> ## Documentation Index
> Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Task Response Caching

> Cache task responses to improve performance and reduce API costs

Enable caching to store and reuse task responses for similar inputs, reducing API costs and improving performance.

## Quick Start

```python theme={null}
from upsonic import Agent, Task

agent = Agent(model="anthropic/claude-sonnet-4-5")
task = Task(
    description="Summarize this article about AI advancements",
    enable_cache=True
)

agent.print_do(task)
```

## Cache Methods

### Vector Search (Default)

Uses semantic similarity to find cached responses for similar inputs.

```python theme={null}
from upsonic import Agent, Task

agent = Agent(model="anthropic/claude-sonnet-4-5")
task = Task(
    description="Analyze this text about climate change",
    enable_cache=True,
    cache_method="vector_search",
    cache_threshold=0.8  # Higher = stricter matching
)

agent.print_do(task)
```

### LLM Call

Uses an LLM to determine if cached responses are applicable.

```python theme={null}
from upsonic import Agent, Task

agent = Agent(model="anthropic/claude-sonnet-4-5")
task = Task(
    description="Analyze this text about renewable energy",
    enable_cache=True,
    cache_method="llm_call"
)

agent.print_do(task)
```

## Configuration Options

| Parameter                  | Type  | Default           | Description                       |
| -------------------------- | ----- | ----------------- | --------------------------------- |
| `enable_cache`             | bool  | `False`           | Enable/disable caching            |
| `cache_method`             | str   | `"vector_search"` | `"vector_search"` or `"llm_call"` |
| `cache_threshold`          | float | `0.7`             | Similarity threshold (0.0-1.0)    |
| `cache_duration_minutes`   | int   | `60`              | Cache expiration time             |
| `cache_embedding_provider` | Any   | Auto-detected     | Custom embedding provider         |

## Full Example

```python theme={null}
from upsonic import Agent, Task

agent = Agent(model="anthropic/claude-sonnet-4-5")
task = Task(
    description="Explain quantum computing",
    enable_cache=True,
    cache_method="vector_search",
    cache_threshold=0.75,
    cache_duration_minutes=120
)

agent.print_do(task)

# Check if response came from cache
cache_stats = task.get_cache_stats()
if cache_stats.get('cache_hit'):
    print("Response retrieved from cache!")
else:
    print("Fresh response generated")
```

## Task Cache Methods

### get\_cache\_stats()

Get cache statistics including hit rate and configuration.

```python theme={null}
stats = task.get_cache_stats()
print(f"Hit rate: {stats['hit_rate']}")
print(f"Total entries: {stats['total_entries']}")
```

**Returns:**

* `total_entries`: Number of cached entries
* `cache_hits`: Number of cache hits
* `cache_misses`: Number of cache misses
* `hit_rate`: Cache hit rate (0.0-1.0)
* `cache_method`: Current cache method
* `cache_threshold`: Current threshold
* `cache_hit`: Whether last request was a cache hit
* `session_id`: Current session ID

### clear\_cache()

Clear all cache entries.

```python theme={null}
task.clear_cache()
```

## Best Practices

* **Threshold Tuning**: Start with `0.7`, increase for stricter matching
* **Duration**: Set based on how often your data changes
* **Method Choice**: Use `vector_search` for speed, `llm_call` for accuracy
* **Embedding Provider**: Auto-detected if not specified