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7679e42736
Adds owned local TCP peers, a Redis process helper and SigV4 helpers to tests/integration, and integration contracts for Anthropic and Bedrock wire shapes, Bedrock role configuration, S3 wire, observed routing, Redis recovery and stream contracts. Consolidates the four commits previously stacked on litellm_integration_accounting onto its main-merged tip
61 lines
4.6 KiB
Python
61 lines
4.6 KiB
Python
import json
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import uuid
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from typing import Final
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import pytest
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from integration._support.client import Gateway, eventually, object_value
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from integration._support.database import read_rows
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from integration._support.wire import Reply, Request, wire_server
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@pytest.mark.covers("other.provider_wire.anthropic.tool_history_system_cache_and_internal_fields", "quota_management.spend_tracking.cache_tokens.disjoint_classes_use_explicit_rates")
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def test_anthropic_tool_history_and_cache_tokens_keep_wire_and_accounting_contracts(gateway: Gateway) -> None:
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identity: Final = "anthropic-wire-" + uuid.uuid4().hex
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tool_schema: Final = {"type": "object", "properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}, "required": ["x", "y"]}
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def respond(request: Request) -> Reply:
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assert request.method == "POST" and request.target == "/v1/messages"
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assert request.headers["x-api-key"] == "synthetic-anthropic-key"
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body: Final = json.loads(request.body)
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assert body["model"] == "claude-sonnet-4-5-20250929"
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assert body["system"] == [{"type": "text", "text": "synthetic policy", "cache_control": {"type": "ephemeral"}}]
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assert body["tools"][0]["name"] == "add" and body["tools"][0]["input_schema"] == tool_schema
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assert body["max_tokens"] == 16
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assert not {"timeout", "stream_chunk_size", "litellm_params", "litellm_metadata", "rpm", "tpm"}.intersection(body)
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messages: Final = body["messages"]
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assert [message["role"] for message in messages] == ["user", "assistant", "user"]
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assert messages[0]["content"] == [{"type": "text", "text": "first"}]
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assert messages[1]["content"] == [{"type": "tool_use", "id": "history-call", "name": "add", "input": {"x": 1, "y": 2}}]
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assert messages[2]["content"] == [{"type": "tool_result", "tool_use_id": "history-call", "content": "3"}, {"type": "text", "text": "next"}]
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return Reply(body=json.dumps({"id": identity, "type": "message", "role": "assistant", "model": "claude-sonnet-4-5-20250929", "content": [{"type": "tool_use", "id": "next-call", "name": "add", "input": {"x": 3, "y": 4}}], "stop_reason": "tool_use", "stop_sequence": None, "usage": {"input_tokens": 10, "output_tokens": 4, "cache_read_input_tokens": 5, "cache_creation_input_tokens": 7}}).encode())
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with wire_server(respond) as wire, gateway.scenario() as scenario:
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model: Final = scenario.model(model="anthropic/claude-sonnet-4-5-20250929", api_base=wire.url, api_key="synthetic-anthropic-key", input_cost_per_token=0.001, output_cost_per_token=0.002, cache_read_input_token_cost=0.0001, cache_creation_input_token_cost=0.002)
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response: Final = gateway.request("POST", "/v1/chat/completions", {
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"model": model, "max_tokens": 16, "timeout": 5,
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"messages": [
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{"role": "system", "content": [{"type": "text", "text": "synthetic policy", "cache_control": {"type": "ephemeral"}}]},
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{"role": "user", "content": "first"},
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{"role": "assistant", "tool_calls": [{"id": "history-call", "type": "function", "function": {"name": "add", "arguments": '{"x":1,"y":2}'}}]},
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{"role": "tool", "tool_call_id": "history-call", "content": "3"},
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{"role": "user", "content": "next"},
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],
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"tools": [{"type": "function", "function": {"name": "add", "parameters": tool_schema}}],
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})
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assert response.status_code == 200, response.text
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body: Final = response.json()
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assert body["id"].startswith("chatcmpl-")
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assert body["choices"][0]["finish_reason"] == "tool_calls"
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tool: Final = body["choices"][0]["message"]["tool_calls"][0]
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assert tool["id"] == "next-call" and tool["function"]["name"] == "add"
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assert json.loads(tool["function"]["arguments"]) == {"x": 3, "y": 4}
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assert body["usage"]["prompt_tokens"] == 22 and body["usage"]["completion_tokens"] == 4
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assert len(wire.drain()) == 1
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rows: Final = eventually(lambda: read_rows('SELECT spend, prompt_tokens, completion_tokens, metadata FROM "LiteLLM_SpendLogs" WHERE request_id=%s', (body["id"],)), lambda values: len(values) == 1, seconds=70)
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assert float(rows[0]["spend"]) == pytest.approx(10 * 0.001 + 5 * 0.0001 + 7 * 0.002 + 4 * 0.002)
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assert rows[0]["prompt_tokens"] == 22 and rows[0]["completion_tokens"] == 4
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metadata: Final = rows[0]["metadata"]
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parsed: Final = json.loads(metadata) if isinstance(metadata, str) else object_value(metadata)
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assert parsed["cost_breakdown"]["input_cost"] == pytest.approx(0.0245)
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assert parsed["cost_breakdown"]["output_cost"] == pytest.approx(0.008)
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