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fix(memory): inject stored facts into system prompt memory context (#1083)

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* 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>
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Willem Jiang
2026-03-13 14:37:40 +08:00
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backend/docs/MEMORY_IMPROVEMENTS.md
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@@ -1,281 +1,65 @@
# Memory System Improvements
This document describes recent improvements to the memory system's fact injection mechanism.
This document tracks memory injection behavior and roadmap status.
## Overview
## Status (As Of 2026-03-10)
Two major improvements have been made to the `format_memory_for_injection` function:
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.
1. **Similarity-Based Fact Retrieval**: Uses TF-IDF to select facts most relevant to current conversation context
2. **Accurate Token Counting**: Uses tiktoken for precise token estimation instead of rough character-based approximation
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.
## 1. Similarity-Based Fact Retrieval
## Current Behavior
### Problem
The original implementation selected facts based solely on confidence scores, taking the top 15 highest-confidence facts regardless of their relevance to the current conversation. This could result in injecting irrelevant facts while omitting contextually important ones.
### Solution
The new implementation uses **TF-IDF (Term Frequency-Inverse Document Frequency)** vectorization with cosine similarity to measure how relevant each fact is to the current conversation context.
**Scoring Formula**:
```
final_score = (similarity × 0.6) + (confidence × 0.4)
```
- **Similarity (60% weight)**: Cosine similarity between fact content and current context
- **Confidence (40% weight)**: LLM-assigned confidence score (0-1)
### Benefits
- **Context-Aware**: Prioritizes facts relevant to what the user is currently discussing
- **Dynamic**: Different facts surface based on conversation topic
- **Balanced**: Considers both relevance and reliability
- **Fallback**: Gracefully degrades to confidence-only ranking if context is unavailable
### Example
Given facts about Python, React, and Docker:
- User asks: *"How should I write Python tests?"*
- Prioritizes: Python testing, type hints, pytest
- User asks: *"How to optimize my Next.js app?"*
- Prioritizes: React/Next.js experience, performance optimization
### Configuration
Customize weights in `config.yaml` (optional):
```yaml
memory:
similarity_weight: 0.6 # Weight for TF-IDF similarity (0-1)
confidence_weight: 0.4 # Weight for confidence score (0-1)
```
**Note**: Weights should sum to 1.0 for best results.
## 2. Accurate Token Counting
### Problem
The original implementation estimated tokens using a simple formula:
```python
max_chars = max_tokens * 4
```
This assumes ~4 characters per token, which is:
- Inaccurate for many languages and content types
- Can lead to over-injection (exceeding token limits)
- Can lead to under-injection (wasting available budget)
### Solution
The new implementation uses **tiktoken**, OpenAI's official tokenizer library, to count tokens accurately:
Function today:
```python
import tiktoken
def _count_tokens(text: str, encoding_name: str = "cl100k_base") -> int:
encoding = tiktoken.get_encoding(encoding_name)
return len(encoding.encode(text))
def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
```
- Uses `cl100k_base` encoding (GPT-4, GPT-3.5, text-embedding-ada-002)
- Provides exact token counts for budget management
- Falls back to character-based estimation if tiktoken fails
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
### Benefits
- **Precision**: Exact token counts match what the model sees
- **Budget Optimization**: Maximizes use of available token budget
- **No Overflows**: Prevents exceeding `max_injection_tokens` limit
- **Better Planning**: Each section's token cost is known precisely
Token counting:
- Uses `tiktoken` (`cl100k_base`) when available
- Falls back to `len(text) // 4` if tokenizer import fails
### Example
```python
text = "This is a test string to count tokens accurately using tiktoken."
## Known Gap
# Old method
char_count = len(text) # 64 characters
old_estimate = char_count // 4 # 16 tokens (overestimate)
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.
# New method
accurate_count = _count_tokens(text) # 13 tokens (exact)
Issue reference: `#1059`
## Roadmap (Planned)
Planned scoring strategy:
```text
final_score = (similarity * 0.6) + (confidence * 0.4)
```
**Result**: 3-token difference (18.75% error rate)
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.
In production, errors can be much larger for:
- Code snippets (more tokens per character)
- Non-English text (variable token ratios)
- Technical jargon (often multi-token words)
## Validation
## Implementation Details
Current regression coverage includes:
- facts inclusion in memory injection output
- confidence ordering
- token-budget-limited fact inclusion
### Function Signature
```python
def format_memory_for_injection(
memory_data: dict[str, Any],
max_tokens: int = 2000,
current_context: str | None = None,
) -> str:
```
**New Parameter**:
- `current_context`: Optional string containing recent conversation messages for similarity calculation
### Backward Compatibility
The function remains **100% backward compatible**:
- If `current_context` is `None` or empty, falls back to confidence-only ranking
- Existing callers without the parameter work exactly as before
- Token counting is always accurate (transparent improvement)
### Integration Point
Memory is **dynamically injected** via `MemoryMiddleware.before_model()`:
```python
# src/agents/middlewares/memory_middleware.py
def _extract_conversation_context(messages: list, max_turns: int = 3) -> str:
"""Extract recent conversation (user input + final responses only)."""
context_parts = []
turn_count = 0
for msg in reversed(messages):
if msg.type == "human":
# Always include user messages
context_parts.append(extract_text(msg))
turn_count += 1
if turn_count >= max_turns:
break
elif msg.type == "ai" and not msg.tool_calls:
# Only include final AI responses (no tool_calls)
context_parts.append(extract_text(msg))
# Skip tool messages and AI messages with tool_calls
return " ".join(reversed(context_parts))
class MemoryMiddleware:
def before_model(self, state, runtime):
"""Inject memory before EACH LLM call (not just before_agent)."""
# Get recent conversation context (filtered)
conversation_context = _extract_conversation_context(
state["messages"],
max_turns=3
)
# Load memory with context-aware fact selection
memory_data = get_memory_data()
memory_content = format_memory_for_injection(
memory_data,
max_tokens=config.max_injection_tokens,
current_context=conversation_context, # ✅ Clean conversation only
)
# Inject as system message
memory_message = SystemMessage(
content=f"<memory>\n{memory_content}\n</memory>",
name="memory_context",
)
return {"messages": [memory_message] + state["messages"]}
```
### How It Works
1. **User continues conversation**:
```
Turn 1: "I'm working on a Python project"
Turn 2: "It uses FastAPI and SQLAlchemy"
Turn 3: "How do I write tests?" ← Current query
```
2. **Extract recent context**: Last 3 turns combined:
```
"I'm working on a Python project. It uses FastAPI and SQLAlchemy. How do I write tests?"
```
3. **TF-IDF scoring**: Ranks facts by relevance to this context
- High score: "Prefers pytest for testing" (testing + Python)
- High score: "Likes type hints in Python" (Python related)
- High score: "Expert in Python and FastAPI" (Python + FastAPI)
- Low score: "Uses Docker for containerization" (less relevant)
4. **Injection**: Top-ranked facts injected into system prompt's `<memory>` section
5. **Agent sees**: Full system prompt with relevant memory context
### Benefits of Dynamic System Prompt
- **Multi-Turn Context**: Uses last 3 turns, not just current question
- Captures ongoing conversation flow
- Better understanding of user's current focus
- **Query-Specific Facts**: Different facts surface based on conversation topic
- **Clean Architecture**: No middleware message manipulation
- **LangChain Native**: Uses built-in dynamic system prompt support
- **Runtime Flexibility**: Memory regenerated for each agent invocation
## Dependencies
New dependencies added to `pyproject.toml`:
```toml
dependencies = [
# ... existing dependencies ...
"tiktoken>=0.8.0", # Accurate token counting
"scikit-learn>=1.6.1", # TF-IDF vectorization
]
```
Install with:
```bash
cd backend
uv sync
```
## Testing
Run the test script to verify improvements:
```bash
cd backend
python test_memory_improvement.py
```
Expected output shows:
- Different fact ordering based on context
- Accurate token counts vs old estimates
- Budget-respecting fact selection
## Performance Impact
### Computational Cost
- **TF-IDF Calculation**: O(n × m) where n=facts, m=vocabulary
- Negligible for typical fact counts (10-100 facts)
- Caching opportunities if context doesn't change
- **Token Counting**: ~10-100µs per call
- Faster than the old character-counting approach
- Minimal overhead compared to LLM inference
### Memory Usage
- **TF-IDF Vectorizer**: ~1-5MB for typical vocabulary
- Instantiated once per injection call
- Garbage collected after use
- **Tiktoken Encoding**: ~1MB (cached singleton)
- Loaded once per process lifetime
### Recommendations
- Current implementation is optimized for accuracy over caching
- For high-throughput scenarios, consider:
- Pre-computing fact embeddings (store in memory.json)
- Caching TF-IDF vectorizer between calls
- Using approximate nearest neighbor search for >1000 facts
## Summary
| Aspect | Before | After |
|--------|--------|-------|
| Fact Selection | Top 15 by confidence only | Relevance-based (similarity + confidence) |
| Token Counting | `len(text) // 4` | `tiktoken.encode(text)` |
| Context Awareness | None | TF-IDF cosine similarity |
| Accuracy | ±25% token estimate | Exact token count |
| Configuration | Fixed weights | Customizable similarity/confidence weights |
These improvements result in:
- **More relevant** facts injected into context
- **Better utilization** of available token budget
- **Fewer hallucinations** due to focused context
- **Higher quality** agent responses
Tests:
- `backend/tests/test_memory_prompt_injection.py`

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backend/docs/MEMORY_IMPROVEMENTS_SUMMARY.md
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# Memory System Improvements - Summary
## 改进概述
## Sync Note (2026-03-10)
针对你提出的两个问题进行了优化：
1. ✅ **粗糙的 token 计算**（`字符数 * 4`）→ 使用 tiktoken 精确计算
2. ✅ **缺乏相似度召回** → 使用 TF-IDF + 最近对话上下文
This summary is synchronized with the `main` branch implementation.
TF-IDF/context-aware retrieval is **planned**, not merged yet.
## 核心改进
## Implemented
### 1. 基于对话上下文的智能 Facts 召回
- Accurate token counting with `tiktoken` in memory injection.
- Facts are injected into `<memory>` prompt content.
- Facts are ordered by confidence and bounded by `max_injection_tokens`.
**之前**：
- 只按 confidence 排序取前 15 个
- 无论用户在讨论什么都注入相同的 facts
## Planned (Not Yet Merged)
**现在**：
- 提取最近 **3 轮对话**（human + AI 消息）作为上下文
- 使用 **TF-IDF 余弦相似度**计算每个 fact 与对话的相关性
- 综合评分：`相似度(60%) + 置信度(40%)`
- 动态选择最相关的 facts
- TF-IDF cosine similarity recall based on recent conversation context.
- `current_context` parameter for `format_memory_for_injection`.
- Weighted ranking (`similarity` + `confidence`).
- Runtime extraction/injection flow for context-aware fact selection.
**示例**：
```
对话历史：
Turn 1: "我在做一个 Python 项目"
Turn 2: "使用 FastAPI 和 SQLAlchemy"
Turn 3: "怎么写测试？"
## Why This Sync Was Needed
上下文: "我在做一个 Python 项目 使用 FastAPI 和 SQLAlchemy 怎么写测试？"
Earlier docs described TF-IDF behavior as already implemented, which did not match code in `main`.
This mismatch is tracked in issue `#1059`.
相关度高的 facts:
✓ "Prefers pytest for testing" (Python + 测试)
✓ "Expert in Python and FastAPI" (Python + FastAPI)
✓ "Likes type hints in Python" (Python)
相关度低的 facts:
✗ "Uses Docker for containerization" (不相关)
```
### 2. 精确的 Token 计算
**之前**：
```python
max_chars = max_tokens * 4 # 粗糙估算
```
**现在**：
```python
import tiktoken
def _count_tokens(text: str) -> int:
encoding = tiktoken.get_encoding("cl100k_base") # GPT-4/3.5
return len(encoding.encode(text))
```
**效果对比**：
```python
text = "This is a test string to count tokens accurately."
旧方法: len(text) // 4 = 12 tokens (估算)
新方法: tiktoken.encode = 10 tokens (精确)
误差: 20%
```
### 3. 多轮对话上下文
**之前的担心**：
> "只传最近一条 human message 会不会上下文不太够？"
**现在的解决方案**：
- 提取最近 **3 轮对话**（可配置）
- 包括 human 和 AI 消息
- 更完整的对话上下文
**示例**：
```
单条消息: "怎么写测试？"
→ 缺少上下文，不知道是什么项目
3轮对话: "Python 项目 + FastAPI + 怎么写测试？"
→ 完整上下文，能选择更相关的 facts
```
## 实现方式
### Middleware 动态注入
使用 `before_model` 钩子在**每次 LLM 调用前**注入 memory：
## Current API Shape
```python
# src/agents/middlewares/memory_middleware.py
def _extract_conversation_context(messages: list, max_turns: int = 3) -> str:
"""提取最近 3 轮对话（只包含用户输入和最终回复）"""
context_parts = []
turn_count = 0
for msg in reversed(messages):
msg_type = getattr(msg, "type", None)
if msg_type == "human":
# ✅ 总是包含用户消息
content = extract_text(msg)
if content:
context_parts.append(content)
turn_count += 1
if turn_count >= max_turns:
break
elif msg_type == "ai":
# ✅ 只包含没有 tool_calls 的 AI 消息（最终回复）
tool_calls = getattr(msg, "tool_calls", None)
if not tool_calls:
content = extract_text(msg)
if content:
context_parts.append(content)
# ✅ 跳过 tool messages 和带 tool_calls 的 AI 消息
return " ".join(reversed(context_parts))
class MemoryMiddleware:
def before_model(self, state, runtime):
"""在每次 LLM 调用前注入 memory（不是 before_agent）"""
# 1. 提取最近 3 轮对话（过滤掉 tool calls）
messages = state["messages"]
conversation_context = _extract_conversation_context(messages, max_turns=3)
# 2. 使用干净的对话上下文选择相关 facts
memory_data = get_memory_data()
memory_content = format_memory_for_injection(
memory_data,
max_tokens=config.max_injection_tokens,
current_context=conversation_context, # ✅ 只包含真实对话内容
)
# 3. 作为 system message 注入到消息列表开头
memory_message = SystemMessage(
content=f"<memory>\n{memory_content}\n</memory>",
name="memory_context", # 用于去重检测
)
# 4. 插入到消息列表开头
updated_messages = [memory_message] + messages
return {"messages": updated_messages}
def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
```
### 为什么这样设计？
No `current_context` argument is currently available in `main`.
基于你的三个重要观察：
## Verification Pointers
1. **应该用 `before_model` 而不是 `before_agent`**
- ✅ `before_agent`: 只在整个 agent 开始时调用一次
- ✅ `before_model`: 在**每次 LLM 调用前**都会调用
- ✅ 这样每次 LLM 推理都能看到最新的相关 memory
2. **messages 数组里只有 human/ai/tool，没有 system**
- ✅ 虽然不常见，但 LangChain 允许在对话中插入 system message
- ✅ Middleware 可以修改 messages 数组
- ✅ 使用 `name="memory_context"` 防止重复注入
3. **应该剔除 tool call 的 AI messages，只传用户输入和最终输出**
- ✅ 过滤掉带 `tool_calls` 的 AI 消息（中间步骤）
- ✅ 只保留： - Human 消息（用户输入）
- AI 消息但无 tool_calls（最终回复）
- ✅ 上下文更干净，TF-IDF 相似度计算更准确
## 配置选项
在 `config.yaml` 中可以调整：
```yaml
memory:
enabled: true
max_injection_tokens: 2000 # ✅ 使用精确 token 计数
# 高级设置（可选）
# max_context_turns: 3 # 对话轮数（默认 3）
# similarity_weight: 0.6 # 相似度权重
# confidence_weight: 0.4 # 置信度权重
```
## 依赖变更
新增依赖：
```toml
dependencies = [
"tiktoken>=0.8.0", # 精确 token 计数
"scikit-learn>=1.6.1", # TF-IDF 向量化
]
```
安装：
```bash
cd backend
uv sync
```
## 性能影响
- **TF-IDF 计算**：O(n × m)，n=facts 数量，m=词汇表大小
- 典型场景（10-100 facts）：< 10ms
- **Token 计数**：~100µs per call
- 比字符计数还快
- **总开销**：可忽略（相比 LLM 推理）
## 向后兼容性
✅ 完全向后兼容：
- 如果没有 `current_context`，退化为按 confidence 排序
- 所有现有配置继续工作
- 不影响其他功能
## 文件变更清单
1. **核心功能**
- `src/agents/memory/prompt.py` - 添加 TF-IDF 召回和精确 token 计数
- `src/agents/lead_agent/prompt.py` - 动态系统提示
- `src/agents/lead_agent/agent.py` - 传入函数而非字符串
2. **依赖**
- `pyproject.toml` - 添加 tiktoken 和 scikit-learn
3. **文档**
- `docs/MEMORY_IMPROVEMENTS.md` - 详细技术文档
- `docs/MEMORY_IMPROVEMENTS_SUMMARY.md` - 改进总结（本文件）
- `CLAUDE.md` - 更新架构说明
- `config.example.yaml` - 添加配置说明
## 测试验证
运行项目验证：
```bash
cd backend
make dev
```
在对话中测试：
1. 讨论不同主题（Python、React、Docker 等）
2. 观察不同对话注入的 facts 是否不同
3. 检查 token 预算是否被准确控制
## 总结
| 问题 | 之前 | 现在 |
|------|------|------|
| Token 计算 | `len(text) // 4` (±25% 误差) | `tiktoken.encode()` (精确) |
| Facts 选择 | 按 confidence 固定排序 | TF-IDF 相似度 + confidence |
| 上下文 | 无 | 最近 3 轮对话 |
| 实现方式 | 静态系统提示 | 动态系统提示函数 |
| 配置灵活性 | 有限 | 可调轮数和权重 |
所有改进都实现了，并且：
- ✅ 不修改 messages 数组
- ✅ 使用多轮对话上下文
- ✅ 精确 token 计数
- ✅ 智能相似度召回
- ✅ 完全向后兼容
- Implementation: `backend/src/agents/memory/prompt.py`
- Prompt assembly: `backend/src/agents/lead_agent/prompt.py`
- Regression tests: `backend/tests/test_memory_prompt_injection.py`

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backend/src/agents/memory/prompt.py
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@@ -1,5 +1,6 @@
"""Prompt templates for memory update and injection."""
import math
import re
from typing import Any
@@ -166,6 +167,22 @@ def _count_tokens(text: str, encoding_name: str = "cl100k_base") -> int:
return len(text) // 4
def _coerce_confidence(value: Any, default: float = 0.0) -> float:
"""Coerce a confidence-like value to a bounded float in [0, 1].
Non-finite values (NaN, inf, -inf) are treated as invalid and fall back
to the default before clamping, preventing them from dominating ranking.
The ``default`` parameter is assumed to be a finite value.
"""
try:
confidence = float(value)
except (TypeError, ValueError):
return max(0.0, min(1.0, default))
if not math.isfinite(confidence):
return max(0.0, min(1.0, default))
return max(0.0, min(1.0, confidence))
def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
"""Format memory data for injection into system prompt.
@@ -217,6 +234,55 @@ def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2
if history_sections:
sections.append("History:\n" + "\n".join(f"- {s}" for s in history_sections))
# Format facts (sorted by confidence; include as many as token budget allows)
facts_data = memory_data.get("facts", [])
if isinstance(facts_data, list) and facts_data:
ranked_facts = sorted(
(
f
for f in facts_data
if isinstance(f, dict)
and isinstance(f.get("content"), str)
and f.get("content").strip()
),
key=lambda fact: _coerce_confidence(fact.get("confidence"), default=0.0),
reverse=True,
)
# Compute token count for existing sections once, then account
# incrementally for each fact line to avoid full-string re-tokenization.
base_text = "\n\n".join(sections)
base_tokens = _count_tokens(base_text) if base_text else 0
# Account for the separator between existing sections and the facts section.
facts_header = "Facts:\n"
separator_tokens = _count_tokens("\n\n" + facts_header) if base_text else _count_tokens(facts_header)
running_tokens = base_tokens + separator_tokens
fact_lines: list[str] = []
for fact in ranked_facts:
content_value = fact.get("content")
if not isinstance(content_value, str):
continue
content = content_value.strip()
if not content:
continue
category = str(fact.get("category", "context")).strip() or "context"
confidence = _coerce_confidence(fact.get("confidence"), default=0.0)
line = f"- [{category} | {confidence:.2f}] {content}"
# Each additional line is preceded by a newline (except the first).
line_text = ("\n" + line) if fact_lines else line
line_tokens = _count_tokens(line_text)
if running_tokens + line_tokens <= max_tokens:
fact_lines.append(line)
running_tokens += line_tokens
else:
break
if fact_lines:
sections.append("Facts:\n" + "\n".join(fact_lines))
if not sections:
return ""

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backend/tests/test_memory_prompt_injection.py Normal file
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@@ -0,0 +1,122 @@
"""Tests for memory prompt injection formatting."""
import math
from src.agents.memory.prompt import _coerce_confidence, format_memory_for_injection
def test_format_memory_includes_facts_section() -> None:
memory_data = {
"user": {},
"history": {},
"facts": [
{"content": "User uses PostgreSQL", "category": "knowledge", "confidence": 0.9},
{"content": "User prefers SQLAlchemy", "category": "preference", "confidence": 0.8},
],
}
result = format_memory_for_injection(memory_data, max_tokens=2000)
assert "Facts:" in result
assert "User uses PostgreSQL" in result
assert "User prefers SQLAlchemy" in result
def test_format_memory_sorts_facts_by_confidence_desc() -> None:
memory_data = {
"user": {},
"history": {},
"facts": [
{"content": "Low confidence fact", "category": "context", "confidence": 0.4},
{"content": "High confidence fact", "category": "knowledge", "confidence": 0.95},
],
}
result = format_memory_for_injection(memory_data, max_tokens=2000)
assert result.index("High confidence fact") < result.index("Low confidence fact")
def test_format_memory_respects_budget_when_adding_facts(monkeypatch) -> None:
# Make token counting deterministic for this test by counting characters.
monkeypatch.setattr("src.agents.memory.prompt._count_tokens", lambda text, encoding_name="cl100k_base": len(text))
memory_data = {
"user": {},
"history": {},
"facts": [
{"content": "First fact should fit", "category": "knowledge", "confidence": 0.95},
{"content": "Second fact should not fit in tiny budget", "category": "knowledge", "confidence": 0.90},
],
}
first_fact_only_memory_data = {
"user": {},
"history": {},
"facts": [
{"content": "First fact should fit", "category": "knowledge", "confidence": 0.95},
],
}
one_fact_result = format_memory_for_injection(first_fact_only_memory_data, max_tokens=2000)
two_facts_result = format_memory_for_injection(memory_data, max_tokens=2000)
# Choose a budget that can include exactly one fact section line.
max_tokens = (len(one_fact_result) + len(two_facts_result)) // 2
first_only_result = format_memory_for_injection(memory_data, max_tokens=max_tokens)
assert "First fact should fit" in first_only_result
assert "Second fact should not fit in tiny budget" not in first_only_result
def test_coerce_confidence_nan_falls_back_to_default() -> None:
"""NaN should not be treated as a valid confidence value."""
result = _coerce_confidence(math.nan, default=0.5)
assert result == 0.5
def test_coerce_confidence_inf_falls_back_to_default() -> None:
"""Infinite values should fall back to default rather than clamping to 1.0."""
assert _coerce_confidence(math.inf, default=0.3) == 0.3
assert _coerce_confidence(-math.inf, default=0.3) == 0.3
def test_coerce_confidence_valid_values_are_clamped() -> None:
"""Valid floats outside [0, 1] are clamped; values inside are preserved."""
assert _coerce_confidence(1.5) == 1.0
assert _coerce_confidence(-0.5) == 0.0
assert abs(_coerce_confidence(0.75) - 0.75) < 1e-9
def test_format_memory_skips_none_content_facts() -> None:
"""Facts with content=None must not produce a 'None' line in the output."""
memory_data = {
"facts": [
{"content": None, "category": "knowledge", "confidence": 0.9},
{"content": "Real fact", "category": "knowledge", "confidence": 0.8},
],
}
result = format_memory_for_injection(memory_data, max_tokens=2000)
assert "None" not in result
assert "Real fact" in result
def test_format_memory_skips_non_string_content_facts() -> None:
"""Facts with non-string content (e.g. int/list) must be ignored."""
memory_data = {
"facts": [
{"content": 42, "category": "knowledge", "confidence": 0.9},
{"content": ["list"], "category": "knowledge", "confidence": 0.85},
{"content": "Valid fact", "category": "knowledge", "confidence": 0.7},
],
}
result = format_memory_for_injection(memory_data, max_tokens=2000)
# The formatted line for an integer content would be "- [knowledge | 0.90] 42".
assert "| 0.90] 42" not in result
# The formatted line for a list content would be "- [knowledge | 0.85] ['list']".
assert "| 0.85]" not in result
assert "Valid fact" in result