fix: only exclude confirmed jargon from candidate statistics

[EterUltimate]

问题

修复 #245「黑话被收录后长时间无法解释」：候选统计的排除查询未区分确认状态，把 is_jargon IS NULL 的待确认词条一并排除，导致候选出现次数始终无法累加到推断阈值，黑话词条被收录后长期停留在待确认状态、无法触发含义推断。

修复

• services/learning/jargon_learning.py：统计排除查询显式传入 only_confirmed=True，只排除已确认黑话；待确认候选继续参与统计，重复出现时正常累加 count，达到 3 次后触发含义推断。同步更新 _get_existing_jargon_terms() 方法说明。
• services/core_learning/v2_learning_integration.py：V2 批处理路径应用相同过滤，避免启用 V2 学习时出现相同问题。

测试

• 新增 tests/unit/test_jargon_learning.py，覆盖：查询正确传递 only_confirmed=True；旧版学习流程不再排除待确认候选；V2 黑话批处理流程使用相同的确认状态过滤。

Fixes #245

Summary by Sourcery

Fix jargon candidate tracking so pending entries remain eligible for repeated-occurrence counting and meaning inference.

Bug Fixes:

• Ensure jargon candidate statistics exclude only confirmed jargon, allowing pending candidates to continue accumulating occurrences and reach meaning-inference thresholds in both learning paths.

Documentation:

• Document the 3.6.4 release and update user-facing version references.

Tests:

• Add unit coverage for confirmed-only filtering in legacy and V2 jargon learning flows.

Chores:

• Bump the plugin, runtime package, Dashboard, and documentation versions to 3.6.4.

[sourcery-ai]

Reviewer's Guide

Fix jargon candidate statistics by excluding only confirmed jargon entries in both legacy and V2 learning flows, allowing pending candidates to accumulate occurrences and reach meaning-inference thresholds; add regression coverage and release the changes as 3.6.4.

Sequence diagram for confirmed-only jargon candidate statistics

sequenceDiagram
    participant Learning as JargonLearning
    participant DB as Database
    participant Candidate as PendingCandidate
    participant Inference as MeaningInference

    Learning->>DB: get_recent_jargon_list(chat_id, limit, only_confirmed=True)
    DB-->>Learning: confirmed jargon terms
    Learning->>Candidate: accumulate repeated occurrences
    Candidate-->>Learning: count reaches threshold
    Learning->>Inference: infer meaning

Loading

File-Level Changes

Change	Details	Files
Restrict jargon exclusion during candidate mining to confirmed entries so pending candidates remain eligible for repeated accumulation and inference.	Pass only_confirmed=True when loading exclusion terms in the legacy learning path. Update the helper documentation to describe confirmed-only results. Add tests covering query arguments, pending-candidate eligibility, and candidate propagation to inference.	services/learning/jargon_learning.py tests/unit/test_jargon_learning.py
Apply confirmed-only jargon filtering to the V2 batch-learning path.	Pass only_confirmed=True when loading terms before V2 candidate filtering. Add an integration-level unit test verifying pending candidates are processed and saved with inferred meaning.	services/core_learning/v2_learning_integration.py tests/unit/test_jargon_learning.py
Release the fix as version 3.6.4 and document the behavior change.	Add changelog notes for the bug fix, tests, and release. Synchronize plugin, metadata, dashboard, README, and documentation versions.	CHANGELOG.md __init__.py metadata.yaml README.md README_EN.md docs/README.md web_src/package.json

Assessment against linked issues

Issue	Objective	Addressed	Explanation
#245	Ensure pending jargon entries with is_jargon = NULL are not excluded from statistical candidate selection, so repeated occurrences can accumulate their count and reach the inference threshold.	✅
#245	Apply the same confirmed-only exclusion behavior to the V2 jargon batch-learning path, preventing the bug when V2 learning is enabled.	✅
#245	Add regression tests and update relevant documentation/version information for the fix.	✅

Possibly linked issues

• 黑话被收录后长时间无法解释 #245: PR通过仅排除已确认词条，修复待确认黑话计数停滞及长期无法解释的问题。

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[sourcery-ai]

Hey - I've reviewed your changes and they look great!

Sourcery assessment

Needs a human reviewer. If the confirmation filter is wrong, pending terms could be promoted and their inferred meanings persisted as jargon records. Reverting would stop further processing but would not remove records already written; those records are bounded and can be corrected or recomputed.

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[EterUltimate]

@sourcery-ai review

[sourcery-ai]

Hey - I've reviewed your changes and they look great!

Sourcery assessment

Needs a human reviewer. If the filtering change is wrong, pending terms may be inferred and written as confirmed jargon or have their counts updated in the database. Reverting stops the new behavior, but records created or modified before the revert outlive it and would need manual cleanup or recomputation.

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