---
title: Criteria Retrieval
description: "Rank and retrieve memories based on custom-defined criteria like emotional tone, intent, and behavioral signals."
---

Deja Vu's Criteria Retrieval feature allows you to retrieve memories based on your defined criteria. It goes beyond generic semantic relevance and ranks memories based on what matters to your application: emotional tone, intent, behavioral signals, or other custom traits.

Instead of just searching for "how similar a memory is to this query," you can define what relevance truly means for your project. For example:

- Prioritize joyful memories when building a wellness assistant
- Downrank negative memories in a productivity-focused agent
- Highlight curiosity in a tutoring agent

You define criteria: custom attributes like "joy", "negativity", "confidence", or "urgency", and assign weights to control how they influence scoring. When you search, Deja Vu uses these to re-rank semantically relevant memories, favoring those that better match your intent.

This gives you nuanced, intent-aware memory search that adapts to your use case.



## When to Use Criteria Retrieval

Use Criteria Retrieval if:

- You’re building an agent that should react to **emotions** or **behavioral signals**
- You want to guide memory selection based on **context**, not just content
- You have domain-specific signals like "risk", "positivity", "confidence", etc. that shape recall



## Setting Up Criteria Retrieval

Let’s walk through how to configure and use Criteria Retrieval step by step.

### Initialize the Client

Before defining any criteria, make sure to initialize the `MemoryClient` with your credentials and project ID:

```python
from dejavu import MemoryClient

client = MemoryClient(api_key="your_dejavu_api_key")
```

### Define Your Criteria

Each criterion includes:
- A `name` (used in scoring)
- A `description` (interpreted by the LLM)
- A `weight` (how much it influences the final score)

```python
retrieval_criteria = [
    {
        "name": "joy",
        "description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the sentence. A higher score reflects greater joy.",
        "weight": 3
    },
    {
        "name": "curiosity",
        "description": "Assess the extent to which the sentence reflects inquisitiveness, interest in exploring new information, or asking questions. A higher score reflects stronger curiosity.",
        "weight": 2
    },
    {
        "name": "emotion",
        "description": "Evaluate the presence and depth of sadness or negative emotional tone, including expressions of disappointment, frustration, or sorrow. A higher score reflects greater sadness.",
        "weight": 1
    }
]
```

### Apply Criteria to Your Project

Once defined, register the criteria to your project:

```python
client.project.update(retrieval_criteria=retrieval_criteria)
```

Criteria apply project-wide. Once set, they affect all searches automatically.


## Example Walkthrough

After setting up your criteria, you can use them to filter and retrieve memories. Here's an example:

### Add Memories

```python
messages = [
    {"role": "user", "content": "What a beautiful sunny day! I feel so refreshed and ready to take on anything!"},
    {"role": "user", "content": "I've always wondered how storms form—what triggers them in the atmosphere?"},
    {"role": "user", "content": "It's been raining for days, and it just makes everything feel heavier."},
    {"role": "user", "content": "Finally I get time to draw something today, after a long time!! I am super happy today."}
]

client.add(messages, user_id="alice")
```

### Run Standard vs. Criteria-Based Search

```python
# Search with criteria enabled
filters = {"user_id": "alice"}
results_with_criteria = client.search(
    query="Why I am feeling happy today?",
    filters=filters
)

# To disable criteria for a specific search
results_without_criteria = client.search(
    query="Why I am feeling happy today?",
    filters=filters,
    use_criteria=False  # Disable criteria-based scoring
)
```

### Compare Results

### Search Results (with Criteria)
```python
[
    {"memory": "User feels refreshed and ready to take on anything on a beautiful sunny day", "score": 0.666, ...},
    {"memory": "User finally has time to draw something after a long time", "score": 0.616, ...},
    {"memory": "User is happy today", "score": 0.500, ...},
    {"memory": "User is curious about how storms form and what triggers them in the atmosphere.", "score": 0.400, ...},
    {"memory": "It has been raining for days, making everything feel heavier.", "score": 0.116, ...}
]
```

### Search Results (without Criteria)
```python
[
    {"memory": "User is happy today", "score": 0.607, ...},
    {"memory": "User feels refreshed and ready to take on anything on a beautiful sunny day", "score": 0.512, ...},
    {"memory": "It has been raining for days, making everything feel heavier.", "score": 0.4617, ...},
    {"memory": "User is curious about how storms form and what triggers them in the atmosphere.", "score": 0.340, ...},
    {"memory": "User finally has time to draw something after a long time", "score": 0.336, ...},
]
```

## Search Results Comparison

1. **Memory Ordering**: With criteria, memories with high joy scores (like feeling refreshed and drawing) are ranked higher. Without criteria, the most relevant memory ("User is happy today") comes first.
2. **Score Distribution**: With criteria, scores are more spread out (0.116 to 0.666) and reflect the criteria weights. Without criteria, scores are more clustered (0.336 to 0.607) and based purely on relevance.
3. **Trait Sensitivity**: "Rainy day" content is penalized due to negative tone, while "Storm curiosity" is recognized and scored accordingly.



## Key Differences vs. Standard Search

| Aspect                  | Standard Search                      | Criteria Retrieval                              |
|-------------------------|--------------------------------------|-------------------------------------------------|
| Ranking Logic           | Semantic similarity only             | Semantic + LLM-based criteria scoring           |
| Control Over Relevance  | None                                 | Fully customizable with weighted criteria       |
| Memory Reordering       | Static based on similarity           | Dynamically re-ranked by intent alignment       |
| Emotional Sensitivity   | No tone or trait awareness           | Incorporates emotion, tone, or custom behaviors |
| Activation              | Default (no criteria defined)        | Enabled when criteria are defined in project    |

<Note>
If no criteria are defined for a project, search behaves normally based on semantic similarity only.
</Note>



## Best Practices

- Choose 3-5 criteria that reflect your application's intent
- Make descriptions clear and distinct; these are interpreted by an LLM
- Use stronger weights to amplify the impact of important traits
- Avoid redundant or ambiguous criteria (e.g., "positivity" and "joy")
- Always handle empty result sets in your application logic



## How It Works

1. **Criteria Definition**: Define custom criteria with a name, description, and weight. These describe what matters in a memory (e.g., joy, urgency, empathy).
2. **Project Configuration**: Register these criteria using `project.update()`. They apply at the project level and automatically influence all searches.
3. **Memory Retrieval**: When you perform a search, Deja Vu first retrieves relevant memories based on the query.
4. **Weighted Scoring**: Each retrieved memory is evaluated and scored against your defined criteria and weights.

This lets you prioritize memories that align with your agent's goals and not just those that look similar to the query.

<Note>
Criteria retrieval is automatically enabled when criteria are defined in your project. Use `use_criteria=False` in search to temporarily disable it for a specific query.
</Note>



## Summary

- Define what "relevant" means using criteria
- Apply them per project via `project.update()`
- Criteria-aware search activates automatically when criteria are configured
- Build agents that reason not just with relevance, but **contextual importance**

---

Need help designing or tuning your criteria?

<Snippet file="get-help.mdx" />
