mirror of
https://github.com/infiniflow/ragflow.git
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84 lines
2.9 KiB
Go
84 lines
2.9 KiB
Go
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//
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// Copyright 2026 The InfiniFlow Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//
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package component
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import (
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"context"
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"testing"
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"time"
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)
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// TestLLM_Stream_ExposesThinkingAndContentForDownstreamMessage is a
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// regression guard for the Downstream Message detect path.
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//
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// Python's `_invoke_async` returns a `functools.partial` callable
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// when it detects a Message component downstream, deferring
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// streaming evaluation until the consumer actually pulls from
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// the channel. The Go port exposes the streaming surface via the
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// goroutine + channel + select pattern; this test pins the
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// contract that any LLM `Stream()` consumer (Message component
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// or otherwise) can rely on:
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//
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// 1. The stream emits a chunk with key "content" and key "thinking".
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// 2. The stream eventually closes (no leaked goroutines).
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// 3. The consumer can read at its own pace — backpressure is bounded
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// by the 16-element channel buffer.
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//
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// The actual "detect Message downstream" decision is a canvas-scheduler
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// concern (it would look at the DAG children of an LLM node). That
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// introspection lives in `internal/agent/canvas/` rather than this
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// component package. For v1, every LLM Stream() is the same shape
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// regardless of downstream topology.
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func TestLLM_Stream_ExposesThinkingAndContentForDownstreamMessage(t *testing.T) {
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stub := &stubInvoker{resp: &ChatInvokeResponse{Content: "streamed answer", Model: "echo"}}
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withStubInvoker(t, stub)
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c := NewLLMComponent(LLMParam{ModelID: "echo"})
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ch, err := c.Stream(context.Background(), map[string]any{"user_prompt": "go"})
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if err != nil {
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t.Fatalf("Stream: %v", err)
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}
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// Simulate a slow downstream consumer (the Message component
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// template-rendering path) that reads one chunk at a time.
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got := []map[string]any{}
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deadline := time.After(2 * time.Second)
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for {
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select {
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case chunk, open := <-ch:
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if !open {
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if len(got) != 2 {
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t.Fatalf("expected 2 chunks (content + done), got %d: %+v", len(got), got)
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}
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if got[0]["content"] != "streamed answer" {
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t.Errorf("content=%v, want 'streamed answer'", got[0]["content"])
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}
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if got[0]["thinking"] != "" {
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t.Errorf("thinking=%v, want empty (no think chain in v1)", got[0]["thinking"])
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}
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if got[1]["done"] != true {
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t.Errorf("done=%v, want true", got[1]["done"])
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}
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return
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}
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got = append(got, chunk)
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case <-deadline:
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t.Fatal("Stream did not close within 2s")
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}
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}
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}
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