Help with this. I am using big-agi and even though it loads in LM-studio, I get errors in big-agi::tRPC-edge failed on llmOpenAI.chatGenerateWithFunctions: [Issue] OpenAI/lmstudio: Not Found –
Failed to load model “text-embedding-nomic-embed-text-v1.5@q8_0”. Error: Model is not llm. – “http://localhost:5151/v1/chat/completions” cannot be found by the server
[POST] OpenAI/lmstudio error (upstream): 404 Not Found {
error: {
message: ‘Failed to load model “text-embedding-nomic-embed-text-v1.5@q8_0”. Error: Model is not llm.’,
type: ‘invalid_request_error’,
param: null,
code: ‘model_not_found’
}
}
This issue seems to involve multiple layers, including the Big-AGI framework, LM Studio, and the model loading. The error indicates that Big-AGI is trying to access a model (`text-embedding-nomic-embed-text-v1.5@q8_0`) that is either misconfigured or not properly loaded into LM Studio. Here’s a concise troubleshooting guide:
### Steps to Resolve:
1. **Verify LM Studio Configuration**:
– Open LM Studio and confirm that the model `text-embedding-nomic-embed-text-v1.5@q8_0` is downloaded, installed, and accessible.
– Ensure the model is correctly labeled as an LLM (Large Language Model). The error suggests it may not be classified as such.
2. **Check Big-AGI Configuration**:
– Confirm that Big-AGI is pointing to the correct server and port for LM Studio (`http://localhost:5151/v1/chat/completions`).
– Ensure Big-AGI is configured to use models that LM Studio supports. The model might not align with the expected capabilities (e.g., chat vs. embeddings).
3. **Inspect the Model Type**:
– The model `text-embedding-nomic-embed-text-v1.5@q8_0` seems to be an embedding model, not a chat-capable LLM. Big-AGI might expect a chat model, causing the “model is not llm” error. Switch to a chat-compatible model, such as a GPT-based model, if required by your use case.
4. **Restart Services**:
– Restart both LM Studio and Big-AGI to ensure configuration changes are applied.
5. **Update Big-AGI and LM Studio**:
– Ensure you’re using the latest versions of both software to avoid compatibility issues. Check release notes for any specific requirements regarding model types or configurations.
6. **Review Logs**:
– Examine detailed logs for both LM Studio and Big-AGI for additional hints about the misconfiguration.
### Additional Steps if Problem Persists:
– **Manually Load Model**:
– Try manually loading the model into LM Studio and running test queries to confirm its functionality outside Big-AGI.
– **Change the Model Endpoint**:
– Test with an alternative model endpoint known to work with LM Studio to isolate the issue.
– **Check Dependencies**:
– Confirm dependencies and plugins for both Big-AGI and LM Studio are properly installed, especially if you’re using a custom model format or quantization like `q8_0`.
If none of these steps resolve the issue, consider sharing your configuration files or specific logs for deeper analysis.
Where would I make the cahnge in big-agi?
In Big-AGI, you typically need to modify configuration files or scripts to adjust model settings or endpoint connections. Here’s where you can make the change:
### 1. **Big-AGI Configuration Files**
Locate the configuration file that Big-AGI uses to define its model and API settings. Common locations include:
– A `.env` file in the project directory.
– A `config.json` or `settings.yaml` file in the source code.
**Steps:**
1. **Locate the Configuration File:**
– Open the Big-AGI project folder.
– Look for a file that contains API or model settings, such as `.env`, `config.json`, or similar.
2. **Modify the Model or Endpoint:**
– Update the model name to a valid chat-capable LLM available in LM Studio. For example:
“`env
MODEL_NAME=text-davinci-003 # Or the model you’re using in LM Studio.
MODEL_ENDPOINT=http://localhost:5151/v1/chat/completions
“`
– Ensure the endpoint is correctly set to point to LM Studio.
3. **Save and Restart:**
– Save the changes and restart Big-AGI to apply the new configuration.
—
### 2. **Direct Code Changes**
If no configuration file exists, the model settings might be hardcoded in the source code.
**Steps:**
1. **Search for Model References:**
– Open the source code in a text editor or IDE.
– Search for keywords like `MODEL_NAME`, `chat/completions`, or `localhost`.
2. **Update the Model or Endpoint:**
– Replace the model name (`text-embedding-nomic-embed-text-v1.5@q8_0`) with a valid one, such as a GPT-based model.
– Ensure the endpoint matches your LM Studio instance.
Example in JavaScript or TypeScript:
“`javascript
const MODEL_NAME = “gpt-3.5-turbo”; // Replace with your model.
const API_URL = “http://localhost:5151/v1/chat/completions”;
“`
3. **Rebuild the Project (if Necessary):**
– If changes are made in source code, rebuild the project using the relevant command, such as `npm run build`.
—
### 3. **Adjust Runtime Commands**
Some configurations can be passed directly during runtime.
**Steps:**
– Check the `README` or documentation for Big-AGI to see if you can specify a model or endpoint as a runtime argument.
– Example:
“`bash
big-agi –model-name “gpt-3.5-turbo” –api-url “http://localhost:5151/v1/chat/completions”
“`
—
### 4. **Validate Changes**
After making changes:
– Restart LM Studio and Big-AGI.
– Test a query to ensure Big-AGI can now access the correct model.
Let me know if you need specific help with locating these files or commands!
within vscode, can I repull file from git?
Yes, you can repull a file or an entire repository from Git within VS Code. Here’s how:
—
### **Repull an Entire Repository**
If you want to ensure your local repository matches the remote:
1. **Open the Terminal in VS Code**:
– Use the shortcut: `Ctrl + “ (backtick)` to open the terminal.
2. **Pull Changes**:
– Run the following command:
“`bash
git pull
“`
– This will fetch and merge the latest changes from the remote repository.
—
### **Repull a Specific File**
If you only want to repull a specific file:
1. **Discard Local Changes** (if any):
– Open the file in VS Code.
– Right-click in the editor and choose **Source Control** > **Discard Changes**.
– Or use the terminal:
“`bash
git checkout —
“`
2. **Pull the File from Remote**:
– Run the command:
“`bash
git checkout origin/main —
“`
Replace `main` with the appropriate branch name and “ with the path to the file.
—
### **Refresh and Verify**
1. Check the **Source Control** panel in VS Code to ensure the file or repository is up to date.
2. If the file does not appear correctly, reload the window with `Ctrl+Shift+P`, then select **Developer: Reload Window**.
This should update the file or repository to match the remote version.
> Build error occurred
Error: Failed to collect page data for /api/elevenlabs/speech
at F:BigAgibig-aginode_modulesnextdistbuildutils.js:1258:15
at process.processTicksAndRejections (node:internal/process/task_queues:95:5) {
type: ‘Error’
The error indicates that the Next.js application encountered an issue while building or running the `elevenlabs/speech` API route. Here’s how you can troubleshoot and resolve the issue:
—
### **1. Check the `elevenlabs/speech` API Route**
The error may originate from missing or misconfigured dependencies or code issues in the API route.
**Steps**:
1. Navigate to `F:BigAgibig-agipagesapielevenlabsspeech.js` or `.ts` (depending on your setup).
2. Look for:
– Missing imports or undefined variables.
– Errors in the logic or unexpected dependencies.
3. Ensure the `elevenlabs` library or any related API is correctly installed and configured:
“`bash
npm install elevenlabs
“`
—
### **2. Verify API Keys or Environment Variables**
If the `elevenlabs` API requires an API key or configuration:
1. Open the `.env.local` file or other environment configuration files in your project.
2. Confirm that the necessary environment variables are set, e.g.:
“`env
ELEVENLABS_API_KEY=your_api_key
“`
3. Restart the server after any environment variable change:
“`bash
npm run dev
“`
—
### **3. Check Data Fetching Logic**
If the `speech` route fetches data from Eleven Labs or other APIs, ensure:
1. The correct API endpoints and parameters are used.
2. Error handling is in place to catch issues during API requests.
3. Example (Node.js with `axios`):
“`javascript
const axios = require(‘axios’);
export default async function handler(req, res) {
try {
const response = await axios.post(‘https://api.elevenlabs.io/speech’, {
headers: {
‘Authorization’: `Bearer ${process.env.ELEVENLABS_API_KEY}`,
},
data: req.body,
});
res.status(200).json(response.data);
} catch (error) {
console.error(‘API error:’, error);
res.status(500).json({ error: ‘Failed to process request’ });
}
}
“`
—
### **4. Debug the Build Process**
Sometimes, the error can occur due to misconfigured imports or build issues.
1. Run a **build check**:
“`bash
npm run build
“`
This will pinpoint any syntax or module-related errors.
2. If the error persists, inspect the exact line in the provided path:
`F:BigAgibig-aginode_modulesnextdistbuildutils.js:1258`
Check if the issue is due to missing module dependencies or misconfigured paths.
—
### **5. Update Dependencies**
Ensure all dependencies are updated to their latest compatible versions:
“`bash
npm install
npm update
“`
—
### **6. Delete and Rebuild**
If the issue persists:
1. Delete `node_modules` and `.next` directories:
“`bash
rm -rf node_modules .next
“`
2. Reinstall dependencies and rebuild:
“`bash
npm install
npm run dev
“`
—
### **7. Test Locally**
If the error is resolved, confirm the `/api/elevenlabs/speech` route works as intended:
– Test it directly via browser or API tools like Postman:
“`
http://localhost:3000/api/elevenlabs/speech
“`
If further issues arise, let me know!
Yesterday we wrote an excellent persona for a full-on personal AI Assistant, do you recall that
I don’t seem to have direct access to yesterday’s persona you mentioned. However, if you share some key details or aspects of it, I can help refine or expand upon it based on what you envision! Let me know how you’d like to proceed.