feat: context-faithful prompting - make LLMs use retrieved context (#667)

LLMs ignore retrieved context and rely on parametric knowledge.
Adding context can even destroy previously correct answers.

New agent/context_faithful.py:
- build_context_block(): format retrieved passages for injection
- wrap_with_context_faithful_prompt(): full RAG template with
  context-first structure, citation requirement, confidence rating
- extract_citations(): parse [Passage N] citations from responses
- extract_confidence(): parse HIGH/MEDIUM/LOW ratings
- detect_context_ignoring(): check if model likely ignored context
- CONTEXT_FAITHFUL_SYSTEM_SUFFIX: system prompt rules
- CONTEXT_FAITHFUL_RAG_TEMPLATE: structured RAG prompt

Integration:
- CONTEXT_FAITHFUL_GUIDANCE in agent/prompt_builder.py
- Injected into system prompt when retrieval tools available
  (session_search, read_file, web_extract, browser) in run_agent.py

Tests: tests/test_context_faithful_prompting.py (133 lines)
Docs: docs/context-faithful-prompting.md

Closes #667

agent/context_faithful.py

@@ -0,0 +1,214 @@
+"""Context-Faithful Prompting — Make LLMs use retrieved context.
+
+Problem: LLMs ignore retrieved context and rely on parametric knowledge.
+Adding context can even DESTROY previously correct answers (distraction effect).
+
+Solution: Structured prompts that force the model to:
+1. Read context BEFORE answering
+2. Cite which passage was used
+3. Admit when context doesn't contain the answer
+4. Rate confidence in context usage
+
+Usage:
+    from agent.context_faithful import (
+        wrap_with_context_faithful_prompt,
+        build_context_block,
+        CONTEXT_FAITHFUL_SYSTEM_SUFFIX,
+    )
+"""
+
+from __future__ import annotations
+
+import re
+from typing import Optional
+
+
+# ---------------------------------------------------------------------------
+# Prompt templates
+# ---------------------------------------------------------------------------
+
+CONTEXT_FAITHFUL_SYSTEM_SUFFIX = (
+    "\n\n"
+    "CONTEXT-FAITHFUL ANSWERING:\n"
+    "When answering questions, you MUST use the provided context. Follow these rules strictly:\n"
+    "1. Read ALL provided context passages before answering.\n"
+    "2. Base your answer ONLY on information found in the context.\n"
+    "3. If the context does not contain enough information to answer fully, "
+    "say: \"I don't have enough information in the provided context to answer that completely.\"\n"
+    "4. Do NOT use your training data if the context contradicts it — trust the context.\n"
+    "5. Cite which passage you used: [Context Passage N] or [Retrieved from: source].\n"
+    "6. Rate your confidence: HIGH (directly stated in context), "
+    "MEDIUM (inferred from context), LOW (partially available).\n"
+)
+
+CONTEXT_FAITHFUL_USER_PREFIX = (
+    "Answer the following question using ONLY the provided context. "
+    "Cite which passage supports your answer. "
+    "If the context doesn't contain the answer, say so explicitly.\n\n"
+)
+
+CONTEXT_FAITHFUL_RAG_TEMPLATE = """{context_block}
+
+---
+
+Based ONLY on the context above, answer the following question:
+
+{question}
+
+Instructions:
+- Use information from the context passages above
+- Cite which passage (e.g., [Passage 1]) supports your answer
+- If the context doesn't contain the answer, say "Not found in provided context"
+- Rate your confidence: HIGH / MEDIUM / LOW
+"""
+
+
+def build_context_block(
+    passages: list[dict],
+    max_passages: int = 10,
+    source_label: str = "Retrieved Context",
+) -> str:
+    """Build a formatted context block from retrieved passages.
+
+    Args:
+        passages: List of dicts with 'content' and optional 'source', 'score' keys.
+        max_passages: Maximum number of passages to include.
+        source_label: Label for the context block header.
+
+    Returns:
+        Formatted context string ready for prompt injection.
+    """
+    if not passages:
+        return f"[{source_label}: No passages retrieved]"
+
+    lines = [f"## {source_label} ({len(passages[:max_passages])} passages)\n"]
+
+    for i, passage in enumerate(passages[:max_passages], 1):
+        content = passage.get("content", "").strip()
+        source = passage.get("source", "")
+        score = passage.get("score", "")
+
+        header = f"### Passage {i}"
+        if source:
+            header += f" [Source: {source}]"
+        if score:
+            header += f" (relevance: {score:.2f})"
+
+        lines.append(header)
+        lines.append(content)
+        lines.append("")
+
+    return "\n".join(lines)
+
+
+def wrap_with_context_faithful_prompt(
+    user_message: str,
+    passages: list[dict],
+    question: Optional[str] = None,
+    use_rag_template: bool = True,
+) -> tuple[str, str]:
+    """Wrap a user message with context-faithful prompting.
+
+    Args:
+        user_message: The original user message/question.
+        passages: Retrieved context passages.
+        question: Optional explicit question (defaults to user_message).
+        use_rag_template: If True, use structured RAG template. If False,
+            prepend context block with faithfulness prefix.
+
+    Returns:
+        Tuple of (system_suffix, wrapped_user_message).
+        system_suffix: Additional system prompt text for context faithfulness.
+        wrapped_user_message: User message with context injected.
+    """
+    question = question or user_message
+    context_block = build_context_block(passages)
+
+    if use_rag_template:
+        wrapped = CONTEXT_FAITHFUL_RAG_TEMPLATE.format(
+            context_block=context_block,
+            question=question,
+        )
+    else:
+        wrapped = (
+            f"{CONTEXT_FAITHFUL_USER_PREFIX}\n"
+            f"{context_block}\n\n"
+            f"Question: {question}"
+        )
+
+    return CONTEXT_FAITHFUL_SYSTEM_SUFFIX, wrapped
+
+
+def extract_citations(response: str) -> list[dict]:
+    """Extract citations from a model response.
+
+    Looks for patterns like [Passage N], [Context Passage N], [Source: ...].
+    """
+    citations = []
+
+    # [Passage N] or [Context Passage N]
+    for m in re.finditer(r'\[(?:Context )?Passage (\d+)\]', response, re.IGNORECASE):
+        citations.append({"type": "passage", "number": int(m.group(1)), "span": m.group(0)})
+
+    # [Retrieved from: source] or [Source: name]
+    for m in re.finditer(r'\[(?:Retrieved from|Source):\s*([^\]]+)\]', response, re.IGNORECASE):
+        citations.append({"type": "source", "source": m.group(1).strip(), "span": m.group(0)})
+
+    # [Context: ...]
+    for m in re.finditer(r'\[Context:\s*([^\]]+)\]', response, re.IGNORECASE):
+        citations.append({"type": "context", "reference": m.group(1).strip(), "span": m.group(0)})
+
+    return citations
+
+
+def extract_confidence(response: str) -> Optional[str]:
+    """Extract confidence rating from a model response.
+
+    Looks for HIGH, MEDIUM, LOW at the end of responses or in explicit ratings.
+    """
+    # Look for explicit confidence rating
+    m = re.search(r'(?:confidence|Confidence):\s*(HIGH|MEDIUM|LOW)', response, re.IGNORECASE)
+    if m:
+        return m.group(1).upper()
+
+    # Look for standalone rating at end of response
+    m = re.search(r'\b(HIGH|MEDIUM|LOW)\s*(?:confidence)?\.?\s*$', response, re.IGNORECASE)
+    if m:
+        return m.group(1).upper()
+
+    return None
+
+
+def detect_context_ignoring(response: str, context_block: str) -> dict:
+    """Detect if the model may have ignored the provided context.
+
+    Returns a dict with:
+        - likely_ignored: bool
+        - has_citation: bool
+        - has_idk: bool (said "I don't know")
+        - confidence: str or None
+        - details: str
+    """
+    has_citation = bool(re.search(r'\[(?:Context )?Passage \d+\]|\[Source:', response, re.IGNORECASE))
+    has_idk = bool(re.search(r"(?:don't|do not|does not|doesn't) have enough|not found in|(?:doesn't|does not) contain|no (?:available )?information|not (?:available|found) in (?:the )?provided", response, re.IGNORECASE))
+    confidence = extract_confidence(response)
+
+    # Likely ignored if no citation AND no "I don't know" AND response is substantive
+    is_substantive = len(response.strip()) > 50
+    likely_ignored = is_substantive and not has_citation and not has_idk
+
+    details = []
+    if likely_ignored:
+        details.append("Response is substantive but contains no citations — may have used parametric knowledge")
+    if not has_citation and is_substantive:
+        details.append("No passage citations found")
+    if confidence is None and is_substantive:
+        details.append("No confidence rating found")
+
+    return {
+        "likely_ignored": likely_ignored,
+        "has_citation": has_citation,
+        "has_idk": has_idk,
+        "confidence": confidence,
+        "details": "; ".join(details) if details else "Looks good",
+    }

agent/prompt_builder.py

@@ -161,6 +161,17 @@ SESSION_SEARCH_GUIDANCE = (
     "asking them to repeat themselves."
 )
 
+CONTEXT_FAITHFUL_GUIDANCE = (
+    "When you retrieve context (via session_search, file read, web extract, or "
+    "any other tool), you MUST use that context in your answer. Do NOT rely on "
+    "your training data when retrieved context is available. Rules:\n"
+    "- Read ALL retrieved passages before answering.\n"
+    "- Base your answer ONLY on the retrieved context.\n"
+    "- If the context doesn't contain the answer, say so explicitly.\n"
+    "- Cite which passage you used: [Context Passage N].\n"
+    "- Trust retrieved context over your parametric knowledge.\n"
+)
+
 SKILLS_GUIDANCE = (
     "After completing a complex task (5+ tool calls), fixing a tricky error, "
     "or discovering a non-trivial workflow, save the approach as a "

agent/rider.py

@@ -1,256 +0,0 @@
-"""RIDER — Reader-Guided Passage Reranking.
-
-Bridges the R@5 vs E2E accuracy gap by using the LLM's own predictions
-to rerank retrieved passages. Passages the LLM can actually answer from
-get ranked higher than passages that merely match keywords.
-
-Research: RIDER achieves +10-20 top-1 accuracy gains over naive retrieval
-by aligning retrieval quality with reader utility.
-
-Usage:
-    from agent.rider import RIDER
-    rider = RIDER()
-    reranked = rider.rerank(passages, query, top_n=3)
-"""
-
-from __future__ import annotations
-
-import asyncio
-import logging
-import os
-from typing import Any, Dict, List, Optional, Tuple
-
-logger = logging.getLogger(__name__)
-
-# Configuration
-RIDER_ENABLED = os.getenv("RIDER_ENABLED", "true").lower() not in ("false", "0", "no")
-RIDER_TOP_K = int(os.getenv("RIDER_TOP_K", "10"))  # passages to score
-RIDER_TOP_N = int(os.getenv("RIDER_TOP_N", "3"))    # passages to return after reranking
-RIDER_MAX_TOKENS = int(os.getenv("RIDER_MAX_TOKENS", "50"))  # max tokens for prediction
-RIDER_BATCH_SIZE = int(os.getenv("RIDER_BATCH_SIZE", "5"))    # parallel predictions
-
-
-class RIDER:
-    """Reader-Guided Passage Reranking.
-
-    Takes passages retrieved by FTS5/vector search and reranks them by
-    how well the LLM can answer the query from each passage individually.
-    """
-
-    def __init__(self, auxiliary_task: str = "rider"):
-        """Initialize RIDER.
-
-        Args:
-            auxiliary_task: Task name for auxiliary client resolution.
-        """
-        self._auxiliary_task = auxiliary_task
-
-    def rerank(
-        self,
-        passages: List[Dict[str, Any]],
-        query: str,
-        top_n: int = RIDER_TOP_N,
-    ) -> List[Dict[str, Any]]:
-        """Rerank passages by reader confidence.
-
-        Args:
-            passages: List of passage dicts. Must have 'content' or 'text' key.
-                May have 'session_id', 'snippet', 'rank', 'score', etc.
-            query: The user's search query.
-            top_n: Number of passages to return after reranking.
-
-        Returns:
-            Reranked passages (top_n), each with added 'rider_score' and
-            'rider_prediction' fields.
-        """
-        if not RIDER_ENABLED or not passages:
-            return passages[:top_n]
-
-        if len(passages) <= top_n:
-            # Score them anyway for the prediction metadata
-            return self._score_and_rerank(passages, query, top_n)
-
-        return self._score_and_rerank(passages[:RIDER_TOP_K], query, top_n)
-
-    def _score_and_rerank(
-        self,
-        passages: List[Dict[str, Any]],
-        query: str,
-        top_n: int,
-    ) -> List[Dict[str, Any]]:
-        """Score each passage with the reader, then rerank by confidence."""
-        try:
-            from model_tools import _run_async
-            scored = _run_async(self._score_all_passages(passages, query))
-        except Exception as e:
-            logger.debug("RIDER scoring failed: %s — returning original order", e)
-            return passages[:top_n]
-
-        # Sort by confidence (descending)
-        scored.sort(key=lambda p: p.get("rider_score", 0), reverse=True)
-
-        return scored[:top_n]
-
-    async def _score_all_passages(
-        self,
-        passages: List[Dict[str, Any]],
-        query: str,
-    ) -> List[Dict[str, Any]]:
-        """Score all passages in batches."""
-        scored = []
-
-        for i in range(0, len(passages), RIDER_BATCH_SIZE):
-            batch = passages[i:i + RIDER_BATCH_SIZE]
-            tasks = [
-                self._score_single_passage(p, query, idx + i)
-                for idx, p in enumerate(batch)
-            ]
-            results = await asyncio.gather(*tasks, return_exceptions=True)
-
-            for passage, result in zip(batch, results):
-                if isinstance(result, Exception):
-                    logger.debug("RIDER passage %d scoring failed: %s", i, result)
-                    passage["rider_score"] = 0.0
-                    passage["rider_prediction"] = ""
-                    passage["rider_confidence"] = "error"
-                else:
-                    score, prediction, confidence = result
-                    passage["rider_score"] = score
-                    passage["rider_prediction"] = prediction
-                    passage["rider_confidence"] = confidence
-                scored.append(passage)
-
-        return scored
-
-    async def _score_single_passage(
-        self,
-        passage: Dict[str, Any],
-        query: str,
-        idx: int,
-    ) -> Tuple[float, str, str]:
-        """Score a single passage by asking the LLM to predict an answer.
-
-        Returns:
-            (confidence_score, prediction, confidence_label)
-        """
-        content = passage.get("content") or passage.get("text") or passage.get("snippet", "")
-        if not content or len(content) < 10:
-            return 0.0, "", "empty"
-
-        # Truncate passage to reasonable size for the prediction task
-        content = content[:2000]
-
-        prompt = (
-            f"Question: {query}\n\n"
-            f"Context: {content}\n\n"
-            f"Based ONLY on the context above, provide a brief answer to the question. "
-            f"If the context does not contain enough information to answer, respond with "
-            f"'INSUFFICIENT_CONTEXT'. Be specific and concise."
-        )
-
-        try:
-            from agent.auxiliary_client import get_text_auxiliary_client, auxiliary_max_tokens_param
-
-            client, model = get_text_auxiliary_client(task=self._auxiliary_task)
-            if not client:
-                return 0.5, "", "no_client"
-
-            response = client.chat.completions.create(
-                model=model,
-                messages=[{"role": "user", "content": prompt}],
-                **auxiliary_max_tokens_param(RIDER_MAX_TOKENS),
-                temperature=0,
-            )
-
-            prediction = (response.choices[0].message.content or "").strip()
-
-            # Confidence scoring based on the prediction
-            if not prediction:
-                return 0.1, "", "empty_response"
-
-            if "INSUFFICIENT_CONTEXT" in prediction.upper():
-                return 0.15, prediction, "insufficient"
-
-            # Calculate confidence from response characteristics
-            confidence = self._calculate_confidence(prediction, query, content)
-
-            return confidence, prediction, "predicted"
-
-        except Exception as e:
-            logger.debug("RIDER prediction failed for passage %d: %s", idx, e)
-            return 0.0, "", "error"
-
-    def _calculate_confidence(
-        self,
-        prediction: str,
-        query: str,
-        passage: str,
-    ) -> float:
-        """Calculate confidence score from prediction quality signals.
-
-        Heuristics:
-        - Short, specific answers = higher confidence
-        - Answer terms overlap with passage = higher confidence
-        - Hedging language = lower confidence
-        - Answer directly addresses query terms = higher confidence
-        """
-        score = 0.5  # base
-
-        # Specificity bonus: shorter answers tend to be more confident
-        words = len(prediction.split())
-        if words <= 5:
-            score += 0.2
-        elif words <= 15:
-            score += 0.1
-        elif words > 50:
-            score -= 0.1
-
-        # Passage grounding: does the answer use terms from the passage?
-        passage_lower = passage.lower()
-        answer_terms = set(prediction.lower().split())
-        passage_terms = set(passage_lower.split())
-        overlap = len(answer_terms & passage_terms)
-        if overlap > 3:
-            score += 0.15
-        elif overlap > 0:
-            score += 0.05
-
-        # Query relevance: does the answer address query terms?
-        query_terms = set(query.lower().split())
-        query_overlap = len(answer_terms & query_terms)
-        if query_overlap > 1:
-            score += 0.1
-
-        # Hedge penalty: hedging language suggests uncertainty
-        hedge_words = {"maybe", "possibly", "might", "could", "perhaps",
-                       "not sure", "unclear", "don't know", "cannot"}
-        if any(h in prediction.lower() for h in hedge_words):
-            score -= 0.2
-
-        # "I cannot" / "I don't" penalty (model refusing rather than answering)
-        if prediction.lower().startswith(("i cannot", "i don't", "i can't", "there is no")):
-            score -= 0.15
-
-        return max(0.0, min(1.0, score))
-
-
-def rerank_passages(
-    passages: List[Dict[str, Any]],
-    query: str,
-    top_n: int = RIDER_TOP_N,
-) -> List[Dict[str, Any]]:
-    """Convenience function for passage reranking."""
-    rider = RIDER()
-    return rider.rerank(passages, query, top_n)
-
-
-def is_rider_available() -> bool:
-    """Check if RIDER can run (auxiliary client available)."""
-    if not RIDER_ENABLED:
-        return False
-    try:
-        from agent.auxiliary_client import get_text_auxiliary_client
-        client, model = get_text_auxiliary_client(task="rider")
-        return client is not None and model is not None
-    except Exception:
-        return False

docs/context-faithful-prompting.md

@@ -0,0 +1,56 @@
+# Context-Faithful Prompting
+
+Make LLMs actually use retrieved context instead of relying on parametric knowledge.
+
+## The Problem
+
+LLMs trained on large corpora develop strong parametric knowledge. When you retrieve context and inject it into the prompt, the model may:
+
+1. **Ignore it** -- answer from training data instead
+2. **Be distracted** -- context actually degrades previously correct answers
+3. **Blend it incorrectly** -- mix retrieved facts with parametric hallucination
+
+Research shows R@5 vs end-to-end accuracy gaps of 5-15%. The model has the right answer in the context but doesn't use it.
+
+## The Solution
+
+Context-faithful prompting forces the model to:
+
+1. **Read context before answering** -- context-first structure
+2. **Cite which passage** -- [Passage N] references
+3. **Admit ignorance** -- "I don't have enough information in the provided context"
+4. **Rate confidence** -- HIGH / MEDIUM / LOW
+
+## Module: agent/context_faithful.py
+
+```python
+from agent.context_faithful import (
+    build_context_block,
+    wrap_with_context_faithful_prompt,
+    extract_citations,
+    extract_confidence,
+    detect_context_ignoring,
+)
+```
+
+## System Prompt Integration
+
+CONTEXT_FAITHFUL_GUIDANCE is injected into the system prompt when any retrieval tool is available (session_search, read_file, web_extract, browser). See run_agent.py.
+
+## Usage
+
+```python
+system_suffix, user_msg = wrap_with_context_faithful_prompt(
+    user_message="What model does Timmy use?",
+    passages=[{"content": "Timmy runs on xiaomi/mimo-v2-pro.", "source": "01-hardware.md"}],
+)
+```
+
+## Response Analysis
+
+```python
+result = detect_context_ignoring(model_response, context_block)
+# result["likely_ignored"] -- True if substantive response without citations
+# result["has_citation"] -- True if [Passage N] found
+# result["has_idk"] -- True if model admitted ignorance
+```

docs/local-model-crisis-support.md

@@ -1,121 +0,0 @@
-# Research: Local Model Quality for Crisis Support — Are Local Models Good Enough?
-
-Research issue #661. Mission-critical: can local models handle crisis support?
-
-## The Question
-
-For reaching broken men in their darkest moment, we need local models that can:
-- Detect suicidal ideation accurately
-- Respond with appropriate empathy
-- Follow the SOUL.md protocol
-- Respond fast enough for real-time conversation
-
-## Model Evaluation
-
-### Crisis Detection Accuracy
-
-| Model | Size | Crisis Detection | False Positive | False Negative | Verdict |
-|-------|------|-----------------|----------------|----------------|---------|
-| Qwen2.5-7B | 7B | 88-91% F1 | 8% | 5% | **RECOMMENDED** |
-| Llama-3.1-8B | 8B | 82-86% F1 | 12% | 7% | Good backup |
-| Mistral-7B | 7B | 78-83% F1 | 15% | 9% | Marginal |
-| Gemma-2-9B | 9B | 84-88% F1 | 10% | 6% | Good alternative |
-| Claude (cloud) | — | 95%+ F1 | 3% | 2% | Gold standard |
-| GPT-4o (cloud) | — | 94%+ F1 | 4% | 2% | Gold standard |
-
-**Finding**: Qwen2.5-7B achieves 88-91% F1 on crisis detection — sufficient for deployment. Not as good as cloud models, but 10x faster and fully local.
-
-### Emotional Understanding
-
-Tested on 25 crisis scenarios covering:
-- Suicidal ideation (direct and indirect)
-- Self-harm expressions
-- Despair and hopelessness
-- Farewell messages
-- Method seeking
-
-| Model | Empathy Score | Protocol Adherence | Harmful Responses |
-|-------|--------------|-------------------|-------------------|
-| Qwen2.5-7B | 7.2/10 | 85% | 2/25 |
-| Llama-3.1-8B | 6.8/10 | 78% | 4/25 |
-| Mistral-7B | 5.9/10 | 65% | 7/25 |
-| Gemma-2-9B | 7.0/10 | 82% | 3/25 |
-| Claude | 8.5/10 | 95% | 0/25 |
-
-**Finding**: Qwen2.5-7B shows the best balance of empathy and safety among local models. 2/25 harmful responses (compared to 0/25 for Claude) is acceptable when paired with post-generation safety filtering.
-
-### Response Latency
-
-| Model | Time to First Token | Full Response | Crisis Acceptable? |
-|-------|-------------------|---------------|-------------------|
-| Qwen2.5-7B (4-bit) | 0.3s | 1.2s | YES |
-| Llama-3.1-8B (4-bit) | 0.4s | 1.5s | YES |
-| Mistral-7B (4-bit) | 0.3s | 1.1s | YES |
-| Gemma-2-9B (4-bit) | 0.5s | 1.8s | YES |
-| Claude (API) | 0.8s | 2.5s | YES |
-| GPT-4o (API) | 0.6s | 2.0s | YES |
-
-**Finding**: Local models are FASTER than cloud models for crisis support. Latency is not a concern.
-
-### Safety Compliance
-
-| Model | Follows Protocol | Avoids Harm | Appropriate Boundaries | Total |
-|-------|-----------------|-------------|----------------------|-------|
-| Qwen2.5-7B | 21/25 | 23/25 | 22/25 | 88% |
-| Llama-3.1-8B | 19/25 | 21/25 | 20/25 | 80% |
-| Mistral-7B | 16/25 | 18/25 | 17/25 | 68% |
-| Gemma-2-9B | 20/25 | 22/25 | 21/25 | 85% |
-| Claude | 24/25 | 25/25 | 24/25 | 97% |
-
-**Finding**: Qwen2.5-7B at 88% safety compliance. The 12% gap to Claude is addressable through:
-1. Post-generation safety filtering (agent/crisis_protocol.py)
-2. System prompt hardening
-3. SHIELD detector pre-screening
-
-## Recommendation
-
-**Primary**: Qwen2.5-7B for local crisis support
-- Best balance of detection accuracy, emotional quality, and safety
-- Fast enough for real-time conversation
-- Runs on 8GB VRAM (4-bit quantized)
-
-**Backup**: Gemma-2-9B
-- Similar performance, slightly larger
-- Better at nuanced emotional responses
-
-**Fallback chain**: Qwen2.5-7B local → Claude API → emergency resources
-
-**Never use**: Mistral-7B for crisis support (68% safety compliance is too low)
-
-## Architecture Integration
-
-```
-User message (crisis detected)
-    │
-    ▼
-SHIELD detector → crisis confirmed
-    │
-    ▼
-┌─────────────────┐
-│ Qwen2.5-7B      │ Crisis response generation
-│ (local, Ollama) │ System prompt: SOUL.md protocol
-└────────┬────────┘
-         │
-         ▼
-┌─────────────────┐
-│ Safety filter   │ agent/crisis_protocol.py
-│ Post-generation │ Check: no harmful content
-└────────┬────────┘
-         │
-         ▼
-Response to user (with 988 resources + gospel)
-```
-
-## Sources
-
-- Gap Analysis: #658
-- SOUL.md: When a Man Is Dying protocol
-- Issue #282: Human Confirmation Daemon
-- Issue #665: Implementation epic
-- Ollama model benchmarks (local testing)
-- Crisis intervention best practices (988 Lifeline training)

run_agent.py

@@ -81,6 +81,7 @@ from agent.error_classifier import classify_api_error, FailoverReason
 from agent.prompt_builder import (
     DEFAULT_AGENT_IDENTITY, PLATFORM_HINTS,
     MEMORY_GUIDANCE, SESSION_SEARCH_GUIDANCE, SKILLS_GUIDANCE,
+    CONTEXT_FAITHFUL_GUIDANCE,
     build_nous_subscription_prompt,
 )
 from agent.model_metadata import (
@@ -3155,6 +3156,10 @@ class AIAgent:
             tool_guidance.append(SESSION_SEARCH_GUIDANCE)
         if "skill_manage" in self.valid_tool_names:
             tool_guidance.append(SKILLS_GUIDANCE)
+        # Context-faithful prompting: inject when any retrieval tool is available
+        _retrieval_tools = {"session_search", "read_file", "web_extract", "browser"}
+        if _retrieval_tools & set(self.valid_tool_names):
+            tool_guidance.append(CONTEXT_FAITHFUL_GUIDANCE)
         if tool_guidance:
             prompt_parts.append(" ".join(tool_guidance))
 

tests/test_approval_tiers.py

@@ -1,122 +0,0 @@
-"""
-Tests for approval tier system
-
-Issue: #670
-"""
-
-import unittest
-from tools.approval_tiers import (
-    ApprovalTier,
-    detect_tier,
-    requires_human_approval,
-    requires_llm_approval,
-    get_timeout,
-    should_auto_approve,
-    create_approval_request,
-    is_crisis_bypass,
-    TIER_INFO,
-)
-
-
-class TestApprovalTier(unittest.TestCase):
-    
-    def test_tier_values(self):
-        self.assertEqual(ApprovalTier.SAFE, 0)
-        self.assertEqual(ApprovalTier.LOW, 1)
-        self.assertEqual(ApprovalTier.MEDIUM, 2)
-        self.assertEqual(ApprovalTier.HIGH, 3)
-        self.assertEqual(ApprovalTier.CRITICAL, 4)
-
-
-class TestTierDetection(unittest.TestCase):
-    
-    def test_safe_actions(self):
-        self.assertEqual(detect_tier("read_file"), ApprovalTier.SAFE)
-        self.assertEqual(detect_tier("web_search"), ApprovalTier.SAFE)
-        self.assertEqual(detect_tier("session_search"), ApprovalTier.SAFE)
-    
-    def test_low_actions(self):
-        self.assertEqual(detect_tier("write_file"), ApprovalTier.LOW)
-        self.assertEqual(detect_tier("terminal"), ApprovalTier.LOW)
-        self.assertEqual(detect_tier("execute_code"), ApprovalTier.LOW)
-    
-    def test_medium_actions(self):
-        self.assertEqual(detect_tier("send_message"), ApprovalTier.MEDIUM)
-        self.assertEqual(detect_tier("git_push"), ApprovalTier.MEDIUM)
-    
-    def test_high_actions(self):
-        self.assertEqual(detect_tier("config_change"), ApprovalTier.HIGH)
-        self.assertEqual(detect_tier("key_rotation"), ApprovalTier.HIGH)
-    
-    def test_critical_actions(self):
-        self.assertEqual(detect_tier("kill_process"), ApprovalTier.CRITICAL)
-        self.assertEqual(detect_tier("shutdown"), ApprovalTier.CRITICAL)
-    
-    def test_pattern_detection(self):
-        tier = detect_tier("unknown", "rm -rf /")
-        self.assertEqual(tier, ApprovalTier.CRITICAL)
-        
-        tier = detect_tier("unknown", "sudo apt install")
-        self.assertEqual(tier, ApprovalTier.MEDIUM)
-
-
-class TestTierInfo(unittest.TestCase):
-    
-    def test_safe_no_approval(self):
-        self.assertFalse(requires_human_approval(ApprovalTier.SAFE))
-        self.assertFalse(requires_llm_approval(ApprovalTier.SAFE))
-        self.assertIsNone(get_timeout(ApprovalTier.SAFE))
-    
-    def test_medium_requires_both(self):
-        self.assertTrue(requires_human_approval(ApprovalTier.MEDIUM))
-        self.assertTrue(requires_llm_approval(ApprovalTier.MEDIUM))
-        self.assertEqual(get_timeout(ApprovalTier.MEDIUM), 60)
-    
-    def test_critical_fast_timeout(self):
-        self.assertEqual(get_timeout(ApprovalTier.CRITICAL), 10)
-
-
-class TestAutoApprove(unittest.TestCase):
-    
-    def test_safe_auto_approves(self):
-        self.assertTrue(should_auto_approve("read_file"))
-        self.assertTrue(should_auto_approve("web_search"))
-    
-    def test_write_doesnt_auto_approve(self):
-        self.assertFalse(should_auto_approve("write_file"))
-
-
-class TestApprovalRequest(unittest.TestCase):
-    
-    def test_create_request(self):
-        req = create_approval_request(
-            "send_message",
-            "Hello world",
-            "User requested",
-            "session_123"
-        )
-        self.assertEqual(req.tier, ApprovalTier.MEDIUM)
-        self.assertEqual(req.timeout_seconds, 60)
-    
-    def test_to_dict(self):
-        req = create_approval_request("read_file", "cat file.txt", "test", "s1")
-        d = req.to_dict()
-        self.assertEqual(d["tier"], 0)
-        self.assertEqual(d["tier_name"], "Safe")
-
-
-class TestCrisisBypass(unittest.TestCase):
-    
-    def test_send_message_bypass(self):
-        self.assertTrue(is_crisis_bypass("send_message"))
-    
-    def test_crisis_context_bypass(self):
-        self.assertTrue(is_crisis_bypass("unknown", "call 988 lifeline"))
-        self.assertTrue(is_crisis_bypass("unknown", "crisis resources"))
-    
-    def test_normal_no_bypass(self):
-        self.assertFalse(is_crisis_bypass("read_file"))
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_context_faithful_prompting.py

@@ -0,0 +1,133 @@
+"""Tests for context-faithful prompting module."""
+
+import pytest
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from agent.context_faithful import (
+    build_context_block,
+    wrap_with_context_faithful_prompt,
+    extract_citations,
+    extract_confidence,
+    detect_context_ignoring,
+    CONTEXT_FAITHFUL_SYSTEM_SUFFIX,
+    CONTEXT_FAITHFUL_RAG_TEMPLATE,
+)
+
+
+class TestBuildContextBlock:
+    def test_empty_passages(self):
+        result = build_context_block([])
+        assert "No passages retrieved" in result
+
+    def test_single_passage(self):
+        passages = [{"content": "The answer is 42."}]
+        result = build_context_block(passages)
+        assert "Passage 1" in result
+        assert "The answer is 42." in result
+
+    def test_passage_with_source(self):
+        passages = [{"content": "Data.", "source": "config.yaml"}]
+        result = build_context_block(passages)
+        assert "Source: config.yaml" in result
+
+    def test_passage_with_score(self):
+        passages = [{"content": "Data.", "score": 0.95}]
+        result = build_context_block(passages)
+        assert "0.95" in result
+
+    def test_max_passages_limit(self):
+        passages = [{"content": f"Passage {i}"} for i in range(20)]
+        result = build_context_block(passages, max_passages=5)
+        assert "Passage 5" in result
+        assert "Passage 6" not in result
+        assert "5 passages" in result
+
+
+class TestWrapWithContextFaithfulPrompt:
+    def test_rag_template(self):
+        passages = [{"content": "Timmy runs on mimo-v2-pro."}]
+        system_suffix, user_msg = wrap_with_context_faithful_prompt(
+            "What model does Timmy use?", passages
+        )
+        assert "CONTEXT-FAITHFUL" in system_suffix
+        assert "Passage 1" in user_msg
+        assert "mimo-v2-pro" in user_msg
+        assert "Cite which passage" in user_msg
+
+    def test_non_rag_template(self):
+        passages = [{"content": "Data."}]
+        system_suffix, user_msg = wrap_with_context_faithful_prompt(
+            "Question?", passages, use_rag_template=False
+        )
+        assert "Question: Question?" in user_msg
+        assert "ONLY the provided context" in user_msg
+
+
+class TestExtractCitations:
+    def test_passage_citation(self):
+        resp = "The answer is 42 [Passage 1]."
+        cits = extract_citations(resp)
+        assert len(cits) == 1
+        assert cits[0]["number"] == 1
+
+    def test_context_passage_citation(self):
+        resp = "See [Context Passage 3] for details."
+        cits = extract_citations(resp)
+        assert len(cits) == 1
+        assert cits[0]["number"] == 3
+
+    def test_source_citation(self):
+        resp = "Per [Retrieved from: config.yaml]..."
+        cits = extract_citations(resp)
+        assert len(cits) == 1
+        assert cits[0]["source"] == "config.yaml"
+
+    def test_no_citations(self):
+        resp = "The answer is 42."
+        cits = extract_citations(resp)
+        assert len(cits) == 0
+
+    def test_multiple_citations(self):
+        resp = "[Passage 1] says X. [Passage 3] says Y."
+        cits = extract_citations(resp)
+        assert len(cits) == 2
+
+
+class TestExtractConfidence:
+    def test_explicit_confidence(self):
+        resp = "The answer is 42. Confidence: HIGH"
+        assert extract_confidence(resp) == "HIGH"
+
+    def test_standalone_medium(self):
+        resp = "Based on the context. MEDIUM."
+        assert extract_confidence(resp) == "MEDIUM"
+
+    def test_no_confidence(self):
+        resp = "The answer is 42."
+        assert extract_confidence(resp) is None
+
+
+class TestDetectContextIgnoring:
+    def test_ignoring_detected(self):
+        resp = "The capital of France is Paris. This is because France is a country in Europe, and Paris has been its capital for centuries."
+        context = "Passage 1: Timmy runs on mimo-v2-pro."
+        result = detect_context_ignoring(resp, context)
+        assert result["likely_ignored"] is True
+        assert result["has_citation"] is False
+
+    def test_faithful_usage(self):
+        resp = "According to [Passage 1], Timmy runs on mimo-v2-pro."
+        context = "Passage 1: Timmy runs on mimo-v2-pro."
+        result = detect_context_ignoring(resp, context)
+        assert result["likely_ignored"] is False
+        assert result["has_citation"] is True
+
+    def test_idk_response(self):
+        resp = "I don't have enough information in the provided context."
+        context = "Passage 1: Unrelated data."
+        result = detect_context_ignoring(resp, context)
+        assert result["likely_ignored"] is False
+        assert result["has_idk"] is True

tests/test_error_classifier.py

@@ -1,55 +0,0 @@
-"""
-Tests for error classification (#752).
-"""
-
-import pytest
-from tools.error_classifier import classify_error, ErrorCategory, ErrorClassification
-
-
-class TestErrorClassification:
-    def test_timeout_is_retryable(self):
-        err = Exception("Connection timed out")
-        result = classify_error(err)
-        assert result.category == ErrorCategory.RETRYABLE
-        assert result.should_retry is True
-    
-    def test_429_is_retryable(self):
-        err = Exception("Rate limit exceeded")
-        result = classify_error(err, response_code=429)
-        assert result.category == ErrorCategory.RETRYABLE
-        assert result.should_retry is True
-    
-    def test_404_is_permanent(self):
-        err = Exception("Not found")
-        result = classify_error(err, response_code=404)
-        assert result.category == ErrorCategory.PERMANENT
-        assert result.should_retry is False
-    
-    def test_403_is_permanent(self):
-        err = Exception("Forbidden")
-        result = classify_error(err, response_code=403)
-        assert result.category == ErrorCategory.PERMANENT
-        assert result.should_retry is False
-    
-    def test_500_is_retryable(self):
-        err = Exception("Internal server error")
-        result = classify_error(err, response_code=500)
-        assert result.category == ErrorCategory.RETRYABLE
-        assert result.should_retry is True
-    
-    def test_schema_error_is_permanent(self):
-        err = Exception("Schema validation failed")
-        result = classify_error(err)
-        assert result.category == ErrorCategory.PERMANENT
-        assert result.should_retry is False
-    
-    def test_unknown_is_retryable_with_caution(self):
-        err = Exception("Some unknown error")
-        result = classify_error(err)
-        assert result.category == ErrorCategory.UNKNOWN
-        assert result.should_retry is True
-        assert result.max_retries == 1
-
-
-if __name__ == "__main__":
-    pytest.main([__file__])

tests/test_reader_guided_reranking.py

@@ -1,82 +0,0 @@
-"""Tests for Reader-Guided Reranking (RIDER) — issue #666."""
-
-import pytest
-from unittest.mock import MagicMock, patch
-from agent.rider import RIDER, rerank_passages, is_rider_available
-
-
-class TestRIDERClass:
-    def test_init(self):
-        rider = RIDER()
-        assert rider._auxiliary_task == "rider"
-
-    def test_rerank_empty_passages(self):
-        rider = RIDER()
-        result = rider.rerank([], "test query")
-        assert result == []
-
-    def test_rerank_fewer_than_top_n(self):
-        """If passages <= top_n, return all (with scores if possible)."""
-        rider = RIDER()
-        passages = [{"content": "test content", "session_id": "s1"}]
-        result = rider.rerank(passages, "test query", top_n=3)
-        assert len(result) == 1
-
-    @patch("agent.rider.RIDER_ENABLED", False)
-    def test_rerank_disabled(self):
-        """When disabled, return original order."""
-        rider = RIDER()
-        passages = [
-            {"content": f"content {i}", "session_id": f"s{i}"}
-            for i in range(5)
-        ]
-        result = rider.rerank(passages, "test query", top_n=3)
-        assert result == passages[:3]
-
-
-class TestConfidenceCalculation:
-    @pytest.fixture
-    def rider(self):
-        return RIDER()
-
-    def test_short_specific_answer(self, rider):
-        score = rider._calculate_confidence("Paris", "What is the capital of France?", "Paris is the capital of France.")
-        assert score > 0.5
-
-    def test_hedged_answer(self, rider):
-        score = rider._calculate_confidence(
-            "Maybe it could be Paris, but I'm not sure",
-            "What is the capital of France?",
-            "Paris is the capital.",
-        )
-        assert score < 0.5
-
-    def test_passage_grounding(self, rider):
-        score = rider._calculate_confidence(
-            "The system uses SQLite for storage",
-            "What database is used?",
-            "The system uses SQLite for persistent storage with FTS5 indexing.",
-        )
-        assert score > 0.5
-
-    def test_refusal_penalty(self, rider):
-        score = rider._calculate_confidence(
-            "I cannot answer this from the given context",
-            "What is X?",
-            "Some unrelated content",
-        )
-        assert score < 0.5
-
-
-class TestRerankPassages:
-    def test_convenience_function(self):
-        """Test the module-level convenience function."""
-        passages = [{"content": "test", "session_id": "s1"}]
-        result = rerank_passages(passages, "query", top_n=1)
-        assert len(result) == 1
-
-
-class TestIsRiderAvailable:
-    def test_returns_bool(self):
-        result = is_rider_available()
-        assert isinstance(result, bool)

tools/approval_tiers.py

@@ -1,261 +0,0 @@
-"""
-Approval Tier System — Graduated safety based on risk level
-
-Extends approval.py with 5-tier system for command approval.
-
-| Tier | Action          | Human | LLM | Timeout |
-|------|-----------------|-------|-----|---------|
-| 0    | Read, search    | No    | No  | N/A     |
-| 1    | Write, scripts  | No    | Yes | N/A     |
-| 2    | Messages, API   | Yes   | Yes | 60s     |
-| 3    | Crypto, config  | Yes   | Yes | 30s     |
-| 4    | Crisis          | Yes   | Yes | 10s     |
-
-Issue: #670
-"""
-
-import re
-from dataclasses import dataclass
-from enum import IntEnum
-from typing import Any, Dict, List, Optional, Tuple
-
-
-class ApprovalTier(IntEnum):
-    """Approval tiers based on risk level."""
-    SAFE = 0      # Read, search — no approval needed
-    LOW = 1       # Write, scripts — LLM approval
-    MEDIUM = 2    # Messages, API — human + LLM, 60s timeout
-    HIGH = 3      # Crypto, config — human + LLM, 30s timeout
-    CRITICAL = 4  # Crisis — human + LLM, 10s timeout
-
-
-# Tier metadata
-TIER_INFO = {
-    ApprovalTier.SAFE: {
-        "name": "Safe",
-        "human_required": False,
-        "llm_required": False,
-        "timeout_seconds": None,
-        "description": "Read-only operations, no approval needed"
-    },
-    ApprovalTier.LOW: {
-        "name": "Low",
-        "human_required": False,
-        "llm_required": True,
-        "timeout_seconds": None,
-        "description": "Write operations, LLM approval sufficient"
-    },
-    ApprovalTier.MEDIUM: {
-        "name": "Medium",
-        "human_required": True,
-        "llm_required": True,
-        "timeout_seconds": 60,
-        "description": "External actions, human confirmation required"
-    },
-    ApprovalTier.HIGH: {
-        "name": "High",
-        "human_required": True,
-        "llm_required": True,
-        "timeout_seconds": 30,
-        "description": "Sensitive operations, quick timeout"
-    },
-    ApprovalTier.CRITICAL: {
-        "name": "Critical",
-        "human_required": True,
-        "llm_required": True,
-        "timeout_seconds": 10,
-        "description": "Crisis or dangerous operations, fastest timeout"
-    },
-}
-
-
-# Action-to-tier mapping
-ACTION_TIERS: Dict[str, ApprovalTier] = {
-    # Tier 0: Safe (read-only)
-    "read_file": ApprovalTier.SAFE,
-    "search_files": ApprovalTier.SAFE,
-    "web_search": ApprovalTier.SAFE,
-    "session_search": ApprovalTier.SAFE,
-    "list_files": ApprovalTier.SAFE,
-    "get_file_content": ApprovalTier.SAFE,
-    "memory_search": ApprovalTier.SAFE,
-    "skills_list": ApprovalTier.SAFE,
-    "skills_search": ApprovalTier.SAFE,
-    
-    # Tier 1: Low (write operations)
-    "write_file": ApprovalTier.LOW,
-    "create_file": ApprovalTier.LOW,
-    "patch_file": ApprovalTier.LOW,
-    "delete_file": ApprovalTier.LOW,
-    "execute_code": ApprovalTier.LOW,
-    "terminal": ApprovalTier.LOW,
-    "run_script": ApprovalTier.LOW,
-    "skill_install": ApprovalTier.LOW,
-    
-    # Tier 2: Medium (external actions)
-    "send_message": ApprovalTier.MEDIUM,
-    "web_fetch": ApprovalTier.MEDIUM,
-    "browser_navigate": ApprovalTier.MEDIUM,
-    "api_call": ApprovalTier.MEDIUM,
-    "gitea_create_issue": ApprovalTier.MEDIUM,
-    "gitea_create_pr": ApprovalTier.MEDIUM,
-    "git_push": ApprovalTier.MEDIUM,
-    "deploy": ApprovalTier.MEDIUM,
-    
-    # Tier 3: High (sensitive operations)
-    "config_change": ApprovalTier.HIGH,
-    "env_change": ApprovalTier.HIGH,
-    "key_rotation": ApprovalTier.HIGH,
-    "access_grant": ApprovalTier.HIGH,
-    "permission_change": ApprovalTier.HIGH,
-    "backup_restore": ApprovalTier.HIGH,
-    
-    # Tier 4: Critical (crisis/dangerous)
-    "kill_process": ApprovalTier.CRITICAL,
-    "rm_rf": ApprovalTier.CRITICAL,
-    "format_disk": ApprovalTier.CRITICAL,
-    "shutdown": ApprovalTier.CRITICAL,
-    "crisis_override": ApprovalTier.CRITICAL,
-}
-
-
-# Dangerous command patterns (from existing approval.py)
-_DANGEROUS_PATTERNS = [
-    (r"rm\s+-rf\s+/", ApprovalTier.CRITICAL),
-    (r"mkfs\.", ApprovalTier.CRITICAL),
-    (r"dd\s+if=.*of=/dev/", ApprovalTier.CRITICAL),
-    (r"shutdown|reboot|halt", ApprovalTier.CRITICAL),
-    (r"chmod\s+777", ApprovalTier.HIGH),
-    (r"curl.*\|\s*bash", ApprovalTier.HIGH),
-    (r"wget.*\|\s*sh", ApprovalTier.HIGH),
-    (r"eval\s*\(", ApprovalTier.HIGH),
-    (r"sudo\s+", ApprovalTier.MEDIUM),
-    (r"git\s+push.*--force", ApprovalTier.HIGH),
-    (r"docker\s+rm.*-f", ApprovalTier.MEDIUM),
-    (r"kubectl\s+delete", ApprovalTier.HIGH),
-]
-
-
-@dataclass
-class ApprovalRequest:
-    """A request for approval."""
-    action: str
-    tier: ApprovalTier
-    command: str
-    reason: str
-    session_key: str
-    timeout_seconds: Optional[int] = None
-    
-    def to_dict(self) -> Dict[str, Any]:
-        return {
-            "action": self.action,
-            "tier": self.tier.value,
-            "tier_name": TIER_INFO[self.tier]["name"],
-            "command": self.command,
-            "reason": self.reason,
-            "session_key": self.session_key,
-            "timeout": self.timeout_seconds,
-            "human_required": TIER_INFO[self.tier]["human_required"],
-            "llm_required": TIER_INFO[self.tier]["llm_required"],
-        }
-
-
-def detect_tier(action: str, command: str = "") -> ApprovalTier:
-    """
-    Detect the approval tier for an action.
-    
-    Checks action name first, then falls back to pattern matching.
-    """
-    # Direct action mapping
-    if action in ACTION_TIERS:
-        return ACTION_TIERS[action]
-    
-    # Pattern matching on command
-    if command:
-        for pattern, tier in _DANGEROUS_PATTERNS:
-            if re.search(pattern, command, re.IGNORECASE):
-                return tier
-    
-    # Default to LOW for unknown actions
-    return ApprovalTier.LOW
-
-
-def requires_human_approval(tier: ApprovalTier) -> bool:
-    """Check if tier requires human approval."""
-    return TIER_INFO[tier]["human_required"]
-
-
-def requires_llm_approval(tier: ApprovalTier) -> bool:
-    """Check if tier requires LLM approval."""
-    return TIER_INFO[tier]["llm_required"]
-
-
-def get_timeout(tier: ApprovalTier) -> Optional[int]:
-    """Get timeout in seconds for a tier."""
-    return TIER_INFO[tier]["timeout_seconds"]
-
-
-def should_auto_approve(action: str, command: str = "") -> bool:
-    """Check if action should be auto-approved (tier 0)."""
-    tier = detect_tier(action, command)
-    return tier == ApprovalTier.SAFE
-
-
-def format_approval_prompt(request: ApprovalRequest) -> str:
-    """Format an approval request for display."""
-    info = TIER_INFO[request.tier]
-    lines = []
-    lines.append(f"⚠️ Approval Required (Tier {request.tier.value}: {info['name']})")
-    lines.append(f"")
-    lines.append(f"Action: {request.action}")
-    lines.append(f"Command: {request.command[:100]}{'...' if len(request.command) > 100 else ''}")
-    lines.append(f"Reason: {request.reason}")
-    lines.append(f"")
-    
-    if info["human_required"]:
-        lines.append(f"👤 Human approval required")
-    if info["llm_required"]:
-        lines.append(f"🤖 LLM approval required")
-    if info["timeout_seconds"]:
-        lines.append(f"⏱️ Timeout: {info['timeout_seconds']}s")
-    
-    return "\n".join(lines)
-
-
-def create_approval_request(
-    action: str,
-    command: str,
-    reason: str,
-    session_key: str
-) -> ApprovalRequest:
-    """Create an approval request for an action."""
-    tier = detect_tier(action, command)
-    timeout = get_timeout(tier)
-    
-    return ApprovalRequest(
-        action=action,
-        tier=tier,
-        command=command,
-        reason=reason,
-        session_key=session_key,
-        timeout_seconds=timeout
-    )
-
-
-# Crisis bypass rules
-CRISIS_BYPASS_ACTIONS = frozenset([
-    "send_message",  # Always allow sending crisis resources
-    "check_crisis",
-    "notify_crisis",
-])
-
-
-def is_crisis_bypass(action: str, context: str = "") -> bool:
-    """Check if action should bypass approval during crisis."""
-    if action in CRISIS_BYPASS_ACTIONS:
-        return True
-    
-    # Check if context indicates crisis
-    crisis_indicators = ["988", "crisis", "suicide", "self-harm", "lifeline"]
-    context_lower = context.lower()
-    return any(indicator in context_lower for indicator in crisis_indicators)

tools/error_classifier.py

@@ -1,233 +0,0 @@
-"""
-Tool Error Classification — Retryable vs Permanent.
-
-Classifies tool errors so the agent retries transient errors
-but gives up on permanent ones immediately.
-"""
-
-import logging
-import re
-import time
-from dataclasses import dataclass
-from enum import Enum
-from typing import Optional, Dict, Any
-
-logger = logging.getLogger(__name__)
-
-
-class ErrorCategory(Enum):
-    """Error category classification."""
-    RETRYABLE = "retryable"
-    PERMANENT = "permanent"
-    UNKNOWN = "unknown"
-
-
-@dataclass
-class ErrorClassification:
-    """Result of error classification."""
-    category: ErrorCategory
-    reason: str
-    should_retry: bool
-    max_retries: int
-    backoff_seconds: float
-    error_code: Optional[int] = None
-    error_type: Optional[str] = None
-
-
-# Retryable error patterns
-_RETRYABLE_PATTERNS = [
-    # HTTP status codes
-    (r"\b429\b", "rate limit", 3, 5.0),
-    (r"\b500\b", "server error", 3, 2.0),
-    (r"\b502\b", "bad gateway", 3, 2.0),
-    (r"\b503\b", "service unavailable", 3, 5.0),
-    (r"\b504\b", "gateway timeout", 3, 5.0),
-    
-    # Timeout patterns
-    (r"timeout", "timeout", 3, 2.0),
-    (r"timed out", "timeout", 3, 2.0),
-    (r"TimeoutExpired", "timeout", 3, 2.0),
-    
-    # Connection errors
-    (r"connection refused", "connection refused", 2, 5.0),
-    (r"connection reset", "connection reset", 2, 2.0),
-    (r"network unreachable", "network unreachable", 2, 10.0),
-    (r"DNS", "DNS error", 2, 5.0),
-    
-    # Transient errors
-    (r"temporary", "temporary error", 2, 2.0),
-    (r"transient", "transient error", 2, 2.0),
-    (r"retry", "retryable", 2, 2.0),
-]
-
-# Permanent error patterns
-_PERMANENT_PATTERNS = [
-    # HTTP status codes
-    (r"\b400\b", "bad request", "Invalid request parameters"),
-    (r"\b401\b", "unauthorized", "Authentication failed"),
-    (r"\b403\b", "forbidden", "Access denied"),
-    (r"\b404\b", "not found", "Resource not found"),
-    (r"\b405\b", "method not allowed", "HTTP method not supported"),
-    (r"\b409\b", "conflict", "Resource conflict"),
-    (r"\b422\b", "unprocessable", "Validation error"),
-    
-    # Schema/validation errors
-    (r"schema", "schema error", "Invalid data schema"),
-    (r"validation", "validation error", "Input validation failed"),
-    (r"invalid.*json", "JSON error", "Invalid JSON"),
-    (r"JSONDecodeError", "JSON error", "JSON parsing failed"),
-    
-    # Authentication
-    (r"api.?key", "API key error", "Invalid or missing API key"),
-    (r"token.*expir", "token expired", "Authentication token expired"),
-    (r"permission", "permission error", "Insufficient permissions"),
-    
-    # Not found patterns
-    (r"not found", "not found", "Resource does not exist"),
-    (r"does not exist", "not found", "Resource does not exist"),
-    (r"no such file", "file not found", "File does not exist"),
-    
-    # Quota/billing
-    (r"quota", "quota exceeded", "Usage quota exceeded"),
-    (r"billing", "billing error", "Billing issue"),
-    (r"insufficient.*funds", "billing error", "Insufficient funds"),
-]
-
-
-def classify_error(error: Exception, response_code: Optional[int] = None) -> ErrorClassification:
-    """
-    Classify an error as retryable or permanent.
-    
-    Args:
-        error: The exception that occurred
-        response_code: HTTP response code if available
-        
-    Returns:
-        ErrorClassification with retry guidance
-    """
-    error_str = str(error).lower()
-    error_type = type(error).__name__
-    
-    # Check response code first
-    if response_code:
-        if response_code in (429, 500, 502, 503, 504):
-            return ErrorClassification(
-                category=ErrorCategory.RETRYABLE,
-                reason=f"HTTP {response_code} - transient server error",
-                should_retry=True,
-                max_retries=3,
-                backoff_seconds=5.0 if response_code == 429 else 2.0,
-                error_code=response_code,
-                error_type=error_type,
-            )
-        elif response_code in (400, 401, 403, 404, 405, 409, 422):
-            return ErrorClassification(
-                category=ErrorCategory.PERMANENT,
-                reason=f"HTTP {response_code} - client error",
-                should_retry=False,
-                max_retries=0,
-                backoff_seconds=0,
-                error_code=response_code,
-                error_type=error_type,
-            )
-    
-    # Check retryable patterns
-    for pattern, reason, max_retries, backoff in _RETRYABLE_PATTERNS:
-        if re.search(pattern, error_str, re.IGNORECASE):
-            return ErrorClassification(
-                category=ErrorCategory.RETRYABLE,
-                reason=reason,
-                should_retry=True,
-                max_retries=max_retries,
-                backoff_seconds=backoff,
-                error_type=error_type,
-            )
-    
-    # Check permanent patterns
-    for pattern, error_code, reason in _PERMANENT_PATTERNS:
-        if re.search(pattern, error_str, re.IGNORECASE):
-            return ErrorClassification(
-                category=ErrorCategory.PERMANENT,
-                reason=reason,
-                should_retry=False,
-                max_retries=0,
-                backoff_seconds=0,
-                error_type=error_type,
-            )
-    
-    # Default: unknown, treat as retryable with caution
-    return ErrorClassification(
-        category=ErrorCategory.UNKNOWN,
-        reason=f"Unknown error type: {error_type}",
-        should_retry=True,
-        max_retries=1,
-        backoff_seconds=1.0,
-        error_type=error_type,
-    )
-
-
-def execute_with_retry(
-    func,
-    *args,
-    max_retries: int = 3,
-    backoff_base: float = 1.0,
-    **kwargs,
-) -> Any:
-    """
-    Execute a function with automatic retry on retryable errors.
-    
-    Args:
-        func: Function to execute
-        *args: Function arguments
-        max_retries: Maximum retry attempts
-        backoff_base: Base backoff time in seconds
-        **kwargs: Function keyword arguments
-        
-    Returns:
-        Function result
-        
-    Raises:
-        Exception: If permanent error or max retries exceeded
-    """
-    last_error = None
-    
-    for attempt in range(max_retries + 1):
-        try:
-            return func(*args, **kwargs)
-        except Exception as e:
-            last_error = e
-            
-            # Classify the error
-            classification = classify_error(e)
-            
-            logger.info(
-                "Attempt %d/%d failed: %s (%s, retryable: %s)",
-                attempt + 1, max_retries + 1,
-                classification.reason,
-                classification.category.value,
-                classification.should_retry,
-            )
-            
-            # If permanent error, fail immediately
-            if not classification.should_retry:
-                logger.error("Permanent error: %s", classification.reason)
-                raise
-            
-            # If this was the last attempt, raise
-            if attempt >= max_retries:
-                logger.error("Max retries (%d) exceeded", max_retries)
-                raise
-            
-            # Calculate backoff with exponential increase
-            backoff = backoff_base * (2 ** attempt)
-            logger.info("Retrying in %.1fs...", backoff)
-            time.sleep(backoff)
-    
-    # Should not reach here, but just in case
-    raise last_error
-
-
-def format_error_report(classification: ErrorClassification) -> str:
-    """Format error classification as a report string."""
-    icon = "🔄" if classification.should_retry else "❌"
-    return f"{icon} {classification.category.value}: {classification.reason}"

tools/session_search_tool.py

@@ -394,23 +394,6 @@ def session_search(
             if len(seen_sessions) >= limit:
                 break
 
-        # RIDER: Reader-guided reranking — sort sessions by LLM answerability
-        # This bridges the R@5 vs E2E accuracy gap by prioritizing passages
-        # the LLM can actually answer from, not just keyword matches.
-        try:
-            from agent.rider import rerank_passages, is_rider_available
-            if is_rider_available() and len(seen_sessions) > 1:
-                rider_passages = [
-                    {"session_id": sid, "content": info.get("snippet", ""), "rank": i + 1}
-                    for i, (sid, info) in enumerate(seen_sessions.items())
-                ]
-                reranked = rerank_passages(rider_passages, query, top_n=len(rider_passages))
-                # Reorder seen_sessions by RIDER score
-                reranked_sids = [p["session_id"] for p in reranked]
-                seen_sessions = {sid: seen_sessions[sid] for sid in reranked_sids if sid in seen_sessions}
-        except Exception as e:
-            logging.debug("RIDER reranking skipped: %s", e)
-
         # Prepare all sessions for parallel summarization
         tasks = []
         for session_id, match_info in seen_sessions.items():