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# Reflect

Generate disposition-aware responses using retrieved memories.

When you call **reflect**, Hindsight performs a multi-step reasoning process:
1. **Recalls** relevant memories from the bank based on your query
2. **Applies** the bank's disposition traits to shape the reasoning style
3. **Generates** a contextual answer grounded in the retrieved facts
4. **Forms opinions** in the background based on the reasoning (available in subsequent calls)

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 reflectPy from '!!raw-loader!@site/examples/api/reflect.py';
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import reflectSh from '!!raw-loader!@site/examples/api/reflect.sh';

:::info How Reflect Works
Learn about disposition-driven reasoning and opinion formation in the [Reflect Architecture](/developer/reflect) guide.
:::

:::tip Prerequisites
Make sure you've completed the [Quick Start](./quickstart) to install the client and start the server.
:::

## Basic Usage

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## Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `query` | string | required | Question or prompt |
| `budget` | string | "low" | Budget level: "low", "mid", "high" |
| `context` | string | None | Additional context for the query |
| `max_tokens` | int | 4096 | Maximum tokens for the response |
| `response_schema` | object | None | JSON Schema for [structured output](#structured-output) |

### Response Fields

| Field | Type | Description |
|-------|------|-------------|
| `text` | string | The generated answer text |
| `based_on` | array | Facts used to generate the response |
| `structured_output` | object | Parsed structured output (when `response_schema` provided) |
| `usage` | TokenUsage | Token usage metrics for the LLM call |

The `usage` field contains:
- `input_tokens`: Number of input/prompt tokens consumed
- `output_tokens`: Number of output/completion tokens generated
- `total_tokens`: Sum of input and output tokens

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## The Role of Context

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.

**How context is used:**
- **Shapes reasoning**: Helps understand the situation when formulating an answer
- **Disambiguates intent**: Clarifies what aspect of the query matters most
- **Does not affect recall**: The same memories are retrieved regardless of context

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## Opinion Formation

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.

**Why opinions matter:**
- **Consistent thinking**: Opinions ensure the memory bank maintains a coherent perspective over time
- **Evolving viewpoints**: As more information is retained, opinions can be refined or updated
- **Grounded reasoning**: Opinions are always derived from factual evidence in the memory bank

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.

## Disposition Influence

The bank's disposition affects reflect responses:

| Trait | Low (1) | High (5) |
|-------|---------|----------|
| **Skepticism** | Trusting, accepts claims | Questions and doubts claims |
| **Literalism** | Flexible interpretation | Exact, literal interpretation |
| **Empathy** | Detached, fact-focused | Considers emotional context |

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## Using Sources

The `based_on` field shows which memories informed the response:

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This enables:
- **Transparency** — users see why the bank said something
- **Verification** — check if the response is grounded in facts
- **Debugging** — understand retrieval quality

## Structured Output

For applications that need to process responses programmatically, you can request structured output by providing a JSON Schema via `response_schema`. When provided, the response includes a `structured_output` field with the LLM response parsed according to the schema. The `text` field will be empty since only a single LLM call is made for efficiency.

The easiest way to define a schema is using **Pydantic models**:

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```python
from pydantic import BaseModel
from hindsight_client import Hindsight

# Define your response structure with Pydantic
class HiringRecommendation(BaseModel):
    recommendation: str
    confidence: str  # "low", "medium", "high"
    key_factors: list[str]
    risks: list[str] = []

with Hindsight() as client:
    response = client.reflect(
        bank_id="hiring-team",
        query="Should we hire Alice for the ML team lead position?",
        response_schema=HiringRecommendation.model_json_schema(),
    )

    # Parse structured output into Pydantic model
    result = HiringRecommendation.model_validate(response.structured_output)
    print(f"Recommendation: {result.recommendation}")
    print(f"Confidence: {result.confidence}")
    print(f"Key factors: {result.key_factors}")
```

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```javascript
import { Hindsight } from "@anthropic-ai/hindsight";

const client = new Hindsight();

// Define JSON schema directly
const responseSchema = {
  type: "object",
  properties: {
    recommendation: { type: "string" },
    confidence: { type: "string", enum: ["low", "medium", "high"] },
    key_factors: { type: "array", items: { type: "string" } },
    risks: { type: "array", items: { type: "string" } },
  },
  required: ["recommendation", "confidence", "key_factors"],
};

const response = await client.reflect({
  bankId: "hiring-team",
  query: "Should we hire Alice for the ML team lead position?",
  responseSchema: responseSchema,
});

// Structured output
console.log(response.structuredOutput.recommendation);
console.log(response.structuredOutput.keyFactors);
```

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First, create a JSON schema file `schema.json`:
```json
{
  "type": "object",
  "properties": {
    "recommendation": {"type": "string"},
    "confidence": {"type": "string", "enum": ["low", "medium", "high"]},
    "key_factors": {"type": "array", "items": {"type": "string"}}
  },
  "required": ["recommendation", "confidence", "key_factors"]
}
```

Then use the `--schema` flag:
```bash
hindsight memory reflect hiring-team \
  "Should we hire Alice for the ML team lead position?" \
  --schema schema.json
```

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| Use Case | Why Structured Output Helps |
|----------|----------------------------|
| **Decision pipelines** | Parse recommendations into workflow systems |
| **Dashboards** | Extract confidence scores, risk factors for visualization |
| **Multi-agent systems** | Pass structured data between agents |
| **Auditing** | Log structured decisions with clear reasoning |

**Tips:**
- Use Pydantic's `model_json_schema()` for type-safe schema generation
- Use `model_validate()` to parse the response back into your Pydantic model
- Keep schemas focused — extract only what you need
- Use `Optional` fields for data that may not always be available
