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diff --git a/docs/content/configuration/memory.md b/docs/content/configuration/memory.md deleted file mode 100644 index 7606f1ae1..000000000 --- a/docs/content/configuration/memory.md +++ /dev/null @@ -1,251 +0,0 @@ -!!! warning - The Pinecone, Milvus, Redis, and Weaviate memory backends were rendered incompatible - by work on the memory system, and have been removed. - Whether support will be added back in the future is subject to discussion, - feel free to pitch in: https://github.com/Significant-Gravitas/AutoGPT/discussions/4280 - -## Setting Your Cache Type - -By default, AutoGPT set up with Docker Compose will use Redis as its memory backend. -Otherwise, the default is LocalCache (which stores memory in a JSON file). - -To switch to a different backend, change the `MEMORY_BACKEND` in `.env` -to the value that you want: - -* `json_file` uses a local JSON cache file -* `pinecone` uses the Pinecone.io account you configured in your ENV settings -* `redis` will use the redis cache that you configured -* `milvus` will use the milvus cache that you configured -* `weaviate` will use the weaviate cache that you configured - -!!! warning - The Pinecone, Milvus, Redis, and Weaviate memory backends were rendered incompatible - by work on the memory system, and have been removed. - Whether support will be added back in the future is subject to discussion, - feel free to pitch in: https://github.com/Significant-Gravitas/AutoGPT/discussions/4280 - -## Memory Backend Setup - -Links to memory backends - -- [Pinecone](https://www.pinecone.io/) -- [Milvus](https://milvus.io/) – [self-hosted](https://milvus.io/docs), or managed with [Zilliz Cloud](https://zilliz.com/) -- [Redis](https://redis.io) -- [Weaviate](https://weaviate.io) - -!!! warning - The Pinecone, Milvus, Redis, and Weaviate memory backends were rendered incompatible - by work on the memory system, and have been removed. - Whether support will be added back in the future is subject to discussion, - feel free to pitch in: https://github.com/Significant-Gravitas/AutoGPT/discussions/4280 - -### Redis Setup - -!!! important - If you have set up AutoGPT using Docker Compose, then Redis is included, no further - setup needed. - -!!! caution - This setup is not intended to be publicly accessible and lacks security measures. - Avoid exposing Redis to the internet without a password or at all! - -1. Launch Redis container - - ```shell - docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest - ``` - -3. Set the following settings in `.env` - - ```shell - MEMORY_BACKEND=redis - REDIS_HOST=localhost - REDIS_PORT=6379 - REDIS_PASSWORD=<PASSWORD> - ``` - - Replace `<PASSWORD>` by your password, omitting the angled brackets (<>). - - Optional configuration: - - - `WIPE_REDIS_ON_START=False` to persist memory stored in Redis between runs. - - `MEMORY_INDEX=<WHATEVER>` to specify a name for the memory index in Redis. - The default is `auto-gpt`. - -!!! info - See [redis-stack-server](https://hub.docker.com/r/redis/redis-stack-server) for - setting a password and additional configuration. - -!!! warning - The Pinecone, Milvus, Redis, and Weaviate memory backends were rendered incompatible - by work on the memory system, and have been removed. - Whether support will be added back in the future is subject to discussion, - feel free to pitch in: https://github.com/Significant-Gravitas/AutoGPT/discussions/4280 - -### 🌲 Pinecone API Key Setup - -Pinecone lets you store vast amounts of vector-based memory, allowing the agent to load only relevant memories at any given time. - -1. Go to [pinecone](https://app.pinecone.io/) and make an account if you don't already have one. -2. Choose the `Starter` plan to avoid being charged. -3. Find your API key and region under the default project in the left sidebar. - -In the `.env` file set: - -- `PINECONE_API_KEY` -- `PINECONE_ENV` (example: `us-east4-gcp`) -- `MEMORY_BACKEND=pinecone` - -!!! warning - The Pinecone, Milvus, Redis, and Weaviate memory backends were rendered incompatible - by work on the memory system, and have been removed. - Whether support will be added back in the future is subject to discussion, - feel free to pitch in: https://github.com/Significant-Gravitas/AutoGPT/discussions/4280 - -### Milvus Setup - -[Milvus](https://milvus.io/) is an open-source, highly scalable vector database to store -huge amounts of vector-based memory and provide fast relevant search. It can be quickly -deployed with docker, or as a cloud service provided by [Zilliz Cloud](https://zilliz.com/). - -1. Deploy your Milvus service, either locally using docker or with a managed Zilliz Cloud database: - - [Install and deploy Milvus locally](https://milvus.io/docs/install_standalone-operator.md) - - - Set up a managed Zilliz Cloud database - 1. Go to [Zilliz Cloud](https://zilliz.com/) and sign up if you don't already have account. - 2. In the *Databases* tab, create a new database. - - Remember your username and password - - Wait until the database status is changed to RUNNING. - 3. In the *Database detail* tab of the database you have created, the public cloud endpoint, such as: - `https://xxx-xxxx.xxxx.xxxx.zillizcloud.com:443`. - -2. Run `pip3 install pymilvus` to install the required client library. - Make sure your PyMilvus version and Milvus version are [compatible](https://github.com/milvus-io/pymilvus#compatibility) - to avoid issues. - See also the [PyMilvus installation instructions](https://github.com/milvus-io/pymilvus#installation). - -3. Update `.env`: - - `MEMORY_BACKEND=milvus` - - One of: - - `MILVUS_ADDR=host:ip` (for local instance) - - `MILVUS_ADDR=https://xxx-xxxx.xxxx.xxxx.zillizcloud.com:443` (for Zilliz Cloud) - - The following settings are **optional**: - - - `MILVUS_USERNAME='username-of-your-milvus-instance'` - - `MILVUS_PASSWORD='password-of-your-milvus-instance'` - - `MILVUS_SECURE=True` to use a secure connection. - Only use if your Milvus instance has TLS enabled. - *Note: setting `MILVUS_ADDR` to a `https://` URL will override this setting.* - - `MILVUS_COLLECTION` to change the collection name to use in Milvus. - Defaults to `autogpt`. - -!!! warning - The Pinecone, Milvus, Redis, and Weaviate memory backends were rendered incompatible - by work on the memory system, and have been removed. - Whether support will be added back in the future is subject to discussion, - feel free to pitch in: https://github.com/Significant-Gravitas/AutoGPT/discussions/4280 - -### Weaviate Setup -[Weaviate](https://weaviate.io/) is an open-source vector database. It allows to store -data objects and vector embeddings from ML-models and scales seamlessly to billion of -data objects. To set up a Weaviate database, check out their [Quickstart Tutorial](https://weaviate.io/developers/weaviate/quickstart). - -Although still experimental, [Embedded Weaviate](https://weaviate.io/developers/weaviate/installation/embedded) -is supported which allows the AutoGPT process itself to start a Weaviate instance. -To enable it, set `USE_WEAVIATE_EMBEDDED` to `True` and make sure you `poetry add weaviate-client@^3.15.4`. - -#### Install the Weaviate client - -Install the Weaviate client before usage. - -```shell -$ poetry add weaviate-client -``` - -#### Setting up environment variables - -In your `.env` file set the following: - -```ini -MEMORY_BACKEND=weaviate -WEAVIATE_HOST="127.0.0.1" # the IP or domain of the running Weaviate instance -WEAVIATE_PORT="8080" -WEAVIATE_PROTOCOL="http" -WEAVIATE_USERNAME="your username" -WEAVIATE_PASSWORD="your password" -WEAVIATE_API_KEY="your weaviate API key if you have one" -WEAVIATE_EMBEDDED_PATH="/home/me/.local/share/weaviate" # this is optional and indicates where the data should be persisted when running an embedded instance -USE_WEAVIATE_EMBEDDED=False # set to True to run Embedded Weaviate -MEMORY_INDEX="Autogpt" # name of the index to create for the application -``` - -## View Memory Usage - -View memory usage by using the `--debug` flag :) - - -## 🧠Memory pre-seeding - -!!! warning - 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. - [Issue 4435](https://github.com/Significant-Gravitas/AutoGPT/issues/4435) - [Issue 4024](https://github.com/Significant-Gravitas/AutoGPT/issues/4024) - [Issue 2076](https://github.com/Significant-Gravitas/AutoGPT/issues/2076) - - - -Memory pre-seeding allows you to ingest files into memory and pre-seed it before running AutoGPT. - -```shell -$ python data_ingestion.py -h -usage: data_ingestion.py [-h] (--file FILE | --dir DIR) [--init] [--overlap OVERLAP] [--max_length MAX_LENGTH] - -Ingest a file or a directory with multiple files into memory. Make sure to set your .env before running this script. - -options: - -h, --help show this help message and exit - --file FILE The file to ingest. - --dir DIR The directory containing the files to ingest. - --init Init the memory and wipe its content (default: False) - --overlap OVERLAP The overlap size between chunks when ingesting files (default: 200) - --max_length MAX_LENGTH The max_length of each chunk when ingesting files (default: 4000) - -# python data_ingestion.py --dir DataFolder --init --overlap 100 --max_length 2000 -``` - -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. - -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. - -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. - -You can adjust the `max_length` and `overlap` parameters to fine-tune the way the - documents are presented to the AI when it "recall" that memory: - -- Adjusting the overlap value allows the AI to access more contextual information - from each chunk when recalling information, but will result in more chunks being - created and therefore increase memory backend usage and OpenAI API requests. -- Reducing the `max_length` value will create more chunks, which can save prompt - tokens by allowing for more message history in the context, but will also - increase the number of chunks. -- Increasing the `max_length` value will provide the AI with more contextual - information from each chunk, reducing the number of chunks created and saving on - OpenAI API requests. However, this may also use more prompt tokens and decrease - the overall context available to the AI. - -Memory pre-seeding is a technique for improving AI accuracy by ingesting relevant data -into its memory. Chunks of data are split and added to memory, allowing the AI to access -them quickly and generate more accurate responses. It's useful for large datasets or when -specific information needs to be accessed quickly. Examples include ingesting API or -GitHub documentation before running AutoGPT. - -!!! attention - If you use Redis for memory, make sure to run AutoGPT with `WIPE_REDIS_ON_START=False` - - For other memory backends, we currently forcefully wipe the memory when starting - AutoGPT. To ingest data with those memory backends, you can call the - `data_ingestion.py` script anytime during an AutoGPT run. - -Memories will be available to the AI immediately as they are ingested, even if ingested -while AutoGPT is running. |