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docs/configuration/imagegen.md

@@ -4,10 +4,12 @@ By default, Auto-GPT uses DALL-e for image generation. To use Stable Diffusion,
 
 Once you have a token, set these variables in your `.env`:
 
-```IMAGE_PROVIDER=huggingface
+``` shell
+IMAGE_PROVIDER=huggingface
 HUGGINGFACE_API_TOKEN="YOUR_HUGGINGFACE_API_TOKEN"
 ```
 
 ## Selenium
-```sudo Xvfb :10 -ac -screen 0 1024x768x24 & DISPLAY=:10 <YOUR_CLIENT>
+``` shell
+sudo Xvfb :10 -ac -screen 0 1024x768x24 & DISPLAY=:10 <YOUR_CLIENT>
 ```

docs/configuration/memory.md

@@ -12,32 +12,39 @@ To switch to either, change the `MEMORY_BACKEND` env variable to the value that
 
 ## Memory Backend Setup
 
-- Links to memory backends
- - [Pinecone](https://www.pinecone.io/)
- - [Milvus](https://milvus.io/)
- - [Redis](https://redis.io)
- - [Weaviate](https://weaviate.io)
+Links to memory backends
+
+- [Pinecone](https://www.pinecone.io/)
+- [Milvus](https://milvus.io/)
+- [Redis](https://redis.io)
+- [Weaviate](https://weaviate.io)
 
 ### Redis Setup
 > _**CAUTION**_ \
 This is not intended to be publicly accessible and lacks security measures. Therefore, avoid exposing Redis to the internet without a password or at all
 1. Install docker (or Docker Desktop on Windows).
 2. Launch Redis container.
- ``` docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest
- ```
- > See https://hub.docker.com/r/redis/redis-stack-server for setting a password and additional configuration.
+
+``` shell 
+ docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest
+```
+> See https://hub.docker.com/r/redis/redis-stack-server for setting a password and additional configuration.
+
 3. Set the following settings in `.env`.
  > Replace **PASSWORD** in angled brackets (<>)
- ``` MEMORY_BACKEND=redis
- REDIS_HOST=localhost
- REDIS_PORT=6379
- REDIS_PASSWORD=<PASSWORD>
- ```
+
+``` shell
+MEMORY_BACKEND=redis
+REDIS_HOST=localhost
+REDIS_PORT=6379
+REDIS_PASSWORD=<PASSWORD>
+```
 
  You can optionally set `WIPE_REDIS_ON_START=False` to persist memory stored in Redis.
 
 You can specify the memory index for redis using the following:
-```MEMORY_INDEX=<WHATEVER>
+``` shell
+MEMORY_INDEX=<WHATEVER>
 ```
 
 ### 🌲 Pinecone API Key Setup
@@ -57,14 +64,16 @@ Alternatively, you can set them from the command line (advanced):
 
 For Windows Users:
 
-```setx PINECONE_API_KEY "<YOUR_PINECONE_API_KEY>"
+``` shell
+setx PINECONE_API_KEY "<YOUR_PINECONE_API_KEY>"
 setx PINECONE_ENV "<YOUR_PINECONE_REGION>" # e.g: "us-east4-gcp"
 setx MEMORY_BACKEND "pinecone"
 ```
 
 For macOS and Linux users:
 
-```export PINECONE_API_KEY="<YOUR_PINECONE_API_KEY>"
+``` shell
+export PINECONE_API_KEY="<YOUR_PINECONE_API_KEY>"
 export PINECONE_ENV="<YOUR_PINECONE_REGION>" # e.g: "us-east4-gcp"
 export MEMORY_BACKEND="pinecone"
 ```
@@ -91,15 +100,15 @@ Although still experimental, [Embedded Weaviate](https://weaviate.io/developers/
 
 Install the Weaviate client before usage.
 
-```
+``` shell
 $ pip install weaviate-client
 ```
 
 #### Setting up environment variables
 
 In your `.env` file set the following:
 
-```
+``` shell
 MEMORY_BACKEND=weaviate
 WEAVIATE_HOST="127.0.0.1" # the IP or domain of the running Weaviate instance
 WEAVIATE_PORT="8080" 
@@ -120,7 +129,8 @@ View memory usage by using the `--debug` flag :)
 ## 🧠 Memory pre-seeding
 Memory pre-seeding allows you to ingest files into memory and pre-seed it before running Auto-GPT.
 
-```# python data_ingestion.py -h 
+``` 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.
@@ -135,6 +145,7 @@ options:
 
 # 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 `Auto-Gpt/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.

docs/configuration/voice.md

@@ -2,7 +2,8 @@
 
 Enter this command to use TTS _(Text-to-Speech)_ for Auto-GPT
 
-```python -m autogpt --speak
+``` shell
+python -m autogpt --speak
 ```
 
 ### List of IDs with names from eleven labs. You can use the name or ID:

docs/installation.md

@@ -26,18 +26,21 @@ _To execute the following commands, open a CMD, Bash, or Powershell window by na
 2. Clone the repository: For this step, you need Git installed. 
 Note: If you don't have Git, you can just download the [latest stable release](https://github.com/Significant-Gravitas/Auto-GPT/releases/latest) instead (`Source code (zip)`, at the bottom of the page).
 
- ``` git clone -b stable https://github.com/Significant-Gravitas/Auto-GPT.git
- ```
+``` shell
+ git clone -b stable https://github.com/Significant-Gravitas/Auto-GPT.git
+```
 
 3. Navigate to the directory where you downloaded the repository.
 
- ``` cd Auto-GPT
- ```
+``` shell
+ cd Auto-GPT
+```
 
 4. Install the required dependencies.
 
- ``` pip install -r requirements.txt
- ```
+``` shell
+ pip install -r requirements.txt
+```
 
 5. Configure Auto-GPT:
  1. Find the file named `.env.template` in the main /Auto-GPT folder. This file may be hidden by default in some operating systems due to the dot prefix. To reveal hidden files, follow the instructions for your specific operating system (e.g., in Windows, click on the "View" tab in File Explorer and check the "Hidden items" box; in macOS, press Cmd + Shift + .).
@@ -60,33 +63,34 @@ Note: If you don't have Git, you can just download the [latest stable release](h
  - `embedding_model_deployment_id` - your text-embedding-ada-002 v2 deployment ID
  - Please specify all of these values as double-quoted strings
 
- ```
- # Replace string in angled brackets (<>) to your own ID
- azure_model_map:
-  fast_llm_model_deployment_id: "<my-fast-llm-deployment-id>"
-  ...
- ```
+``` shell
+# Replace string in angled brackets (<>) to your own ID
+azure_model_map:
+ fast_llm_model_deployment_id: "<my-fast-llm-deployment-id>"
+ ...
+```
  - Details can be found here: https://pypi.org/project/openai/ in the `Microsoft Azure Endpoints` section and here: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/tutorials/embeddings?tabs=command-line for the embedding model.
 
 ## Docker
 
 You can also build this into a docker image and run it:
 
-```
+``` shell
 docker build -t autogpt .
 docker run -it --env-file=./.env -v $PWD/auto_gpt_workspace:/home/appuser/auto_gpt_workspace autogpt
 ```
 
 Or if you have `docker-compose`:
-```
+``` shell
 docker-compose run --build --rm auto-gpt
 ```
 
 You can pass extra arguments, for instance, running with `--gpt3only` and `--continuous` mode:
-```
+``` shell
 docker run -it --env-file=./.env -v $PWD/auto_gpt_workspace:/home/appuser/auto_gpt_workspace autogpt --gpt3only --continuous
 ```
-```
+
+``` shell
 docker-compose run --build --rm auto-gpt --gpt3only --continuous
 ```
 

docs/plugins.md

@@ -14,7 +14,8 @@ Use the [Auto-GPT Plugin Template](https://github.com/Significant-Gravitas/Auto-
 2. **Install the plugin's dependencies (if any):**
  Navigate to the plugin's folder in your terminal, and run the following command to install any required dependencies:
 
- ``` pip install -r requirements.txt
+ ``` shell
+ pip install -r requirements.txt
  ```
 
 3. **Package the plugin as a Zip file:**
@@ -26,7 +27,7 @@ Use the [Auto-GPT Plugin Template](https://github.com/Significant-Gravitas/Auto-
 5. **Allowlist the plugin (optional):**
  Add the plugin's class name to the `ALLOWLISTED_PLUGINS` in the `.env` file to avoid being prompted with a warning when loading the plugin:
 
- ```
+ ``` shell
  ALLOWLISTED_PLUGINS=example-plugin1,example-plugin2,example-plugin3
  ```
 

docs/usage.md

@@ -65,15 +65,15 @@ Use at your own risk.
 
 If you don't have access to the GPT4 api, this mode will allow you to use Auto-GPT!
 
-```
+``` shell
 python -m autogpt --gpt3only
 ```
 
 ### GPT4 ONLY Mode
 
 If you do have access to the GPT4 api, this mode will allow you to use Auto-GPT solely using the GPT-4 API for increased intelligence (and cost!)
 
-```
+``` shell
 python -m autogpt --gpt4only
 ```
 
@@ -83,6 +83,6 @@ Activity and error logs are located in the `./output/logs`
 
 To print out debug logs:
 
-```
+``` shell
 python -m autogpt --debug
 ```