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chore(docs): Delete deprecated memory configuration guide
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Reinier van der Leer
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!!! warning
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The Pinecone, Milvus, Redis, and Weaviate memory backends were rendered incompatible
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by work on the memory system, and have been removed.
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Whether support will be added back in the future is subject to discussion,
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feel free to pitch in: https://github.com/Significant-Gravitas/AutoGPT/discussions/4280
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## Setting Your Cache Type
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By default, AutoGPT set up with Docker Compose will use Redis as its memory backend.
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Otherwise, the default is LocalCache (which stores memory in a JSON file).
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To switch to a different backend, change the `MEMORY_BACKEND` in `.env`
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to the value that you want:
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* `json_file` uses a local JSON cache file
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* `pinecone` uses the Pinecone.io account you configured in your ENV settings
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* `redis` will use the redis cache that you configured
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* `milvus` will use the milvus cache that you configured
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* `weaviate` will use the weaviate cache that you configured
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!!! warning
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The Pinecone, Milvus, Redis, and Weaviate memory backends were rendered incompatible
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by work on the memory system, and have been removed.
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Whether support will be added back in the future is subject to discussion,
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feel free to pitch in: https://github.com/Significant-Gravitas/AutoGPT/discussions/4280
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## Memory Backend Setup
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Links to memory backends
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- [Pinecone](https://www.pinecone.io/)
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- [Milvus](https://milvus.io/) – [self-hosted](https://milvus.io/docs), or managed with [Zilliz Cloud](https://zilliz.com/)
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- [Redis](https://redis.io)
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- [Weaviate](https://weaviate.io)
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!!! warning
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The Pinecone, Milvus, Redis, and Weaviate memory backends were rendered incompatible
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by work on the memory system, and have been removed.
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Whether support will be added back in the future is subject to discussion,
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feel free to pitch in: https://github.com/Significant-Gravitas/AutoGPT/discussions/4280
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### Redis Setup
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!!! important
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If you have set up AutoGPT using Docker Compose, then Redis is included, no further
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setup needed.
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!!! caution
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This setup is not intended to be publicly accessible and lacks security measures.
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Avoid exposing Redis to the internet without a password or at all!
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1. Launch Redis container
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```shell
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docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest
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```
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3. Set the following settings in `.env`
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```shell
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MEMORY_BACKEND=redis
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REDIS_HOST=localhost
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REDIS_PORT=6379
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REDIS_PASSWORD=<PASSWORD>
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```
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Replace `<PASSWORD>` by your password, omitting the angled brackets (<>).
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Optional configuration:
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- `WIPE_REDIS_ON_START=False` to persist memory stored in Redis between runs.
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- `MEMORY_INDEX=<WHATEVER>` to specify a name for the memory index in Redis.
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The default is `auto-gpt`.
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!!! info
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See [redis-stack-server](https://hub.docker.com/r/redis/redis-stack-server) for
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setting a password and additional configuration.
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!!! warning
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The Pinecone, Milvus, Redis, and Weaviate memory backends were rendered incompatible
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by work on the memory system, and have been removed.
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Whether support will be added back in the future is subject to discussion,
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feel free to pitch in: https://github.com/Significant-Gravitas/AutoGPT/discussions/4280
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### 🌲 Pinecone API Key Setup
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Pinecone lets you store vast amounts of vector-based memory, allowing the agent to load only relevant memories at any given time.
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1. Go to [pinecone](https://app.pinecone.io/) and make an account if you don't already have one.
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2. Choose the `Starter` plan to avoid being charged.
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3. Find your API key and region under the default project in the left sidebar.
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In the `.env` file set:
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- `PINECONE_API_KEY`
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- `PINECONE_ENV` (example: `us-east4-gcp`)
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- `MEMORY_BACKEND=pinecone`
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!!! warning
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The Pinecone, Milvus, Redis, and Weaviate memory backends were rendered incompatible
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by work on the memory system, and have been removed.
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Whether support will be added back in the future is subject to discussion,
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feel free to pitch in: https://github.com/Significant-Gravitas/AutoGPT/discussions/4280
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### Milvus Setup
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[Milvus](https://milvus.io/) is an open-source, highly scalable vector database to store
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huge amounts of vector-based memory and provide fast relevant search. It can be quickly
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deployed with docker, or as a cloud service provided by [Zilliz Cloud](https://zilliz.com/).
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1. Deploy your Milvus service, either locally using docker or with a managed Zilliz Cloud database:
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- [Install and deploy Milvus locally](https://milvus.io/docs/install_standalone-operator.md)
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- Set up a managed Zilliz Cloud database
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1. Go to [Zilliz Cloud](https://zilliz.com/) and sign up if you don't already have account.
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2. In the *Databases* tab, create a new database.
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- Remember your username and password
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- Wait until the database status is changed to RUNNING.
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3. In the *Database detail* tab of the database you have created, the public cloud endpoint, such as:
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`https://xxx-xxxx.xxxx.xxxx.zillizcloud.com:443`.
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2. Run `pip3 install pymilvus` to install the required client library.
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Make sure your PyMilvus version and Milvus version are [compatible](https://github.com/milvus-io/pymilvus#compatibility)
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to avoid issues.
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See also the [PyMilvus installation instructions](https://github.com/milvus-io/pymilvus#installation).
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3. Update `.env`:
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- `MEMORY_BACKEND=milvus`
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- One of:
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- `MILVUS_ADDR=host:ip` (for local instance)
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- `MILVUS_ADDR=https://xxx-xxxx.xxxx.xxxx.zillizcloud.com:443` (for Zilliz Cloud)
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The following settings are **optional**:
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- `MILVUS_USERNAME='username-of-your-milvus-instance'`
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- `MILVUS_PASSWORD='password-of-your-milvus-instance'`
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- `MILVUS_SECURE=True` to use a secure connection.
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Only use if your Milvus instance has TLS enabled.
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*Note: setting `MILVUS_ADDR` to a `https://` URL will override this setting.*
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- `MILVUS_COLLECTION` to change the collection name to use in Milvus.
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Defaults to `autogpt`.
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!!! warning
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The Pinecone, Milvus, Redis, and Weaviate memory backends were rendered incompatible
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by work on the memory system, and have been removed.
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Whether support will be added back in the future is subject to discussion,
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feel free to pitch in: https://github.com/Significant-Gravitas/AutoGPT/discussions/4280
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### Weaviate Setup
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[Weaviate](https://weaviate.io/) is an open-source vector database. It allows to store
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data objects and vector embeddings from ML-models and scales seamlessly to billion of
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data objects. To set up a Weaviate database, check out their [Quickstart Tutorial](https://weaviate.io/developers/weaviate/quickstart).
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Although still experimental, [Embedded Weaviate](https://weaviate.io/developers/weaviate/installation/embedded)
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is supported which allows the AutoGPT process itself to start a Weaviate instance.
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To enable it, set `USE_WEAVIATE_EMBEDDED` to `True` and make sure you `poetry add weaviate-client@^3.15.4`.
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#### Install the Weaviate client
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Install the Weaviate client before usage.
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```shell
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$ poetry add weaviate-client
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```
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#### Setting up environment variables
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In your `.env` file set the following:
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```ini
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MEMORY_BACKEND=weaviate
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WEAVIATE_HOST="127.0.0.1" # the IP or domain of the running Weaviate instance
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WEAVIATE_PORT="8080"
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WEAVIATE_PROTOCOL="http"
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WEAVIATE_USERNAME="your username"
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WEAVIATE_PASSWORD="your password"
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WEAVIATE_API_KEY="your weaviate API key if you have one"
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WEAVIATE_EMBEDDED_PATH="/home/me/.local/share/weaviate" # this is optional and indicates where the data should be persisted when running an embedded instance
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USE_WEAVIATE_EMBEDDED=False # set to True to run Embedded Weaviate
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MEMORY_INDEX="Autogpt" # name of the index to create for the application
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```
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## View Memory Usage
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View memory usage by using the `--debug` flag :)
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## 🧠 Memory pre-seeding
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!!! warning
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Data ingestion is broken in v0.4.7 and possibly earlier versions. This is a known issue that will be addressed in future releases. Follow these issues for updates.
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[Issue 4435](https://github.com/Significant-Gravitas/AutoGPT/issues/4435)
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[Issue 4024](https://github.com/Significant-Gravitas/AutoGPT/issues/4024)
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[Issue 2076](https://github.com/Significant-Gravitas/AutoGPT/issues/2076)
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Memory pre-seeding allows you to ingest files into memory and pre-seed it before running AutoGPT.
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```shell
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$ python data_ingestion.py -h
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usage: data_ingestion.py [-h] (--file FILE | --dir DIR) [--init] [--overlap OVERLAP] [--max_length MAX_LENGTH]
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Ingest a file or a directory with multiple files into memory. Make sure to set your .env before running this script.
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options:
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-h, --help show this help message and exit
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--file FILE The file to ingest.
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--dir DIR The directory containing the files to ingest.
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--init Init the memory and wipe its content (default: False)
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--overlap OVERLAP The overlap size between chunks when ingesting files (default: 200)
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--max_length MAX_LENGTH The max_length of each chunk when ingesting files (default: 4000)
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# python data_ingestion.py --dir DataFolder --init --overlap 100 --max_length 2000
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```
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In the example above, the script initializes the memory, ingests all files within the `AutoGPT/auto_gpt_workspace/DataFolder` directory into memory with an overlap between chunks of 100 and a maximum length of each chunk of 2000.
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Note that you can also use the `--file` argument to ingest a single file into memory and that data_ingestion.py will only ingest files within the `/auto_gpt_workspace` directory.
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The DIR path is relative to the auto_gpt_workspace directory, so `python data_ingestion.py --dir . --init` will ingest everything in `auto_gpt_workspace` directory.
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You can adjust the `max_length` and `overlap` parameters to fine-tune the way the
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documents are presented to the AI when it "recall" that memory:
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- Adjusting the overlap value allows the AI to access more contextual information
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from each chunk when recalling information, but will result in more chunks being
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created and therefore increase memory backend usage and OpenAI API requests.
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- Reducing the `max_length` value will create more chunks, which can save prompt
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tokens by allowing for more message history in the context, but will also
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increase the number of chunks.
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- Increasing the `max_length` value will provide the AI with more contextual
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information from each chunk, reducing the number of chunks created and saving on
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OpenAI API requests. However, this may also use more prompt tokens and decrease
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the overall context available to the AI.
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Memory pre-seeding is a technique for improving AI accuracy by ingesting relevant data
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into its memory. Chunks of data are split and added to memory, allowing the AI to access
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them quickly and generate more accurate responses. It's useful for large datasets or when
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specific information needs to be accessed quickly. Examples include ingesting API or
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GitHub documentation before running AutoGPT.
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!!! attention
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If you use Redis for memory, make sure to run AutoGPT with `WIPE_REDIS_ON_START=False`
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For other memory backends, we currently forcefully wipe the memory when starting
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AutoGPT. To ingest data with those memory backends, you can call the
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`data_ingestion.py` script anytime during an AutoGPT run.
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Memories will be available to the AI immediately as they are ingested, even if ingested
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while AutoGPT is running.
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