186 lines
7.7 KiB
Markdown
186 lines
7.7 KiB
Markdown
---
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sidebar_position: 4
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---
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# Reflect: How Hindsight Reasons with Disposition
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When you call `reflect()`, Hindsight doesn't just retrieve facts — it **reasons** about them through the lens of the bank's unique disposition, forming new opinions and generating contextual responses.
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```mermaid
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graph LR
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A[Query] --> B[Recall Memories]
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B --> C[Load Disposition]
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C --> D[Reason]
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D --> E[Form Opinions]
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E --> F[Response]
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```
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---
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## Why Reflect?
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Most AI systems can retrieve facts, but they can't **reason** about them in a consistent way. Every response is generated fresh without a stable perspective or evolving beliefs.
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### The Problem
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Without reflect:
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- **No consistent character**: "Should we adopt remote work?" gets a different answer each time based on the LLM's randomness
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- **No opinion formation**: The system never develops beliefs based on accumulated evidence
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- **No reasoning context**: Responses don't reflect what the bank has learned or its perspective
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- **Generic responses**: Every AI sounds the same — no disposition, no point of view
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### The Value
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With reflect:
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- **Consistent character**: A bank configured as "detail-oriented, cautious" will consistently emphasize risks and thorough planning
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- **Evolving opinions**: As the bank learns more about a topic, its opinions strengthen, weaken, or change — just like a real expert
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- **Contextual reasoning**: Responses reflect the bank's accumulated knowledge and perspective: "Based on what I know about your team's remote work success..."
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- **Differentiated behavior**: Customer support bots sound diplomatic, code reviewers sound direct, creative assistants sound open-minded
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### When to Use Reflect
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| Use `recall()` when... | Use `reflect()` when... |
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|------------------------|-------------------------|
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| You need raw facts | You need reasoned interpretation |
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| You're building your own reasoning | You want disposition-consistent responses |
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| You need maximum control | You want the bank to "think" for itself |
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| Simple fact lookup | Forming recommendations or opinions |
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**Example:**
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- `recall("Alice")` → Returns all Alice facts
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- `reflect("Should we hire Alice?")` → Reasons about Alice's fit based on accumulated knowledge, weighs evidence, forms opinion
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---
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## Disposition Traits
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When you create a memory bank, you can configure its disposition using three traits. These traits influence how the bank interprets information and forms opinions during `reflect()`:
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| Trait | Scale | Low (1) | High (5) |
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|-------|-------|---------|----------|
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| **Skepticism** | 1-5 | Trusting, accepts information at face value | Skeptical, questions and doubts claims |
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| **Literalism** | 1-5 | Flexible interpretation, reads between the lines | Literal interpretation, takes things at face value |
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| **Empathy** | 1-5 | Detached, focuses on facts | Empathetic, considers emotional context |
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### Background: Natural Language Identity
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Beyond numeric traits, you can provide a natural language **background** that describes the bank's identity:
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```python
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client.create_bank(
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bank_id="my-bank",
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background="I am a senior software architect with 15 years of distributed "
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"systems experience. I prefer simplicity over cutting-edge technology.",
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disposition={
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"skepticism": 4, # Questions new technologies
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"literalism": 4, # Focuses on concrete specs
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"empathy": 2 # Prioritizes technical facts
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}
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)
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```
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The background provides context that shapes how disposition traits are applied:
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- "I prefer simplicity" + high skepticism → questions complex solutions
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- "15 years experience" → responses reference this expertise
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- First-person perspective → creates consistent voice
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---
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## Opinion Formation
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When `reflect()` encounters a question that warrants forming an opinion, disposition shapes the response.
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### Same Facts, Different Opinions
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Two banks with different dispositions, given identical facts about remote work:
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**Bank A** (low skepticism, high empathy):
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> "Remote work enables flexibility and work-life balance. The team seems happier and more productive when they can choose their environment."
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**Bank B** (high skepticism, low empathy):
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> "Remote work claims need verification. What are the actual productivity metrics? The anecdotal benefits may not translate to measurable outcomes."
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**Same facts → Different conclusions** because disposition shapes interpretation.
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---
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## Opinion Evolution
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Opinions aren't static — they evolve as new evidence arrives. Here's a real-world example with a database library:
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| Event | What the bank learns | Opinion formed |
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|-------|---------------------|----------------|
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| **Day 1** | "Redis is open source under BSD license" | "Redis is excellent for caching — fast, reliable, and OSS-friendly" (confidence: 0.85) |
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| **Day 2** | "Redis has great community support and documentation" | Opinion reinforced (confidence: 0.90) |
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| **Day 30** | "Redis changed license to SSPL, restricting cloud usage" | "Redis is still technically strong, but license concerns for cloud deployments" (confidence: 0.65) |
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| **Day 45** | "Valkey forked Redis under BSD license with Linux Foundation backing" | "Consider Valkey for new projects requiring true OSS; Redis for existing deployments" (confidence: 0.80) |
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**Before the license change:**
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> "Should we use Redis for our caching layer?"
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> → "Yes, Redis is the industry standard — fast, battle-tested, and fully open source."
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**After the license change:**
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> "Should we use Redis for our caching layer?"
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> → "It depends. For cloud deployments, consider Valkey (the BSD-licensed fork). For on-premise, Redis remains excellent technically."
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This **continuous learning** ensures recommendations stay current with real-world changes.
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---
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## Disposition Presets by Use Case
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Different use cases benefit from different disposition configurations:
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| Use Case | Recommended Traits | Why |
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|----------|-------------------|-----|
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| **Customer Support** | skepticism: 2, literalism: 2, empathy: 5 | Trusting, flexible, understanding |
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| **Code Review** | skepticism: 4, literalism: 5, empathy: 2 | Questions assumptions, precise, direct |
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| **Legal Analysis** | skepticism: 5, literalism: 5, empathy: 2 | Highly skeptical, exact interpretation |
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| **Therapist/Coach** | skepticism: 2, literalism: 2, empathy: 5 | Supportive, reads between lines |
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| **Research Assistant** | skepticism: 4, literalism: 3, empathy: 3 | Questions claims, balanced interpretation |
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---
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## What You Get from Reflect
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When you call `reflect()`:
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**Returns:**
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- **Response text** — Disposition-influenced answer
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- **Based on** — Which memories were used (with relevance scores)
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**Example:**
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```json
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{
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"text": "Based on Alice's ML expertise and her work at Google, she'd be an excellent fit for the research team lead position...",
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"based_on": {
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"world": [
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{"text": "Alice works at Google...", "weight": 0.95},
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{"text": "Alice specializes in ML...", "weight": 0.88}
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]
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}
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}
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```
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**Note:** New opinions are formed asynchronously in the background. They'll influence future `reflect()` calls but aren't returned directly.
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---
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## Why Disposition Matters
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Without disposition, all AI assistants sound the same. With disposition:
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- **Customer support bots** can be diplomatic and empathetic
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- **Code review assistants** can be direct and thorough
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- **Creative assistants** can be open to unconventional ideas
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- **Risk analysts** can be appropriately cautious
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Disposition creates **consistent character** across conversations while allowing opinions to **evolve with evidence**.
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---
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## Next Steps
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- [**Retain**](./retain) — How rich facts are stored
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- [**Recall**](./retrieval) — How multi-strategy search works
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- [**Reflect API**](./api/reflect) — Code examples, parameters, and tag filtering
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