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* test(e2e): cover customer chat/messages cost + streaming paths Fills five uncovered P0 registry cells matching the customer's confirmed stack (OpenAI SDK, Bedrock, /v1/messages) and their per-request cost dependency: - /v1/messages logs cost that matches the x-litellm-response-cost header (LIT-4076) - OpenAI /chat/completions streams real content, and a non-streamed call is costed - Bedrock Converse /chat/completions returns real content non-streamed and streamed The streaming checks aggregate delta content and parse every chunk as JSON, so a clean-but-empty stream or a truncated chunk fails instead of passing on a bare 200. * test(e2e): add tool-use coverage for openai, bedrock converse, anthropic responses Function-calling regression guards on the paths the customer's agentic SDK usage exercises: OpenAI and Bedrock Converse /chat/completions, and Anthropic /v1/responses. The model is forced to call a weather tool and the test asserts the returned tool call names the function and carries JSON-parseable arguments with the expected field, so a dropped tool_call or malformed argument JSON fails instead of passing on a bare 200. Adds a minimal tool_calls field to the response OutMessage. * test(e2e): cover bedrock converse responses + thinking Adds llm.responses.bedrock_converse.basic/tool_use and llm.chat_completions.bedrock_converse.thinking. The thinking test enables extended thinking and requires reasoning_content plus a real answer, so a path that drops the reasoning block fails rather than passing. * test(e2e): cover bedrock embeddings + openai structured output and reasoning Bedrock Titan embeddings return a real vector; OpenAI structured output must yield schema-conforming JSON with the correct extracted values (age==42, not just valid JSON); an OpenAI reasoning call must report reasoning tokens, so a non-reasoning fallback fails. Adds response_format to ChatBody and reasoning-token details to Usage. * test(e2e): cover vision + streaming tool calls on openai and bedrock converse Vision on both providers must describe the image (not just 200); the streamed OpenAI tool call is reassembled from its fragments and its argument JSON parsed, so a stream that never completes the call or splits its JSON fails. Extends ChatMessage content to a typed text/image union. * test(e2e): cover openai prompt caching hit on repeated large prefix A repeated large-prefix prompt must report cached prompt tokens on the second call, so a cache regression that stops reusing the prefix (and silently re-bills full input) fails here. * test(e2e): cover openai audio speech + bedrock rerank and image generation Marks the OpenAI TTS cell and adds Bedrock Titan rerank (top_n honored, scored) and Bedrock Titan image generation (returns b64/url), the customer's non-chat AWS surfaces. * test(e2e): cover end-user (customer) create persistence mgmt.end_user.new.happy_path: create an end-user via /customer/new and confirm /customer/info reports it, the end-user-identity surface the customer relies on for per-customer controls. Adds customer models + management-client methods. * test(e2e): enforce key model allow-list on the passthrough route other.auth.passthrough.model_allowlist_enforced: a key scoped to gemini must be denied a claude call through the anthropic passthrough route (403), so custom-auth scoping is not bypassable by going through passthrough instead of /chat/completions. * test(e2e): address Greptile - assert stream data events, correlate messages spend by key - streaming: assert len(stream_events) > 1 instead of chunks > 1, since chunks counts the terminal data: [DONE] marker and would pass a single content event - messages cost: correlate the spend row by the unique scoped key rather than the Anthropic response id, which need not equal the proxy spend-log request_id
182 lines
6.4 KiB
Python
182 lines
6.4 KiB
Python
"""Live e2e for LLM-translation passthrough endpoints.
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Each test sends a NATIVE provider request through the proxy's passthrough route
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and verifies the proxy still logged a costed SpendLogs row
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(call_type="pass_through_endpoint"), correlated by the x-litellm-call-id header.
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Covered: gemini ("gemini-2.5-flash") + anthropic ("claude-haiku-4-5"), streaming +
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non-streaming, plus native tool calls. See LLM_TRANSLATION_COVERAGE_MATRIX.md.
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A passthrough call returning non-2xx fails hard (never a skip); once it returns
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2xx, a missing or zero-cost SpendLogs row fails too.
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"""
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import pytest
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from e2e_config import unique_marker
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from e2e_http import StreamingResponse, require_successful_call
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from lifecycle import ResourceManager
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from models import KeyGenerateBody, SpendLogRow
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from passthrough_client import (
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AnthropicTool,
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GeminiFunctionDeclaration,
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GeminiTool,
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JsonSchema,
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JsonSchemaProperty,
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PassthroughClient,
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)
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pytestmark = pytest.mark.e2e
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def _fetch_cost_breakdown(client: PassthroughClient, result: StreamingResponse) -> SpendLogRow:
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"""The passthrough call's logged row, polled until it carries a cost.
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Asserts (not skips) that a 2xx passthrough call produced a costed row - the
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whole point of passthrough spend tracking.
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"""
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assert result.call_id, "passthrough response had no x-litellm-call-id header"
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rows = client.proxy.poll_logs_for_request_id(
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result.call_id,
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predicate=lambda rs: (rs[0].spend or 0) > 0,
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)
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assert rows, f"no SpendLogs row for passthrough call_id {result.call_id}"
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row = rows[0]
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assert row.call_type == "pass_through_endpoint"
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assert (row.spend or 0) > 0, f"passthrough call was not costed: {row}"
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assert row.status == "success"
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return row
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# ---- Gemini passthrough ------------------------------------------------
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def test_gemini_passthrough_nonstreaming_logs_cost(
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client: PassthroughClient, scoped_key: str
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) -> None:
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tag = f"e2e-passthrough-{unique_marker()}"
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result = client.gemini_generate(
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scoped_key, "gemini-2.5-flash", "Say hello in one word", tags=[tag, "gemini"]
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)
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require_successful_call(result)
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row = _fetch_cost_breakdown(client, result)
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assert row.custom_llm_provider == "gemini"
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assert "gemini" in (row.model or "")
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assert tag in (row.request_tags or []), f"tags not logged: {row.request_tags}"
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def test_gemini_passthrough_streaming_logs_cost(
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client: PassthroughClient, scoped_key: str
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) -> None:
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result = client.gemini_stream(scoped_key, "gemini-2.5-flash", "Count to five")
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require_successful_call(result)
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assert result.chunks > 0, "streaming passthrough produced no events"
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row = _fetch_cost_breakdown(client, result)
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assert row.custom_llm_provider == "gemini"
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def test_gemini_passthrough_tool_call_logs_cost(
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client: PassthroughClient, scoped_key: str
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) -> None:
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result = client.gemini_generate(
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scoped_key,
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"gemini-2.5-flash",
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"What is the weather in Paris? Use the get_weather tool.",
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tools=[
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GeminiTool(
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function_declarations=[
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GeminiFunctionDeclaration(
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name="get_weather",
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description="Get the weather for a city",
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parameters=JsonSchema(
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type="object",
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properties={"city": JsonSchemaProperty(type="string")},
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required=["city"],
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),
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)
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]
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)
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],
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)
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require_successful_call(result)
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assert "functionCall" in result.body, "gemini did not emit a tool call"
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row = _fetch_cost_breakdown(client, result)
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assert row.custom_llm_provider == "gemini"
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# ---- Anthropic passthrough ---------------------------------------------
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def test_anthropic_passthrough_nonstreaming_logs_cost(
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client: PassthroughClient, scoped_key: str
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) -> None:
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result = client.anthropic_message(scoped_key, "claude-haiku-4-5", "Say hello")
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require_successful_call(result)
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row = _fetch_cost_breakdown(client, result)
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assert row.custom_llm_provider == "anthropic"
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assert "claude" in (row.model or "")
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def test_anthropic_passthrough_streaming_logs_cost(
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client: PassthroughClient, scoped_key: str
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) -> None:
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result = client.anthropic_message(
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scoped_key, "claude-haiku-4-5", "Count to five", stream=True
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)
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require_successful_call(result)
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assert result.chunks > 0, "streaming passthrough produced no events"
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row = _fetch_cost_breakdown(client, result)
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assert row.custom_llm_provider == "anthropic"
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def test_anthropic_passthrough_tool_call_logs_cost(
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client: PassthroughClient, scoped_key: str
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) -> None:
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result = client.anthropic_message(
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scoped_key,
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"claude-haiku-4-5",
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"What is the weather in Paris? Use the get_weather tool.",
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tools=[
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AnthropicTool(
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name="get_weather",
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description="Get the weather for a city",
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input_schema=JsonSchema(
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type="object",
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properties={"city": JsonSchemaProperty(type="string")},
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required=["city"],
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),
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)
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],
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)
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require_successful_call(result)
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assert "tool_use" in result.body, "anthropic did not emit a tool call"
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row = _fetch_cost_breakdown(client, result)
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assert row.custom_llm_provider == "anthropic"
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class TestPassthroughModelAllowlist:
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"""A passthrough route must honor the calling key's model allow-list.
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The customer fronts native provider calls through the proxy with custom auth,
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so a key scoped to one model must not reach a different model just because the
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request goes through the passthrough route rather than /chat/completions.
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"""
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@pytest.mark.covers("other.auth.passthrough.model_allowlist_enforced")
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def test_passthrough_denies_model_outside_key_allowlist(
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self, client: PassthroughClient, resources: ResourceManager
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) -> None:
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key = client.proxy.generate_key(KeyGenerateBody(models=["gemini-2.5-flash"]))
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resources.defer(lambda: client.proxy.delete_key(key))
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result = client.anthropic_message(key, "claude-haiku-4-5", f"say hi {unique_marker()}")
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assert result.status_code == 403, (
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"a key restricted to gemini-2.5-flash must be denied a claude passthrough call, "
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f"got {result.status_code}: {result.body[:300]}"
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)
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