyncio +import aiohttp +import asyncio class _OPTIONAL_PresidioPIIMasking(CustomLogger): @@ -138,7 +138,12 @@ class _OPTIONAL_PresidioPIIMasking(CustomLogger): else: raise Exception(f"Invalid anonymizer response: {redacted_text}") except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.hooks.presidio_pii_masking.py::async_pre_call_hook(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) raise e async def async_pre_call_hook( diff --git a/litellm/proxy/hooks/prompt_injection_detection.py b/litellm/proxy/hooks/prompt_injection_detection.py index 08dbedd8c8..ed33e3b519 100644 --- a/litellm/proxy/hooks/prompt_injection_detection.py +++ b/litellm/proxy/hooks/prompt_injection_detection.py @@ -204,7 +204,12 @@ class _OPTIONAL_PromptInjectionDetection(CustomLogger): return e.detail["error"] raise e except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.hooks.prompt_injection_detection.py::async_pre_call_hook(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) async def async_moderation_hook( self, diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index 88fc0e9136..df6dfd1394 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -21,7 +21,14 @@ model_list: general_settings: master_key: sk-1234 + alerting: ["slack"] litellm_settings: callbacks: ["otel"] - store_audit_logs: true \ No newline at end of file + store_audit_logs: true + redact_messages_in_exceptions: True + enforced_params: + - user + - metadata + - metadata.generation_name + diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 25e46269e1..2071ef63bc 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -111,6 +111,7 @@ from litellm.proxy.utils import ( encrypt_value, decrypt_value, _to_ns, + get_error_message_str, ) from litellm import ( CreateBatchRequest, @@ -120,7 +121,10 @@ from litellm import ( CreateFileRequest, ) from litellm.proxy.secret_managers.google_kms import load_google_kms -from litellm.proxy.secret_managers.aws_secret_manager import load_aws_secret_manager +from litellm.proxy.secret_managers.aws_secret_manager import ( + load_aws_secret_manager, + load_aws_kms, +) import pydantic from litellm.proxy._types import * from litellm.caching import DualCache, RedisCache @@ -133,7 +137,10 @@ from litellm.router import ( AssistantsTypedDict, ) from litellm.router import ModelInfo as RouterModelInfo -from litellm._logging import verbose_router_logger, verbose_proxy_logger +from litellm._logging import ( + verbose_router_logger, + verbose_proxy_logger, +) from litellm.proxy.auth.handle_jwt import JWTHandler from litellm.proxy.auth.litellm_license import LicenseCheck from litellm.proxy.auth.model_checks import ( @@ -1515,7 +1522,12 @@ async def user_api_key_auth( else: raise Exception() except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.user_api_key_auth(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, litellm.BudgetExceededError): raise ProxyException( message=e.message, type="auth_error", param=None, code=400 @@ -2782,10 +2794,12 @@ class ProxyConfig: load_google_kms(use_google_kms=True) elif ( key_management_system - == KeyManagementSystem.AWS_SECRET_MANAGER.value + == KeyManagementSystem.AWS_SECRET_MANAGER.value # noqa: F405 ): ### LOAD FROM AWS SECRET MANAGER ### load_aws_secret_manager(use_aws_secret_manager=True) + elif key_management_system == KeyManagementSystem.AWS_KMS.value: + load_aws_kms(use_aws_kms=True) else: raise ValueError("Invalid Key Management System selected") key_management_settings = general_settings.get( @@ -2819,6 +2833,7 @@ class ProxyConfig: master_key = general_settings.get( "master_key", litellm.get_secret("LITELLM_MASTER_KEY", None) ) + if master_key and master_key.startswith("os.environ/"): master_key = litellm.get_secret(master_key) if not isinstance(master_key, str): @@ -2909,6 +2924,16 @@ class ProxyConfig: ) health_check_interval = general_settings.get("health_check_interval", 300) + ## check if user has set a premium feature in general_settings + if ( + general_settings.get("enforced_params") is not None + and premium_user is not True + ): + raise ValueError( + "Trying to use `enforced_params`" + + CommonProxyErrors.not_premium_user.value + ) + router_params: dict = { "cache_responses": litellm.cache != None, # cache if user passed in cache values @@ -3522,7 +3547,12 @@ async def generate_key_helper_fn( ) key_data["token_id"] = getattr(create_key_response, "token", None) except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.generate_key_helper_fn(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise e raise HTTPException( @@ -3561,7 +3591,12 @@ async def delete_verification_token(tokens: List, user_id: Optional[str] = None) else: raise Exception("DB not connected. prisma_client is None") except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.delete_verification_token(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) raise e return deleted_tokens @@ -3722,7 +3757,12 @@ async def async_assistants_data_generator( done_message = "[DONE]" yield f"data: {done_message}\n\n" except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.async_assistants_data_generator(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, @@ -3732,9 +3772,6 @@ async def async_assistants_data_generator( f"\033[1;31mAn error occurred: {e}\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`" ) router_model_names = llm_router.model_names if llm_router is not None else [] - if user_debug: - traceback.print_exc() - if isinstance(e, HTTPException): raise e else: @@ -3774,7 +3811,12 @@ async def async_data_generator( done_message = "[DONE]" yield f"data: {done_message}\n\n" except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.async_data_generator(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, @@ -3784,8 +3826,6 @@ async def async_data_generator( f"\033[1;31mAn error occurred: {e}\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`" ) router_model_names = llm_router.model_names if llm_router is not None else [] - if user_debug: - traceback.print_exc() if isinstance(e, HTTPException): raise e @@ -3846,6 +3886,18 @@ def on_backoff(details): verbose_proxy_logger.debug("Backing off... this was attempt # %s", details["tries"]) +def giveup(e): + result = not ( + isinstance(e, ProxyException) + and getattr(e, "message", None) is not None + and isinstance(e.message, str) + and "Max parallel request limit reached" in e.message + ) + if result: + verbose_proxy_logger.info(json.dumps({"event": "giveup", "exception": str(e)})) + return result + + @router.on_event("startup") async def startup_event(): global prisma_client, master_key, use_background_health_checks, llm_router, llm_model_list, general_settings, proxy_budget_rescheduler_min_time, proxy_budget_rescheduler_max_time, litellm_proxy_admin_name, db_writer_client, store_model_in_db @@ -4130,12 +4182,8 @@ def model_list( max_tries=litellm.num_retries or 3, # maximum number of retries max_time=litellm.request_timeout or 60, # maximum total time to retry for on_backoff=on_backoff, # specifying the function to call on backoff - giveup=lambda e: not ( - isinstance(e, ProxyException) - and getattr(e, "message", None) is not None - and isinstance(e.message, str) - and "Max parallel request limit reached" in e.message - ), # the result of the logical expression is on the second position + giveup=giveup, + logger=verbose_proxy_logger, ) async def chat_completion( request: Request, @@ -4144,6 +4192,7 @@ async def chat_completion( user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), ): global general_settings, user_debug, proxy_logging_obj, llm_model_list + data = {} try: body = await request.body() @@ -4434,7 +4483,12 @@ async def chat_completion( return _chat_response except Exception as e: data["litellm_status"] = "fail" # used for alerting - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.chat_completion(): Exception occured - {}".format( + get_error_message_str(e=e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) @@ -4445,8 +4499,6 @@ async def chat_completion( litellm_debug_info, ) router_model_names = llm_router.model_names if llm_router is not None else [] - if user_debug: - traceback.print_exc() if isinstance(e, HTTPException): raise ProxyException( @@ -4678,15 +4730,12 @@ async def completion( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - verbose_proxy_logger.debug("EXCEPTION RAISED IN PROXY MAIN.PY") - litellm_debug_info = getattr(e, "litellm_debug_info", "") - verbose_proxy_logger.debug( - "\033[1;31mAn error occurred: %s %s\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`", - e, - litellm_debug_info, + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.completion(): Exception occured - {}".format( + str(e) + ) ) - traceback.print_exc() - error_traceback = traceback.format_exc() + verbose_proxy_logger.debug(traceback.format_exc()) error_msg = f"{str(e)}" raise ProxyException( message=getattr(e, "message", error_msg), @@ -4896,7 +4945,12 @@ async def embeddings( e, litellm_debug_info, ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.embeddings(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e)), @@ -5075,7 +5129,12 @@ async def image_generation( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.image_generation(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e)), @@ -5253,7 +5312,12 @@ async def audio_speech( ) except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.audio_speech(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) raise e @@ -5442,7 +5506,12 @@ async def audio_transcriptions( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.audio_transcription(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e.detail)), @@ -5451,7 +5520,6 @@ async def audio_transcriptions( code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST), ) else: - error_traceback = traceback.format_exc() error_msg = f"{str(e)}" raise ProxyException( message=getattr(e, "message", error_msg), @@ -5579,7 +5647,12 @@ async def get_assistants( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.get_assistants(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e.detail)), @@ -5588,7 +5661,6 @@ async def get_assistants( code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST), ) else: - error_traceback = traceback.format_exc() error_msg = f"{str(e)}" raise ProxyException( message=getattr(e, "message", error_msg), @@ -5708,7 +5780,12 @@ async def create_threads( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.create_threads(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e.detail)), @@ -5717,7 +5794,6 @@ async def create_threads( code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST), ) else: - error_traceback = traceback.format_exc() error_msg = f"{str(e)}" raise ProxyException( message=getattr(e, "message", error_msg), @@ -5836,7 +5912,12 @@ async def get_thread( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.get_thread(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e.detail)), @@ -5845,7 +5926,6 @@ async def get_thread( code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST), ) else: - error_traceback = traceback.format_exc() error_msg = f"{str(e)}" raise ProxyException( message=getattr(e, "message", error_msg), @@ -5967,7 +6047,12 @@ async def add_messages( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.add_messages(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e.detail)), @@ -5976,7 +6061,6 @@ async def add_messages( code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST), ) else: - error_traceback = traceback.format_exc() error_msg = f"{str(e)}" raise ProxyException( message=getattr(e, "message", error_msg), @@ -6094,7 +6178,12 @@ async def get_messages( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.get_messages(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e.detail)), @@ -6103,7 +6192,6 @@ async def get_messages( code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST), ) else: - error_traceback = traceback.format_exc() error_msg = f"{str(e)}" raise ProxyException( message=getattr(e, "message", error_msg), @@ -6235,7 +6323,12 @@ async def run_thread( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.run_thread(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e.detail)), @@ -6244,7 +6337,6 @@ async def run_thread( code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST), ) else: - error_traceback = traceback.format_exc() error_msg = f"{str(e)}" raise ProxyException( message=getattr(e, "message", error_msg), @@ -6383,7 +6475,12 @@ async def create_batch( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.create_batch(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e.detail)), @@ -6392,7 +6489,6 @@ async def create_batch( code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST), ) else: - error_traceback = traceback.format_exc() error_msg = f"{str(e)}" raise ProxyException( message=getattr(e, "message", error_msg), @@ -6526,7 +6622,12 @@ async def retrieve_batch( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.retrieve_batch(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e.detail)), @@ -6679,7 +6780,12 @@ async def create_file( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.create_file(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e.detail)), @@ -6688,7 +6794,6 @@ async def create_file( code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST), ) else: - error_traceback = traceback.format_exc() error_msg = f"{str(e)}" raise ProxyException( message=getattr(e, "message", error_msg), @@ -6864,7 +6969,12 @@ async def moderations( await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data ) - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.moderations(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "message", str(e)), @@ -6873,7 +6983,6 @@ async def moderations( code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST), ) else: - error_traceback = traceback.format_exc() error_msg = f"{str(e)}" raise ProxyException( message=getattr(e, "message", error_msg), @@ -7184,7 +7293,12 @@ async def generate_key_fn( return GenerateKeyResponse(**response) except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.generate_key_fn(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "detail", f"Authentication Error({str(e)})"), @@ -9639,7 +9753,12 @@ async def user_info( } return response_data except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.user_info(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "detail", f"Authentication Error({str(e)})"), @@ -9734,7 +9853,12 @@ async def user_update(data: UpdateUserRequest): return response # update based on remaining passed in values except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.user_update(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "detail", f"Authentication Error({str(e)})"), @@ -9787,7 +9911,12 @@ async def user_request_model(request: Request): return {"status": "success"} # update based on remaining passed in values except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.user_request_model(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "detail", f"Authentication Error({str(e)})"), @@ -9829,7 +9958,12 @@ async def user_get_requests(): return {"requests": response} # update based on remaining passed in values except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.user_get_requests(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "detail", f"Authentication Error({str(e)})"), @@ -10219,7 +10353,12 @@ async def update_end_user( # update based on remaining passed in values except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.update_end_user(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "detail", f"Internal Server Error({str(e)})"), @@ -10303,7 +10442,12 @@ async def delete_end_user( # update based on remaining passed in values except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.delete_end_user(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "detail", f"Internal Server Error({str(e)})"), @@ -11606,7 +11750,12 @@ async def add_new_model( return model_response except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.add_new_model(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "detail", f"Authentication Error({str(e)})"), @@ -11720,7 +11869,12 @@ async def update_model( return model_response except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.update_model(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "detail", f"Authentication Error({str(e)})"), @@ -13954,7 +14108,12 @@ async def update_config(config_info: ConfigYAML): return {"message": "Config updated successfully"} except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.update_config(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "detail", f"Authentication Error({str(e)})"), @@ -14427,7 +14586,12 @@ async def get_config(): "available_callbacks": all_available_callbacks, } except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.get_config(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "detail", f"Authentication Error({str(e)})"), @@ -14678,7 +14842,12 @@ async def health_services_endpoint( } except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.health_services_endpoint(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) if isinstance(e, HTTPException): raise ProxyException( message=getattr(e, "detail", f"Authentication Error({str(e)})"), @@ -14757,7 +14926,12 @@ async def health_endpoint( "unhealthy_count": len(unhealthy_endpoints), } except Exception as e: - traceback.print_exc() + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.py::health_endpoint(): Exception occured - {}".format( + str(e) + ) + ) + verbose_proxy_logger.debug(traceback.format_exc()) raise e diff --git a/litellm/proxy/secret_managers/aws_secret_manager.py b/litellm/proxy/secret_managers/aws_secret_manager.py index a40b1dffa2..8dd6772cf7 100644 --- a/litellm/proxy/secret_managers/aws_secret_manager.py +++ b/litellm/proxy/secret_managers/aws_secret_manager.py @@ -8,7 +8,8 @@ Requires: * `pip install boto3>=1.28.57` """ -import litellm, os +import litellm +import os from typing import Optional from litellm.proxy._types import KeyManagementSystem @@ -38,3 +39,21 @@ def load_aws_secret_manager(use_aws_secret_manager: Optional[bool]): except Exception as e: raise e + + +def load_aws_kms(use_aws_kms: Optional[bool]): + if use_aws_kms is None or use_aws_kms is False: + return + try: + import boto3 + + validate_environment() + + # Create a Secrets Manager client + kms_client = boto3.client("kms", region_name=os.getenv("AWS_REGION_NAME")) + + litellm.secret_manager_client = kms_client + litellm._key_management_system = KeyManagementSystem.AWS_KMS + + except Exception as e: + raise e diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index 8ae811f112..0b5dcd85ba 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -2889,3 +2889,16 @@ missing_keys_html_form = """ def _to_ns(dt): return int(dt.timestamp() * 1e9) + +def get_error_message_str(e: Exception) -> str: + error_message = "" + if isinstance(e, HTTPException): + if isinstance(e.detail, str): + error_message = e.detail + elif isinstance(e.detail, dict): + error_message = json.dumps(e.detail) + else: + error_message = str(e) + else: + error_message = str(e) + return error_message diff --git a/litellm/py.typed b/litellm/py.typed new file mode 100644 index 0000000000..5686005abc --- /dev/null +++ b/litellm/py.typed @@ -0,0 +1,2 @@ +# Marker file to instruct type checkers to look for inline type annotations in this package. +# See PEP 561 for more information. diff --git a/litellm/router.py b/litellm/router.py index e3fed496f1..0187b441f7 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -220,8 +220,6 @@ class Router: [] ) # names of models under litellm_params. ex. azure/chatgpt-v-2 self.deployment_latency_map = {} - ### SCHEDULER ### - self.scheduler = Scheduler(polling_interval=polling_interval) ### CACHING ### cache_type: Literal["local", "redis"] = "local" # default to an in-memory cache redis_cache = None @@ -259,6 +257,10 @@ class Router: redis_cache=redis_cache, in_memory_cache=InMemoryCache() ) # use a dual cache (Redis+In-Memory) for tracking cooldowns, usage, etc. + ### SCHEDULER ### + self.scheduler = Scheduler( + polling_interval=polling_interval, redis_cache=redis_cache + ) self.default_deployment = None # use this to track the users default deployment, when they want to use model = * self.default_max_parallel_requests = default_max_parallel_requests @@ -2096,8 +2098,8 @@ class Router: except Exception as e: raise e except Exception as e: - verbose_router_logger.debug(f"An exception occurred - {str(e)}") - traceback.print_exc() + verbose_router_logger.error(f"An exception occurred - {str(e)}") + verbose_router_logger.debug(traceback.format_exc()) raise original_exception async def async_function_with_retries(self, *args, **kwargs): @@ -4048,6 +4050,12 @@ class Router: for idx in reversed(invalid_model_indices): _returned_deployments.pop(idx) + ## ORDER FILTERING ## -> if user set 'order' in deployments, return deployments with lowest order (e.g. order=1 > order=2) + if len(_returned_deployments) > 0: + _returned_deployments = litellm.utils._get_order_filtered_deployments( + _returned_deployments + ) + return _returned_deployments def _common_checks_available_deployment( diff --git a/litellm/router_strategy/lowest_cost.py b/litellm/router_strategy/lowest_cost.py index 1670490e16..46cbb2181e 100644 --- a/litellm/router_strategy/lowest_cost.py +++ b/litellm/router_strategy/lowest_cost.py @@ -1,11 +1,9 @@ #### What this does #### # picks based on response time (for streaming, this is time to first token) -from pydantic import BaseModel, Extra, Field, root_validator -import os, requests, random # type: ignore +from pydantic import BaseModel from typing import Optional, Union, List, Dict from datetime import datetime, timedelta -import random - +from litellm import verbose_logger import traceback from litellm.caching import DualCache from litellm.integrations.custom_logger import CustomLogger @@ -119,7 +117,12 @@ class LowestCostLoggingHandler(CustomLogger): if self.test_flag: self.logged_success += 1 except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.proxy.hooks.prompt_injection_detection.py::async_pre_call_hook(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) pass async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): @@ -201,7 +204,12 @@ class LowestCostLoggingHandler(CustomLogger): if self.test_flag: self.logged_success += 1 except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.proxy.hooks.prompt_injection_detection.py::async_pre_call_hook(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) pass async def async_get_available_deployments( diff --git a/litellm/router_strategy/lowest_latency.py b/litellm/router_strategy/lowest_latency.py index 1e4b151ada..5d71847510 100644 --- a/litellm/router_strategy/lowest_latency.py +++ b/litellm/router_strategy/lowest_latency.py @@ -1,16 +1,16 @@ #### What this does #### # picks based on response time (for streaming, this is time to first token) -from pydantic import BaseModel, Extra, Field, root_validator # type: ignore -import dotenv, os, requests, random # type: ignore +from pydantic import BaseModel +import random from typing import Optional, Union, List, Dict from datetime import datetime, timedelta -import random import traceback from litellm.caching import DualCache from litellm.integrations.custom_logger import CustomLogger from litellm import ModelResponse from litellm import token_counter import litellm +from litellm import verbose_logger class LiteLLMBase(BaseModel): @@ -165,7 +165,12 @@ class LowestLatencyLoggingHandler(CustomLogger): if self.test_flag: self.logged_success += 1 except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.proxy.hooks.prompt_injection_detection.py::async_pre_call_hook(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) pass async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): @@ -229,7 +234,12 @@ class LowestLatencyLoggingHandler(CustomLogger): # do nothing if it's not a timeout error return except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.proxy.hooks.prompt_injection_detection.py::async_pre_call_hook(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) pass async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): @@ -352,7 +362,12 @@ class LowestLatencyLoggingHandler(CustomLogger): if self.test_flag: self.logged_success += 1 except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.router_strategy.lowest_latency.py::async_log_success_event(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) pass def get_available_deployments( diff --git a/litellm/router_strategy/lowest_tpm_rpm.py b/litellm/router_strategy/lowest_tpm_rpm.py index 15460051b8..a21c69abf7 100644 --- a/litellm/router_strategy/lowest_tpm_rpm.py +++ b/litellm/router_strategy/lowest_tpm_rpm.py @@ -11,6 +11,7 @@ from litellm.integrations.custom_logger import CustomLogger from litellm._logging import verbose_router_logger from litellm.utils import print_verbose + class LiteLLMBase(BaseModel): """ Implements default functions, all pydantic objects should have. @@ -23,16 +24,20 @@ class LiteLLMBase(BaseModel): # if using pydantic v1 return self.dict() + class RoutingArgs(LiteLLMBase): - ttl: int = 1 * 60 # 1min (RPM/TPM expire key) - + ttl: int = 1 * 60 # 1min (RPM/TPM expire key) + + class LowestTPMLoggingHandler(CustomLogger): test_flag: bool = False logged_success: int = 0 logged_failure: int = 0 default_cache_time_seconds: int = 1 * 60 * 60 # 1 hour - def __init__(self, router_cache: DualCache, model_list: list, routing_args: dict = {}): + def __init__( + self, router_cache: DualCache, model_list: list, routing_args: dict = {} + ): self.router_cache = router_cache self.model_list = model_list self.routing_args = RoutingArgs(**routing_args) @@ -72,19 +77,28 @@ class LowestTPMLoggingHandler(CustomLogger): request_count_dict = self.router_cache.get_cache(key=tpm_key) or {} request_count_dict[id] = request_count_dict.get(id, 0) + total_tokens - self.router_cache.set_cache(key=tpm_key, value=request_count_dict, ttl=self.routing_args.ttl) + self.router_cache.set_cache( + key=tpm_key, value=request_count_dict, ttl=self.routing_args.ttl + ) ## RPM request_count_dict = self.router_cache.get_cache(key=rpm_key) or {} request_count_dict[id] = request_count_dict.get(id, 0) + 1 - self.router_cache.set_cache(key=rpm_key, value=request_count_dict, ttl=self.routing_args.ttl) + self.router_cache.set_cache( + key=rpm_key, value=request_count_dict, ttl=self.routing_args.ttl + ) ### TESTING ### if self.test_flag: self.logged_success += 1 except Exception as e: - traceback.print_exc() + verbose_router_logger.error( + "litellm.router_strategy.lowest_tpm_rpm.py::async_log_success_event(): Exception occured - {}".format( + str(e) + ) + ) + verbose_router_logger.debug(traceback.format_exc()) pass async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): @@ -123,19 +137,28 @@ class LowestTPMLoggingHandler(CustomLogger): request_count_dict = self.router_cache.get_cache(key=tpm_key) or {} request_count_dict[id] = request_count_dict.get(id, 0) + total_tokens - self.router_cache.set_cache(key=tpm_key, value=request_count_dict, ttl=self.routing_args.ttl) + self.router_cache.set_cache( + key=tpm_key, value=request_count_dict, ttl=self.routing_args.ttl + ) ## RPM request_count_dict = self.router_cache.get_cache(key=rpm_key) or {} request_count_dict[id] = request_count_dict.get(id, 0) + 1 - self.router_cache.set_cache(key=rpm_key, value=request_count_dict, ttl=self.routing_args.ttl) + self.router_cache.set_cache( + key=rpm_key, value=request_count_dict, ttl=self.routing_args.ttl + ) ### TESTING ### if self.test_flag: self.logged_success += 1 except Exception as e: - traceback.print_exc() + verbose_router_logger.error( + "litellm.router_strategy.lowest_tpm_rpm.py::async_log_success_event(): Exception occured - {}".format( + str(e) + ) + ) + verbose_router_logger.debug(traceback.format_exc()) pass def get_available_deployments( diff --git a/litellm/router_strategy/lowest_tpm_rpm_v2.py b/litellm/router_strategy/lowest_tpm_rpm_v2.py index 40e75031ad..e3b8c8b770 100644 --- a/litellm/router_strategy/lowest_tpm_rpm_v2.py +++ b/litellm/router_strategy/lowest_tpm_rpm_v2.py @@ -1,19 +1,19 @@ #### What this does #### # identifies lowest tpm deployment from pydantic import BaseModel -import dotenv, os, requests, random +import random from typing import Optional, Union, List, Dict -import datetime as datetime_og -from datetime import datetime -import traceback, asyncio, httpx +import traceback +import httpx import litellm from litellm import token_counter from litellm.caching import DualCache from litellm.integrations.custom_logger import CustomLogger -from litellm._logging import verbose_router_logger +from litellm._logging import verbose_router_logger, verbose_logger from litellm.utils import print_verbose, get_utc_datetime from litellm.types.router import RouterErrors + class LiteLLMBase(BaseModel): """ Implements default functions, all pydantic objects should have. @@ -22,12 +22,14 @@ class LiteLLMBase(BaseModel): def json(self, **kwargs): try: return self.model_dump() # noqa - except: + except Exception as e: # if using pydantic v1 return self.dict() + class RoutingArgs(LiteLLMBase): - ttl: int = 1 * 60 # 1min (RPM/TPM expire key) + ttl: int = 1 * 60 # 1min (RPM/TPM expire key) + class LowestTPMLoggingHandler_v2(CustomLogger): """ @@ -47,7 +49,9 @@ class LowestTPMLoggingHandler_v2(CustomLogger): logged_failure: int = 0 default_cache_time_seconds: int = 1 * 60 * 60 # 1 hour - def __init__(self, router_cache: DualCache, model_list: list, routing_args: dict = {}): + def __init__( + self, router_cache: DualCache, model_list: list, routing_args: dict = {} + ): self.router_cache = router_cache self.model_list = model_list self.routing_args = RoutingArgs(**routing_args) @@ -104,7 +108,9 @@ class LowestTPMLoggingHandler_v2(CustomLogger): ) else: # if local result below limit, check redis ## prevent unnecessary redis checks - result = self.router_cache.increment_cache(key=rpm_key, value=1, ttl=self.routing_args.ttl) + result = self.router_cache.increment_cache( + key=rpm_key, value=1, ttl=self.routing_args.ttl + ) if result is not None and result > deployment_rpm: raise litellm.RateLimitError( message="Deployment over defined rpm limit={}. current usage={}".format( @@ -244,12 +250,19 @@ class LowestTPMLoggingHandler_v2(CustomLogger): # update cache ## TPM - self.router_cache.increment_cache(key=tpm_key, value=total_tokens, ttl=self.routing_args.ttl) + self.router_cache.increment_cache( + key=tpm_key, value=total_tokens, ttl=self.routing_args.ttl + ) ### TESTING ### if self.test_flag: self.logged_success += 1 except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.proxy.hooks.prompt_injection_detection.py::async_pre_call_hook(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) pass async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): @@ -295,7 +308,12 @@ class LowestTPMLoggingHandler_v2(CustomLogger): if self.test_flag: self.logged_success += 1 except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.proxy.hooks.prompt_injection_detection.py::async_pre_call_hook(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) pass def _common_checks_available_deployment( diff --git a/litellm/scheduler.py b/litellm/scheduler.py index aad4ba4e3e..8ee7f0e078 100644 --- a/litellm/scheduler.py +++ b/litellm/scheduler.py @@ -1,13 +1,14 @@ -import heapq, time +import heapq from pydantic import BaseModel from typing import Optional import enum -from litellm.caching import DualCache +from litellm.caching import DualCache, RedisCache from litellm import print_verbose class SchedulerCacheKeys(enum.Enum): queue = "scheduler:queue" + default_in_memory_ttl = 5 # cache queue in-memory for 5s when redis cache available class DefaultPriorities(enum.Enum): @@ -25,18 +26,24 @@ class FlowItem(BaseModel): class Scheduler: cache: DualCache - def __init__(self, polling_interval: Optional[float] = None): + def __init__( + self, + polling_interval: Optional[float] = None, + redis_cache: Optional[RedisCache] = None, + ): """ polling_interval: float or null - frequency of polling queue. Default is 3ms. """ self.queue: list = [] - self.cache = DualCache() + default_in_memory_ttl: Optional[float] = None + if redis_cache is not None: + # if redis-cache available frequently poll that instead of using in-memory. + default_in_memory_ttl = SchedulerCacheKeys.default_in_memory_ttl.value + self.cache = DualCache( + redis_cache=redis_cache, default_in_memory_ttl=default_in_memory_ttl + ) self.polling_interval = polling_interval or 0.03 # default to 3ms - def update_variables(self, cache: Optional[DualCache] = None): - if cache is not None: - self.cache = cache - async def add_request(self, request: FlowItem): # We use the priority directly, as lower values indicate higher priority # get the queue diff --git a/litellm/tests/log.txt b/litellm/tests/log.txt deleted file mode 100644 index ea07ca7e12..0000000000 --- a/litellm/tests/log.txt +++ /dev/null @@ -1,4274 +0,0 @@ -============================= test session starts ============================== -platform darwin -- Python 3.11.4, pytest-8.2.0, pluggy-1.5.0 -rootdir: /Users/krrishdholakia/Documents/litellm -configfile: pyproject.toml -plugins: asyncio-0.23.6, mock-3.14.0, anyio-4.2.0 -asyncio: mode=Mode.STRICT -collected 1 item - -test_amazing_vertex_completion.py F [100%] - -=================================== FAILURES =================================== -____________________________ test_gemini_pro_vision ____________________________ - -model = 'gemini-1.5-flash-preview-0514' -messages = [{'content': [{'text': 'Whats in this image?', 'type': 'text'}, {'image_url': {'url': 'gs://cloud-samples-data/generative-ai/image/boats.jpeg'}, 'type': 'image_url'}], 'role': 'user'}] -model_response = ModelResponse(id='chatcmpl-722df0e7-4e2d-44e6-9e2c-49823faa0189', choices=[Choices(finish_reason='stop', index=0, mess... role='assistant'))], created=1716145725, model=None, object='chat.completion', system_fingerprint=None, usage=Usage()) -print_verbose = -encoding = -logging_obj = -vertex_project = None, vertex_location = None, vertex_credentials = None -optional_params = {} -litellm_params = {'acompletion': False, 'api_base': '', 'api_key': None, 'completion_call_id': None, ...} -logger_fn = None, acompletion = False - - def completion( - model: str, - messages: list, - model_response: ModelResponse, - print_verbose: Callable, - encoding, - logging_obj, - vertex_project=None, - vertex_location=None, - vertex_credentials=None, - optional_params=None, - litellm_params=None, - logger_fn=None, - acompletion: bool = False, - ): - try: - import vertexai - except: - raise VertexAIError( - status_code=400, - message="vertexai import failed please run `pip install google-cloud-aiplatform`", - ) - - if not ( - hasattr(vertexai, "preview") or hasattr(vertexai.preview, "language_models") - ): - raise VertexAIError( - status_code=400, - message="""Upgrade vertex ai. Run `pip install "google-cloud-aiplatform>=1.38"`""", - ) - try: - from vertexai.preview.language_models import ( - ChatModel, - CodeChatModel, - InputOutputTextPair, - ) - from vertexai.language_models import TextGenerationModel, CodeGenerationModel - from vertexai.preview.generative_models import ( - GenerativeModel, - Part, - GenerationConfig, - ) - from google.cloud import aiplatform # type: ignore - from google.protobuf import json_format # type: ignore - from google.protobuf.struct_pb2 import Value # type: ignore - from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types # type: ignore - import google.auth # type: ignore - import proto # type: ignore - - ## Load credentials with the correct quota project ref: https://github.com/googleapis/python-aiplatform/issues/2557#issuecomment-1709284744 - print_verbose( - f"VERTEX AI: vertex_project={vertex_project}; vertex_location={vertex_location}" - ) - if vertex_credentials is not None and isinstance(vertex_credentials, str): - import google.oauth2.service_account - - json_obj = json.loads(vertex_credentials) - - creds = google.oauth2.service_account.Credentials.from_service_account_info( - json_obj, - scopes=["https://www.googleapis.com/auth/cloud-platform"], - ) - else: - creds, _ = google.auth.default(quota_project_id=vertex_project) - print_verbose( - f"VERTEX AI: creds={creds}; google application credentials: {os.getenv('GOOGLE_APPLICATION_CREDENTIALS')}" - ) - vertexai.init( - project=vertex_project, location=vertex_location, credentials=creds - ) - - ## Load Config - config = litellm.VertexAIConfig.get_config() - for k, v in config.items(): - if k not in optional_params: - optional_params[k] = v - - ## Process safety settings into format expected by vertex AI - safety_settings = None - if "safety_settings" in optional_params: - safety_settings = optional_params.pop("safety_settings") - if not isinstance(safety_settings, list): - raise ValueError("safety_settings must be a list") - if len(safety_settings) > 0 and not isinstance(safety_settings[0], dict): - raise ValueError("safety_settings must be a list of dicts") - safety_settings = [ - gapic_content_types.SafetySetting(x) for x in safety_settings - ] - - # vertexai does not use an API key, it looks for credentials.json in the environment - - prompt = " ".join( - [ - message["content"] - for message in messages - if isinstance(message["content"], str) - ] - ) - - mode = "" - - request_str = "" - response_obj = None - async_client = None - instances = None - client_options = { - "api_endpoint": f"{vertex_location}-aiplatform.googleapis.com" - } - if ( - model in litellm.vertex_language_models - or model in litellm.vertex_vision_models - ): - llm_model = GenerativeModel(model) - mode = "vision" - request_str += f"llm_model = GenerativeModel({model})\n" - elif model in litellm.vertex_chat_models: - llm_model = ChatModel.from_pretrained(model) - mode = "chat" - request_str += f"llm_model = ChatModel.from_pretrained({model})\n" - elif model in litellm.vertex_text_models: - llm_model = TextGenerationModel.from_pretrained(model) - mode = "text" - request_str += f"llm_model = TextGenerationModel.from_pretrained({model})\n" - elif model in litellm.vertex_code_text_models: - llm_model = CodeGenerationModel.from_pretrained(model) - mode = "text" - request_str += f"llm_model = CodeGenerationModel.from_pretrained({model})\n" - elif model in litellm.vertex_code_chat_models: # vertex_code_llm_models - llm_model = CodeChatModel.from_pretrained(model) - mode = "chat" - request_str += f"llm_model = CodeChatModel.from_pretrained({model})\n" - elif model == "private": - mode = "private" - model = optional_params.pop("model_id", None) - # private endpoint requires a dict instead of JSON - instances = [optional_params.copy()] - instances[0]["prompt"] = prompt - llm_model = aiplatform.PrivateEndpoint( - endpoint_name=model, - project=vertex_project, - location=vertex_location, - ) - request_str += f"llm_model = aiplatform.PrivateEndpoint(endpoint_name={model}, project={vertex_project}, location={vertex_location})\n" - else: # assume vertex model garden on public endpoint - mode = "custom" - - instances = [optional_params.copy()] - instances[0]["prompt"] = prompt - instances = [ - json_format.ParseDict(instance_dict, Value()) - for instance_dict in instances - ] - # Will determine the API used based on async parameter - llm_model = None - - # NOTE: async prediction and streaming under "private" mode isn't supported by aiplatform right now - if acompletion == True: - data = { - "llm_model": llm_model, - "mode": mode, - "prompt": prompt, - "logging_obj": logging_obj, - "request_str": request_str, - "model": model, - "model_response": model_response, - "encoding": encoding, - "messages": messages, - "print_verbose": print_verbose, - "client_options": client_options, - "instances": instances, - "vertex_location": vertex_location, - "vertex_project": vertex_project, - "safety_settings": safety_settings, - **optional_params, - } - if optional_params.get("stream", False) is True: - # async streaming - return async_streaming(**data) - - return async_completion(**data) - - if mode == "vision": - print_verbose("\nMaking VertexAI Gemini Pro / Pro Vision Call") - print_verbose(f"\nProcessing input messages = {messages}") - tools = optional_params.pop("tools", None) - content = _gemini_convert_messages_text(messages=messages) - stream = optional_params.pop("stream", False) - if stream == True: - request_str += f"response = llm_model.generate_content({content}, generation_config=GenerationConfig(**{optional_params}), safety_settings={safety_settings}, stream={stream})\n" - logging_obj.pre_call( - input=prompt, - api_key=None, - additional_args={ - "complete_input_dict": optional_params, - "request_str": request_str, - }, - ) - - model_response = llm_model.generate_content( - contents={"content": content}, - generation_config=optional_params, - safety_settings=safety_settings, - stream=True, - tools=tools, - ) - - return model_response - - request_str += f"response = llm_model.generate_content({content})\n" - ## LOGGING - logging_obj.pre_call( - input=prompt, - api_key=None, - additional_args={ - "complete_input_dict": optional_params, - "request_str": request_str, - }, - ) - - ## LLM Call -> response = llm_model.generate_content( - contents=content, - generation_config=optional_params, - safety_settings=safety_settings, - tools=tools, - ) - -../llms/vertex_ai.py:740: -_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ -../proxy/myenv/lib/python3.11/site-packages/vertexai/generative_models/_generative_models.py:405: in generate_content - return self._generate_content( -../proxy/myenv/lib/python3.11/site-packages/vertexai/generative_models/_generative_models.py:487: in _generate_content - request = self._prepare_request( -../proxy/myenv/lib/python3.11/site-packages/vertexai/generative_models/_generative_models.py:274: in _prepare_request - contents = [ -../proxy/myenv/lib/python3.11/site-packages/vertexai/generative_models/_generative_models.py:275: in - gapic_content_types.Content(content_dict) for content_dict in contents -_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ - -self = <[AttributeError('Unknown field for Content: _pb') raised in repr()] Content object at 0x1646aaa90> -mapping = {'parts': [{'text': 'Whats in this image?'}, file_data { - mime_type: "image/jpeg" - file_uri: "gs://cloud-samples-data/generative-ai/image/boats.jpeg" -} -], 'role': 'user'} -ignore_unknown_fields = False, kwargs = {} -params = {'parts': [text: "Whats in this image?" -, file_data { - mime_type: "image/jpeg" - file_uri: "gs://cloud-samples-data/generative-ai/image/boats.jpeg" -} -], 'role': 'user'} -marshal = , key = 'parts' -value = [{'text': 'Whats in this image?'}, file_data { - mime_type: "image/jpeg" - file_uri: "gs://cloud-samples-data/generative-ai/image/boats.jpeg" -} -] -pb_value = [text: "Whats in this image?" -, file_data { - mime_type: "image/jpeg" - file_uri: "gs://cloud-samples-data/generative-ai/image/boats.jpeg" -} -] - - def __init__( - self, - mapping=None, - *, - ignore_unknown_fields=False, - **kwargs, - ): - # We accept several things for `mapping`: - # * An instance of this class. - # * An instance of the underlying protobuf descriptor class. - # * A dict - # * Nothing (keyword arguments only). - if mapping is None: - if not kwargs: - # Special fast path for empty construction. - super().__setattr__("_pb", self._meta.pb()) - return - - mapping = kwargs - elif isinstance(mapping, self._meta.pb): - # Make a copy of the mapping. - # This is a constructor for a new object, so users will assume - # that it will not have side effects on the arguments being - # passed in. - # - # The `wrap` method on the metaclass is the public API for taking - # ownership of the passed in protobuf object. - mapping = copy.deepcopy(mapping) - if kwargs: - mapping.MergeFrom(self._meta.pb(**kwargs)) - - super().__setattr__("_pb", mapping) - return - elif isinstance(mapping, type(self)): - # Just use the above logic on mapping's underlying pb. - self.__init__(mapping=mapping._pb, **kwargs) - return - elif isinstance(mapping, collections.abc.Mapping): - # Can't have side effects on mapping. - mapping = copy.copy(mapping) - # kwargs entries take priority for duplicate keys. - mapping.update(kwargs) - else: - # Sanity check: Did we get something not a map? Error if so. - raise TypeError( - "Invalid constructor input for %s: %r" - % ( - self.__class__.__name__, - mapping, - ) - ) - - params = {} - # Update the mapping to address any values that need to be - # coerced. - marshal = self._meta.marshal - for key, value in mapping.items(): - (key, pb_type) = self._get_pb_type_from_key(key) - if pb_type is None: - if ignore_unknown_fields: - continue - - raise ValueError( - "Unknown field for {}: {}".format(self.__class__.__name__, key) - ) - - try: - pb_value = marshal.to_proto(pb_type, value) - except ValueError: - # Underscores may be appended to field names - # that collide with python or proto-plus keywords. - # In case a key only exists with a `_` suffix, coerce the key - # to include the `_` suffix. It's not possible to - # natively define the same field with a trailing underscore in protobuf. - # See related issue - # https://github.com/googleapis/python-api-core/issues/227 - if isinstance(value, dict): - if _upb: - # In UPB, pb_type is MessageMeta which doesn't expose attrs like it used to in Python/CPP. - keys_to_update = [ - item - for item in value - if item not in pb_type.DESCRIPTOR.fields_by_name - and f"{item}_" in pb_type.DESCRIPTOR.fields_by_name - ] - else: - keys_to_update = [ - item - for item in value - if not hasattr(pb_type, item) - and hasattr(pb_type, f"{item}_") - ] - for item in keys_to_update: - value[f"{item}_"] = value.pop(item) - - pb_value = marshal.to_proto(pb_type, value) - - if pb_value is not None: - params[key] = pb_value - - # Create the internal protocol buffer. -> super().__setattr__("_pb", self._meta.pb(**params)) -E TypeError: Parameter to MergeFrom() must be instance of same class: expected got . - -../proxy/myenv/lib/python3.11/site-packages/proto/message.py:615: TypeError - -During handling of the above exception, another exception occurred: - -model = 'gemini-1.5-flash-preview-0514' -messages = [{'content': [{'text': 'Whats in this image?', 'type': 'text'}, {'image_url': {'url': 'gs://cloud-samples-data/generative-ai/image/boats.jpeg'}, 'type': 'image_url'}], 'role': 'user'}] -timeout = 600.0, temperature = None, top_p = None, n = None, stream = None -stream_options = None, stop = None, max_tokens = None, presence_penalty = None -frequency_penalty = None, logit_bias = None, user = None, response_format = None -seed = None, tools = None, tool_choice = None, logprobs = None -top_logprobs = None, deployment_id = None, extra_headers = None -functions = None, function_call = None, base_url = None, api_version = None -api_key = None, model_list = None -kwargs = {'litellm_call_id': '7f48b7ab-47b3-4beb-b2b5-fa298be49d3f', 'litellm_logging_obj': } -args = {'acompletion': False, 'api_base': None, 'api_key': None, 'api_version': None, ...} -api_base = None, mock_response = None, force_timeout = 600, logger_fn = None -verbose = False, custom_llm_provider = 'vertex_ai' - - @client - def completion( - model: str, - # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create - messages: List = [], - timeout: Optional[Union[float, str, httpx.Timeout]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - n: Optional[int] = None, - stream: Optional[bool] = None, - stream_options: Optional[dict] = None, - stop=None, - max_tokens: Optional[int] = None, - presence_penalty: Optional[float] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[dict] = None, - user: Optional[str] = None, - # openai v1.0+ new params - response_format: Optional[dict] = None, - seed: Optional[int] = None, - tools: Optional[List] = None, - tool_choice: Optional[str] = None, - logprobs: Optional[bool] = None, - top_logprobs: Optional[int] = None, - deployment_id=None, - extra_headers: Optional[dict] = None, - # soon to be deprecated params by OpenAI - functions: Optional[List] = None, - function_call: Optional[str] = None, - # set api_base, api_version, api_key - base_url: Optional[str] = None, - api_version: Optional[str] = None, - api_key: Optional[str] = None, - model_list: Optional[list] = None, # pass in a list of api_base,keys, etc. - # Optional liteLLM function params - **kwargs, - ) -> Union[ModelResponse, CustomStreamWrapper]: - """ - Perform a completion() using any of litellm supported llms (example gpt-4, gpt-3.5-turbo, claude-2, command-nightly) - Parameters: - model (str): The name of the language model to use for text completion. see all supported LLMs: https://docs.litellm.ai/docs/providers/ - messages (List): A list of message objects representing the conversation context (default is an empty list). - - OPTIONAL PARAMS - functions (List, optional): A list of functions to apply to the conversation messages (default is an empty list). - function_call (str, optional): The name of the function to call within the conversation (default is an empty string). - temperature (float, optional): The temperature parameter for controlling the randomness of the output (default is 1.0). - top_p (float, optional): The top-p parameter for nucleus sampling (default is 1.0). - n (int, optional): The number of completions to generate (default is 1). - stream (bool, optional): If True, return a streaming response (default is False). - stream_options (dict, optional): A dictionary containing options for the streaming response. Only set this when you set stream: true. - stop(string/list, optional): - Up to 4 sequences where the LLM API will stop generating further tokens. - max_tokens (integer, optional): The maximum number of tokens in the generated completion (default is infinity). - presence_penalty (float, optional): It is used to penalize new tokens based on their existence in the text so far. - frequency_penalty: It is used to penalize new tokens based on their frequency in the text so far. - logit_bias (dict, optional): Used to modify the probability of specific tokens appearing in the completion. - user (str, optional): A unique identifier representing your end-user. This can help the LLM provider to monitor and detect abuse. - logprobs (bool, optional): Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message - top_logprobs (int, optional): An integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used. - metadata (dict, optional): Pass in additional metadata to tag your completion calls - eg. prompt version, details, etc. - api_base (str, optional): Base URL for the API (default is None). - api_version (str, optional): API version (default is None). - api_key (str, optional): API key (default is None). - model_list (list, optional): List of api base, version, keys - extra_headers (dict, optional): Additional headers to include in the request. - - LITELLM Specific Params - mock_response (str, optional): If provided, return a mock completion response for testing or debugging purposes (default is None). - custom_llm_provider (str, optional): Used for Non-OpenAI LLMs, Example usage for bedrock, set model="amazon.titan-tg1-large" and custom_llm_provider="bedrock" - max_retries (int, optional): The number of retries to attempt (default is 0). - Returns: - ModelResponse: A response object containing the generated completion and associated metadata. - - Note: - - This function is used to perform completions() using the specified language model. - - It supports various optional parameters for customizing the completion behavior. - - If 'mock_response' is provided, a mock completion response is returned for testing or debugging. - """ - ######### unpacking kwargs ##################### - args = locals() - api_base = kwargs.get("api_base", None) - mock_response = kwargs.get("mock_response", None) - force_timeout = kwargs.get("force_timeout", 600) ## deprecated - logger_fn = kwargs.get("logger_fn", None) - verbose = kwargs.get("verbose", False) - custom_llm_provider = kwargs.get("custom_llm_provider", None) - litellm_logging_obj = kwargs.get("litellm_logging_obj", None) - id = kwargs.get("id", None) - metadata = kwargs.get("metadata", None) - model_info = kwargs.get("model_info", None) - proxy_server_request = kwargs.get("proxy_server_request", None) - fallbacks = kwargs.get("fallbacks", None) - headers = kwargs.get("headers", None) or extra_headers - num_retries = kwargs.get("num_retries", None) ## deprecated - max_retries = kwargs.get("max_retries", None) - context_window_fallback_dict = kwargs.get("context_window_fallback_dict", None) - organization = kwargs.get("organization", None) - ### CUSTOM MODEL COST ### - input_cost_per_token = kwargs.get("input_cost_per_token", None) - output_cost_per_token = kwargs.get("output_cost_per_token", None) - input_cost_per_second = kwargs.get("input_cost_per_second", None) - output_cost_per_second = kwargs.get("output_cost_per_second", None) - ### CUSTOM PROMPT TEMPLATE ### - initial_prompt_value = kwargs.get("initial_prompt_value", None) - roles = kwargs.get("roles", None) - final_prompt_value = kwargs.get("final_prompt_value", None) - bos_token = kwargs.get("bos_token", None) - 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) - ### ASYNC CALLS ### - acompletion = kwargs.get("acompletion", False) - client = kwargs.get("client", None) - ### Admin Controls ### - no_log = kwargs.get("no-log", False) - ######## end of unpacking kwargs ########### - openai_params = [ - "functions", - "function_call", - "temperature", - "temperature", - "top_p", - "n", - "stream", - "stream_options", - "stop", - "max_tokens", - "presence_penalty", - "frequency_penalty", - "logit_bias", - "user", - "request_timeout", - "api_base", - "api_version", - "api_key", - "deployment_id", - "organization", - "base_url", - "default_headers", - "timeout", - "response_format", - "seed", - "tools", - "tool_choice", - "max_retries", - "logprobs", - "top_logprobs", - "extra_headers", - ] - litellm_params = [ - "metadata", - "acompletion", - "atext_completion", - "text_completion", - "caching", - "mock_response", - "api_key", - "api_version", - "api_base", - "force_timeout", - "logger_fn", - "verbose", - "custom_llm_provider", - "litellm_logging_obj", - "litellm_call_id", - "use_client", - "id", - "fallbacks", - "azure", - "headers", - "model_list", - "num_retries", - "context_window_fallback_dict", - "retry_policy", - "roles", - "final_prompt_value", - "bos_token", - "eos_token", - "request_timeout", - "complete_response", - "self", - "client", - "rpm", - "tpm", - "max_parallel_requests", - "input_cost_per_token", - "output_cost_per_token", - "input_cost_per_second", - "output_cost_per_second", - "hf_model_name", - "model_info", - "proxy_server_request", - "preset_cache_key", - "caching_groups", - "ttl", - "cache", - "no-log", - "base_model", - "stream_timeout", - "supports_system_message", - "region_name", - "allowed_model_region", - "model_config", - ] - - 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 - - try: - if base_url is not None: - api_base = base_url - if max_retries is not None: # openai allows openai.OpenAI(max_retries=3) - num_retries = max_retries - logging = litellm_logging_obj - fallbacks = fallbacks or litellm.model_fallbacks - if fallbacks is not None: - return completion_with_fallbacks(**args) - if model_list is not None: - deployments = [ - m["litellm_params"] for m in model_list if m["model_name"] == model - ] - return batch_completion_models(deployments=deployments, **args) - if litellm.model_alias_map and model in litellm.model_alias_map: - model = litellm.model_alias_map[ - model - ] # update the model to the actual value if an alias has been passed in - model_response = ModelResponse() - setattr(model_response, "usage", litellm.Usage()) - if ( - kwargs.get("azure", False) == True - ): # don't remove flag check, to remain backwards compatible for repos like Codium - custom_llm_provider = "azure" - if deployment_id != None: # azure llms - model = deployment_id - custom_llm_provider = "azure" - model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider( - model=model, - custom_llm_provider=custom_llm_provider, - api_base=api_base, - api_key=api_key, - ) - if model_response is not None and hasattr(model_response, "_hidden_params"): - model_response._hidden_params["custom_llm_provider"] = custom_llm_provider - model_response._hidden_params["region_name"] = kwargs.get( - "aws_region_name", None - ) # support region-based pricing for bedrock - - ### TIMEOUT LOGIC ### - timeout = timeout or kwargs.get("request_timeout", 600) or 600 - # set timeout for 10 minutes by default - if isinstance(timeout, httpx.Timeout) and not supports_httpx_timeout( - custom_llm_provider - ): - timeout = timeout.read or 600 # default 10 min timeout - elif not isinstance(timeout, httpx.Timeout): - timeout = float(timeout) # type: ignore - - ### REGISTER CUSTOM MODEL PRICING -- IF GIVEN ### - if input_cost_per_token is not None and output_cost_per_token is not None: - litellm.register_model( - { - f"{custom_llm_provider}/{model}": { - "input_cost_per_token": input_cost_per_token, - "output_cost_per_token": output_cost_per_token, - "litellm_provider": custom_llm_provider, - }, - model: { - "input_cost_per_token": input_cost_per_token, - "output_cost_per_token": output_cost_per_token, - "litellm_provider": custom_llm_provider, - }, - } - ) - elif ( - input_cost_per_second is not None - ): # time based pricing just needs cost in place - output_cost_per_second = output_cost_per_second - litellm.register_model( - { - f"{custom_llm_provider}/{model}": { - "input_cost_per_second": input_cost_per_second, - "output_cost_per_second": output_cost_per_second, - "litellm_provider": custom_llm_provider, - }, - model: { - "input_cost_per_second": input_cost_per_second, - "output_cost_per_second": output_cost_per_second, - "litellm_provider": custom_llm_provider, - }, - } - ) - ### BUILD CUSTOM PROMPT TEMPLATE -- IF GIVEN ### - custom_prompt_dict = {} # type: ignore - if ( - initial_prompt_value - or roles - or final_prompt_value - or bos_token - or eos_token - ): - custom_prompt_dict = {model: {}} - if initial_prompt_value: - custom_prompt_dict[model]["initial_prompt_value"] = initial_prompt_value - if roles: - custom_prompt_dict[model]["roles"] = roles - if final_prompt_value: - custom_prompt_dict[model]["final_prompt_value"] = final_prompt_value - if bos_token: - 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 - - if dynamic_api_key is not None: - api_key = dynamic_api_key - # check if user passed in any of the OpenAI optional params - optional_params = get_optional_params( - functions=functions, - function_call=function_call, - temperature=temperature, - top_p=top_p, - n=n, - stream=stream, - stream_options=stream_options, - stop=stop, - max_tokens=max_tokens, - presence_penalty=presence_penalty, - frequency_penalty=frequency_penalty, - logit_bias=logit_bias, - user=user, - # params to identify the model - model=model, - custom_llm_provider=custom_llm_provider, - response_format=response_format, - seed=seed, - tools=tools, - tool_choice=tool_choice, - max_retries=max_retries, - logprobs=logprobs, - top_logprobs=top_logprobs, - extra_headers=extra_headers, - **non_default_params, - ) - - if litellm.add_function_to_prompt and optional_params.get( - "functions_unsupported_model", None - ): # if user opts to add it to prompt, when API doesn't support function calling - functions_unsupported_model = optional_params.pop( - "functions_unsupported_model" - ) - messages = function_call_prompt( - messages=messages, functions=functions_unsupported_model - ) - - # For logging - save the values of the litellm-specific params passed in - litellm_params = get_litellm_params( - acompletion=acompletion, - api_key=api_key, - force_timeout=force_timeout, - logger_fn=logger_fn, - verbose=verbose, - custom_llm_provider=custom_llm_provider, - api_base=api_base, - litellm_call_id=kwargs.get("litellm_call_id", None), - model_alias_map=litellm.model_alias_map, - completion_call_id=id, - metadata=metadata, - model_info=model_info, - proxy_server_request=proxy_server_request, - preset_cache_key=preset_cache_key, - no_log=no_log, - input_cost_per_second=input_cost_per_second, - input_cost_per_token=input_cost_per_token, - output_cost_per_second=output_cost_per_second, - output_cost_per_token=output_cost_per_token, - ) - logging.update_environment_variables( - model=model, - user=user, - optional_params=optional_params, - litellm_params=litellm_params, - ) - if mock_response: - return mock_completion( - model, - messages, - stream=stream, - mock_response=mock_response, - logging=logging, - acompletion=acompletion, - ) - if custom_llm_provider == "azure": - # azure configs - api_type = get_secret("AZURE_API_TYPE") or "azure" - - api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE") - - api_version = ( - api_version or litellm.api_version or get_secret("AZURE_API_VERSION") - ) - - api_key = ( - api_key - or litellm.api_key - or litellm.azure_key - or get_secret("AZURE_OPENAI_API_KEY") - or get_secret("AZURE_API_KEY") - ) - - azure_ad_token = optional_params.get("extra_body", {}).pop( - "azure_ad_token", None - ) or get_secret("AZURE_AD_TOKEN") - - headers = headers or litellm.headers - - ## LOAD CONFIG - if set - config = litellm.AzureOpenAIConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - - ## COMPLETION CALL - response = azure_chat_completions.completion( - model=model, - messages=messages, - headers=headers, - api_key=api_key, - api_base=api_base, - api_version=api_version, - api_type=api_type, - azure_ad_token=azure_ad_token, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - logging_obj=logging, - acompletion=acompletion, - timeout=timeout, # type: ignore - client=client, # pass AsyncAzureOpenAI, AzureOpenAI client - ) - - if optional_params.get("stream", False) or acompletion == True: - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - additional_args={ - "headers": headers, - "api_version": api_version, - "api_base": api_base, - }, - ) - elif custom_llm_provider == "azure_text": - # azure configs - api_type = get_secret("AZURE_API_TYPE") or "azure" - - api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE") - - api_version = ( - api_version or litellm.api_version or get_secret("AZURE_API_VERSION") - ) - - api_key = ( - api_key - or litellm.api_key - or litellm.azure_key - or get_secret("AZURE_OPENAI_API_KEY") - or get_secret("AZURE_API_KEY") - ) - - azure_ad_token = optional_params.get("extra_body", {}).pop( - "azure_ad_token", None - ) or get_secret("AZURE_AD_TOKEN") - - headers = headers or litellm.headers - - ## LOAD CONFIG - if set - config = litellm.AzureOpenAIConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - - ## COMPLETION CALL - response = azure_text_completions.completion( - model=model, - messages=messages, - headers=headers, - api_key=api_key, - api_base=api_base, - api_version=api_version, - api_type=api_type, - azure_ad_token=azure_ad_token, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - logging_obj=logging, - acompletion=acompletion, - timeout=timeout, - client=client, # pass AsyncAzureOpenAI, AzureOpenAI client - ) - - if optional_params.get("stream", False) or acompletion == True: - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - additional_args={ - "headers": headers, - "api_version": api_version, - "api_base": api_base, - }, - ) - elif ( - model in litellm.open_ai_chat_completion_models - or custom_llm_provider == "custom_openai" - or custom_llm_provider == "deepinfra" - or custom_llm_provider == "perplexity" - or custom_llm_provider == "groq" - or custom_llm_provider == "deepseek" - or custom_llm_provider == "anyscale" - or custom_llm_provider == "mistral" - or custom_llm_provider == "openai" - or custom_llm_provider == "together_ai" - or custom_llm_provider in litellm.openai_compatible_providers - or "ft:gpt-3.5-turbo" in model # finetune gpt-3.5-turbo - ): # allow user to make an openai call with a custom base - # note: if a user sets a custom base - we should ensure this works - # allow for the setting of dynamic and stateful api-bases - api_base = ( - api_base # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there - or litellm.api_base - or get_secret("OPENAI_API_BASE") - or "https://api.openai.com/v1" - ) - openai.organization = ( - organization - or litellm.organization - or get_secret("OPENAI_ORGANIZATION") - or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 - ) - # set API KEY - api_key = ( - api_key - or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there - or litellm.openai_key - or get_secret("OPENAI_API_KEY") - ) - - headers = headers or litellm.headers - - ## LOAD CONFIG - if set - config = litellm.OpenAIConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - - ## COMPLETION CALL - try: - response = openai_chat_completions.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - print_verbose=print_verbose, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - timeout=timeout, # type: ignore - custom_prompt_dict=custom_prompt_dict, - client=client, # pass AsyncOpenAI, OpenAI client - organization=organization, - custom_llm_provider=custom_llm_provider, - ) - except Exception as e: - ## LOGGING - log the original exception returned - logging.post_call( - input=messages, - api_key=api_key, - original_response=str(e), - additional_args={"headers": headers}, - ) - raise e - - if optional_params.get("stream", False): - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - additional_args={"headers": headers}, - ) - elif ( - custom_llm_provider == "text-completion-openai" - or "ft:babbage-002" in model - or "ft:davinci-002" in model # support for finetuned completion models - ): - openai.api_type = "openai" - - api_base = ( - api_base - or litellm.api_base - or get_secret("OPENAI_API_BASE") - or "https://api.openai.com/v1" - ) - - openai.api_version = None - # set API KEY - - api_key = ( - api_key - or litellm.api_key - or litellm.openai_key - or get_secret("OPENAI_API_KEY") - ) - - headers = headers or litellm.headers - - ## LOAD CONFIG - if set - config = litellm.OpenAITextCompletionConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > openai_text_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - if litellm.organization: - openai.organization = litellm.organization - - if ( - len(messages) > 0 - and "content" in messages[0] - and type(messages[0]["content"]) == list - ): - # text-davinci-003 can accept a string or array, if it's an array, assume the array is set in messages[0]['content'] - # https://platform.openai.com/docs/api-reference/completions/create - prompt = messages[0]["content"] - else: - prompt = " ".join([message["content"] for message in messages]) # type: ignore - - ## COMPLETION CALL - _response = openai_text_completions.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - client=client, # pass AsyncOpenAI, OpenAI client - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - timeout=timeout, # type: ignore - ) - - if ( - optional_params.get("stream", False) == False - and acompletion == False - and text_completion == False - ): - # convert to chat completion response - _response = litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object( - response_object=_response, model_response_object=model_response - ) - - if optional_params.get("stream", False) or acompletion == True: - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=_response, - additional_args={"headers": headers}, - ) - response = _response - elif ( - "replicate" in model - or custom_llm_provider == "replicate" - or model in litellm.replicate_models - ): - # Setting the relevant API KEY for replicate, replicate defaults to using os.environ.get("REPLICATE_API_TOKEN") - replicate_key = None - replicate_key = ( - api_key - or litellm.replicate_key - or litellm.api_key - or get_secret("REPLICATE_API_KEY") - or get_secret("REPLICATE_API_TOKEN") - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("REPLICATE_API_BASE") - or "https://api.replicate.com/v1" - ) - - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - - model_response = replicate.completion( # type: ignore - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, # for calculating input/output tokens - api_key=replicate_key, - logging_obj=logging, - custom_prompt_dict=custom_prompt_dict, - acompletion=acompletion, - ) - - if optional_params.get("stream", False) == True: - ## LOGGING - logging.post_call( - input=messages, - api_key=replicate_key, - original_response=model_response, - ) - - response = model_response - elif ( - "clarifai" in model - or custom_llm_provider == "clarifai" - or model in litellm.clarifai_models - ): - clarifai_key = None - clarifai_key = ( - api_key - or litellm.clarifai_key - or litellm.api_key - or get_secret("CLARIFAI_API_KEY") - or get_secret("CLARIFAI_API_TOKEN") - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("CLARIFAI_API_BASE") - or "https://api.clarifai.com/v2" - ) - - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - model_response = clarifai.completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - acompletion=acompletion, - logger_fn=logger_fn, - encoding=encoding, # for calculating input/output tokens - api_key=clarifai_key, - logging_obj=logging, - custom_prompt_dict=custom_prompt_dict, - ) - - if "stream" in optional_params and optional_params["stream"] == True: - # don't try to access stream object, - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=model_response, - ) - - if optional_params.get("stream", False) or acompletion == True: - ## LOGGING - logging.post_call( - input=messages, - api_key=clarifai_key, - original_response=model_response, - ) - response = model_response - - elif custom_llm_provider == "anthropic": - api_key = ( - api_key - or litellm.anthropic_key - or litellm.api_key - or os.environ.get("ANTHROPIC_API_KEY") - ) - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - - if (model == "claude-2") or (model == "claude-instant-1"): - # call anthropic /completion, only use this route for claude-2, claude-instant-1 - api_base = ( - api_base - or litellm.api_base - or get_secret("ANTHROPIC_API_BASE") - or "https://api.anthropic.com/v1/complete" - ) - response = anthropic_text_completions.completion( - model=model, - messages=messages, - api_base=api_base, - acompletion=acompletion, - custom_prompt_dict=litellm.custom_prompt_dict, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, # for calculating input/output tokens - api_key=api_key, - logging_obj=logging, - headers=headers, - ) - else: - # call /messages - # default route for all anthropic models - api_base = ( - api_base - or litellm.api_base - or get_secret("ANTHROPIC_API_BASE") - or "https://api.anthropic.com/v1/messages" - ) - response = anthropic_chat_completions.completion( - model=model, - messages=messages, - api_base=api_base, - acompletion=acompletion, - custom_prompt_dict=litellm.custom_prompt_dict, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, # for calculating input/output tokens - api_key=api_key, - logging_obj=logging, - headers=headers, - ) - if optional_params.get("stream", False) or acompletion == True: - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - ) - response = response - elif custom_llm_provider == "nlp_cloud": - nlp_cloud_key = ( - api_key - or litellm.nlp_cloud_key - or get_secret("NLP_CLOUD_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("NLP_CLOUD_API_BASE") - or "https://api.nlpcloud.io/v1/gpu/" - ) - - response = nlp_cloud.completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - api_key=nlp_cloud_key, - logging_obj=logging, - ) - - if "stream" in optional_params and optional_params["stream"] == True: - # don't try to access stream object, - response = CustomStreamWrapper( - response, - model, - custom_llm_provider="nlp_cloud", - logging_obj=logging, - ) - - if optional_params.get("stream", False) or acompletion == True: - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - ) - - response = response - elif custom_llm_provider == "aleph_alpha": - aleph_alpha_key = ( - api_key - or litellm.aleph_alpha_key - or get_secret("ALEPH_ALPHA_API_KEY") - or get_secret("ALEPHALPHA_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("ALEPH_ALPHA_API_BASE") - or "https://api.aleph-alpha.com/complete" - ) - - model_response = aleph_alpha.completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - default_max_tokens_to_sample=litellm.max_tokens, - api_key=aleph_alpha_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - ) - - if "stream" in optional_params and optional_params["stream"] == True: - # don't try to access stream object, - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="aleph_alpha", - logging_obj=logging, - ) - return response - response = model_response - elif custom_llm_provider == "cohere": - cohere_key = ( - api_key - or litellm.cohere_key - or get_secret("COHERE_API_KEY") - or get_secret("CO_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("COHERE_API_BASE") - or "https://api.cohere.ai/v1/generate" - ) - - model_response = cohere.completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - api_key=cohere_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - ) - - if "stream" in optional_params and optional_params["stream"] == True: - # don't try to access stream object, - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="cohere", - logging_obj=logging, - ) - return response - response = model_response - elif custom_llm_provider == "cohere_chat": - cohere_key = ( - api_key - or litellm.cohere_key - or get_secret("COHERE_API_KEY") - or get_secret("CO_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("COHERE_API_BASE") - or "https://api.cohere.ai/v1/chat" - ) - - model_response = cohere_chat.completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - api_key=cohere_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - ) - - if "stream" in optional_params and optional_params["stream"] == True: - # don't try to access stream object, - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="cohere_chat", - logging_obj=logging, - ) - return response - response = model_response - elif custom_llm_provider == "maritalk": - maritalk_key = ( - api_key - or litellm.maritalk_key - or get_secret("MARITALK_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("MARITALK_API_BASE") - or "https://chat.maritaca.ai/api/chat/inference" - ) - - model_response = maritalk.completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - api_key=maritalk_key, - logging_obj=logging, - ) - - if "stream" in optional_params and optional_params["stream"] == True: - # don't try to access stream object, - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="maritalk", - logging_obj=logging, - ) - return response - response = model_response - elif custom_llm_provider == "huggingface": - custom_llm_provider = "huggingface" - huggingface_key = ( - api_key - or litellm.huggingface_key - or os.environ.get("HF_TOKEN") - or os.environ.get("HUGGINGFACE_API_KEY") - or litellm.api_key - ) - hf_headers = headers or litellm.headers - - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - model_response = huggingface.completion( - model=model, - messages=messages, - api_base=api_base, # type: ignore - headers=hf_headers, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - api_key=huggingface_key, - acompletion=acompletion, - logging_obj=logging, - custom_prompt_dict=custom_prompt_dict, - timeout=timeout, # type: ignore - ) - if ( - "stream" in optional_params - and optional_params["stream"] == True - and acompletion is False - ): - # don't try to access stream object, - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="huggingface", - logging_obj=logging, - ) - return response - response = model_response - elif custom_llm_provider == "oobabooga": - custom_llm_provider = "oobabooga" - model_response = oobabooga.completion( - model=model, - messages=messages, - model_response=model_response, - api_base=api_base, # type: ignore - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - api_key=None, - logger_fn=logger_fn, - encoding=encoding, - logging_obj=logging, - ) - if "stream" in optional_params and optional_params["stream"] == True: - # don't try to access stream object, - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="oobabooga", - logging_obj=logging, - ) - return response - response = model_response - elif custom_llm_provider == "openrouter": - api_base = api_base or litellm.api_base or "https://openrouter.ai/api/v1" - - api_key = ( - api_key - or litellm.api_key - or litellm.openrouter_key - or get_secret("OPENROUTER_API_KEY") - or get_secret("OR_API_KEY") - ) - - openrouter_site_url = get_secret("OR_SITE_URL") or "https://litellm.ai" - - openrouter_app_name = get_secret("OR_APP_NAME") or "liteLLM" - - headers = ( - headers - or litellm.headers - or { - "HTTP-Referer": openrouter_site_url, - "X-Title": openrouter_app_name, - } - ) - - ## Load Config - config = openrouter.OpenrouterConfig.get_config() - for k, v in config.items(): - if k == "extra_body": - # we use openai 'extra_body' to pass openrouter specific params - transforms, route, models - if "extra_body" in optional_params: - optional_params[k].update(v) - else: - optional_params[k] = v - elif k not in optional_params: - optional_params[k] = v - - data = {"model": model, "messages": messages, **optional_params} - - ## COMPLETION CALL - response = openai_chat_completions.completion( - model=model, - messages=messages, - headers=headers, - api_key=api_key, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - logging_obj=logging, - acompletion=acompletion, - timeout=timeout, # type: ignore - ) - ## LOGGING - logging.post_call( - input=messages, api_key=openai.api_key, original_response=response - ) - elif ( - custom_llm_provider == "together_ai" - or ("togethercomputer" in model) - or (model in litellm.together_ai_models) - ): - """ - Deprecated. We now do together ai calls via the openai client - https://docs.together.ai/docs/openai-api-compatibility - """ - custom_llm_provider = "together_ai" - together_ai_key = ( - api_key - or litellm.togetherai_api_key - or get_secret("TOGETHER_AI_TOKEN") - or get_secret("TOGETHERAI_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("TOGETHERAI_API_BASE") - or "https://api.together.xyz/inference" - ) - - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - - model_response = together_ai.completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - api_key=together_ai_key, - logging_obj=logging, - custom_prompt_dict=custom_prompt_dict, - ) - if ( - "stream_tokens" in optional_params - and optional_params["stream_tokens"] == True - ): - # don't try to access stream object, - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="together_ai", - logging_obj=logging, - ) - return response - response = model_response - elif custom_llm_provider == "palm": - palm_api_key = api_key or get_secret("PALM_API_KEY") or litellm.api_key - - # palm does not support streaming as yet :( - model_response = palm.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - api_key=palm_api_key, - logging_obj=logging, - ) - # fake palm streaming - if "stream" in optional_params and optional_params["stream"] == True: - # fake streaming for palm - resp_string = model_response["choices"][0]["message"]["content"] - response = CustomStreamWrapper( - resp_string, model, custom_llm_provider="palm", logging_obj=logging - ) - return response - response = model_response - elif custom_llm_provider == "gemini": - gemini_api_key = ( - api_key - or get_secret("GEMINI_API_KEY") - or get_secret("PALM_API_KEY") # older palm api key should also work - or litellm.api_key - ) - - # palm does not support streaming as yet :( - model_response = gemini.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - api_key=gemini_api_key, - logging_obj=logging, - acompletion=acompletion, - custom_prompt_dict=custom_prompt_dict, - ) - if ( - "stream" in optional_params - and optional_params["stream"] == True - and acompletion == False - ): - response = CustomStreamWrapper( - iter(model_response), - model, - custom_llm_provider="gemini", - logging_obj=logging, - ) - return response - response = model_response - elif custom_llm_provider == "vertex_ai": - vertex_ai_project = ( - optional_params.pop("vertex_project", None) - or optional_params.pop("vertex_ai_project", None) - or litellm.vertex_project - or get_secret("VERTEXAI_PROJECT") - ) - vertex_ai_location = ( - optional_params.pop("vertex_location", None) - or optional_params.pop("vertex_ai_location", None) - or litellm.vertex_location - or get_secret("VERTEXAI_LOCATION") - ) - vertex_credentials = ( - optional_params.pop("vertex_credentials", None) - or optional_params.pop("vertex_ai_credentials", None) - or get_secret("VERTEXAI_CREDENTIALS") - ) - new_params = deepcopy(optional_params) - if "claude-3" in model: - model_response = vertex_ai_anthropic.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=new_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - vertex_location=vertex_ai_location, - vertex_project=vertex_ai_project, - vertex_credentials=vertex_credentials, - logging_obj=logging, - acompletion=acompletion, - ) - else: -> model_response = vertex_ai.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=new_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - vertex_location=vertex_ai_location, - vertex_project=vertex_ai_project, - vertex_credentials=vertex_credentials, - logging_obj=logging, - acompletion=acompletion, - ) - -../main.py:1824: -_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ - -model = 'gemini-1.5-flash-preview-0514' -messages = [{'content': [{'text': 'Whats in this image?', 'type': 'text'}, {'image_url': {'url': 'gs://cloud-samples-data/generative-ai/image/boats.jpeg'}, 'type': 'image_url'}], 'role': 'user'}] -model_response = ModelResponse(id='chatcmpl-722df0e7-4e2d-44e6-9e2c-49823faa0189', choices=[Choices(finish_reason='stop', index=0, mess... role='assistant'))], created=1716145725, model=None, object='chat.completion', system_fingerprint=None, usage=Usage()) -print_verbose = -encoding = -logging_obj = -vertex_project = None, vertex_location = None, vertex_credentials = None -optional_params = {} -litellm_params = {'acompletion': False, 'api_base': '', 'api_key': None, 'completion_call_id': None, ...} -logger_fn = None, acompletion = False - - def completion( - model: str, - messages: list, - model_response: ModelResponse, - print_verbose: Callable, - encoding, - logging_obj, - vertex_project=None, - vertex_location=None, - vertex_credentials=None, - optional_params=None, - litellm_params=None, - logger_fn=None, - acompletion: bool = False, - ): - try: - import vertexai - except: - raise VertexAIError( - status_code=400, - message="vertexai import failed please run `pip install google-cloud-aiplatform`", - ) - - if not ( - hasattr(vertexai, "preview") or hasattr(vertexai.preview, "language_models") - ): - raise VertexAIError( - status_code=400, - message="""Upgrade vertex ai. Run `pip install "google-cloud-aiplatform>=1.38"`""", - ) - try: - from vertexai.preview.language_models import ( - ChatModel, - CodeChatModel, - InputOutputTextPair, - ) - from vertexai.language_models import TextGenerationModel, CodeGenerationModel - from vertexai.preview.generative_models import ( - GenerativeModel, - Part, - GenerationConfig, - ) - from google.cloud import aiplatform # type: ignore - from google.protobuf import json_format # type: ignore - from google.protobuf.struct_pb2 import Value # type: ignore - from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types # type: ignore - import google.auth # type: ignore - import proto # type: ignore - - ## Load credentials with the correct quota project ref: https://github.com/googleapis/python-aiplatform/issues/2557#issuecomment-1709284744 - print_verbose( - f"VERTEX AI: vertex_project={vertex_project}; vertex_location={vertex_location}" - ) - if vertex_credentials is not None and isinstance(vertex_credentials, str): - import google.oauth2.service_account - - json_obj = json.loads(vertex_credentials) - - creds = google.oauth2.service_account.Credentials.from_service_account_info( - json_obj, - scopes=["https://www.googleapis.com/auth/cloud-platform"], - ) - else: - creds, _ = google.auth.default(quota_project_id=vertex_project) - print_verbose( - f"VERTEX AI: creds={creds}; google application credentials: {os.getenv('GOOGLE_APPLICATION_CREDENTIALS')}" - ) - vertexai.init( - project=vertex_project, location=vertex_location, credentials=creds - ) - - ## Load Config - config = litellm.VertexAIConfig.get_config() - for k, v in config.items(): - if k not in optional_params: - optional_params[k] = v - - ## Process safety settings into format expected by vertex AI - safety_settings = None - if "safety_settings" in optional_params: - safety_settings = optional_params.pop("safety_settings") - if not isinstance(safety_settings, list): - raise ValueError("safety_settings must be a list") - if len(safety_settings) > 0 and not isinstance(safety_settings[0], dict): - raise ValueError("safety_settings must be a list of dicts") - safety_settings = [ - gapic_content_types.SafetySetting(x) for x in safety_settings - ] - - # vertexai does not use an API key, it looks for credentials.json in the environment - - prompt = " ".join( - [ - message["content"] - for message in messages - if isinstance(message["content"], str) - ] - ) - - mode = "" - - request_str = "" - response_obj = None - async_client = None - instances = None - client_options = { - "api_endpoint": f"{vertex_location}-aiplatform.googleapis.com" - } - if ( - model in litellm.vertex_language_models - or model in litellm.vertex_vision_models - ): - llm_model = GenerativeModel(model) - mode = "vision" - request_str += f"llm_model = GenerativeModel({model})\n" - elif model in litellm.vertex_chat_models: - llm_model = ChatModel.from_pretrained(model) - mode = "chat" - request_str += f"llm_model = ChatModel.from_pretrained({model})\n" - elif model in litellm.vertex_text_models: - llm_model = TextGenerationModel.from_pretrained(model) - mode = "text" - request_str += f"llm_model = TextGenerationModel.from_pretrained({model})\n" - elif model in litellm.vertex_code_text_models: - llm_model = CodeGenerationModel.from_pretrained(model) - mode = "text" - request_str += f"llm_model = CodeGenerationModel.from_pretrained({model})\n" - elif model in litellm.vertex_code_chat_models: # vertex_code_llm_models - llm_model = CodeChatModel.from_pretrained(model) - mode = "chat" - request_str += f"llm_model = CodeChatModel.from_pretrained({model})\n" - elif model == "private": - mode = "private" - model = optional_params.pop("model_id", None) - # private endpoint requires a dict instead of JSON - instances = [optional_params.copy()] - instances[0]["prompt"] = prompt - llm_model = aiplatform.PrivateEndpoint( - endpoint_name=model, - project=vertex_project, - location=vertex_location, - ) - request_str += f"llm_model = aiplatform.PrivateEndpoint(endpoint_name={model}, project={vertex_project}, location={vertex_location})\n" - else: # assume vertex model garden on public endpoint - mode = "custom" - - instances = [optional_params.copy()] - instances[0]["prompt"] = prompt - instances = [ - json_format.ParseDict(instance_dict, Value()) - for instance_dict in instances - ] - # Will determine the API used based on async parameter - llm_model = None - - # NOTE: async prediction and streaming under "private" mode isn't supported by aiplatform right now - if acompletion == True: - data = { - "llm_model": llm_model, - "mode": mode, - "prompt": prompt, - "logging_obj": logging_obj, - "request_str": request_str, - "model": model, - "model_response": model_response, - "encoding": encoding, - "messages": messages, - "print_verbose": print_verbose, - "client_options": client_options, - "instances": instances, - "vertex_location": vertex_location, - "vertex_project": vertex_project, - "safety_settings": safety_settings, - **optional_params, - } - if optional_params.get("stream", False) is True: - # async streaming - return async_streaming(**data) - - return async_completion(**data) - - if mode == "vision": - print_verbose("\nMaking VertexAI Gemini Pro / Pro Vision Call") - print_verbose(f"\nProcessing input messages = {messages}") - tools = optional_params.pop("tools", None) - content = _gemini_convert_messages_text(messages=messages) - stream = optional_params.pop("stream", False) - if stream == True: - request_str += f"response = llm_model.generate_content({content}, generation_config=GenerationConfig(**{optional_params}), safety_settings={safety_settings}, stream={stream})\n" - logging_obj.pre_call( - input=prompt, - api_key=None, - additional_args={ - "complete_input_dict": optional_params, - "request_str": request_str, - }, - ) - - model_response = llm_model.generate_content( - contents={"content": content}, - generation_config=optional_params, - safety_settings=safety_settings, - stream=True, - tools=tools, - ) - - return model_response - - request_str += f"response = llm_model.generate_content({content})\n" - ## LOGGING - logging_obj.pre_call( - input=prompt, - api_key=None, - additional_args={ - "complete_input_dict": optional_params, - "request_str": request_str, - }, - ) - - ## LLM Call - response = llm_model.generate_content( - contents=content, - generation_config=optional_params, - safety_settings=safety_settings, - tools=tools, - ) - - if tools is not None and bool( - getattr(response.candidates[0].content.parts[0], "function_call", None) - ): - function_call = response.candidates[0].content.parts[0].function_call - args_dict = {} - - # Check if it's a RepeatedComposite instance - for key, val in function_call.args.items(): - if isinstance( - val, proto.marshal.collections.repeated.RepeatedComposite - ): - # If so, convert to list - args_dict[key] = [v for v in val] - else: - args_dict[key] = val - - try: - args_str = json.dumps(args_dict) - except Exception as e: - raise VertexAIError(status_code=422, message=str(e)) - message = litellm.Message( - content=None, - tool_calls=[ - { - "id": f"call_{str(uuid.uuid4())}", - "function": { - "arguments": args_str, - "name": function_call.name, - }, - "type": "function", - } - ], - ) - completion_response = message - else: - completion_response = response.text - response_obj = response._raw_response - optional_params["tools"] = tools - elif mode == "chat": - chat = llm_model.start_chat() - request_str += f"chat = llm_model.start_chat()\n" - - if "stream" in optional_params and optional_params["stream"] == True: - # NOTE: VertexAI does not accept stream=True as a param and raises an error, - # we handle this by removing 'stream' from optional params and sending the request - # after we get the response we add optional_params["stream"] = True, since main.py needs to know it's a streaming response to then transform it for the OpenAI format - optional_params.pop( - "stream", None - ) # vertex ai raises an error when passing stream in optional params - request_str += ( - f"chat.send_message_streaming({prompt}, **{optional_params})\n" - ) - ## LOGGING - logging_obj.pre_call( - input=prompt, - api_key=None, - additional_args={ - "complete_input_dict": optional_params, - "request_str": request_str, - }, - ) - model_response = chat.send_message_streaming(prompt, **optional_params) - - return model_response - - request_str += f"chat.send_message({prompt}, **{optional_params}).text\n" - ## LOGGING - logging_obj.pre_call( - input=prompt, - api_key=None, - additional_args={ - "complete_input_dict": optional_params, - "request_str": request_str, - }, - ) - completion_response = chat.send_message(prompt, **optional_params).text - elif mode == "text": - if "stream" in optional_params and optional_params["stream"] == True: - optional_params.pop( - "stream", None - ) # See note above on handling streaming for vertex ai - request_str += ( - f"llm_model.predict_streaming({prompt}, **{optional_params})\n" - ) - ## LOGGING - logging_obj.pre_call( - input=prompt, - api_key=None, - additional_args={ - "complete_input_dict": optional_params, - "request_str": request_str, - }, - ) - model_response = llm_model.predict_streaming(prompt, **optional_params) - - return model_response - - request_str += f"llm_model.predict({prompt}, **{optional_params}).text\n" - ## LOGGING - logging_obj.pre_call( - input=prompt, - api_key=None, - additional_args={ - "complete_input_dict": optional_params, - "request_str": request_str, - }, - ) - completion_response = llm_model.predict(prompt, **optional_params).text - elif mode == "custom": - """ - Vertex AI Model Garden - """ - ## LOGGING - logging_obj.pre_call( - input=prompt, - api_key=None, - additional_args={ - "complete_input_dict": optional_params, - "request_str": request_str, - }, - ) - llm_model = aiplatform.gapic.PredictionServiceClient( - client_options=client_options - ) - request_str += f"llm_model = aiplatform.gapic.PredictionServiceClient(client_options={client_options})\n" - endpoint_path = llm_model.endpoint_path( - project=vertex_project, location=vertex_location, endpoint=model - ) - request_str += ( - f"llm_model.predict(endpoint={endpoint_path}, instances={instances})\n" - ) - response = llm_model.predict( - endpoint=endpoint_path, instances=instances - ).predictions - - completion_response = response[0] - if ( - isinstance(completion_response, str) - and "\nOutput:\n" in completion_response - ): - completion_response = completion_response.split("\nOutput:\n", 1)[1] - if "stream" in optional_params and optional_params["stream"] == True: - response = TextStreamer(completion_response) - return response - elif mode == "private": - """ - Vertex AI Model Garden deployed on private endpoint - """ - ## LOGGING - logging_obj.pre_call( - input=prompt, - api_key=None, - additional_args={ - "complete_input_dict": optional_params, - "request_str": request_str, - }, - ) - request_str += f"llm_model.predict(instances={instances})\n" - response = llm_model.predict(instances=instances).predictions - - completion_response = response[0] - if ( - isinstance(completion_response, str) - and "\nOutput:\n" in completion_response - ): - completion_response = completion_response.split("\nOutput:\n", 1)[1] - if "stream" in optional_params and optional_params["stream"] == True: - response = TextStreamer(completion_response) - return response - - ## LOGGING - logging_obj.post_call( - input=prompt, api_key=None, original_response=completion_response - ) - - ## RESPONSE OBJECT - if isinstance(completion_response, litellm.Message): - model_response["choices"][0]["message"] = completion_response - elif len(str(completion_response)) > 0: - model_response["choices"][0]["message"]["content"] = str( - completion_response - ) - model_response["created"] = int(time.time()) - model_response["model"] = model - ## CALCULATING USAGE - if model in litellm.vertex_language_models and response_obj is not None: - model_response["choices"][0].finish_reason = map_finish_reason( - response_obj.candidates[0].finish_reason.name - ) - usage = Usage( - prompt_tokens=response_obj.usage_metadata.prompt_token_count, - completion_tokens=response_obj.usage_metadata.candidates_token_count, - total_tokens=response_obj.usage_metadata.total_token_count, - ) - else: - # init prompt tokens - # this block attempts to get usage from response_obj if it exists, if not it uses the litellm token counter - prompt_tokens, completion_tokens, total_tokens = 0, 0, 0 - if response_obj is not None: - if hasattr(response_obj, "usage_metadata") and hasattr( - response_obj.usage_metadata, "prompt_token_count" - ): - prompt_tokens = response_obj.usage_metadata.prompt_token_count - completion_tokens = ( - response_obj.usage_metadata.candidates_token_count - ) - else: - prompt_tokens = len(encoding.encode(prompt)) - completion_tokens = len( - encoding.encode( - model_response["choices"][0]["message"].get("content", "") - ) - ) - - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - setattr(model_response, "usage", usage) - return model_response - except Exception as e: - if isinstance(e, VertexAIError): - raise e -> raise VertexAIError(status_code=500, message=str(e)) -E litellm.llms.vertex_ai.VertexAIError: Parameter to MergeFrom() must be instance of same class: expected got . - -../llms/vertex_ai.py:971: VertexAIError - -During handling of the above exception, another exception occurred: - -args = () -kwargs = {'litellm_call_id': '7f48b7ab-47b3-4beb-b2b5-fa298be49d3f', 'litellm_logging_obj': -call_type = 'completion', model = 'vertex_ai/gemini-1.5-flash-preview-0514' -k = 'litellm_logging_obj' - - @wraps(original_function) - def wrapper(*args, **kwargs): - # DO NOT MOVE THIS. It always needs to run first - # Check if this is an async function. If so only execute the async function - if ( - kwargs.get("acompletion", False) == True - or kwargs.get("aembedding", False) == True - or kwargs.get("aimg_generation", False) == True - or kwargs.get("amoderation", False) == True - or kwargs.get("atext_completion", False) == True - or kwargs.get("atranscription", False) == True - ): - # [OPTIONAL] CHECK MAX RETRIES / REQUEST - if litellm.num_retries_per_request is not None: - # check if previous_models passed in as ['litellm_params']['metadata]['previous_models'] - previous_models = kwargs.get("metadata", {}).get( - "previous_models", None - ) - if previous_models is not None: - if litellm.num_retries_per_request <= len(previous_models): - raise Exception(f"Max retries per request hit!") - - # MODEL CALL - result = original_function(*args, **kwargs) - if "stream" in kwargs and kwargs["stream"] == True: - if ( - "complete_response" in kwargs - and kwargs["complete_response"] == True - ): - chunks = [] - for idx, chunk in enumerate(result): - chunks.append(chunk) - return litellm.stream_chunk_builder( - chunks, messages=kwargs.get("messages", None) - ) - else: - return result - - return result - - # Prints Exactly what was passed to litellm function - don't execute any logic here - it should just print - print_args_passed_to_litellm(original_function, args, kwargs) - start_time = datetime.datetime.now() - result = None - logging_obj = kwargs.get("litellm_logging_obj", None) - - # only set litellm_call_id if its not in kwargs - call_type = original_function.__name__ - if "litellm_call_id" not in kwargs: - kwargs["litellm_call_id"] = str(uuid.uuid4()) - try: - model = args[0] if len(args) > 0 else kwargs["model"] - except: - model = None - if ( - call_type != CallTypes.image_generation.value - and call_type != CallTypes.text_completion.value - ): - raise ValueError("model param not passed in.") - - try: - if logging_obj is None: - logging_obj, kwargs = function_setup( - original_function.__name__, rules_obj, start_time, *args, **kwargs - ) - kwargs["litellm_logging_obj"] = logging_obj - - # CHECK FOR 'os.environ/' in kwargs - for k, v in kwargs.items(): - if v is not None and isinstance(v, str) and v.startswith("os.environ/"): - kwargs[k] = litellm.get_secret(v) - # [OPTIONAL] CHECK BUDGET - if litellm.max_budget: - if litellm._current_cost > litellm.max_budget: - raise BudgetExceededError( - current_cost=litellm._current_cost, - max_budget=litellm.max_budget, - ) - - # [OPTIONAL] CHECK MAX RETRIES / REQUEST - if litellm.num_retries_per_request is not None: - # check if previous_models passed in as ['litellm_params']['metadata]['previous_models'] - previous_models = kwargs.get("metadata", {}).get( - "previous_models", None - ) - if previous_models is not None: - if litellm.num_retries_per_request <= len(previous_models): - raise Exception(f"Max retries per request hit!") - - # [OPTIONAL] CHECK CACHE - print_verbose( - f"SYNC kwargs[caching]: {kwargs.get('caching', False)}; litellm.cache: {litellm.cache}; kwargs.get('cache')['no-cache']: {kwargs.get('cache', {}).get('no-cache', False)}" - ) - # if caching is false or cache["no-cache"]==True, don't run this - if ( - ( - ( - ( - kwargs.get("caching", None) is None - and litellm.cache is not None - ) - or kwargs.get("caching", False) == True - ) - and kwargs.get("cache", {}).get("no-cache", False) != True - ) - and kwargs.get("aembedding", False) != True - and kwargs.get("atext_completion", False) != True - and kwargs.get("acompletion", False) != True - and kwargs.get("aimg_generation", False) != True - and kwargs.get("atranscription", False) != True - ): # allow users to control returning cached responses from the completion function - # checking cache - print_verbose(f"INSIDE CHECKING CACHE") - if ( - litellm.cache is not None - and str(original_function.__name__) - in litellm.cache.supported_call_types - ): - print_verbose(f"Checking Cache") - preset_cache_key = litellm.cache.get_cache_key(*args, **kwargs) - kwargs["preset_cache_key"] = ( - preset_cache_key # for streaming calls, we need to pass the preset_cache_key - ) - cached_result = litellm.cache.get_cache(*args, **kwargs) - if cached_result != None: - if "detail" in cached_result: - # implies an error occurred - pass - else: - call_type = original_function.__name__ - print_verbose( - f"Cache Response Object routing: call_type - {call_type}; cached_result instace: {type(cached_result)}" - ) - if call_type == CallTypes.completion.value and isinstance( - cached_result, dict - ): - cached_result = convert_to_model_response_object( - response_object=cached_result, - model_response_object=ModelResponse(), - stream=kwargs.get("stream", False), - ) - - if kwargs.get("stream", False) == True: - cached_result = CustomStreamWrapper( - completion_stream=cached_result, - model=model, - custom_llm_provider="cached_response", - logging_obj=logging_obj, - ) - elif call_type == CallTypes.embedding.value and isinstance( - cached_result, dict - ): - cached_result = convert_to_model_response_object( - response_object=cached_result, - response_type="embedding", - ) - - # LOG SUCCESS - cache_hit = True - end_time = datetime.datetime.now() - ( - model, - custom_llm_provider, - dynamic_api_key, - api_base, - ) = litellm.get_llm_provider( - model=model, - custom_llm_provider=kwargs.get( - "custom_llm_provider", None - ), - api_base=kwargs.get("api_base", None), - api_key=kwargs.get("api_key", None), - ) - print_verbose( - f"Async Wrapper: Completed Call, calling async_success_handler: {logging_obj.async_success_handler}" - ) - logging_obj.update_environment_variables( - model=model, - user=kwargs.get("user", None), - optional_params={}, - litellm_params={ - "logger_fn": kwargs.get("logger_fn", None), - "acompletion": False, - "metadata": kwargs.get("metadata", {}), - "model_info": kwargs.get("model_info", {}), - "proxy_server_request": kwargs.get( - "proxy_server_request", None - ), - "preset_cache_key": kwargs.get( - "preset_cache_key", None - ), - "stream_response": kwargs.get( - "stream_response", {} - ), - }, - input=kwargs.get("messages", ""), - api_key=kwargs.get("api_key", None), - original_response=str(cached_result), - additional_args=None, - stream=kwargs.get("stream", False), - ) - threading.Thread( - target=logging_obj.success_handler, - args=(cached_result, start_time, end_time, cache_hit), - ).start() - return cached_result - - # CHECK MAX TOKENS - if ( - kwargs.get("max_tokens", None) is not None - and model is not None - and litellm.modify_params - == True # user is okay with params being modified - and ( - call_type == CallTypes.acompletion.value - or call_type == CallTypes.completion.value - ) - ): - try: - base_model = model - if kwargs.get("hf_model_name", None) is not None: - base_model = f"huggingface/{kwargs.get('hf_model_name')}" - max_output_tokens = ( - get_max_tokens(model=base_model) or 4096 - ) # assume min context window is 4k tokens - user_max_tokens = kwargs.get("max_tokens") - ## Scenario 1: User limit + prompt > model limit - messages = None - if len(args) > 1: - messages = args[1] - elif kwargs.get("messages", None): - messages = kwargs["messages"] - input_tokens = token_counter(model=base_model, messages=messages) - input_tokens += max( - 0.1 * input_tokens, 10 - ) # give at least a 10 token buffer. token counting can be imprecise. - if input_tokens > max_output_tokens: - pass # allow call to fail normally - elif user_max_tokens + input_tokens > max_output_tokens: - user_max_tokens = max_output_tokens - input_tokens - print_verbose(f"user_max_tokens: {user_max_tokens}") - kwargs["max_tokens"] = int( - round(user_max_tokens) - ) # make sure max tokens is always an int - except Exception as e: - print_verbose(f"Error while checking max token limit: {str(e)}") - # MODEL CALL -> result = original_function(*args, **kwargs) - -../utils.py:3211: -_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ -../main.py:2368: in completion - raise exception_type( -../utils.py:9709: in exception_type - raise e -_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ - -model = 'gemini-1.5-flash-preview-0514' -original_exception = VertexAIError("Parameter to MergeFrom() must be instance of same class: expected got .") -custom_llm_provider = 'vertex_ai' -completion_kwargs = {'acompletion': False, 'api_base': None, 'api_key': None, 'api_version': None, ...} -extra_kwargs = {'litellm_call_id': '7f48b7ab-47b3-4beb-b2b5-fa298be49d3f', 'litellm_logging_obj': } - - def exception_type( - model, - original_exception, - custom_llm_provider, - completion_kwargs={}, - extra_kwargs={}, - ): - global user_logger_fn, liteDebuggerClient - exception_mapping_worked = False - if litellm.suppress_debug_info is False: - print() # noqa - print( # noqa - "\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m" # noqa - ) # noqa - print( # noqa - "LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'." # noqa - ) # noqa - print() # noqa - try: - if model: - error_str = str(original_exception) - if isinstance(original_exception, BaseException): - exception_type = type(original_exception).__name__ - else: - exception_type = "" - - ################################################################################ - # Common Extra information needed for all providers - # We pass num retries, api_base, vertex_deployment etc to the exception here - ################################################################################ - extra_information = "" - try: - _api_base = litellm.get_api_base( - model=model, optional_params=extra_kwargs - ) - messages = litellm.get_first_chars_messages(kwargs=completion_kwargs) - _vertex_project = extra_kwargs.get("vertex_project") - _vertex_location = extra_kwargs.get("vertex_location") - _metadata = extra_kwargs.get("metadata", {}) or {} - _model_group = _metadata.get("model_group") - _deployment = _metadata.get("deployment") - extra_information = f"\nModel: {model}" - if _api_base: - extra_information += f"\nAPI Base: {_api_base}" - if messages and len(messages) > 0: - extra_information += f"\nMessages: {messages}" - - if _model_group is not None: - extra_information += f"\nmodel_group: {_model_group}\n" - if _deployment is not None: - extra_information += f"\ndeployment: {_deployment}\n" - if _vertex_project is not None: - extra_information += f"\nvertex_project: {_vertex_project}\n" - if _vertex_location is not None: - extra_information += f"\nvertex_location: {_vertex_location}\n" - - # on litellm proxy add key name + team to exceptions - extra_information = _add_key_name_and_team_to_alert( - request_info=extra_information, metadata=_metadata - ) - except: - # DO NOT LET this Block raising the original exception - pass - - ################################################################################ - # End of Common Extra information Needed for all providers - ################################################################################ - - ################################################################################ - #################### Start of Provider Exception mapping #################### - ################################################################################ - - if "Request Timeout Error" in error_str or "Request timed out" in error_str: - exception_mapping_worked = True - raise Timeout( - message=f"APITimeoutError - Request timed out. \nerror_str: {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - - if ( - custom_llm_provider == "openai" - or custom_llm_provider == "text-completion-openai" - or custom_llm_provider == "custom_openai" - or custom_llm_provider in litellm.openai_compatible_providers - ): - # custom_llm_provider is openai, make it OpenAI - if hasattr(original_exception, "message"): - message = original_exception.message - else: - message = str(original_exception) - if message is not None and isinstance(message, str): - message = message.replace("OPENAI", custom_llm_provider.upper()) - message = message.replace("openai", custom_llm_provider) - message = message.replace("OpenAI", custom_llm_provider) - if custom_llm_provider == "openai": - exception_provider = "OpenAI" + "Exception" - else: - exception_provider = ( - custom_llm_provider[0].upper() - + custom_llm_provider[1:] - + "Exception" - ) - - if "This model's maximum context length is" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif ( - "invalid_request_error" in error_str - and "model_not_found" in error_str - ): - exception_mapping_worked = True - raise NotFoundError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif ( - "invalid_request_error" in error_str - and "content_policy_violation" in error_str - ): - exception_mapping_worked = True - raise ContentPolicyViolationError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif ( - "invalid_request_error" in error_str - and "Incorrect API key provided" not in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif "Request too large" in error_str: - raise RateLimitError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif ( - "The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif "Mistral API raised a streaming error" in error_str: - exception_mapping_worked = True - _request = httpx.Request( - method="POST", url="https://api.openai.com/v1" - ) - raise APIError( - status_code=500, - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - request=_request, - litellm_debug_info=extra_information, - ) - elif hasattr(original_exception, "status_code"): - exception_mapping_worked = True - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - request=original_exception.request, - litellm_debug_info=extra_information, - ) - else: - # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors - raise APIConnectionError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ) - elif custom_llm_provider == "anthropic": # one of the anthropics - if hasattr(original_exception, "message"): - if ( - "prompt is too long" in original_exception.message - or "prompt: length" in original_exception.message - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=original_exception.message, - model=model, - llm_provider="anthropic", - response=original_exception.response, - ) - if "Invalid API Key" in original_exception.message: - exception_mapping_worked = True - raise AuthenticationError( - message=original_exception.message, - model=model, - llm_provider="anthropic", - response=original_exception.response, - ) - if hasattr(original_exception, "status_code"): - print_verbose(f"status_code: {original_exception.status_code}") - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AnthropicException - {original_exception.message}", - llm_provider="anthropic", - model=model, - response=original_exception.response, - ) - elif ( - original_exception.status_code == 400 - or original_exception.status_code == 413 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"AnthropicException - {original_exception.message}", - model=model, - llm_provider="anthropic", - response=original_exception.response, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"AnthropicException - {original_exception.message}", - model=model, - llm_provider="anthropic", - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AnthropicException - {original_exception.message}", - llm_provider="anthropic", - model=model, - response=original_exception.response, - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise APIError( - status_code=500, - message=f"AnthropicException - {original_exception.message}. Handle with `litellm.APIError`.", - llm_provider="anthropic", - model=model, - request=original_exception.request, - ) - elif custom_llm_provider == "replicate": - if "Incorrect authentication token" in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message=f"ReplicateException - {error_str}", - llm_provider="replicate", - model=model, - response=original_exception.response, - ) - elif "input is too long" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"ReplicateException - {error_str}", - model=model, - llm_provider="replicate", - response=original_exception.response, - ) - elif exception_type == "ModelError": - exception_mapping_worked = True - raise BadRequestError( - message=f"ReplicateException - {error_str}", - model=model, - llm_provider="replicate", - response=original_exception.response, - ) - elif "Request was throttled" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"ReplicateException - {error_str}", - llm_provider="replicate", - model=model, - response=original_exception.response, - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=original_exception.response, - ) - elif ( - original_exception.status_code == 400 - or original_exception.status_code == 422 - or original_exception.status_code == 413 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - response=original_exception.response, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=original_exception.response, - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=original_exception.response, - ) - exception_mapping_worked = True - raise APIError( - status_code=500, - message=f"ReplicateException - {str(original_exception)}", - llm_provider="replicate", - model=model, - request=httpx.Request( - method="POST", - url="https://api.replicate.com/v1/deployments", - ), - ) - elif custom_llm_provider == "watsonx": - if "token_quota_reached" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"WatsonxException: Rate Limit Errror - {error_str}", - llm_provider="watsonx", - model=model, - response=original_exception.response, - ) - elif custom_llm_provider == "predibase": - if "authorization denied for" in error_str: - exception_mapping_worked = True - - # Predibase returns the raw API Key in the response - this block ensures it's not returned in the exception - if ( - error_str is not None - and isinstance(error_str, str) - and "bearer" in error_str.lower() - ): - # only keep the first 10 chars after the occurnence of "bearer" - _bearer_token_start_index = error_str.lower().find("bearer") - error_str = error_str[: _bearer_token_start_index + 14] - error_str += "XXXXXXX" + '"' - - raise AuthenticationError( - message=f"PredibaseException: Authentication Error - {error_str}", - llm_provider="predibase", - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif custom_llm_provider == "bedrock": - if ( - "too many tokens" in error_str - or "expected maxLength:" in error_str - or "Input is too long" in error_str - or "prompt: length: 1.." in error_str - or "Too many input tokens" in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"BedrockException: Context Window Error - {error_str}", - model=model, - llm_provider="bedrock", - response=original_exception.response, - ) - elif "Malformed input request" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {error_str}", - model=model, - llm_provider="bedrock", - response=original_exception.response, - ) - elif ( - "Unable to locate credentials" in error_str - or "The security token included in the request is invalid" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"BedrockException Invalid Authentication - {error_str}", - model=model, - llm_provider="bedrock", - response=original_exception.response, - ) - elif "AccessDeniedException" in error_str: - exception_mapping_worked = True - raise PermissionDeniedError( - message=f"BedrockException PermissionDeniedError - {error_str}", - model=model, - llm_provider="bedrock", - response=original_exception.response, - ) - elif ( - "throttlingException" in error_str - or "ThrottlingException" in error_str - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"BedrockException: Rate Limit Error - {error_str}", - model=model, - llm_provider="bedrock", - response=original_exception.response, - ) - elif ( - "Connect timeout on endpoint URL" in error_str - or "timed out" in error_str - ): - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException: Timeout Error - {error_str}", - model=model, - llm_provider="bedrock", - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=httpx.Response( - status_code=500, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ), - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=original_exception.response, - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=original_exception.response, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=original_exception.response, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif custom_llm_provider == "sagemaker": - if "Unable to locate credentials" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"SagemakerException - {error_str}", - model=model, - llm_provider="sagemaker", - response=original_exception.response, - ) - elif ( - "Input validation error: `best_of` must be > 0 and <= 2" - in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"SagemakerException - the value of 'n' must be > 0 and <= 2 for sagemaker endpoints", - model=model, - llm_provider="sagemaker", - response=original_exception.response, - ) - elif ( - "`inputs` tokens + `max_new_tokens` must be <=" in error_str - or "instance type with more CPU capacity or memory" in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"SagemakerException - {error_str}", - model=model, - llm_provider="sagemaker", - response=original_exception.response, - ) - elif custom_llm_provider == "vertex_ai": - if ( - "Vertex AI API has not been used in project" in error_str - or "Unable to find your project" in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"VertexAIException - {error_str}", - model=model, - llm_provider="vertex_ai", - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif ( - "None Unknown Error." in error_str - or "Content has no parts." in error_str - ): - exception_mapping_worked = True - raise APIError( - message=f"VertexAIException - {error_str}", - status_code=500, - model=model, - llm_provider="vertex_ai", - request=original_exception.request, - litellm_debug_info=extra_information, - ) - elif "403" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"VertexAIException - {error_str}", - model=model, - llm_provider="vertex_ai", - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif "The response was blocked." in error_str: - exception_mapping_worked = True - raise UnprocessableEntityError( - message=f"VertexAIException - {error_str}", - model=model, - llm_provider="vertex_ai", - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - elif ( - "429 Quota exceeded" in error_str - or "IndexError: list index out of range" in error_str - or "429 Unable to submit request because the service is temporarily out of capacity." - in error_str - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"VertexAIException - {error_str}", - model=model, - llm_provider="vertex_ai", - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"VertexAIException - {error_str}", - model=model, - llm_provider="vertex_ai", - litellm_debug_info=extra_information, - response=original_exception.response, - ) - if original_exception.status_code == 500: - exception_mapping_worked = True -> raise APIError( - message=f"VertexAIException - {error_str}", - status_code=500, - model=model, - llm_provider="vertex_ai", - litellm_debug_info=extra_information, - request=original_exception.request, -E litellm.exceptions.APIError: VertexAIException - Parameter to MergeFrom() must be instance of same class: expected got . - -../utils.py:8922: APIError - -During handling of the above exception, another exception occurred: - - def test_gemini_pro_vision(): - try: - load_vertex_ai_credentials() - litellm.set_verbose = True - litellm.num_retries = 3 -> resp = litellm.completion( - model="vertex_ai/gemini-1.5-flash-preview-0514", - messages=[ - { - "role": "user", - "content": [ - {"type": "text", "text": "Whats in this image?"}, - { - "type": "image_url", - "image_url": { - "url": "gs://cloud-samples-data/generative-ai/image/boats.jpeg" - }, - }, - ], - } - ], - ) - -test_amazing_vertex_completion.py:510: -_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ -../utils.py:3289: in wrapper - return litellm.completion_with_retries(*args, **kwargs) -../main.py:2401: in completion_with_retries - return retryer(original_function, *args, **kwargs) -../proxy/myenv/lib/python3.11/site-packages/tenacity/__init__.py:379: in __call__ - do = self.iter(retry_state=retry_state) -../proxy/myenv/lib/python3.11/site-packages/tenacity/__init__.py:325: in iter - raise retry_exc.reraise() -../proxy/myenv/lib/python3.11/site-packages/tenacity/__init__.py:158: in reraise - raise self.last_attempt.result() -/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/concurrent/futures/_base.py:449: in result - return self.__get_result() -/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/concurrent/futures/_base.py:401: in __get_result - raise self._exception -../proxy/myenv/lib/python3.11/site-packages/tenacity/__init__.py:382: in __call__ - result = fn(*args, **kwargs) -../utils.py:3317: in wrapper - raise e -../utils.py:3211: in wrapper - result = original_function(*args, **kwargs) -../main.py:2368: in completion - raise exception_type( -../utils.py:9709: in exception_type - raise e -_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ - -model = 'gemini-1.5-flash-preview-0514' -original_exception = VertexAIError("Parameter to MergeFrom() must be instance of same class: expected got .") -custom_llm_provider = 'vertex_ai' -completion_kwargs = {'acompletion': False, 'api_base': None, 'api_key': None, 'api_version': None, ...} -extra_kwargs = {'litellm_call_id': '7f48b7ab-47b3-4beb-b2b5-fa298be49d3f', 'litellm_logging_obj': } - - def exception_type( - model, - original_exception, - custom_llm_provider, - completion_kwargs={}, - extra_kwargs={}, - ): - global user_logger_fn, liteDebuggerClient - exception_mapping_worked = False - if litellm.suppress_debug_info is False: - print() # noqa - print( # noqa - "\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m" # noqa - ) # noqa - print( # noqa - "LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'." # noqa - ) # noqa - print() # noqa - try: - if model: - error_str = str(original_exception) - if isinstance(original_exception, BaseException): - exception_type = type(original_exception).__name__ - else: - exception_type = "" - - ################################################################################ - # Common Extra information needed for all providers - # We pass num retries, api_base, vertex_deployment etc to the exception here - ################################################################################ - extra_information = "" - try: - _api_base = litellm.get_api_base( - model=model, optional_params=extra_kwargs - ) - messages = litellm.get_first_chars_messages(kwargs=completion_kwargs) - _vertex_project = extra_kwargs.get("vertex_project") - _vertex_location = extra_kwargs.get("vertex_location") - _metadata = extra_kwargs.get("metadata", {}) or {} - _model_group = _metadata.get("model_group") - _deployment = _metadata.get("deployment") - extra_information = f"\nModel: {model}" - if _api_base: - extra_information += f"\nAPI Base: {_api_base}" - if messages and len(messages) > 0: - extra_information += f"\nMessages: {messages}" - - if _model_group is not None: - extra_information += f"\nmodel_group: {_model_group}\n" - if _deployment is not None: - extra_information += f"\ndeployment: {_deployment}\n" - if _vertex_project is not None: - extra_information += f"\nvertex_project: {_vertex_project}\n" - if _vertex_location is not None: - extra_information += f"\nvertex_location: {_vertex_location}\n" - - # on litellm proxy add key name + team to exceptions - extra_information = _add_key_name_and_team_to_alert( - request_info=extra_information, metadata=_metadata - ) - except: - # DO NOT LET this Block raising the original exception - pass - - ################################################################################ - # End of Common Extra information Needed for all providers - ################################################################################ - - ################################################################################ - #################### Start of Provider Exception mapping #################### - ################################################################################ - - if "Request Timeout Error" in error_str or "Request timed out" in error_str: - exception_mapping_worked = True - raise Timeout( - message=f"APITimeoutError - Request timed out. \nerror_str: {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - - if ( - custom_llm_provider == "openai" - or custom_llm_provider == "text-completion-openai" - or custom_llm_provider == "custom_openai" - or custom_llm_provider in litellm.openai_compatible_providers - ): - # custom_llm_provider is openai, make it OpenAI - if hasattr(original_exception, "message"): - message = original_exception.message - else: - message = str(original_exception) - if message is not None and isinstance(message, str): - message = message.replace("OPENAI", custom_llm_provider.upper()) - message = message.replace("openai", custom_llm_provider) - message = message.replace("OpenAI", custom_llm_provider) - if custom_llm_provider == "openai": - exception_provider = "OpenAI" + "Exception" - else: - exception_provider = ( - custom_llm_provider[0].upper() - + custom_llm_provider[1:] - + "Exception" - ) - - if "This model's maximum context length is" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif ( - "invalid_request_error" in error_str - and "model_not_found" in error_str - ): - exception_mapping_worked = True - raise NotFoundError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif ( - "invalid_request_error" in error_str - and "content_policy_violation" in error_str - ): - exception_mapping_worked = True - raise ContentPolicyViolationError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif ( - "invalid_request_error" in error_str - and "Incorrect API key provided" not in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif "Request too large" in error_str: - raise RateLimitError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif ( - "The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif "Mistral API raised a streaming error" in error_str: - exception_mapping_worked = True - _request = httpx.Request( - method="POST", url="https://api.openai.com/v1" - ) - raise APIError( - status_code=500, - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - request=_request, - litellm_debug_info=extra_information, - ) - elif hasattr(original_exception, "status_code"): - exception_mapping_worked = True - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - request=original_exception.request, - litellm_debug_info=extra_information, - ) - else: - # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors - raise APIConnectionError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ) - elif custom_llm_provider == "anthropic": # one of the anthropics - if hasattr(original_exception, "message"): - if ( - "prompt is too long" in original_exception.message - or "prompt: length" in original_exception.message - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=original_exception.message, - model=model, - llm_provider="anthropic", - response=original_exception.response, - ) - if "Invalid API Key" in original_exception.message: - exception_mapping_worked = True - raise AuthenticationError( - message=original_exception.message, - model=model, - llm_provider="anthropic", - response=original_exception.response, - ) - if hasattr(original_exception, "status_code"): - print_verbose(f"status_code: {original_exception.status_code}") - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AnthropicException - {original_exception.message}", - llm_provider="anthropic", - model=model, - response=original_exception.response, - ) - elif ( - original_exception.status_code == 400 - or original_exception.status_code == 413 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"AnthropicException - {original_exception.message}", - model=model, - llm_provider="anthropic", - response=original_exception.response, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"AnthropicException - {original_exception.message}", - model=model, - llm_provider="anthropic", - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AnthropicException - {original_exception.message}", - llm_provider="anthropic", - model=model, - response=original_exception.response, - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise APIError( - status_code=500, - message=f"AnthropicException - {original_exception.message}. Handle with `litellm.APIError`.", - llm_provider="anthropic", - model=model, - request=original_exception.request, - ) - elif custom_llm_provider == "replicate": - if "Incorrect authentication token" in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message=f"ReplicateException - {error_str}", - llm_provider="replicate", - model=model, - response=original_exception.response, - ) - elif "input is too long" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"ReplicateException - {error_str}", - model=model, - llm_provider="replicate", - response=original_exception.response, - ) - elif exception_type == "ModelError": - exception_mapping_worked = True - raise BadRequestError( - message=f"ReplicateException - {error_str}", - model=model, - llm_provider="replicate", - response=original_exception.response, - ) - elif "Request was throttled" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"ReplicateException - {error_str}", - llm_provider="replicate", - model=model, - response=original_exception.response, - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=original_exception.response, - ) - elif ( - original_exception.status_code == 400 - or original_exception.status_code == 422 - or original_exception.status_code == 413 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - response=original_exception.response, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=original_exception.response, - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=original_exception.response, - ) - exception_mapping_worked = True - raise APIError( - status_code=500, - message=f"ReplicateException - {str(original_exception)}", - llm_provider="replicate", - model=model, - request=httpx.Request( - method="POST", - url="https://api.replicate.com/v1/deployments", - ), - ) - elif custom_llm_provider == "watsonx": - if "token_quota_reached" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"WatsonxException: Rate Limit Errror - {error_str}", - llm_provider="watsonx", - model=model, - response=original_exception.response, - ) - elif custom_llm_provider == "predibase": - if "authorization denied for" in error_str: - exception_mapping_worked = True - - # Predibase returns the raw API Key in the response - this block ensures it's not returned in the exception - if ( - error_str is not None - and isinstance(error_str, str) - and "bearer" in error_str.lower() - ): - # only keep the first 10 chars after the occurnence of "bearer" - _bearer_token_start_index = error_str.lower().find("bearer") - error_str = error_str[: _bearer_token_start_index + 14] - error_str += "XXXXXXX" + '"' - - raise AuthenticationError( - message=f"PredibaseException: Authentication Error - {error_str}", - llm_provider="predibase", - model=model, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif custom_llm_provider == "bedrock": - if ( - "too many tokens" in error_str - or "expected maxLength:" in error_str - or "Input is too long" in error_str - or "prompt: length: 1.." in error_str - or "Too many input tokens" in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"BedrockException: Context Window Error - {error_str}", - model=model, - llm_provider="bedrock", - response=original_exception.response, - ) - elif "Malformed input request" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {error_str}", - model=model, - llm_provider="bedrock", - response=original_exception.response, - ) - elif ( - "Unable to locate credentials" in error_str - or "The security token included in the request is invalid" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"BedrockException Invalid Authentication - {error_str}", - model=model, - llm_provider="bedrock", - response=original_exception.response, - ) - elif "AccessDeniedException" in error_str: - exception_mapping_worked = True - raise PermissionDeniedError( - message=f"BedrockException PermissionDeniedError - {error_str}", - model=model, - llm_provider="bedrock", - response=original_exception.response, - ) - elif ( - "throttlingException" in error_str - or "ThrottlingException" in error_str - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"BedrockException: Rate Limit Error - {error_str}", - model=model, - llm_provider="bedrock", - response=original_exception.response, - ) - elif ( - "Connect timeout on endpoint URL" in error_str - or "timed out" in error_str - ): - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException: Timeout Error - {error_str}", - model=model, - llm_provider="bedrock", - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=httpx.Response( - status_code=500, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ), - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=original_exception.response, - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=original_exception.response, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=original_exception.response, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif custom_llm_provider == "sagemaker": - if "Unable to locate credentials" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"SagemakerException - {error_str}", - model=model, - llm_provider="sagemaker", - response=original_exception.response, - ) - elif ( - "Input validation error: `best_of` must be > 0 and <= 2" - in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"SagemakerException - the value of 'n' must be > 0 and <= 2 for sagemaker endpoints", - model=model, - llm_provider="sagemaker", - response=original_exception.response, - ) - elif ( - "`inputs` tokens + `max_new_tokens` must be <=" in error_str - or "instance type with more CPU capacity or memory" in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"SagemakerException - {error_str}", - model=model, - llm_provider="sagemaker", - response=original_exception.response, - ) - elif custom_llm_provider == "vertex_ai": - if ( - "Vertex AI API has not been used in project" in error_str - or "Unable to find your project" in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"VertexAIException - {error_str}", - model=model, - llm_provider="vertex_ai", - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif ( - "None Unknown Error." in error_str - or "Content has no parts." in error_str - ): - exception_mapping_worked = True - raise APIError( - message=f"VertexAIException - {error_str}", - status_code=500, - model=model, - llm_provider="vertex_ai", - request=original_exception.request, - litellm_debug_info=extra_information, - ) - elif "403" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"VertexAIException - {error_str}", - model=model, - llm_provider="vertex_ai", - response=original_exception.response, - litellm_debug_info=extra_information, - ) - elif "The response was blocked." in error_str: - exception_mapping_worked = True - raise UnprocessableEntityError( - message=f"VertexAIException - {error_str}", - model=model, - llm_provider="vertex_ai", - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - elif ( - "429 Quota exceeded" in error_str - or "IndexError: list index out of range" in error_str - or "429 Unable to submit request because the service is temporarily out of capacity." - in error_str - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"VertexAIException - {error_str}", - model=model, - llm_provider="vertex_ai", - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"VertexAIException - {error_str}", - model=model, - llm_provider="vertex_ai", - litellm_debug_info=extra_information, - response=original_exception.response, - ) - if original_exception.status_code == 500: - exception_mapping_worked = True -> raise APIError( - message=f"VertexAIException - {error_str}", - status_code=500, - model=model, - llm_provider="vertex_ai", - litellm_debug_info=extra_information, - request=original_exception.request, -E litellm.exceptions.APIError: VertexAIException - Parameter to MergeFrom() must be instance of same class: expected got . - -../utils.py:8922: APIError - -During handling of the above exception, another exception occurred: - - def test_gemini_pro_vision(): - try: - load_vertex_ai_credentials() - litellm.set_verbose = True - litellm.num_retries = 3 - resp = litellm.completion( - model="vertex_ai/gemini-1.5-flash-preview-0514", - messages=[ - { - "role": "user", - "content": [ - {"type": "text", "text": "Whats in this image?"}, - { - "type": "image_url", - "image_url": { - "url": "gs://cloud-samples-data/generative-ai/image/boats.jpeg" - }, - }, - ], - } - ], - ) - print(resp) - - prompt_tokens = resp.usage.prompt_tokens - - # DO Not DELETE this ASSERT - # Google counts the prompt tokens for us, we should ensure we use the tokens from the orignal response - assert prompt_tokens == 263 # the gemini api returns 263 to us - except litellm.RateLimitError as e: - pass - except Exception as e: - if "500 Internal error encountered.'" in str(e): - pass - else: -> pytest.fail(f"An exception occurred - {str(e)}") -E Failed: An exception occurred - VertexAIException - Parameter to MergeFrom() must be instance of same class: expected got . - -test_amazing_vertex_completion.py:540: Failed ----------------------------- Captured stdout setup ----------------------------- - ------------------------------ Captured stdout call ----------------------------- -loading vertex ai credentials -Read vertexai file path - - -Request to litellm: -litellm.completion(model='vertex_ai/gemini-1.5-flash-preview-0514', messages=[{'role': 'user', 'content': [{'type': 'text', 'text': 'Whats in this image?'}, {'type': 'image_url', 'image_url': {'url': 'gs://cloud-samples-data/generative-ai/image/boats.jpeg'}}]}]) - - -self.optional_params: {} -SYNC kwargs[caching]: False; litellm.cache: None; kwargs.get('cache')['no-cache']: False -(start) INSIDE THE VERTEX AI OPTIONAL PARAM BLOCK -(end) INSIDE THE VERTEX AI OPTIONAL PARAM BLOCK - optional_params: {} -Final returned optional params: {} -self.optional_params: {} -VERTEX AI: vertex_project=None; vertex_location=None -VERTEX AI: creds=; google application credentials: /var/folders/gf/5h3fnlwx40sdrycs4y5qzqx40000gn/T/tmpolsest5s - -Making VertexAI Gemini Pro / Pro Vision Call - -Processing input messages = [{'role': 'user', 'content': [{'type': 'text', 'text': 'Whats in this image?'}, {'type': 'image_url', 'image_url': {'url': 'gs://cloud-samples-data/generative-ai/image/boats.jpeg'}}]}] - -Request Sent from LiteLLM: -llm_model = GenerativeModel(gemini-1.5-flash-preview-0514) -response = llm_model.generate_content([{'role': 'user', 'parts': [{'text': 'Whats in this image?'}, file_data { - mime_type: "image/jpeg" - file_uri: "gs://cloud-samples-data/generative-ai/image/boats.jpeg" -} -]}]) - - - -Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new -LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'. - -Logging Details: logger_fn - None | callable(logger_fn) - False - - -Request to litellm: -litellm.completion(model='vertex_ai/gemini-1.5-flash-preview-0514', messages=[{'role': 'user', 'content': [{'type': 'text', 'text': 'Whats in this image?'}, {'type': 'image_url', 'image_url': {'url': 'gs://cloud-samples-data/generative-ai/image/boats.jpeg'}}]}], litellm_call_id='7f48b7ab-47b3-4beb-b2b5-fa298be49d3f', litellm_logging_obj=) - - -SYNC kwargs[caching]: False; litellm.cache: None; kwargs.get('cache')['no-cache']: False -(start) INSIDE THE VERTEX AI OPTIONAL PARAM BLOCK -(end) INSIDE THE VERTEX AI OPTIONAL PARAM BLOCK - optional_params: {} -Final returned optional params: {} -self.optional_params: {} -VERTEX AI: vertex_project=None; vertex_location=None -VERTEX AI: creds=; google application credentials: /var/folders/gf/5h3fnlwx40sdrycs4y5qzqx40000gn/T/tmpolsest5s - -Making VertexAI Gemini Pro / Pro Vision Call - -Processing input messages = [{'role': 'user', 'content': [{'type': 'text', 'text': 'Whats in this image?'}, {'type': 'image_url', 'image_url': {'url': 'gs://cloud-samples-data/generative-ai/image/boats.jpeg'}}]}] - -Request Sent from LiteLLM: -llm_model = GenerativeModel(gemini-1.5-flash-preview-0514) -response = llm_model.generate_content([{'role': 'user', 'parts': [{'text': 'Whats in this image?'}, file_data { - mime_type: "image/jpeg" - file_uri: "gs://cloud-samples-data/generative-ai/image/boats.jpeg" -} -]}]) - - - -Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new -LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'. - -Logging Details: logger_fn - None | callable(logger_fn) - False -Logging Details LiteLLM-Failure Call -self.failure_callback: [] - - -Request to litellm: -litellm.completion(model='vertex_ai/gemini-1.5-flash-preview-0514', messages=[{'role': 'user', 'content': [{'type': 'text', 'text': 'Whats in this image?'}, {'type': 'image_url', 'image_url': {'url': 'gs://cloud-samples-data/generative-ai/image/boats.jpeg'}}]}], litellm_call_id='7f48b7ab-47b3-4beb-b2b5-fa298be49d3f', litellm_logging_obj=) - - -SYNC kwargs[caching]: False; litellm.cache: None; kwargs.get('cache')['no-cache']: False -(start) INSIDE THE VERTEX AI OPTIONAL PARAM BLOCK -(end) INSIDE THE VERTEX AI OPTIONAL PARAM BLOCK - optional_params: {} -Final returned optional params: {} -self.optional_params: {} -VERTEX AI: vertex_project=None; vertex_location=None -VERTEX AI: creds=; google application credentials: /var/folders/gf/5h3fnlwx40sdrycs4y5qzqx40000gn/T/tmpolsest5s - -Making VertexAI Gemini Pro / Pro Vision Call - -Processing input messages = [{'role': 'user', 'content': [{'type': 'text', 'text': 'Whats in this image?'}, {'type': 'image_url', 'image_url': {'url': 'gs://cloud-samples-data/generative-ai/image/boats.jpeg'}}]}] - -Request Sent from LiteLLM: -llm_model = GenerativeModel(gemini-1.5-flash-preview-0514) -response = llm_model.generate_content([{'role': 'user', 'parts': [{'text': 'Whats in this image?'}, file_data { - mime_type: "image/jpeg" - file_uri: "gs://cloud-samples-data/generative-ai/image/boats.jpeg" -} -]}]) - - - -Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new -LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'. - -Logging Details: logger_fn - None | callable(logger_fn) - False -Logging Details LiteLLM-Failure Call -self.failure_callback: [] - - -Request to litellm: -litellm.completion(model='vertex_ai/gemini-1.5-flash-preview-0514', messages=[{'role': 'user', 'content': [{'type': 'text', 'text': 'Whats in this image?'}, {'type': 'image_url', 'image_url': {'url': 'gs://cloud-samples-data/generative-ai/image/boats.jpeg'}}]}], litellm_call_id='7f48b7ab-47b3-4beb-b2b5-fa298be49d3f', litellm_logging_obj=) - - -SYNC kwargs[caching]: False; litellm.cache: None; kwargs.get('cache')['no-cache']: False -(start) INSIDE THE VERTEX AI OPTIONAL PARAM BLOCK -(end) INSIDE THE VERTEX AI OPTIONAL PARAM BLOCK - optional_params: {} -Final returned optional params: {} -self.optional_params: {} -VERTEX AI: vertex_project=None; vertex_location=None -VERTEX AI: creds=; google application credentials: /var/folders/gf/5h3fnlwx40sdrycs4y5qzqx40000gn/T/tmpolsest5s - -Making VertexAI Gemini Pro / Pro Vision Call - -Processing input messages = [{'role': 'user', 'content': [{'type': 'text', 'text': 'Whats in this image?'}, {'type': 'image_url', 'image_url': {'url': 'gs://cloud-samples-data/generative-ai/image/boats.jpeg'}}]}] - -Request Sent from LiteLLM: -llm_model = GenerativeModel(gemini-1.5-flash-preview-0514) -response = llm_model.generate_content([{'role': 'user', 'parts': [{'text': 'Whats in this image?'}, file_data { - mime_type: "image/jpeg" - file_uri: "gs://cloud-samples-data/generative-ai/image/boats.jpeg" -} -]}]) - - - -Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new -LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'. - -Logging Details: logger_fn - None | callable(logger_fn) - False -Logging Details LiteLLM-Failure Call -self.failure_callback: [] -=============================== warnings summary =============================== -../proxy/myenv/lib/python3.11/site-packages/pydantic/_internal/_config.py:284: 25 warnings - /Users/krrishdholakia/Documents/litellm/litellm/proxy/myenv/lib/python3.11/site-packages/pydantic/_internal/_config.py:284: PydanticDeprecatedSince20: Support for class-based `config` is deprecated, use ConfigDict instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - warnings.warn(DEPRECATION_MESSAGE, DeprecationWarning) - -../proxy/_types.py:255 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:255: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - @root_validator(pre=True) - -../proxy/_types.py:342 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:342: PydanticDeprecatedSince20: `pydantic.config.Extra` is deprecated, use literal values instead (e.g. `extra='allow'`). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - extra = Extra.allow # Allow extra fields - -../proxy/_types.py:345 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:345: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - @root_validator(pre=True) - -../proxy/_types.py:374 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:374: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - @root_validator(pre=True) - -../proxy/_types.py:421 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:421: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - @root_validator(pre=True) - -../proxy/_types.py:490 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:490: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - @root_validator(pre=True) - -../proxy/_types.py:510 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:510: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - @root_validator(pre=True) - -../proxy/_types.py:523 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:523: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - @root_validator(pre=True) - -../proxy/_types.py:568 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:568: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - @root_validator(pre=True) - -../proxy/_types.py:605 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:605: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - @root_validator(pre=True) - -../proxy/_types.py:923 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:923: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - @root_validator(pre=True) - -../proxy/_types.py:950 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:950: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - @root_validator(pre=True) - -../proxy/_types.py:971 - /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:971: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - @root_validator(pre=True) - -../utils.py:60 - /Users/krrishdholakia/Documents/litellm/litellm/utils.py:60: DeprecationWarning: open_text is deprecated. Use files() instead. Refer to https://importlib-resources.readthedocs.io/en/latest/using.html#migrating-from-legacy for migration advice. - with resources.open_text("litellm.llms.tokenizers", "anthropic_tokenizer.json") as f: - --- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html -=========================== short test summary info ============================ -FAILED test_amazing_vertex_completion.py::test_gemini_pro_vision - Failed: An... -======================== 1 failed, 39 warnings in 2.09s ======================== diff --git a/litellm/tests/test_assistants.py b/litellm/tests/test_assistants.py index 5f565f67cc..d5f047a092 100644 --- a/litellm/tests/test_assistants.py +++ b/litellm/tests/test_assistants.py @@ -198,7 +198,11 @@ async def test_aarun_thread_litellm(sync_mode, provider, is_streaming): ) assert isinstance(messages.data[0], Message) else: - pytest.fail("An unexpected error occurred when running the thread") + pytest.fail( + "An unexpected error occurred when running the thread, {}".format( + run + ) + ) else: added_message = await litellm.a_add_message(**data) @@ -226,4 +230,8 @@ async def test_aarun_thread_litellm(sync_mode, provider, is_streaming): ) assert isinstance(messages.data[0], Message) else: - pytest.fail("An unexpected error occurred when running the thread") + pytest.fail( + "An unexpected error occurred when running the thread, {}".format( + run + ) + ) diff --git a/litellm/tests/test_bedrock_completion.py b/litellm/tests/test_bedrock_completion.py index 047f0cb2e2..64e7741e2a 100644 --- a/litellm/tests/test_bedrock_completion.py +++ b/litellm/tests/test_bedrock_completion.py @@ -243,6 +243,7 @@ def test_completion_bedrock_claude_sts_oidc_auth(): except Exception as e: pytest.fail(f"Error occurred: {e}") + @pytest.mark.skipif( os.environ.get("CIRCLE_OIDC_TOKEN_V2") is None, reason="Cannot run without being in CircleCI Runner", @@ -277,7 +278,15 @@ def test_completion_bedrock_httpx_command_r_sts_oidc_auth(): except Exception as e: pytest.fail(f"Error occurred: {e}") -def test_bedrock_claude_3(): + +@pytest.mark.parametrize( + "image_url", + [ + "data:image/png;base64,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", + "https://avatars.githubusercontent.com/u/29436595?v=", + ], +) +def test_bedrock_claude_3(image_url): try: litellm.set_verbose = True data = { @@ -294,7 +303,7 @@ def test_bedrock_claude_3(): { "image_url": { "detail": "high", - "url": "data:image/png;base64,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", + "url": image_url, }, "type": "image_url", }, @@ -313,7 +322,6 @@ def test_bedrock_claude_3(): # Add any assertions here to check the response assert len(response.choices) > 0 assert len(response.choices[0].message.content) > 0 - except RateLimitError: pass except Exception as e: @@ -552,7 +560,7 @@ def test_bedrock_ptu(): assert "url" in mock_client_post.call_args.kwargs assert ( mock_client_post.call_args.kwargs["url"] - == "https://bedrock-runtime.us-west-2.amazonaws.com/model/arn%3Aaws%3Abedrock%3Aus-west-2%3A888602223428%3Aprovisioned-model%2F8fxff74qyhs3/invoke" + == "https://bedrock-runtime.us-west-2.amazonaws.com/model/arn%3Aaws%3Abedrock%3Aus-west-2%3A888602223428%3Aprovisioned-model%2F8fxff74qyhs3/converse" ) mock_client_post.assert_called_once() diff --git a/litellm/tests/test_completion.py b/litellm/tests/test_completion.py index d143d1ab80..98898052b1 100644 --- a/litellm/tests/test_completion.py +++ b/litellm/tests/test_completion.py @@ -300,7 +300,11 @@ def test_completion_claude_3(): pytest.fail(f"Error occurred: {e}") -def test_completion_claude_3_function_call(): +@pytest.mark.parametrize( + "model", + ["anthropic/claude-3-opus-20240229", "anthropic.claude-3-sonnet-20240229-v1:0"], +) +def test_completion_claude_3_function_call(model): litellm.set_verbose = True tools = [ { @@ -331,13 +335,14 @@ def test_completion_claude_3_function_call(): try: # test without max tokens response = completion( - model="anthropic/claude-3-opus-20240229", + model=model, messages=messages, tools=tools, tool_choice={ "type": "function", "function": {"name": "get_current_weather"}, }, + drop_params=True, ) # Add any assertions, here to check response args @@ -364,10 +369,11 @@ def test_completion_claude_3_function_call(): ) # In the second response, Claude should deduce answer from tool results second_response = completion( - model="anthropic/claude-3-opus-20240229", + model=model, messages=messages, tools=tools, tool_choice="auto", + drop_params=True, ) print(second_response) except Exception as e: @@ -2162,6 +2168,7 @@ def test_completion_azure_key_completion_arg(): logprobs=True, max_tokens=10, ) + print(f"response: {response}") print("Hidden Params", response._hidden_params) @@ -2534,6 +2541,7 @@ def test_replicate_custom_prompt_dict(): "content": "what is yc write 1 paragraph", } ], + mock_response="Hello world", repetition_penalty=0.1, num_retries=3, ) diff --git a/litellm/tests/test_image_generation.py b/litellm/tests/test_image_generation.py index fea014e67a..1d3d53f70a 100644 --- a/litellm/tests/test_image_generation.py +++ b/litellm/tests/test_image_generation.py @@ -76,7 +76,7 @@ def test_image_generation_azure_dall_e_3(): ) print(f"response: {response}") assert len(response.data) > 0 - except litellm.RateLimitError as e: + except litellm.InternalServerError as e: pass except litellm.ContentPolicyViolationError: pass # OpenAI randomly raises these errors - skip when they occur diff --git a/litellm/tests/test_key_generate_prisma.py b/litellm/tests/test_key_generate_prisma.py index 148d32cd9c..083d84c2b5 100644 --- a/litellm/tests/test_key_generate_prisma.py +++ b/litellm/tests/test_key_generate_prisma.py @@ -2248,3 +2248,55 @@ async def test_create_update_team(prisma_client): assert _team_info["budget_reset_at"] is not None and isinstance( _team_info["budget_reset_at"], datetime.datetime ) + + +@pytest.mark.asyncio() +async def test_enforced_params(prisma_client): + setattr(litellm.proxy.proxy_server, "prisma_client", prisma_client) + setattr(litellm.proxy.proxy_server, "master_key", "sk-1234") + from litellm.proxy.proxy_server import general_settings + + general_settings["enforced_params"] = [ + "user", + "metadata", + "metadata.generation_name", + ] + + await litellm.proxy.proxy_server.prisma_client.connect() + request = NewUserRequest() + key = await new_user(request) + print(key) + + generated_key = key.key + bearer_token = "Bearer " + generated_key + + request = Request(scope={"type": "http"}) + request._url = URL(url="/chat/completions") + + # Case 1: Missing user + async def return_body(): + return b'{"model": "gemini-pro-vision"}' + + request.body = return_body + try: + await user_api_key_auth(request=request, api_key=bearer_token) + pytest.fail(f"This should have failed!. IT's an invalid request") + except Exception as e: + assert ( + "BadRequest please pass param=user in request body. This is a required param" + in e.message + ) + + # Case 2: Missing metadata["generation_name"] + async def return_body_2(): + return b'{"model": "gemini-pro-vision", "user": "1234", "metadata": {}}' + + request.body = return_body_2 + try: + await user_api_key_auth(request=request, api_key=bearer_token) + pytest.fail(f"This should have failed!. IT's an invalid request") + except Exception as e: + assert ( + "Authentication Error, BadRequest please pass param=[metadata][generation_name] in request body" + in e.message + ) diff --git a/litellm/tests/test_lowest_latency_routing.py b/litellm/tests/test_lowest_latency_routing.py index f9f510673d..d83cdef9ad 100644 --- a/litellm/tests/test_lowest_latency_routing.py +++ b/litellm/tests/test_lowest_latency_routing.py @@ -275,7 +275,7 @@ async def _deploy(lowest_latency_logger, deployment_id, tokens_used, duration): } start_time = time.time() response_obj = {"usage": {"total_tokens": tokens_used}} - time.sleep(duration) + await asyncio.sleep(duration) end_time = time.time() lowest_latency_logger.log_success_event( response_obj=response_obj, @@ -325,6 +325,7 @@ def test_get_available_endpoints_tpm_rpm_check_async(ans_rpm): d1 = [(lowest_latency_logger, "1234", 50, 0.01)] * non_ans_rpm d2 = [(lowest_latency_logger, "5678", 50, 0.01)] * non_ans_rpm asyncio.run(_gather_deploy([*d1, *d2])) + time.sleep(3) ## CHECK WHAT'S SELECTED ## d_ans = lowest_latency_logger.get_available_deployments( model_group=model_group, healthy_deployments=model_list diff --git a/litellm/tests/test_prompt_factory.py b/litellm/tests/test_prompt_factory.py index 2fc04ec528..b3aafab6e6 100644 --- a/litellm/tests/test_prompt_factory.py +++ b/litellm/tests/test_prompt_factory.py @@ -15,6 +15,7 @@ from litellm.llms.prompt_templates.factory import ( claude_2_1_pt, llama_2_chat_pt, prompt_factory, + _bedrock_tools_pt, ) @@ -128,3 +129,27 @@ def test_anthropic_messages_pt(): # codellama_prompt_format() +def test_bedrock_tool_calling_pt(): + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } + ] + converted_tools = _bedrock_tools_pt(tools=tools) + + print(converted_tools) diff --git a/litellm/tests/test_router.py b/litellm/tests/test_router.py index d76dec25c7..02bf9a16b8 100644 --- a/litellm/tests/test_router.py +++ b/litellm/tests/test_router.py @@ -38,6 +38,48 @@ def test_router_sensitive_keys(): assert "special-key" not in str(e) +def test_router_order(): + """ + Asserts for 2 models in a model group, model with order=1 always called first + """ + router = Router( + model_list=[ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-4o", + "api_key": os.getenv("OPENAI_API_KEY"), + "mock_response": "Hello world", + "order": 1, + }, + "model_info": {"id": "1"}, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-4o", + "api_key": "bad-key", + "mock_response": Exception("this is a bad key"), + "order": 2, + }, + "model_info": {"id": "2"}, + }, + ], + num_retries=0, + allowed_fails=0, + enable_pre_call_checks=True, + ) + + for _ in range(100): + response = router.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hey, how's it going?"}], + ) + + assert isinstance(response, litellm.ModelResponse) + assert response._hidden_params["model_id"] == "1" + + @pytest.mark.parametrize("num_retries", [None, 2]) @pytest.mark.parametrize("max_retries", [None, 4]) def test_router_num_retries_init(num_retries, max_retries): diff --git a/litellm/tests/test_streaming.py b/litellm/tests/test_streaming.py index c24de601f5..a5e098b027 100644 --- a/litellm/tests/test_streaming.py +++ b/litellm/tests/test_streaming.py @@ -1284,18 +1284,18 @@ async def test_completion_replicate_llama3_streaming(sync_mode): # pytest.fail(f"Error occurred: {e}") -@pytest.mark.parametrize("sync_mode", [True, False]) +@pytest.mark.parametrize("sync_mode", [True]) # False @pytest.mark.parametrize( "model", [ - # "bedrock/cohere.command-r-plus-v1:0", - # "anthropic.claude-3-sonnet-20240229-v1:0", - # "anthropic.claude-instant-v1", - # "bedrock/ai21.j2-mid", - # "mistral.mistral-7b-instruct-v0:2", - # "bedrock/amazon.titan-tg1-large", - # "meta.llama3-8b-instruct-v1:0", - "cohere.command-text-v14" + "bedrock/cohere.command-r-plus-v1:0", + "anthropic.claude-3-sonnet-20240229-v1:0", + "anthropic.claude-instant-v1", + "bedrock/ai21.j2-mid", + "mistral.mistral-7b-instruct-v0:2", + "bedrock/amazon.titan-tg1-large", + "meta.llama3-8b-instruct-v1:0", + "cohere.command-text-v14", ], ) @pytest.mark.asyncio diff --git a/litellm/tests/test_token_counter.py b/litellm/tests/test_token_counter.py index 194dfb8af3..a6f7cd7616 100644 --- a/litellm/tests/test_token_counter.py +++ b/litellm/tests/test_token_counter.py @@ -186,3 +186,13 @@ def test_load_test_token_counter(model): total_time = end_time - start_time print("model={}, total test time={}".format(model, total_time)) assert total_time < 10, f"Total encoding time > 10s, {total_time}" + +def test_openai_token_with_image_and_text(): + model = "gpt-4o" + full_request = {'model': 'gpt-4o', 'tools': [{'type': 'function', 'function': {'name': 'json', 'parameters': {'type': 'object', 'required': ['clause'], 'properties': {'clause': {'type': 'string'}}}, 'description': 'Respond with a JSON object.'}}], 'logprobs': False, 'messages': [{'role': 'user', 'content': [{'text': '\n Just some long text, long long text, and you know it will be longer than 7 tokens definetly.', 'type': 'text'}]}], 'tool_choice': {'type': 'function', 'function': {'name': 'json'}}, 'exclude_models': [], 'disable_fallback': False, 'exclude_providers': []} + messages = full_request.get("messages", []) + + token_count = token_counter(model=model, messages=messages) + print(token_count) + +test_openai_token_with_image_and_text() \ No newline at end of file diff --git a/litellm/types/files.py b/litellm/types/files.py new file mode 100644 index 0000000000..0545567ece --- /dev/null +++ b/litellm/types/files.py @@ -0,0 +1,267 @@ +from enum import Enum +from types import MappingProxyType +from typing import List, Set + +""" +Base Enums/Consts +""" +class FileType(Enum): + AAC = "AAC" + CSV = "CSV" + DOC = "DOC" + DOCX = "DOCX" + FLAC = "FLAC" + FLV = "FLV" + GIF = "GIF" + GOOGLE_DOC = "GOOGLE_DOC" + GOOGLE_DRAWINGS = "GOOGLE_DRAWINGS" + GOOGLE_SHEETS = "GOOGLE_SHEETS" + GOOGLE_SLIDES = "GOOGLE_SLIDES" + HEIC = "HEIC" + HEIF = "HEIF" + HTML = "HTML" + JPEG = "JPEG" + JSON = "JSON" + M4A = "M4A" + M4V = "M4V" + MOV = "MOV" + MP3 = "MP3" + MP4 = "MP4" + MPEG = "MPEG" + MPEGPS = "MPEGPS" + MPG = "MPG" + MPA = "MPA" + MPGA = "MPGA" + OGG = "OGG" + OPUS = "OPUS" + PDF = "PDF" + PCM = "PCM" + PNG = "PNG" + PPT = "PPT" + PPTX = "PPTX" + RTF = "RTF" + THREE_GPP = "3GPP" + TXT = "TXT" + WAV = "WAV" + WEBM = "WEBM" + WEBP = "WEBP" + WMV = "WMV" + XLS = "XLS" + XLSX = "XLSX" + +FILE_EXTENSIONS: MappingProxyType[FileType, List[str]] = MappingProxyType({ + FileType.AAC: ["aac"], + FileType.CSV: ["csv"], + FileType.DOC: ["doc"], + FileType.DOCX: ["docx"], + FileType.FLAC: ["flac"], + FileType.FLV: ["flv"], + FileType.GIF: ["gif"], + FileType.GOOGLE_DOC: ["gdoc"], + FileType.GOOGLE_DRAWINGS: ["gdraw"], + FileType.GOOGLE_SHEETS: ["gsheet"], + FileType.GOOGLE_SLIDES: ["gslides"], + FileType.HEIC: ["heic"], + FileType.HEIF: ["heif"], + FileType.HTML: ["html", "htm"], + FileType.JPEG: ["jpeg", "jpg"], + FileType.JSON: ["json"], + FileType.M4A: ["m4a"], + FileType.M4V: ["m4v"], + FileType.MOV: ["mov"], + FileType.MP3: ["mp3"], + FileType.MP4: ["mp4"], + FileType.MPEG: ["mpeg"], + FileType.MPEGPS: ["mpegps"], + FileType.MPG: ["mpg"], + FileType.MPA: ["mpa"], + FileType.MPGA: ["mpga"], + FileType.OGG: ["ogg"], + FileType.OPUS: ["opus"], + FileType.PDF: ["pdf"], + FileType.PCM: ["pcm"], + FileType.PNG: ["png"], + FileType.PPT: ["ppt"], + FileType.PPTX: ["pptx"], + FileType.RTF: ["rtf"], + FileType.THREE_GPP: ["3gpp"], + FileType.TXT: ["txt"], + FileType.WAV: ["wav"], + FileType.WEBM: ["webm"], + FileType.WEBP: ["webp"], + FileType.WMV: ["wmv"], + FileType.XLS: ["xls"], + FileType.XLSX: ["xlsx"], +}) + +FILE_MIME_TYPES: MappingProxyType[FileType, str] = MappingProxyType({ + FileType.AAC: "audio/aac", + FileType.CSV: "text/csv", + FileType.DOC: "application/msword", + FileType.DOCX: "application/vnd.openxmlformats-officedocument.wordprocessingml.document", + FileType.FLAC: "audio/flac", + FileType.FLV: "video/x-flv", + FileType.GIF: "image/gif", + FileType.GOOGLE_DOC: "application/vnd.google-apps.document", + FileType.GOOGLE_DRAWINGS: "application/vnd.google-apps.drawing", + FileType.GOOGLE_SHEETS: "application/vnd.google-apps.spreadsheet", + FileType.GOOGLE_SLIDES: "application/vnd.google-apps.presentation", + FileType.HEIC: "image/heic", + FileType.HEIF: "image/heif", + FileType.HTML: "text/html", + FileType.JPEG: "image/jpeg", + FileType.JSON: "application/json", + FileType.M4A: "audio/x-m4a", + FileType.M4V: "video/x-m4v", + FileType.MOV: "video/quicktime", + FileType.MP3: "audio/mpeg", + FileType.MP4: "video/mp4", + FileType.MPEG: "video/mpeg", + FileType.MPEGPS: "video/mpegps", + FileType.MPG: "video/mpg", + FileType.MPA: "audio/m4a", + FileType.MPGA: "audio/mpga", + FileType.OGG: "audio/ogg", + FileType.OPUS: "audio/opus", + FileType.PDF: "application/pdf", + FileType.PCM: "audio/pcm", + FileType.PNG: "image/png", + FileType.PPT: "application/vnd.ms-powerpoint", + FileType.PPTX: "application/vnd.openxmlformats-officedocument.presentationml.presentation", + FileType.RTF: "application/rtf", + FileType.THREE_GPP: "video/3gpp", + FileType.TXT: "text/plain", + FileType.WAV: "audio/wav", + FileType.WEBM: "video/webm", + FileType.WEBP: "image/webp", + FileType.WMV: "video/wmv", + FileType.XLS: "application/vnd.ms-excel", + FileType.XLSX: "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", +}) + +""" +Util Functions +""" +def get_file_mime_type_from_extension(extension: str) -> str: + for file_type, extensions in FILE_EXTENSIONS.items(): + if extension in extensions: + return FILE_MIME_TYPES[file_type] + raise ValueError(f"Unknown mime type for extension: {extension}") + + +def get_file_extension_from_mime_type(mime_type: str) -> str: + for file_type, mime in FILE_MIME_TYPES.items(): + if mime == mime_type: + return FILE_EXTENSIONS[file_type][0] + raise ValueError(f"Unknown extension for mime type: {mime_type}") + + +def get_file_type_from_extension(extension: str) -> FileType: + for file_type, extensions in FILE_EXTENSIONS.items(): + if extension in extensions: + return file_type + + raise ValueError(f"Unknown file type for extension: {extension}") + + +def get_file_extension_for_file_type(file_type: FileType) -> str: + return FILE_EXTENSIONS[file_type][0] + +def get_file_mime_type_for_file_type(file_type: FileType) -> str: + return FILE_MIME_TYPES[file_type] + + +""" +FileType Type Groupings (Videos, Images, etc) +""" + +# Images +IMAGE_FILE_TYPES = { + FileType.PNG, + FileType.JPEG, + FileType.GIF, + FileType.WEBP, + FileType.HEIC, + FileType.HEIF +} + +def is_image_file_type(file_type): + return file_type in IMAGE_FILE_TYPES + +# Videos +VIDEO_FILE_TYPES = { + FileType.MOV, + FileType.MP4, + FileType.MPEG, + FileType.M4V, + FileType.FLV, + FileType.MPEGPS, + FileType.MPG, + FileType.WEBM, + FileType.WMV, + FileType.THREE_GPP +} + +def is_video_file_type(file_type): + return file_type in VIDEO_FILE_TYPES + +# Audio +AUDIO_FILE_TYPES = { + FileType.AAC, + FileType.FLAC, + FileType.MP3, + FileType.MPA, + FileType.MPGA, + FileType.OPUS, + FileType.PCM, + FileType.WAV, +} + +def is_audio_file_type(file_type): + return file_type in AUDIO_FILE_TYPES + +# Text +TEXT_FILE_TYPES = { + FileType.CSV, + FileType.HTML, + FileType.RTF, + FileType.TXT +} + +def is_text_file_type(file_type): + return file_type in TEXT_FILE_TYPES + +""" +Other FileType Groupings +""" +# Accepted file types for GEMINI 1.5 through Vertex AI +# https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/send-multimodal-prompts#gemini-send-multimodal-samples-images-nodejs +GEMINI_1_5_ACCEPTED_FILE_TYPES: Set[FileType] = { + # Image + FileType.PNG, + FileType.JPEG, + # Audio + FileType.AAC, + FileType.FLAC, + FileType.MP3, + FileType.MPA, + FileType.MPGA, + FileType.OPUS, + FileType.PCM, + FileType.WAV, + # Video + FileType.FLV, + FileType.MOV, + FileType.MPEG, + FileType.MPEGPS, + FileType.MPG, + FileType.MP4, + FileType.WEBM, + FileType.WMV, + FileType.THREE_GPP, + # PDF + FileType.PDF, +} + +def is_gemini_1_5_accepted_file_type(file_type: FileType) -> bool: + return file_type in GEMINI_1_5_ACCEPTED_FILE_TYPES diff --git a/litellm/types/llms/bedrock.py b/litellm/types/llms/bedrock.py index 0c82596827..b06075092f 100644 --- a/litellm/types/llms/bedrock.py +++ b/litellm/types/llms/bedrock.py @@ -1,4 +1,4 @@ -from typing import TypedDict, Any, Union, Optional +from typing import TypedDict, Any, Union, Optional, Literal, List import json from typing_extensions import ( Self, @@ -11,10 +11,137 @@ from typing_extensions import ( ) +class SystemContentBlock(TypedDict): + text: str + + +class ImageSourceBlock(TypedDict): + bytes: Optional[str] # base 64 encoded string + + +class ImageBlock(TypedDict): + format: Literal["png", "jpeg", "gif", "webp"] + source: ImageSourceBlock + + +class ToolResultContentBlock(TypedDict, total=False): + image: ImageBlock + json: dict + text: str + + +class ToolResultBlock(TypedDict, total=False): + content: Required[List[ToolResultContentBlock]] + toolUseId: Required[str] + status: Literal["success", "error"] + + +class ToolUseBlock(TypedDict): + input: dict + name: str + toolUseId: str + + +class ContentBlock(TypedDict, total=False): + text: str + image: ImageBlock + toolResult: ToolResultBlock + toolUse: ToolUseBlock + + +class MessageBlock(TypedDict): + content: List[ContentBlock] + role: Literal["user", "assistant"] + + +class ConverseMetricsBlock(TypedDict): + latencyMs: float # time in ms + + +class ConverseResponseOutputBlock(TypedDict): + message: Optional[MessageBlock] + + +class ConverseTokenUsageBlock(TypedDict): + inputTokens: int + outputTokens: int + totalTokens: int + + +class ConverseResponseBlock(TypedDict): + additionalModelResponseFields: dict + metrics: ConverseMetricsBlock + output: ConverseResponseOutputBlock + stopReason: ( + str # end_turn | tool_use | max_tokens | stop_sequence | content_filtered + ) + usage: ConverseTokenUsageBlock + + +class ToolInputSchemaBlock(TypedDict): + json: Optional[dict] + + +class ToolSpecBlock(TypedDict, total=False): + inputSchema: Required[ToolInputSchemaBlock] + name: Required[str] + description: str + + +class ToolBlock(TypedDict): + toolSpec: Optional[ToolSpecBlock] + + +class SpecificToolChoiceBlock(TypedDict): + name: str + + +class ToolChoiceValuesBlock(TypedDict, total=False): + any: dict + auto: dict + tool: SpecificToolChoiceBlock + + +class ToolConfigBlock(TypedDict, total=False): + tools: Required[List[ToolBlock]] + toolChoice: Union[str, ToolChoiceValuesBlock] + + +class InferenceConfig(TypedDict, total=False): + maxTokens: int + stopSequences: List[str] + temperature: float + topP: float + + +class ToolBlockDeltaEvent(TypedDict): + input: str + + +class ContentBlockDeltaEvent(TypedDict, total=False): + """ + Either 'text' or 'toolUse' will be specified for Converse API streaming response. + """ + + text: str + toolUse: ToolBlockDeltaEvent + + +class RequestObject(TypedDict, total=False): + additionalModelRequestFields: dict + additionalModelResponseFieldPaths: List[str] + inferenceConfig: InferenceConfig + messages: Required[List[MessageBlock]] + system: List[SystemContentBlock] + toolConfig: ToolConfigBlock + + class GenericStreamingChunk(TypedDict): text: Required[str] + tool_str: Required[str] is_finished: Required[bool] finish_reason: Required[str] + usage: Optional[ConverseTokenUsageBlock] class Document(TypedDict): diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index bc0c82434f..7861e394cd 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -293,3 +293,20 @@ class ListBatchRequest(TypedDict, total=False): extra_headers: Optional[Dict[str, str]] extra_body: Optional[Dict[str, str]] timeout: Optional[float] + + +class ChatCompletionToolCallFunctionChunk(TypedDict): + name: str + arguments: str + + +class ChatCompletionToolCallChunk(TypedDict): + id: str + type: Literal["function"] + function: ChatCompletionToolCallFunctionChunk + + +class ChatCompletionResponseMessage(TypedDict, total=False): + content: Optional[str] + tool_calls: List[ChatCompletionToolCallChunk] + role: Literal["assistant"] diff --git a/litellm/utils.py b/litellm/utils.py index be7728dfef..ad9779f2d7 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -239,6 +239,8 @@ def map_finish_reason( return "length" elif finish_reason == "tool_use": # anthropic return "tool_calls" + elif finish_reason == "content_filtered": + return "content_filter" return finish_reason @@ -1372,8 +1374,12 @@ class Logging: callback_func=callback, ) except Exception as e: - traceback.print_exc() - print_verbose( + verbose_logger.error( + "litellm.Logging.pre_call(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug( f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while input logging with integrations {traceback.format_exc()}" ) print_verbose( @@ -4060,6 +4066,9 @@ def openai_token_counter( for c in value: if c["type"] == "text": text += c["text"] + num_tokens += len( + encoding.encode(c["text"], disallowed_special=()) + ) elif c["type"] == "image_url": if isinstance(c["image_url"], dict): image_url_dict = c["image_url"] @@ -5634,19 +5643,29 @@ def get_optional_params( optional_params["stream"] = stream elif "anthropic" in model: _check_valid_arg(supported_params=supported_params) - # anthropic params on bedrock - # \"max_tokens_to_sample\":300,\"temperature\":0.5,\"top_p\":1,\"stop_sequences\":[\"\\\\n\\\\nHuman:\"]}" - if model.startswith("anthropic.claude-3"): - optional_params = ( - litellm.AmazonAnthropicClaude3Config().map_openai_params( + if "aws_bedrock_client" in passed_params: # deprecated boto3.invoke route. + if model.startswith("anthropic.claude-3"): + optional_params = ( + litellm.AmazonAnthropicClaude3Config().map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + ) + ) + else: + optional_params = litellm.AmazonAnthropicConfig().map_openai_params( non_default_params=non_default_params, optional_params=optional_params, ) - ) - else: - optional_params = litellm.AmazonAnthropicConfig().map_openai_params( + else: # bedrock httpx route + optional_params = litellm.AmazonConverseConfig().map_openai_params( + model=model, non_default_params=non_default_params, optional_params=optional_params, + drop_params=( + drop_params + if drop_params is not None and isinstance(drop_params, bool) + else False + ), ) elif "amazon" in model: # amazon titan llms _check_valid_arg(supported_params=supported_params) @@ -6198,6 +6217,27 @@ def calculate_max_parallel_requests( return None +def _get_order_filtered_deployments(healthy_deployments: List[Dict]) -> List: + min_order = min( + ( + deployment["litellm_params"]["order"] + for deployment in healthy_deployments + if "order" in deployment["litellm_params"] + ), + default=None, + ) + + if min_order is not None: + filtered_deployments = [ + deployment + for deployment in healthy_deployments + if deployment["litellm_params"].get("order") == min_order + ] + + return filtered_deployments + return healthy_deployments + + def _get_model_region( custom_llm_provider: str, litellm_params: LiteLLM_Params ) -> Optional[str]: @@ -6403,20 +6443,7 @@ def get_supported_openai_params( - None if unmapped """ if custom_llm_provider == "bedrock": - if model.startswith("anthropic.claude-3"): - return litellm.AmazonAnthropicClaude3Config().get_supported_openai_params() - elif model.startswith("anthropic"): - return litellm.AmazonAnthropicConfig().get_supported_openai_params() - elif model.startswith("ai21"): - return ["max_tokens", "temperature", "top_p", "stream"] - elif model.startswith("amazon"): - return ["max_tokens", "temperature", "stop", "top_p", "stream"] - elif model.startswith("meta"): - return ["max_tokens", "temperature", "top_p", "stream"] - elif model.startswith("cohere"): - return ["stream", "temperature", "max_tokens"] - elif model.startswith("mistral"): - return ["max_tokens", "temperature", "stop", "top_p", "stream"] + return litellm.AmazonConverseConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "ollama": return litellm.OllamaConfig().get_supported_openai_params() elif custom_llm_provider == "ollama_chat": @@ -8516,7 +8543,11 @@ def exception_type( extra_information = f"\nModel: {model}" if _api_base: extra_information += f"\nAPI Base: `{_api_base}`" - if messages and len(messages) > 0: + if ( + messages + and len(messages) > 0 + and litellm.redact_messages_in_exceptions is False + ): extra_information += f"\nMessages: `{messages}`" if _model_group is not None: @@ -9803,8 +9834,7 @@ def exception_type( elif custom_llm_provider == "azure": if "Internal server error" in error_str: exception_mapping_worked = True - raise APIError( - status_code=500, + raise litellm.InternalServerError( message=f"AzureException Internal server error - {original_exception.message}", llm_provider="azure", model=model, @@ -10054,6 +10084,8 @@ def get_secret( ): key_management_system = litellm._key_management_system key_management_settings = litellm._key_management_settings + args = locals() + if secret_name.startswith("os.environ/"): secret_name = secret_name.replace("os.environ/", "") @@ -10141,13 +10173,13 @@ def get_secret( key_manager = "local" if ( - key_manager == KeyManagementSystem.AZURE_KEY_VAULT + key_manager == KeyManagementSystem.AZURE_KEY_VAULT.value or type(client).__module__ + "." + type(client).__name__ == "azure.keyvault.secrets._client.SecretClient" ): # support Azure Secret Client - from azure.keyvault.secrets import SecretClient secret = client.get_secret(secret_name).value elif ( - key_manager == KeyManagementSystem.GOOGLE_KMS + key_manager == KeyManagementSystem.GOOGLE_KMS.value or client.__class__.__name__ == "KeyManagementServiceClient" ): encrypted_secret: Any = os.getenv(secret_name) @@ -10175,6 +10207,25 @@ def get_secret( secret = response.plaintext.decode( "utf-8" ) # assumes the original value was encoded with utf-8 + elif key_manager == KeyManagementSystem.AWS_KMS.value: + """ + Only check the tokens which start with 'aws_kms/'. This prevents latency impact caused by checking all keys. + """ + encrypted_value = os.getenv(secret_name, None) + if encrypted_value is None: + raise Exception("encrypted value for AWS KMS cannot be None.") + # Decode the base64 encoded ciphertext + ciphertext_blob = base64.b64decode(encrypted_value) + + # Set up the parameters for the decrypt call + params = {"CiphertextBlob": ciphertext_blob} + + # Perform the decryption + response = client.decrypt(**params) + + # Extract and decode the plaintext + plaintext = response["Plaintext"] + secret = plaintext.decode("utf-8") elif key_manager == KeyManagementSystem.AWS_SECRET_MANAGER.value: try: get_secret_value_response = client.get_secret_value( @@ -10195,10 +10246,14 @@ def get_secret( for k, v in secret_dict.items(): secret = v print_verbose(f"secret: {secret}") + elif key_manager == "local": + secret = os.getenv(secret_name) else: # assume the default is infisicial client secret = client.get_secret(secret_name).secret_value except Exception as e: # check if it's in os.environ - print_verbose(f"An exception occurred - {str(e)}") + verbose_logger.error( + f"An exception occurred - {str(e)}\n\n{traceback.format_exc()}" + ) secret = os.getenv(secret_name) try: secret_value_as_bool = ast.literal_eval(secret) @@ -10532,7 +10587,12 @@ class CustomStreamWrapper: "finish_reason": finish_reason, } except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.CustomStreamWrapper.handle_predibase_chunk(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) raise e def handle_huggingface_chunk(self, chunk): @@ -10576,7 +10636,12 @@ class CustomStreamWrapper: "finish_reason": finish_reason, } except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.CustomStreamWrapper.handle_huggingface_chunk(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) raise e def handle_ai21_chunk(self, chunk): # fake streaming @@ -10811,7 +10876,12 @@ class CustomStreamWrapper: "usage": usage, } except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.CustomStreamWrapper.handle_openai_chat_completion_chunk(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) raise e def handle_azure_text_completion_chunk(self, chunk): @@ -10892,7 +10962,12 @@ class CustomStreamWrapper: else: return "" except: - traceback.print_exc() + verbose_logger.error( + "litellm.CustomStreamWrapper.handle_baseten_chunk(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) return "" def handle_cloudlfare_stream(self, chunk): @@ -11091,7 +11166,12 @@ class CustomStreamWrapper: "is_finished": True, } except: - traceback.print_exc() + verbose_logger.error( + "litellm.CustomStreamWrapper.handle_clarifai_chunk(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) return "" def model_response_creator(self): @@ -11332,12 +11412,27 @@ class CustomStreamWrapper: if response_obj["is_finished"]: self.received_finish_reason = response_obj["finish_reason"] elif self.custom_llm_provider == "bedrock": + from litellm.types.llms.bedrock import GenericStreamingChunk + if self.received_finish_reason is not None: raise StopIteration - response_obj = self.handle_bedrock_stream(chunk) + response_obj: GenericStreamingChunk = chunk completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: self.received_finish_reason = response_obj["finish_reason"] + + if ( + self.stream_options + and self.stream_options.get("include_usage", False) is True + and response_obj["usage"] is not None + ): + self.sent_stream_usage = True + model_response.usage = litellm.Usage( + prompt_tokens=response_obj["usage"]["inputTokens"], + completion_tokens=response_obj["usage"]["outputTokens"], + total_tokens=response_obj["usage"]["totalTokens"], + ) elif self.custom_llm_provider == "sagemaker": print_verbose(f"ENTERS SAGEMAKER STREAMING for chunk {chunk}") response_obj = self.handle_sagemaker_stream(chunk) @@ -11563,7 +11658,12 @@ class CustomStreamWrapper: tool["type"] = "function" model_response.choices[0].delta = Delta(**_json_delta) except Exception as e: - traceback.print_exc() + verbose_logger.error( + "litellm.CustomStreamWrapper.chunk_creator(): Exception occured - {}".format( + str(e) + ) + ) + verbose_logger.debug(traceback.format_exc()) model_response.choices[0].delta = Delta() else: try: @@ -11599,7 +11699,7 @@ class CustomStreamWrapper: and hasattr(model_response, "usage") and hasattr(model_response.usage, "prompt_tokens") ): - if self.sent_first_chunk == False: + if self.sent_first_chunk is False: completion_obj["role"] = "assistant" self.sent_first_chunk = True model_response.choices[0].delta = Delta(**completion_obj) @@ -11767,6 +11867,8 @@ class CustomStreamWrapper: def __next__(self): try: + if self.completion_stream is None: + self.fetch_sync_stream() while True: if ( isinstance(self.completion_stream, str) @@ -11841,6 +11943,14 @@ class CustomStreamWrapper: custom_llm_provider=self.custom_llm_provider, ) + def fetch_sync_stream(self): + if self.completion_stream is None and self.make_call is not None: + # Call make_call to get the completion stream + self.completion_stream = self.make_call(client=litellm.module_level_client) + self._stream_iter = self.completion_stream.__iter__() + + return self.completion_stream + async def fetch_stream(self): if self.completion_stream is None and self.make_call is not None: # Call make_call to get the completion stream diff --git a/pyproject.toml b/pyproject.toml index 648a8b41ac..a472ae1956 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,10 +1,14 @@ [tool.poetry] name = "litellm" -version = "1.40.4" +version = "1.40.5" description = "Library to easily interface with LLM API providers" authors = ["BerriAI"] license = "MIT" readme = "README.md" +packages = [ + { include = "litellm" }, + { include = "litellm/py.typed"}, +] [tool.poetry.urls] homepage = "https://litellm.ai" @@ -80,7 +84,7 @@ requires = ["poetry-core", "wheel"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "1.40.4" +version = "1.40.5" version_files = [ "pyproject.toml:^version" ] diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 0000000000..dfb323c1b3 --- /dev/null +++ b/ruff.toml @@ -0,0 +1,3 @@ +ignore = ["F405"] +extend-select = ["E501"] +line-length = 120