[Infra] Promote Internal Staging to main - #26545
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- Gemini 2.x charges per grounded prompt (flat $0.035), clamped to 1 regardless of internal query count - Gemini 3.x charges per search query ($0.014 each) - Extract web_search_requests from groundingMetadata in non-streaming responses (parity with streaming path) - Add search_context_cost_per_query to vertex_ai and base Gemini entries - Move tests to tests/test_litellm/ (CI directory)
…rature Add tests for the gpt-5.1/5.2/5.4 reasoning.effort interaction: - gpt-5.1 with no reasoning allows flexible temperature - gpt-5.1 with effort='high' drops temperature - gpt-5.4 with effort='none' allows flexible temperature
Document how each provider bills for web search, the search_context_cost_per_query field in model_prices JSON, how to override pricing via proxy config, and how LiteLLM extracts web_search_requests from each provider's response.
…ded model name check Replace _is_gemini_3_model() substring check with a web_search_billing_unit field in model_prices JSON: - "per_query": each search query billed individually (Gemini 3.x) - "per_prompt" (default): flat fee per grounded API call (Gemini 2.x) Add web_search_billing_unit to 23 Gemini 3.x model entries. Update docs and tests accordingly.
Zhipu GLM returns non-standard finish_reason values during streaming when inference fails mid-request, causing Pydantic validation crash: - "network_error" (inference interrupted) → map to "stop" - "sensitive" (content policy violation) → map to "content_filter" Fixes #23386
The vertex_ai cost calculator hardcoded $0.035 and charged for every call with a PromptTokensDetailsWrapper (not just web search calls). Delegate to the shared Gemini calculator which reads pricing and billing unit from model_info, fixing both issues for vertex_ai models.
fix: map Zhipu GLM non-standard finish_reason values
…ture-drop-params fix(responses-api): apply GPT-5 temperature validation
…lock-ordering fix(bedrock): sort assistant content blocks so text precedes toolUse
…pported-params fix(gemini): filter unsupported params from embedding requests
These providers have their own web search systems and pricing, independent of Google's grounding billing model.
fix(gemini): read web search cost from model_info instead of hardcode
Gemini API returns a DOCUMENT modality in promptTokensDetails for PDF inputs, but the token parser only handled TEXT, IMAGE, AUDIO, and VIDEO. DOCUMENT tokens were silently dropped, causing cost to be undercounted by up to 99% for PDF-heavy requests. Map DOCUMENT tokens to text_tokens since Gemini bills documents at the text token rate. Applied to all four modality parser loops: promptTokensDetails, cacheTokensDetails, responseTokensDetails, and candidatesTokensDetails. Fixes #24375
…equests The `dimensions` parameter was correctly mapped to `outputDimensionality` in `optional_params` but never placed in the request body. The Vertex AI predict endpoint expects it under a `parameters` field. Add `parameters` dict to `VertexMultimodalEmbeddingRequest` TypedDict and populate it from `optional_params` in `transform_embedding_request`. Fixes #24392
…mbedding The Vertex AI multimodalembedding predict endpoint expects 'dimension' (not 'outputDimensionality') in the parameters field.
…API and Chat Completions bridges Both bridges (Responses→CC and CC→Responses) independently encoded the same field mapping knowledge. This extracts 4 shared mappings into a single module so future changes only need to happen in one place. Shared mappings: - status ↔ finish_reason bidirectional dicts and functions - response_format ↔ text.format paired conversion functions - provider_specific_fields normalization helper - usage field name translation (input_tokens ↔ prompt_tokens, etc.) No behavioral changes — bridge methods now delegate to the shared module.
…ovider_specific_fields
The observability "View all" page only listed 9 integrations while 27 more had documentation. Added all missing integration cards so users can discover the full catalog from the integrations landing page.
fix(gemini): include DOCUMENT modality tokens in cost calculation
…ding docs: add missing observability integrations to View All page
…sions fix(vertex_ai): forward dimensions parameter in multimodalembedding requests
refactor(responses): extract shared format mapping between Responses API and Chat Completions bridges
Add Bedrock model entries using the aliases feature: - zai.glm-5: $1.00/$3.20 per 1M tokens (us-east-1, us-west-2) - minimax.minimax-m2.5: $0.30/$1.20 per 1M tokens (us-east-1, us-west-2) Source: https://aws.amazon.com/bedrock/pricing/ Refs #24411, #24412
Add base and regional Bedrock entries: - zai.glm-5: $1.00/$3.20 per 1M tokens (us-east-1, us-west-2) - minimax.minimax-m2.5: $0.30/$1.20 per 1M tokens (us-east-1, us-west-2) Source: https://aws.amazon.com/bedrock/pricing/ Refs #24411, #24412
…put_tokens/max_output_tokens Models using only the legacy max_tokens field now have proper max_input_tokens and max_output_tokens based on provider documentation. Providers updated: OpenRouter (13), Gradient AI (13), Heroku (4), Aleph Alpha (6), Together AI (2).
[Feat] Add "My User" tab to team info page
[Fix] broaden RAG ingestion credential cleanup to AWS endpoint/identity fields
Co-authored-by: Michael Riad Zaky <michaelr@Michaels-MacBook-Air.local>
…ocks When a non-streaming request had any post-call guardrail registered, the proxy deferred the async-success logging closure until after post_call_success_hook ran. The finally block fired that closure even when the hook raised — the propagating HTTPException then routed through post_call_failure_hook → _handle_logging_proxy_only_error, which writes its own failure spend log via async_failure_handler. The result was two spend log rows per blocked request: one Success exposing the blocked LLM response and one Failure. Reproduces with both pre and post bedrock guardrails configured for a team when the post-call OUTPUT scan blocks the response. Gate the deferred closure on _exception_raised so the failure path remains the single source of truth for blocked requests. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
… direct test coverage Code review noted the previous test reimplemented the proxy's try/except/finally around post_call_success_hook, so it would not catch a regression that re-introduced the duplicate-log bug in the production code path. Extract the gating logic into `ProxyBaseLLMRequestProcessing._flush_deferred_async_logging` so tests exercise the production helper directly. The proxy finally block becomes a single call to the helper. Tests now invoke the helper itself and additionally assert (via inspect.getsource) that base_process_llm_request continues to delegate to the helper rather than inlining the gate. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
fix(proxy): suppress deferred success log when post-call guardrail blocks
[Infra] Build UI
) * fix(memory): jsonify metadata before Prisma writes on /v1/memory The POST/PUT memory endpoints handed bare dicts (and bare `None`) to prisma-client-python for the `Json?` `metadata` column, which the client rejects with `MissingRequiredValueError` / `DataError: metadata should be of any of the following types: NullableJsonNullValueInput, Json`. Both the create and upsert paths now route writes through the existing `jsonify_object` helper used elsewhere in the proxy for `Json?` columns (e.g. `LiteLLM_VerificationToken.budget_limits`), and omit metadata when None so the column defaults to SQL NULL via the schema. Explicit `metadata: null` on PUT is now a no-op for the column to match how the rest of the proxy handles nullable JSON fields (no `JsonNull`/`DbNull` sentinel exists in prisma-client-python — see RobertCraigie/prisma-client-py#714). A payload with only `metadata: null` returns 400 instead of a misleading 200. Made-with: Cursor * fix(memory): JSON-encode non-dict metadata before Prisma writes `jsonify_object` only stringifies dict values, so list-shaped metadata still hit Prisma as raw Python objects and triggered the same DataError this PR is meant to fix. `metadata` is typed `Optional[Any]` so list payloads are valid input. Replace `jsonify_object` with a local `_serialize_metadata_for_prisma` helper that always `json.dumps` non-string values, applied at all three write sites (POST create, PUT update, PUT-create). Adds regression tests for list metadata on each path. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(memory): always json.dumps metadata, not just non-strings The str-passthrough in `_serialize_metadata_for_prisma` left plain Python strings (e.g. `metadata: "hello"`) unencoded — Postgres `jsonb` rejects bare-word strings as invalid JSON, reproducing the same DataError this PR is meant to fix. Always `json.dumps` regardless of input type so all `Optional[Any]` shapes (dict, list, scalar, str) become valid JSON. Adds a regression test for plain-string metadata. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(memory): jsonify metadata before Prisma writes on /v1/memory The POST/PUT memory endpoints handed bare dicts (and bare `None`) to prisma-client-python for the `Json?` `metadata` column, which the client rejects with `MissingRequiredValueError` / `DataError: metadata should be of any of the following types: NullableJsonNullValueInput, Json`. Both the create and upsert paths now route writes through the existing `jsonify_object` helper used elsewhere in the proxy for `Json?` columns (e.g. `LiteLLM_VerificationToken.budget_limits`), and omit metadata when None so the column defaults to SQL NULL via the schema. Explicit `metadata: null` on PUT is now a no-op for the column to match how the rest of the proxy handles nullable JSON fields (no `JsonNull`/`DbNull` sentinel exists in prisma-client-python — see RobertCraigie/prisma-client-py#714). A payload with only `metadata: null` returns 400 instead of a misleading 200. Made-with: Cursor * fix(memory): JSON-encode non-dict metadata before Prisma writes `jsonify_object` only stringifies dict values, so list-shaped metadata still hit Prisma as raw Python objects and triggered the same DataError this PR is meant to fix. `metadata` is typed `Optional[Any]` so list payloads are valid input. Replace `jsonify_object` with a local `_serialize_metadata_for_prisma` helper that always `json.dumps` non-string values, applied at all three write sites (POST create, PUT update, PUT-create). Adds regression tests for list metadata on each path. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(memory): always json.dumps metadata, not just non-strings The str-passthrough in `_serialize_metadata_for_prisma` left plain Python strings (e.g. `metadata: "hello"`) unencoded — Postgres `jsonb` rejects bare-word strings as invalid JSON, reproducing the same DataError this PR is meant to fix. Always `json.dumps` regardless of input type so all `Optional[Any]` shapes (dict, list, scalar, str) become valid JSON. Adds a regression test for plain-string metadata. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(memory): encode explicit metadata:null as JSON null to clear field prisma-client-python has no JsonNull/DbNull sentinel for writing a true SQL NULL on `Json?` columns (RobertCraigie/prisma-client-py#714), so an earlier iteration of this PR treated `PUT {"metadata": null}` as a no-op. That doesn't match the natural caller expectation that explicit-null clears the field. Encode it as the JSON literal `null` instead — stored as Postgres `jsonb 'null'`, which prisma deserializes back to Python `None` on read. Subsequent reads return `metadata: null`, so the field is effectively cleared from the caller's perspective. Strict SQL NULL remains unreachable via the typed client and would require raw SQL. Also clean up stale `jsonify_object` references in test mock comments (replaced by `_serialize_metadata_for_prisma`). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(memory ui): use shared DeleteResourceModal for memory deletion Swap the imperative `Modal.confirm` in MemoryView for the shared `DeleteResourceModal`, so memory deletion matches the rest of the dashboard: type-to-confirm guard on the key, in-flight loading state on the OK button, cancel disabled while the request is pending, and the modal stays open on error so the user can retry. Made-with: Cursor --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
[Infra] Rebuild UI
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
[Infra] Bump Versions
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Merging this PR will not alter performance
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yuneng-berri
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krrish-berri-2
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shin-berri
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fzowl
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[Infra] Promote Internal Staging to main
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