229 lines
6.5 KiB
Markdown
229 lines
6.5 KiB
Markdown
---
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sidebar_position: 3
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---
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# Reflect
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Generate disposition-aware responses using retrieved memories.
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When you call **reflect**, Hindsight performs a multi-step reasoning process:
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1. **Recalls** relevant memories from the bank based on your query
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2. **Applies** the bank's disposition traits to shape the reasoning style
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3. **Generates** a contextual answer grounded in the retrieved facts
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4. **Forms opinions** in the background based on the reasoning (available in subsequent calls)
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The response includes the generated answer along with the facts that were used, providing full transparency into how the answer was derived.
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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:::info How Reflect Works
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Learn about disposition-driven reasoning and opinion formation in the [Reflect Architecture](/developer/reflect) guide.
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:::
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:::tip Prerequisites
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Make sure you've completed the [Quick Start](./quickstart) to install the client and start the server.
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:::
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## Basic Usage
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<Tabs>
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<TabItem value="python" label="Python">
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```python
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from hindsight_client import Hindsight
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client = Hindsight(base_url="http://localhost:8888")
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client.reflect(bank_id="my-bank", query="What should I know about Alice?")
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```
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</TabItem>
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<TabItem value="node" label="Node.js">
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```typescript
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import { HindsightClient } from '@vectorize-io/hindsight-client';
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const client = new HindsightClient({ baseUrl: 'http://localhost:8888' });
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await client.reflect('my-bank', 'What should I know about Alice?');
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```
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</TabItem>
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<TabItem value="cli" label="CLI">
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```bash
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hindsight memory think my-bank "What should I know about Alice?"
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```
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</TabItem>
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</Tabs>
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## Parameters
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `query` | string | required | Question or prompt |
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| `budget` | string | "low" | Budget level: "low", "mid", "high" |
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| `context` | string | None | Additional context for the query |
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<Tabs>
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<TabItem value="python" label="Python">
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```python
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response = client.reflect(
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bank_id="my-bank",
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query="What do you think about remote work?",
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budget="mid",
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context="We're considering a hybrid work policy"
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)
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```
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</TabItem>
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<TabItem value="node" label="Node.js">
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```typescript
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const response = await client.reflect('my-bank', 'What do you think about remote work?', {
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budget: 'mid',
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context: "We're considering a hybrid work policy"
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});
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```
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</TabItem>
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</Tabs>
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## The Role of Context
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The `context` parameter steers how the reflection is performed without impacting the memory recall. It provides situational information that helps shape the reasoning and response.
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**How context is used:**
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- **Shapes reasoning**: Helps understand the situation when formulating an answer
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- **Disambiguates intent**: Clarifies what aspect of the query matters most
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- **Does not affect recall**: The same memories are retrieved regardless of context
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<Tabs>
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<TabItem value="python" label="Python">
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```python
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# Context is passed to the LLM to help it understand the situation
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response = client.reflect(
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bank_id="my-bank",
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query="What do you think about the proposal?",
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context="We're in a budget review meeting discussing Q4 spending"
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)
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```
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</TabItem>
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<TabItem value="node" label="Node.js">
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```typescript
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// Context helps the LLM understand the current situation
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const response = await client.reflect('my-bank', 'What do you think about the proposal?', {
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context: "We're in a budget review meeting discussing Q4 spending"
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});
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```
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</TabItem>
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</Tabs>
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## Opinion Formation
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When reflect reasons about a question, it may form new **opinions** based on the evidence in the memory bank. These opinions are created in the background and become available in subsequent `reflect` and `recall` calls.
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**Why opinions matter:**
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- **Consistent thinking**: Opinions ensure the memory bank maintains a coherent perspective over time
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- **Evolving viewpoints**: As more information is retained, opinions can be refined or updated
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- **Grounded reasoning**: Opinions are always derived from factual evidence in the memory bank
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Opinions are stored as a special memory type and are automatically retrieved when relevant to future queries. This creates a natural evolution of the bank's perspective, similar to how humans form and refine their views based on accumulated experience.
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## Disposition Influence
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The bank's disposition affects reflect responses:
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| Trait | Low (1) | High (5) |
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|-------|---------|----------|
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| **Skepticism** | Trusting, accepts claims | Questions and doubts claims |
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| **Literalism** | Flexible interpretation | Exact, literal interpretation |
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| **Empathy** | Detached, fact-focused | Considers emotional context |
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<Tabs>
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<TabItem value="python" label="Python">
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```python
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# Create a bank with specific disposition
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client.create_bank(
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bank_id="cautious-advisor",
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background="I am a risk-aware financial advisor",
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disposition={
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"skepticism": 5, # Very skeptical of claims
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"literalism": 4, # Focuses on exact requirements
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"empathy": 2 # Prioritizes facts over feelings
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}
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)
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# Reflect responses will reflect this disposition
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response = client.reflect(
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bank_id="cautious-advisor",
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query="Should I invest in crypto?"
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)
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# Response will likely emphasize risks and caution
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```
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</TabItem>
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<TabItem value="node" label="Node.js">
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```typescript
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// Create a bank with specific disposition
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await client.createBank('cautious-advisor', {
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background: 'I am a risk-aware financial advisor',
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disposition: {
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skepticism: 5,
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literalism: 4,
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empathy: 2
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}
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});
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// Reflect responses will reflect this disposition
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const response = await client.reflect('cautious-advisor', 'Should I invest in crypto?');
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```
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</TabItem>
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</Tabs>
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## Using Sources
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The `based_on` field shows which memories informed the response:
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<Tabs>
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<TabItem value="python" label="Python">
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```python
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response = client.reflect(bank_id="my-bank", query="Tell me about Alice")
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print("Response:", response.text)
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print("\nBased on:")
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for fact in response.based_on or []:
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print(f" - [{fact.type}] {fact.text}")
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```
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</TabItem>
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<TabItem value="node" label="Node.js">
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```typescript
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const response = await client.reflect('my-bank', 'Tell me about Alice');
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console.log('Response:', response.text);
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console.log('\nBased on:');
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for (const fact of response.based_on || []) {
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console.log(` - [${fact.type}] ${fact.text}`);
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}
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```
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</TabItem>
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</Tabs>
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This enables:
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- **Transparency** — users see why the bank said something
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- **Verification** — check if the response is grounded in facts
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- **Debugging** — understand retrieval quality
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