To create an environment where an AI can learn from various code files contained in a directory and its subdirectories, we need a systematic approach. Here is a possible procedure to set up such a `gpt4all Embed4All GPU environment`:

### Steps to Create the Embed4All GPU Environment

1. **Collect and Analyze Files:**
   - Traverse the directory and its subdirectories to collect all relevant code files.
   - Supported file types include: `.sh`, `.bat`, `.ps1`, `.cs`, `.c`, `.cpp`, `.h`, `.cmake`, `.py`, `.git`, `.sql`, `.csv`, `.sqlite`, `.lsl`.

2. **Create Programming Language Module/Plugin:**
   - Develop a module or plugin that supports various programming languages.
   - This module should be able to read and analyze code files of the mentioned languages to extract relevant parameters.

3. **Parameter Detection:**
   - Define the necessary parameters required for the Embed4All environment for each supported file type.
   - Example parameters might include: `dimensionality`, `long_text_mode`, etc.
   - Implement algorithms or rules to extract these parameters from the code files.

4. **Set Up Embed4All Environment:**
   - Configure the Embed4All environment based on the extracted parameters.
   - For instance, specific settings for embedding dimensions or handling long texts can be made according to the needs of the code file.

5. **Training the AI:**
   - Use the configured Embed4All environment to train the AI.
   - Utilize the extracted parameters to adjust and fine-tune the training parameters of the AI.

### Technical Implementation

- **File Crawling and Language Detection:** Use tools like Python (`os` and `glob` libraries) or specific code parsers (e.g., `pygments` for syntax highlighting) to identify files and recognize their language.
  
- **Parameter Extraction:** Implement parsers for each supported programming language that can extract specific parameters from the code. For example, regular expressions or syntax analyses could be used to find relevant information.

- **Embed4All Configuration:** Use the extracted parameters to create a customized configuration for the Embed4All environment. This could be done through scripts that configure the embedding models or through direct APIs provided by Embed4All.

### Further Development and Maintenance

- **Scalability:** Consider the scalability of the solution to handle large volumes of code files.
- **Extensibility:** Keep the solution flexible to add new programming languages or file formats.
- **Maintenance:** Regularly monitor and update the parameter detection and configuration to optimize the performance of the AI and the Embed4All environment.

This approach should provide you with a solid foundation to create an environment where AI models can learn from a variety of code files, supported by a configured Embed4All environment.