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lukaszraczylo
ace50757e4
Reasoning models burn thousands of completion tokens before the answer. Small per-test caps caused silent truncation and zero scores. The runner now floors max_tokens at the config default (12000). The client parses finish_reason. Reports mark truncated cells and the JSON format carries response text for debugging. A stderr progress reporter prints one line per completed request; --quiet disables it.
45 lines
1.3 KiB
Go
45 lines
1.3 KiB
Go
// Package llm defines the OpenAI-compatible chat client interface used to
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// exercise language models under test, plus a concrete HTTP implementation.
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package llm
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import (
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"context"
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"time"
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)
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// Message is a single chat message in an OpenAI-compatible conversation.
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type Message struct {
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Role string
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Content string
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}
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// Request is one chat-completion request sent to a model.
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type Request struct {
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Model string
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Messages []Message
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MaxTokens int
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Temperature float64
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}
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// Response is the normalized result of a chat-completion call.
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type Response struct {
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Text string
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FinishReason string
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PromptTokens int
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CompletionTokens int
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Latency time.Duration
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}
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// FinishReasonLength is the OpenAI-compatible finish_reason value meaning
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// the model hit its token budget (MaxTokens) before it finished: the
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// response, and Text in particular, may be truncated or empty. A reasoning
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// model that spends its whole budget on internal reasoning before writing
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// anything to the answer channel reports this with Text == "".
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const FinishReasonLength = "length"
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// Client completes chat requests against a language model backend.
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// Implementations must be safe for concurrent use.
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type Client interface {
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Complete(ctx context.Context, req Request) (Response, error)
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}
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