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
129 lines
4.9 KiB
Text
129 lines
4.9 KiB
Text
---
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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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import CodeSnippet from '@site/src/components/CodeSnippet';
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{/* Import raw source files */}
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import reflectPy from '!!raw-loader!@site/examples/api/reflect.py';
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import reflectMjs from '!!raw-loader!@site/examples/api/reflect.mjs';
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import reflectSh from '!!raw-loader!@site/examples/api/reflect.sh';
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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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<CodeSnippet code={reflectPy} section="reflect-basic" language="python" />
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</TabItem>
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<TabItem value="node" label="Node.js">
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<CodeSnippet code={reflectMjs} section="reflect-basic" language="javascript" />
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</TabItem>
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<TabItem value="cli" label="CLI">
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<CodeSnippet code={reflectSh} section="reflect-basic" language="bash" />
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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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<CodeSnippet code={reflectPy} section="reflect-with-params" language="python" />
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</TabItem>
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<TabItem value="node" label="Node.js">
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<CodeSnippet code={reflectMjs} section="reflect-with-params" language="javascript" />
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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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<CodeSnippet code={reflectPy} section="reflect-with-context" language="python" />
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</TabItem>
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<TabItem value="node" label="Node.js">
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<CodeSnippet code={reflectMjs} section="reflect-with-context" language="javascript" />
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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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<CodeSnippet code={reflectPy} section="reflect-disposition" language="python" />
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</TabItem>
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<TabItem value="node" label="Node.js">
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<CodeSnippet code={reflectMjs} section="reflect-disposition" language="javascript" />
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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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<CodeSnippet code={reflectPy} section="reflect-sources" language="python" />
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</TabItem>
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<TabItem value="node" label="Node.js">
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<CodeSnippet code={reflectMjs} section="reflect-sources" language="javascript" />
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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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