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b5fcb1334a
* fix(memory): inject stored facts into system prompt memory context - add Facts section rendering in format_memory_for_injection - rank facts by confidence and coerce confidence values safely - enforce max token budget while appending fact lines - add regression tests for fact inclusion, ordering, and budget behavior Fixes #1059 * Update the document with the latest status * fix(memory): harden fact injection — NaN/inf confidence, None content, incremental token budget (#1090) * Initial plan * fix(memory): address review feedback on confidence coercion, None content, and token budget Co-authored-by: WillemJiang <219644+WillemJiang@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: WillemJiang <219644+WillemJiang@users.noreply.github.com> --------- Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
66 lines
1.9 KiB
Markdown
66 lines
1.9 KiB
Markdown
# Memory System Improvements
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This document tracks memory injection behavior and roadmap status.
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## Status (As Of 2026-03-10)
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Implemented in `main`:
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- Accurate token counting via `tiktoken` in `format_memory_for_injection`.
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- Facts are injected into prompt memory context.
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- Facts are ranked by confidence (descending).
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- Injection respects `max_injection_tokens` budget.
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Planned / not yet merged:
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- TF-IDF similarity-based fact retrieval.
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- `current_context` input for context-aware scoring.
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- Configurable similarity/confidence weights (`similarity_weight`, `confidence_weight`).
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- Middleware/runtime wiring for context-aware retrieval before each model call.
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## Current Behavior
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Function today:
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```python
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def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
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```
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Current injection format:
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- `User Context` section from `user.*.summary`
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- `History` section from `history.*.summary`
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- `Facts` section from `facts[]`, sorted by confidence, appended until token budget is reached
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Token counting:
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- Uses `tiktoken` (`cl100k_base`) when available
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- Falls back to `len(text) // 4` if tokenizer import fails
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## Known Gap
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Previous versions of this document described TF-IDF/context-aware retrieval as if it were already shipped.
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That was not accurate for `main` and caused confusion.
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Issue reference: `#1059`
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## Roadmap (Planned)
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Planned scoring strategy:
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```text
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final_score = (similarity * 0.6) + (confidence * 0.4)
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```
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Planned integration shape:
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1. Extract recent conversational context from filtered user/final-assistant turns.
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2. Compute TF-IDF cosine similarity between each fact and current context.
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3. Rank by weighted score and inject under token budget.
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4. Fall back to confidence-only ranking if context is unavailable.
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## Validation
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Current regression coverage includes:
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- facts inclusion in memory injection output
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- confidence ordering
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- token-budget-limited fact inclusion
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Tests:
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- `backend/tests/test_memory_prompt_injection.py`
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