letta-server/docs/data_sources.md

## Loading External Data
MemGPT supports pre-loading data into archival memory. In order to made data accessible to your agent, you must load data in with `memgpt load`, then attach the data source to your agent. You can configure where archival memory is stored by configuring the [storage backend](storage.md).

### Viewing available data sources
You can view available data sources with:
```
memgpt list sources
```
```
+----------------+----------+----------+
|      Name      | Location | Agents   |
+----------------+----------+----------+
| short-stories  |  local   |  agent_1 |
|      arxiv     |  local   |          |
|  memgpt-docs   |  local   |  agent_1 |
+----------------+----------+----------+
```
The `Agents` column indicates which agents have access to the data, while `Location` indicates what storage backend the data has been loaded into.

### Attaching data to agents
Attaching a data source to your agent loads the data into your agent's archival memory to access. You can attach data to your agent in two ways:

*[Option 1]* From the CLI, run:
```
memgpt attach --agent <AGENT-NAME> --data-source <DATA-SOURCE-NAME>
```

*[Option 2]*  While chatting with the agent, enter the `/attach` command and select the data source
```
> Enter your message: /attach
? Select data source (Use arrow keys)
 » short-stories
   arxiv
   memgpt-docs
```


!!! note "Hint"
    To encourage your agent to reference its archival memory, we recommend adding phrases like "_search your archival memory..._" for the best results.



### Loading a file or directory
You can load a file, list of files, or directry into MemGPT with the following command:
```sh
memgpt load directory --name <NAME> \
    [--input-dir <DIRECTORY>] [--input-files <FILE1> <FILE2>...] [--recursive]
```


### Loading a database dump
You can load database into MemGPT, either from a database dump or a database connection, with the following command:
```sh
memgpt load database --name <NAME>  \
    --query <QUERY> \ # Query to run on database to get data
    --dump-path <PATH> \ # Path to dump file
    --scheme <SCHEME> \ # Database scheme
    --host <HOST> \ # Database host
    --port <PORT> \ # Database port
    --user <USER> \ # Database user
    --password <PASSWORD> \ # Database password
    --dbname <DB_NAME> # Database name
```

### Loading a vector database
If you already have a vector database containing passages and embeddings, you can load them into MemGPT by specifying the table name, database URI, and the columns containing the passage text and embeddings.
```sh
memgpt load vector-database --name <NAME> \
    --uri <URI> \ # Database URI
    --table_name <TABLE-NAME> \ # Name of table containing data
    --text_column <TEXT-COL> \ # Name of column containing text
    --embedding_column <EMBEDDING-COL> # Name of column containing embedding
```
Since embeddings are already provided, MemGPT will not re-compute the embeddings.

### Loading a LlamaIndex dump
If you have a Llama Index `VectorIndex` which was saved to disk, you can load it into MemGPT by specifying the directory the index was saved to:
```sh
memgpt load index --name <NAME> --dir <INDEX-DIR>
```
Since Llama Index will have already computing embeddings, MemGPT will not re-compute embeddings.


### Loading other types of data
We highly encourage contributions for new data sources, which can be added as a new [CLI data load command](https://github.com/cpacker/MemGPT/blob/main/memgpt/cli/cli_load.py). We recommend checking for [Llama Index connectors](https://gpt-index.readthedocs.io/en/v0.6.3/how_to/data_connectors.html) that may support ingesting the data you're interested in loading.