Memory System Improvements

This document tracks memory injection behavior and roadmap status.

Status (As Of 2026-03-10)

Implemented in main:

• Accurate token counting via tiktoken in format_memory_for_injection.
• Facts are injected into prompt memory context.
• Facts are ranked by confidence (descending).
• Injection respects max_injection_tokens budget.

Planned / not yet merged:

• TF-IDF similarity-based fact retrieval.
• current_context input for context-aware scoring.
• Configurable similarity/confidence weights (similarity_weight, confidence_weight).
• Middleware/runtime wiring for context-aware retrieval before each model call.

Current Behavior

Function today:

def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:

Current injection format:

• User Context section from user.*.summary
• History section from history.*.summary
• Facts section from facts[], sorted by confidence, appended until token budget is reached

Token counting:

• Uses tiktoken (cl100k_base) when available
• Falls back to len(text) // 4 if tokenizer import fails

Known Gap

Previous versions of this document described TF-IDF/context-aware retrieval as if it were already shipped. That was not accurate for main and caused confusion.

Issue reference: #1059

Roadmap (Planned)

Planned scoring strategy:

final_score = (similarity * 0.6) + (confidence * 0.4)

Planned integration shape:

1. Extract recent conversational context from filtered user/final-assistant turns.
2. Compute TF-IDF cosine similarity between each fact and current context.
3. Rank by weighted score and inject under token budget.
4. Fall back to confidence-only ranking if context is unavailable.

Validation

Current regression coverage includes:

• facts inclusion in memory injection output
• confidence ordering
• token-budget-limited fact inclusion

Tests:

• backend/tests/test_memory_prompt_injection.py