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context-injection

Configure and tune automatic context injection for Memory OS. Covers the Icarus pre_llm_call hook flow, 4 sources (Fabric, Qdrant, Sessions, Facts), threshold gating, per-session dedup, the social closer filter, multi-source deduplication, and injection troubleshooting.

1.0.0

Tuning context injection thresholds

Debugging why context is not being injected

Understanding how the 4 memory sources interact

Reducing token cost from context injection

Diagnosing duplicate context in system prompt

Configuring social closer detection

Improving recall relevance

Context Injection — Multi-Source pre_llm_call Pipeline

Overview

The Icarus plugin hooks into the agent's pre_llm_call lifecycle to inject relevant context before every LLM turn. Four sources are queried in parallel, each gated by relevance thresholds, and results are deduplicated across the session.

Injection Flow

User message
    │
    ▼
pre_llm_call(user_message):
    ├── _is_social_close(message)?
    │     └── Yes → skip ALL search-based injection
    ├── fabric_recall(query) → [fabric] results
    ├── _search_qdrant(query) → [qdrant] results
    ├── _search_sessions(query) → [sessions] results
    └── _search_facts(query) → [facts] results (first turn only)
    │
    ▼
System prompt assembled with injected context

Source Configuration

Source	Method	Threshold	Top-K	Cost	Label
Fabric	`fabric_recall(query)`	overlap > 0.85 → skip	3	low (local markdown)	`[fabric]`
Qdrant	`_search_qdrant(query)` → context_enhancer	threshold ≥ 0.55	2	high (API embedding)	`[qdrant]`
Sessions	FTS5 on state.db	tokens ≥ 4 chars	2	low (local SQLite)	`[sessions]`
Facts	FTS5 on memory_store.db	first turn only	3	low (local SQLite)	`[facts]`

Threshold tuning rationale

Overlap gate (0.85): Was set to 0.6 originally — this suppressed ALL injection from turn 2 onward in long single-topic sessions. The agent went blind to all sources midway through focused conversations. The fix: only suppress near-literal repetition (>0.85) and rely on per-source dedup instead.

Qdrant threshold (0.55): Was set to 0.72 — legitimate queries scored 0.57-0.63 and were silently filtered. 0.55 surfaces them; top_k stays low (2) to bound prompt size.

Facts (first turn only): Facts are stable, rarely change per session. Querying every turn wastes tokens. First-turn-only injection is the right tradeoff.

Trivial messages skip expensive search entirely:

_SOCIAL_CLOSERS = frozenset({
    "ok", "thanks", "yes", "no", "👍", "👌", "✅", "done", "got it",
    "certo", "sim", "claro", "beleza"
})

Also blocks Qdrant/sessions/facts (NOT fabric — fabric has zero network cost) for:

Messages shorter than 6 chars that are ASCII-only without technical markers (://.@#$_?)

Why not len > 20: Messages like "fix the BM25" or "probe Qdrant" are short but technically dense — exactly the queries that benefit most from semantic injection.

Per-Session Deduplication

Module-level sets (_injected_fabric, _injected_qdrant, _injected_sessions) are reset in on_session_start.

Dedup key: Prefer the entry/point id; fall back to a stable content slice (e.g. summary[:60]). A too-short slice ([:40]) collides and produces duplicate lines.

What this replaces: The old approach used a single overlap > 0.6 bail that killed ALL injection after turn 1. Now individual sources are deduplicated independently, so injection stays active across a long session without repeating the same content.

Context Enhancer (Qdrant Pipeline)

The Qdrant search path uses context_enhancer.py — a standalone embedding + search pipeline:

search_with_fallback(query, top_k=2, threshold=0.55):
    1. Embed via Qwen3-Embedding-8B (OpenRouter)
    2. try hybrid (dense + BM25 sparse) → RRF fusion
    3. except -> try dense-only
    4. except -> try lexical (markdown grep)
    5. except -> try SQLite keyword
    6. fail -> return empty

Key injection pitfall: context_enhancer.py reads OPENROUTER_API_KEY (singular) from os.environ, but some environments use split keys (OPENROUTER_FULL_API_KEY, OPENROUTER_DS_API_KEY). Before importing, inject: os.environ["OPENROUTER_API_KEY"] = resolved_key. Without this, embed_query() fails silently and search falls back to lexical-only.

Injecting into System Prompt

Results are labeled by source:

[memory]
[fabric] relevant to your request:
  [2026-05-30] agent: Fixed context injection thresholds

[sessions] prior conversations on this topic:
  [2026-05-24] (untitled): …Discussion about memory thresholds…

[qdrant] semantically similar knowledge:
  [concept] Memory Architecture — Fallback cascade (hybrid → dense → lexical)
  [entity] Qdrant v1.17 — Named vectors required for hybrid search

[facts] structured facts:
  - Context injection thresholds: overlap > 0.85, Qdrant ≥ 0.55

Source labeling lets the agent understand where each piece of information came from and apply the correct priority level from Ground Truth hierarchy.

Troubleshooting

No context being injected

Check _is_social_close() — trivial messages skip search
Verify OPENROUTER_API_KEY is resolvable at import time (hooks.py reads at module level)
Check gateway was restarted after any .env or hooks.py changes
Verify the overlap threshold is 0.85 (not 0.6)

Duplicate context appearing

Check dedup key slice — if too short ([:40]), different entries may collide
Verify _injected_* sets are reset in on_session_start

Qdrant search returning empty but Qdrant has data

Check embedding dimension mismatch (env vs collection)
Check if threshold is too high (> 0.55 hides legitimate matches)
Check if OPENROUTER_API_KEY was injected before context_enhancer import

Fabric results stale or missing

Verify fabric_recall is working (fabric_brief() shows entries)
Check FABRIC_DIR points to a directory with .md files
Check ICARUS_EXTRACTION_MAX_TOKENS is set to 4096 (1024 default truncates)

Pitfalls

ICARUS_EXTRACTION_MAX_TOKENS is frozen at import time — changing .env requires gateway restart. Value is read at hooks.py:15 and cached in process memory.
DeepSeek + response_format: json_object = content: null — use prompt-based JSON + _parse_json_robust() instead
FTS5 snippet() cannot be combined with GROUP BY — fetch ranked rows, dedup in Python
sessions.started_at is a Unix timestamp (float) — not an ISO string. Use datetime.fromtimestamp(float(sa))
Open DBs read-only — sqlite3.connect(f"file:{db}?mode=ro", uri=True). The gateway must never lock or mutate these files from a hook
FTS5 is lexical, not semantic — queries in one language may not match content in another with different wording
Always restart gateway (hermes gateway restart) after editing hooks.py — hooks are loaded at import, changes only apply to new sessions

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