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* fix(cache_control): let a hosted deployment opt into the OpenAI cache dialect _targets_openai_prompt_cache_breakpoint gated on custom_llm_provider == "openai" unconditionally, so an OpenAI-shaped model served by another provider could never qualify, even with supports_prompt_cache_breakpoint set explicitly on its own cost-map entry. bedrock_mantle honours prompt_cache_breakpoint end to end, and had no way to say so. model_cost is keyed per exact deployment string, so a flag on the deployment's own entry states the dialect more precisely than a provider name can. Consult it before the provider check, and require the entry's litellm_provider to match the serving provider so an openai entry cannot license another provider. Entries for the openai provider keep their api_base check, so an OpenAI-compatible third-party host is still not assumed to speak the dialect. Fixes #38666 * test: drop a laziness assertion the existing suite already makes test_provider_lookup_skipped_for_models_below_gpt_5_6 already pins that _resolve_provider is not called for an unflagged model, and it covers the new flag lookup unchanged. The duplicate patched a litellm internal for no added coverage, which the test-quality gate counts against TQ008. * fix(bedrock_mantle): flag GPT-5.6 and newer rows for OpenAI explicit prompt cache breakpoints * fix(responses): predict the chat-completions bridge with the model name the dispatch resolves * fix(cache_control): read a region-prefixed Mantle GPT deployment through its region-free price-map row * fix(cache_control): let a bare hosted deployment name read its own provider's price-map row * fix(responses): hand the hook the provider the router resolved for a Foundry GPT deployment * fix(cache_control): read the deployment breakpoint flag strictly and default a null prompt_cache_options A cost-map or deployment `supports_prompt_cache_breakpoint` that is not the boolean `true` (the string "true", the integer 1, "false", 0, a long string) no longer opts a deployment into the OpenAI prompt cache dialect; only `true` does, the same reading Bedrock Converse applies to its own flag. A client that sends `"prompt_cache_options": null` on `/v1/chat/completions` or `/v1/messages` now gets the implicit default the hook already applied on `/v1/responses` when it placed a breakpoint; before, the null suppressed the default and the request left with a breakpoint and no options. * test(integration): audit cells for the Mantle GPT prompt cache breakpoint dialect Deterministic cells for the configured-breakpoint path on Bedrock Mantle GPT and Azure AI Foundry GPT-6 deployments: every endpoint, streaming and not, sync and async SDKs and raw httpx, the hostile option shapes, flag precedence, the response cache twin, a two-worker burst, a worker kill and a graceful restart. * fix(cache_control): ignore a malformed cache_control_injection_points value instead of failing the request A deployment or client `cache_control_injection_points` that is not a list of points (a string, an integer, a bare point dict, a list of strings) raised inside the prompt hook (`'str' object has no attribute 'get'`, `'int' object is not iterable`) and turned every request to that deployment into a 500 on `/v1/chat/completions`, `/v1/messages` and `/v1/responses`. Every entry point now reads such a value as no configured points, the way a `null` already read, and the request leaves without a breakpoint; entries of a list that are not points are dropped and the point entries kept. * test(integration): cover int, dict and string-list injection point shapes in the Mantle audit cells The D9 cell now also sends an integer, a bare point dict and a list of strings as the deployment's `cache_control_injection_points`, which the merge base answered with a 500 on every request. * fix(cache_control): stamp the OpenAI dialect for a bare deployment name served by a flagged provider A deployment written as `model: openai.gpt-5.6-sol` with `custom_llm_provider: bedrock_mantle` has no cost-map row of its own and no openai row of the same name, so the dialect stamp's cheap gate (`supports_openai_prompt_cache_breakpoint`) returned early and the configured point was never stamped. On `/v1/responses` and on the chat seeding path the hook sees no provider, so it fell back to the Anthropic `cache_control` marker, which Bedrock Mantle strips. The gate now also passes a model whose serving provider is already known and whose provider-keyed row carries the flag, which is the same row the dialect resolution reads and costs no provider lookup. A bare name without a provider is still left alone, so models below GPT-5.6 keep their points untouched and resolve nothing. * test(integration): cover a bare Mantle deployment name with its provider in the audit cells A deployment configured as `model: openai.gpt-5.6-sol` plus `custom_llm_provider: bedrock_mantle` sends the breakpoint and the implicit options on all three endpoints; the merge base leaves it on the Anthropic dialect. * fix(cache_control): keep a configured point that sits beside junk entries on every chat seed path `configured_injection_points` kept the point dicts of a mixed `cache_control_injection_points` list as a tuple, but only read a `list` back. The chat seed and the `/v1/responses` dialect stamp write the normalized value back onto the request, and on a deployment the OpenAI dialect does not stamp (an Anthropic model, Claude on Bedrock Mantle) that value is the tuple itself, so the hook then read it as no configured points and the valid point was silently dropped. The stamped OpenAI dialect and the `/v1/messages` path kept it only because they build a new list. The normalizer now reads back the tuple it wrote, so the point reaches the wire on every path; an all-dict list still passes through as the same object. * test(integration): cover a configured point beside junk entries on a Claude Mantle deployment A deployment configured with `cache_control_injection_points: ["system", {system point}, 3]` marks the system block on the Anthropic dialect on all three endpoints; the merge base answers 500 on the junk entry. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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| .. | ||
| _types | ||
| agentops | ||
| arize | ||
| azure_sentinel | ||
| azure_storage | ||
| bitbucket | ||
| clickhouse | ||
| cloudzero | ||
| code_interpreter_interception | ||
| compression_interception | ||
| datadog | ||
| deepeval | ||
| dotprompt | ||
| email_templates | ||
| focus | ||
| gcs_bucket | ||
| gcs_pubsub | ||
| generic_api | ||
| generic_prompt_management | ||
| gitlab | ||
| langfuse | ||
| levo | ||
| litellm_agent | ||
| mavvrik_focus | ||
| newrelic | ||
| opentelemetry_utils | ||
| opik | ||
| otel | ||
| pointfive | ||
| prometheus_helpers | ||
| SlackAlerting | ||
| vantage | ||
| vector_store_integrations | ||
| weave | ||
| websearch_interception | ||
| zerobus | ||
| __init__.py | ||
| adaptive_concurrency.py | ||
| additional_logging_utils.py | ||
| anthropic_cache_control_hook.py | ||
| argilla.py | ||
| athina.py | ||
| batch_utils.py | ||
| braintrust_logging.py | ||
| braintrust_mock_client.py | ||
| callback_configs.json | ||
| custom_batch_logger.py | ||
| custom_guardrail.py | ||
| custom_logger.py | ||
| custom_prompt_management.py | ||
| custom_secret_manager.py | ||
| custom_sso_handler.py | ||
| dynamodb.py | ||
| email_alerting.py | ||
| galileo.py | ||
| greenscale.py | ||
| helicone.py | ||
| helicone_mock_client.py | ||
| humanloop.py | ||
| lago.py | ||
| langsmith.py | ||
| langsmith_mock_client.py | ||
| langtrace.py | ||
| literal_ai.py | ||
| logfire_logger.py | ||
| lunary.py | ||
| mlflow.py | ||
| mock_client_factory.py | ||
| openmeter.py | ||
| opentelemetry.py | ||
| posthog.py | ||
| posthog_mock_client.py | ||
| prometheus.py | ||
| prometheus_metrics_endpoint.py | ||
| prometheus_services.py | ||
| prompt_layer.py | ||
| prompt_management_base.py | ||
| Readme.md | ||
| rubrik.py | ||
| s3.py | ||
| s3_v2.py | ||
| shadow_eval_logger.py | ||
| sqs.py | ||
| supabase.py | ||
| test_httpx.py | ||
| traceloop.py | ||
| weights_biases.py | ||
Integrations
This folder contains logging integrations for litellm
eg. logging to Datadog, Langfuse, Prometheus, s3, GCS Bucket, etc.