> For the complete documentation index, see [llms.txt](https://openagi.aiplanet.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://openagi.aiplanet.com/components/vectorstore/chromastorage.md).

# 💾 ChromaStorage

The `ChromaStorage` class is a specific implementation of the `Storage` class using `ChromaDB`. Here is how you can use it:

```python
class ChromaMemory(BaseModel):
    storage: BaseStorage = Field(
        default=ChromaStorage,
        description="Storage to be used for the Memory.",
        exclude=True,
    )

    def model_post_init(self, __context: Any) -> None:
        instance = super().model_post_init(__context)
        self.storage = ChromaStorage.from_kwargs(collection_name=self.sessiond_id)
        return instance
```

```python
class ChromaMemory(BaseModel):
    sessiond_id: str = Field(default=uuid4().hex)
    storage: BaseStorage = Field(
        default=ChromaStorage,
        description="Storage to be used for the Memory.",
        exclude=True,
    )

    def model_post_init(self, __context: Any) -> None:
        inst = super().model_post_init(__context)
        logging.info(f"{self.sessiond_id=}")
        self.storage = ChromaStorage.from_kwargs(collection_name=self.sessiond_id)
        return inst

    def search(self, query: str, n_results: int = 10, **kwargs) -> Dict[str, Any]:
        """Search for similar tasks based on a query."""
        query_data = {
            "query_texts": query,
            "n_results": n_results,
            "where": {"$contains": self.sessiond_id},
            **kwargs,
        }
        return self.storage.query_documents(**query_data)

    def display_memory(self) -> Dict[str, Any]:
        """Retrieve and display the current memory state from the database."""
        result = self.storage.query_documents(self.session_id, n_results=2)
        if result:
            return result
        return {}

    def save_task(self, task: Task) -> None:
        """Save execution details into Memory."""
        document = task.result
        metadata = {
            "task_id": task.id,
            "session_id": self.sessiond_id,
            "task_name": task.name,
            "task_description": task.description,
            "task_result": task.result,
            "task_actions": task.actions,
        }

        return self.storage.save_document(
            id=task.id,
            document=document,
            metadata=metadata,
        )

    def save_planned_tasks(self, tasks: TaskLists):
        for task in tasks:
            self.save_task(task=task)
```


---

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