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Making tool_calls a vector.

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Nicolas Patry 2025-03-05 15:13:25 +01:00
parent 31766dad77
commit 175a059061
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5 changed files with 1040 additions and 5 deletions
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2
clients/python/text_generation/types.py
View File

@ -67,7 +67,7 @@ class ChoiceDeltaToolCall(BaseModel):
class ChoiceDelta(BaseModel):
role: str
content: Optional[str] = None
tool_calls: Optional[ChoiceDeltaToolCall] = None
tool_calls: Optional[List[ChoiceDeltaToolCall]] = None
class Choice(BaseModel):

12
integration-tests/conftest.py
View File

@ -269,7 +269,17 @@ class ResponseComparator(JSONSnapshotExtension):
def eq_chat_complete_chunk(
response: ChatCompletionChunk, other: ChatCompletionChunk
) -> bool:
return response.choices[0].delta.content == other.choices[0].delta.content
if response.choices[0].delta.content:
return (
response.choices[0].delta.content == other.choices[0].delta.content
)
elif response.choices[0].delta.tool_calls:
return (
response.choices[0].delta.tool_calls
== other.choices[0].delta.tool_calls
)
else:
raise RuntimeError("Invalid empty chat chunk")
def eq_response(response: Response, other: Response) -> bool:
return response.generated_text == other.generated_text and eq_details(

992
integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_openai.json Normal file
View File

@ -0,0 +1,992 @@
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{
"choices": [
{
"delta": {
"content": null,
"function_call": null,
"refusal": null,
"role": "assistant",
"tool_calls": [
{
"function": {
"arguments": "{\"",
"name": null
},
"id": "",
"index": 0,
"type": "function"
}
]
},
"finish_reason": null,
"index": 0,
"logprobs": null
}
],
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},
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"function": {
"arguments": "function",
"name": null
},
"id": "",
"index": 0,
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}
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33
integration-tests/models/test_tools_llama.py
View File

@ -1,6 +1,7 @@
import pytest
import requests
import json
from openai import OpenAI
@pytest.fixture(scope="module")
@ -108,6 +109,38 @@ async def test_flash_llama_grammar_tools(flash_llama_grammar_tools, response_sna
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_grammar_tools_openai(
flash_llama_grammar_tools, response_snapshot
):
client = OpenAI(api_key="xx", base_url=f"{flash_llama_grammar_tools.base_url}/v1")
stream = client.chat.completions.create(
model="tgi",
max_tokens=100,
seed=1,
tools=tools,
stream=True,
temperature=0.0,
messages=[
{
"role": "system",
"content": "Youre a helpful assistant! Answer the users question best you can.",
},
{
"role": "user",
"content": "What is the weather like in Brooklyn, New York?",
},
],
)
chunks = []
for chunk in stream:
chunks.append(chunk)
assert chunks == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_grammar_tools_auto(

6
router/src/lib.rs
View File

@ -730,7 +730,7 @@ pub(crate) struct ChatCompletionChoice {
pub struct ToolCallDelta {
#[schema(example = "assistant")]
role: String,
tool_calls: DeltaToolCall,
tool_calls: Vec<DeltaToolCall>,
}
#[derive(Clone, Debug, Serialize, ToSchema)]
@ -774,7 +774,7 @@ impl ChatCompletionChunk {
}),
(None, Some(tool_calls)) => ChatCompletionDelta::Tool(ToolCallDelta {
role: "assistant".to_string(),
tool_calls: DeltaToolCall {
tool_calls: vec![DeltaToolCall {
index: 0,
id: String::new(),
r#type: "function".to_string(),
@ -782,7 +782,7 @@ impl ChatCompletionChunk {
name: None,
arguments: tool_calls[0].to_string(),
},
},
}],
}),
(None, None) => ChatCompletionDelta::Chat(TextMessage {
role: "assistant".to_string(),