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4e78f1f440
Port Python rag/advanced_rag agentic search to Go: ES-backed dataset-nav service, agentic-search harness, and agent tools. Includes agentic-search port plan and self-review docs.
125 lines
3.8 KiB
Go
125 lines
3.8 KiB
Go
//
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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 harness
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import (
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"context"
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"fmt"
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"strings"
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"github.com/cloudwego/eino/schema"
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"gorm.io/gorm"
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"ragflow/internal/agent/chat"
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)
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// FINAL_ANSWER_SYSTEM mirrors Python report_prompt.FINAL_ANSWER_SYSTEM.
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const finalAnswerSystem = `You are a smart agent. Answer the user's question using ONLY the evidence provided below. Do not invent facts: if the evidence cannot support a claim, say so plainly instead of guessing.
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# Citation rules
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{cite_rules}
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# Language
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Answer in the SAME language as the question. Translate retrieved evidence into that language as part of composing the answer; only verbatim quoted snippets may stay in their source language.
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# Fallback
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If the evidence does not answer the question, reply with a clear statement that you don't have enough information based on the available sources (in the user's language).
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`
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const partialAnswerPreamble = "Note: the following answer is based on partial information and may be incomplete."
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const defaultCiteRules = "Cite passages with their source documents where available."
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const (
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abstainMessage = "I cannot answer this question based on the available information."
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emptyResultMessage = "I don't have enough information based on the available sources."
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)
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// AnswerResult mirrors the formalize_answer node output.
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type AnswerResult struct {
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FinalAnswer string
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Abstained bool
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Empty bool
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}
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// FormalizeAnswer generates the final answer from the gathered kbinfos. Mirrors
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// Python's formalize_answer node: abstain/empty short-circuits, otherwise builds
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// system+user and calls the chat invoker.
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func FormalizeAnswer(ctx context.Context, db *gorm.DB, question string, kb *Kbinfos, partial, abstain, empty bool) AnswerResult {
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if abstain {
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return AnswerResult{FinalAnswer: abstainMessage, Abstained: true}
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}
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if empty || kb == nil || len(kb.Chunks) == 0 {
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return AnswerResult{FinalAnswer: emptyResultMessage, Empty: true}
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}
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var b strings.Builder
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b.WriteString("Question:\n" + question + "\n")
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if partial {
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b.WriteString(partialAnswerPreamble + "\n")
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}
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b.WriteString("\nEvidence:\n")
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for i, c := range kb.Chunks {
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text := chunkText(c)
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doc := chunkDoc(c)
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if text == "" {
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continue
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}
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b.WriteString(fmt.Sprintf("[%d] %s", i+1, text))
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if doc != "" {
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b.WriteString(fmt.Sprintf(" (source: %s)", doc))
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}
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b.WriteString("\n")
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}
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system := strings.ReplaceAll(finalAnswerSystem, "{cite_rules}", defaultCiteRules)
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inv := chat.GetDefaultInvoker()
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if inv == nil {
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return AnswerResult{FinalAnswer: "I'm sorry, the chat invoker is not configured."}
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}
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resp, err := inv.Invoke(ctx, db, chat.Request{
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Messages: []schema.Message{
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{Role: schema.System, Content: system},
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{Role: schema.User, Content: b.String()},
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},
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})
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if err != nil {
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return AnswerResult{FinalAnswer: "I'm sorry, I encountered an error while composing the answer."}
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}
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return AnswerResult{FinalAnswer: resp.Content}
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}
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func chunkText(c map[string]interface{}) string {
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if t, ok := c["content_with_weight"].(string); ok && t != "" {
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return t
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}
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if t, ok := c["content"].(string); ok {
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return t
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}
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return ""
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}
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func chunkDoc(c map[string]interface{}) string {
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if d, ok := c["docnm_kwd"].(string); ok {
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return d
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}
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if d, ok := c["doc_id"].(string); ok {
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return d
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}
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return ""
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}
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