From e3aea6d6c40119bdf7094a6e85691a2448a1abc3 Mon Sep 17 00:00:00 2001 From: cs0lar Date: Wed, 12 Apr 2023 17:21:37 +0100 Subject: [PATCH] added weaviate embedded section in README --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 18809f58..9959f55d 100644 --- a/README.md +++ b/README.md @@ -207,6 +207,7 @@ export PINECONE_ENV="Your pinecone region" # something like: us-east4-gcp ## 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. [An instance of Weaviate can be created locally (using Docker), on Kubernetes or using Weaviate Cloud Services](https://weaviate.io/developers/weaviate/quickstart). +Although still experimental, [Embedded Weaviate](https://weaviate.io/developers/weaviate/installation/embedded) is supported which allows the Auto-GPT process itself to start a Weaviate instance. To enable it, set `USE_WEAVIATE_EMBEDDED` to `True`. #### Setting up enviornment variables @@ -214,10 +215,13 @@ In your `.env` file set the following: ``` MEMORY_BACKEND=weaviate -WEAVIATE_HOST="http://127.0.0.1" # the URL of the running Weaviate instance +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_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 MEMORY_INDEX="Autogpt" # name of the index to create for the application ```