v2.17.0 (#822)
Co-authored-by: Clément Sirieix <clement.sirieix@mistral.ai> Co-authored-by: Guillaume LE GOFF <guillaume.lgf@gmail.com> Co-authored-by: Hdandria <henri.dandria@mistral.ai> Co-authored-by: Ivana Dunisijevic <ivana.dunisijevic@mistral.ai> Co-authored-by: Jean Burellier <sheplu@users.noreply.github.com> Co-authored-by: Mathias Gesbert <mathias.gesbert@mistral.ai> Co-authored-by: Mert Unsal <mert.unsal@mistral.ai> Co-authored-by: Michel Thomazo <51709227+michelTho@users.noreply.github.com> Co-authored-by: Paul VEZIA <166131032+le-codeur-rapide@users.noreply.github.com> Co-authored-by: Pierre Rossinès <pierre.rossines@mistral.ai> Co-authored-by: Val <102326092+vdeva@users.noreply.github.com> Co-authored-by: Vincent G <10739306+VinceOPS@users.noreply.github.com> Co-authored-by: renovate-mistral[bot] <253709520+renovate-mistral[bot]@users.noreply.github.com> Co-authored-by: Mistral Vibe <vibe@mistral.ai>
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Clément Drouin
GitHub
parent
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commit
6bedf271ce
223 changed files with 10533 additions and 6947 deletions
132
tests/backend/data/anthropic.py
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132
tests/backend/data/anthropic.py
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from __future__ import annotations
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import json
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from typing import Any
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from tests.backend.data import Chunk, JsonResponse
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def _sse_event(data: dict[str, Any]) -> Chunk:
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return f"data: {json.dumps(data, separators=(',', ':'))}".encode()
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def anthropic_request_content_blocks(payload: dict[str, Any]) -> list[dict[str, Any]]:
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# Flatten every structured content block across a request's messages
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# (text/thinking/tool_use/tool_result blocks).
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return [
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block
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for message in payload["messages"]
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if isinstance(message["content"], list)
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for block in message["content"]
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]
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def anthropic_message(
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text: str,
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*,
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input_tokens: int = 12,
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output_tokens: int = 3,
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stop_reason: str = "end_turn",
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) -> JsonResponse:
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return {
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"content": [{"type": "text", "text": text}],
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"usage": {"input_tokens": input_tokens, "output_tokens": output_tokens},
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"stop_reason": stop_reason,
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}
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def anthropic_tool_use(
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name: str,
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tool_input: dict[str, Any],
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*,
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tool_id: str = "toolu_1",
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input_tokens: int = 20,
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output_tokens: int = 5,
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) -> JsonResponse:
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return {
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"content": [
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{"type": "tool_use", "id": tool_id, "name": name, "input": tool_input}
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],
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"usage": {"input_tokens": input_tokens, "output_tokens": output_tokens},
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"stop_reason": "tool_use",
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}
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def anthropic_reasoning_tool_use_stream(
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name: str,
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arguments: str,
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*,
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reasoning: str = "thinking...",
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signature: str = "sig",
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tool_id: str = "toolu_1",
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input_tokens: int = 20,
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output_tokens: int = 5,
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) -> list[Chunk]:
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return [
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_sse_event(e)
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for e in (
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{
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"type": "message_start",
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"message": {"usage": {"input_tokens": input_tokens}},
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},
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{
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"type": "content_block_start",
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"index": 0,
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"content_block": {"type": "thinking", "thinking": reasoning},
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},
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{
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"type": "content_block_delta",
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"index": 0,
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"delta": {"type": "signature_delta", "signature": signature},
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},
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{"type": "content_block_stop", "index": 0},
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{
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"type": "content_block_start",
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"index": 1,
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"content_block": {"type": "tool_use", "id": tool_id, "name": name},
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},
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{
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"type": "content_block_delta",
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"index": 1,
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"delta": {"type": "input_json_delta", "partial_json": arguments},
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},
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{"type": "content_block_stop", "index": 1},
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{
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"type": "message_delta",
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"delta": {"stop_reason": "tool_use"},
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"usage": {"output_tokens": output_tokens},
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},
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{"type": "message_stop"},
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)
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]
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def anthropic_text_stream(
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text: str, *, input_tokens: int = 12, output_tokens: int = 3
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) -> list[Chunk]:
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return [
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_sse_event(e)
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for e in (
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{
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"type": "message_start",
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"message": {"usage": {"input_tokens": input_tokens}},
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},
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{
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"type": "content_block_start",
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"index": 0,
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"content_block": {"type": "text", "text": ""},
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},
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{
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"type": "content_block_delta",
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"index": 0,
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"delta": {"type": "text_delta", "text": text},
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},
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{"type": "content_block_stop", "index": 0},
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{
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"type": "message_delta",
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"delta": {"stop_reason": "end_turn"},
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"usage": {"output_tokens": output_tokens},
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},
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{"type": "message_stop"},
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)
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]
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@ -1,7 +1,41 @@
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from __future__ import annotations
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from typing import Any
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from tests.backend.data import Chunk, JsonResponse, ResultData, Url
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def mistral_completion(
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content: str,
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*,
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prompt_tokens: int = 100,
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completion_tokens: int = 50,
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tool_calls: list[dict[str, Any]] | None = None,
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) -> JsonResponse:
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return {
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"id": "cmpl_test",
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"created": 1234567890,
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"model": "devstral-latest",
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"object": "chat.completion",
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"usage": {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": prompt_tokens + completion_tokens,
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},
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"choices": [
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{
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"index": 0,
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"finish_reason": "tool_calls" if tool_calls else "stop",
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"message": {
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"role": "assistant",
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"content": content,
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"tool_calls": tool_calls,
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},
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}
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],
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}
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SIMPLE_CONVERSATION_PARAMS: list[tuple[Url, JsonResponse, ResultData]] = [
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(
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"https://api.mistral.ai",
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@ -4,8 +4,7 @@ import json
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from typing import Any
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from tests.backend.data import Chunk, JsonResponse, ResultData, Url
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OPENAI_RESPONSES_TEST_BASE_URL = "https://api.openai.com"
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from tests.constants import OPENAI_BASE_URL
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def _sse_event(data: dict[str, Any] | str) -> Chunk:
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@ -90,9 +89,134 @@ def _function_call_item(
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}
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def openai_message_item(
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text: str, *, phase: str | None = None, message_id: str = "msg_1"
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) -> dict[str, Any]:
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return _message_output_item(message_id, text, phase=phase)
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def openai_function_call_item(
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name: str, arguments: str, *, item_id: str = "fc_1", call_id: str = "call_1"
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) -> dict[str, Any]:
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return _function_call_item(item_id, call_id, name, arguments)
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def openai_response(
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output: list[dict[str, Any]],
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*,
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input_tokens: int = 10,
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output_tokens: int = 2,
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response_id: str = "resp_1",
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model: str = "gpt-test",
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) -> JsonResponse:
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return {
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"id": response_id,
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"object": "response",
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"created_at": 1234567890,
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"model": model,
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"output": output,
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"usage": {
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"input_tokens": input_tokens,
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"output_tokens": output_tokens,
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"total_tokens": input_tokens + output_tokens,
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},
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}
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def openai_sse(*events: dict[str, Any] | str) -> list[Chunk]:
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return [_sse_event(event) for event in events]
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def openai_reasoning_tool_call_stream(
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name: str,
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arguments: str,
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*,
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reasoning: str = "thinking...",
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call_id: str = "call_1",
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item_id: str = "fc_1",
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input_tokens: int = 20,
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output_tokens: int = 5,
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) -> list[Chunk]:
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return openai_sse(
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{"type": "response.created", "response": {"id": "resp_1", "output": []}},
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{
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"type": "response.output_item.added",
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"output_index": 0,
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"item": _stream_message_item("msg_r", phase="commentary"),
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},
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{
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"type": "response.reasoning_summary_text.delta",
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"output_index": 0,
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"delta": reasoning,
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},
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{
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"type": "response.output_item.added",
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"output_index": 1,
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"item": _function_call_item(
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item_id, call_id, name, "", status="in_progress"
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),
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},
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{
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"type": "response.function_call_arguments.delta",
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"output_index": 1,
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"call_id": call_id,
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"delta": arguments,
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},
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{
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"type": "response.function_call_arguments.done",
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"output_index": 1,
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"call_id": call_id,
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"name": name,
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"arguments": arguments,
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},
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{
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"type": "response.output_item.done",
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"output_index": 1,
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"item": _function_call_item(item_id, call_id, name, arguments),
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},
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{
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"type": "response.completed",
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"response": openai_response(
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[_function_call_item(item_id, call_id, name, arguments)],
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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),
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},
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"[DONE]",
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)
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def openai_text_stream(
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text: str, *, input_tokens: int = 10, output_tokens: int = 2
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) -> list[Chunk]:
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return openai_sse(
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{"type": "response.created", "response": {"id": "resp_1", "output": []}},
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{
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"type": "response.output_item.added",
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"output_index": 0,
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"item": _stream_message_item("msg_1"),
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},
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{
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"type": "response.output_text.delta",
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"output_index": 0,
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"content_index": 0,
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"delta": text,
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},
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{
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"type": "response.completed",
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"response": openai_response(
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[openai_message_item(text)],
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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),
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},
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"[DONE]",
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)
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SIMPLE_CONVERSATION_PARAMS: list[tuple[Url, JsonResponse, ResultData]] = [
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(
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OPENAI_RESPONSES_TEST_BASE_URL,
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OPENAI_BASE_URL,
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{
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"id": "resp_fake_id_1234",
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"object": "response",
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@ -113,7 +237,7 @@ SIMPLE_CONVERSATION_PARAMS: list[tuple[Url, JsonResponse, ResultData]] = [
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TOOL_CONVERSATION_PARAMS: list[tuple[Url, JsonResponse, ResultData]] = [
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(
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OPENAI_RESPONSES_TEST_BASE_URL,
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OPENAI_BASE_URL,
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{
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"id": "resp_fake_id_9012",
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"object": "response",
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@ -144,7 +268,7 @@ TOOL_CONVERSATION_PARAMS: list[tuple[Url, JsonResponse, ResultData]] = [
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STREAMED_SIMPLE_CONVERSATION_PARAMS: list[tuple[Url, list[Chunk], list[ResultData]]] = [
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(
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OPENAI_RESPONSES_TEST_BASE_URL,
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OPENAI_BASE_URL,
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[
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_sse_event({
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"type": "response.created",
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@ -235,7 +359,7 @@ STREAMED_SIMPLE_CONVERSATION_PARAMS: list[tuple[Url, list[Chunk], list[ResultDat
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COMMENTARY_CONVERSATION_PARAMS: list[tuple[Url, JsonResponse, ResultData]] = [
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(
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OPENAI_RESPONSES_TEST_BASE_URL,
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OPENAI_BASE_URL,
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{
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"id": "resp_thinking_1234",
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"object": "response",
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@ -268,7 +392,7 @@ STREAMED_COMMENTARY_CONVERSATION_PARAMS: list[
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tuple[Url, list[Chunk], list[ResultData]]
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] = [
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(
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OPENAI_RESPONSES_TEST_BASE_URL,
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OPENAI_BASE_URL,
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[
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_sse_event({
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"type": "response.created",
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@ -419,7 +543,7 @@ STREAMED_COMMENTARY_CONVERSATION_PARAMS: list[
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STREAMED_TOOL_CONVERSATION_PARAMS: list[tuple[Url, list[Chunk], list[ResultData]]] = [
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(
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OPENAI_RESPONSES_TEST_BASE_URL,
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OPENAI_BASE_URL,
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[
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_sse_event({
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"type": "response.created",
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