d405b4feed
* Fix main-methods.py: entities is a dict, use .items() and .canonical_name * Migrate docs to use CodeSnippet components - Convert quickstart.md, retain.md, recall.md, reflect.md, memory-banks.md to .mdx - Use CodeSnippet to pull code from validated example scripts - Add missing 'name' parameter to create_bank calls - Fix main-methods.py entities iteration (dict not list) - Remove retain-new.mdx demo file * Migrate existing docs to match testing pattern with code snippet and add CLI tests to the CI * Fix doc-id issue + add main-method tests * CLI fixes * Update openAPI json * Fix rust build issues * increase sleep time for Hindsight to process the document * Added a polling sleep instead of fixed * Delete immediately fails, so create the doc a earlier in the test to get the doc ready * Add debug logs * Remove debug logs
135 lines
3.7 KiB
Text
135 lines
3.7 KiB
Text
---
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sidebar_position: 5
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---
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# Opinions
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How memory banks form, store, and evolve beliefs.
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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import CodeSnippet from '@site/src/components/CodeSnippet';
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{/* Import raw source files */}
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import opinionsPy from '!!raw-loader!@site/examples/api/opinions.py';
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import opinionsMjs from '!!raw-loader!@site/examples/api/opinions.mjs';
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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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## What Are Opinions?
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Opinions are beliefs formed by the memory bank based on evidence and disposition. Unlike world facts (objective information received) or experience (conversations and events), opinions are **judgments** with confidence scores.
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| Type | Example | Confidence |
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|------|---------|------------|
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| World Fact | "Python was created in 1991" | — |
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| Experience | "I recommended Python to Bob" | — |
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| Opinion | "Python is the best language for data science" | 0.85 |
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## How Opinions Form
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Opinions are created during `reflect` operations when the memory bank:
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1. Retrieves relevant facts
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2. Applies disposition traits
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3. Forms a judgment
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4. Assigns a confidence score
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```mermaid
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graph LR
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F[Facts] --> D[Disposition Filter]
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D --> J[Judgment]
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J --> O[Opinion + Confidence]
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O --> S[(Store)]
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```
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<Tabs>
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<TabItem value="python" label="Python">
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<CodeSnippet code={opinionsPy} section="opinion-form" language="python" />
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</TabItem>
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<TabItem value="node" label="Node.js">
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<CodeSnippet code={opinionsMjs} section="opinion-form" language="javascript" />
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</TabItem>
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</Tabs>
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## Searching Opinions
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<Tabs>
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<TabItem value="python" label="Python">
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<CodeSnippet code={opinionsPy} section="opinion-search" language="python" />
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</TabItem>
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<TabItem value="node" label="Node.js">
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<CodeSnippet code={opinionsMjs} section="opinion-search" language="javascript" />
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</TabItem>
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<TabItem value="cli" label="CLI">
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```bash
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hindsight recall my-bank "programming" --types opinion
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```
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</TabItem>
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</Tabs>
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## Opinion Evolution
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Opinions change as new evidence arrives:
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| Evidence Type | Effect |
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|---------------|--------|
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| **Reinforcing** | Confidence increases (+0.1) |
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| **Weakening** | Confidence decreases (-0.15) |
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| **Contradicting** | Opinion revised, confidence reset |
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**Example evolution:**
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```
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t=0: "Python is best for data science" (0.70)
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↓ New evidence: Python dominates ML libraries
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t=1: "Python is best for data science" (0.85)
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↓ New evidence: Julia is 10x faster for numerical computing
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t=2: "Python is best for data science, though Julia is faster" (0.75)
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↓ New evidence: Most teams still use Python
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t=3: "Python is best for data science" (0.82)
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```
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## Disposition Influence
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Different dispositions form different opinions from the same facts:
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<Tabs>
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<TabItem value="python" label="Python">
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<CodeSnippet code={opinionsPy} section="opinion-disposition" language="python" />
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</TabItem>
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<TabItem value="node" label="Node.js">
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<CodeSnippet code={opinionsMjs} section="opinion-disposition" language="javascript" />
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</TabItem>
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</Tabs>
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## Opinions in Reflect Responses
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When `reflect` uses opinions, they appear in `based_on`:
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<Tabs>
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<TabItem value="python" label="Python">
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<CodeSnippet code={opinionsPy} section="opinion-in-reflect" language="python" />
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</TabItem>
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<TabItem value="node" label="Node.js">
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<CodeSnippet code={opinionsMjs} section="opinion-in-reflect" language="javascript" />
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</TabItem>
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</Tabs>
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## Confidence Thresholds
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Opinions below a confidence threshold may be:
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- Excluded from responses
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- Marked as uncertain
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- Revised more easily
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```python
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# Low confidence opinions are held loosely
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# "I think Python might be good for this" (0.45)
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# High confidence opinions are stated firmly
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# "Python is definitely the right choice" (0.92)
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```
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