Ollama Model with Windsurf

how to use a local ollama model with windsurf

Using a local Ollama model with **Windsurf** involves the integration of the Ollama local model server and the Windsurf agentic framework to streamline agent-based operations. Here's a step-by-step guide: ### Prerequisites 1. **Install Windsurf**: Ensure that the Windsurf framework is set up in your environment. Install it via: ```bash pip install windsurf ``` 2. **Set Up Ollama Local Model**: - Download and install the Ollama model and server. Instructions can be found in their [documentation](https://ollama.ai/docs). - Start the Ollama server locally. For example: ```bash ollama serve ``` - Confirm the server is running by checking its default endpoint: `http://localhost:11434`. 3. **Prepare Dependencies**: - Install required Python packages for HTTP requests (e.g., `requests`) and Windsurf. ### Steps to Use Ollama with Windsurf #### 1. Start the Ollama Model Server Start your model server with a specific model loaded. For example: ```bash ollama serve --model-path path/to/your/model ``` #### 2. Create an Ollama Agent in Windsurf Define a custom agent in Windsurf that communicates with the local Ollama server. Here's an example implementation: ```python from windsurf import Agent import requests class OllamaAgent(Agent): def __init__(self, base_url="http://localhost:11434"): super().__init__() self.base_url = base_url def generate(self, prompt): # POST request to the local Ollama server response = requests.post( f"{self.base_url}/api/generate", json={"prompt": prompt} ) if response.status_code == 200: return response.json().get("response", "") else: raise ValueError(f"Ollama server error: {response.status_code} - {response.text}") ``` #### 3. Orchestrate Agents with Windsurf Windsurf allows for multiple agents to interact. Define the flow or interaction among agents, with Ollama as one of the key nodes. ```python from windsurf import Orchestrator # Define your orchestration logic orchestrator = Orchestrator() # Add the Ollama agent ollama_agent = OllamaAgent() orchestrator.add_agent("ollama", ollama_agent) # Define a task def task(): result = orchestrator.get_agent("ollama").generate("What is the capital of France?") print(result) # Run the task task() ``` #### 4. Test the Setup Run your script to verify the Ollama model processes requests and returns outputs through Windsurf's orchestration system. ```bash python your_script.py ``` ### Advanced Features - **Multiple Agents**: Add additional agents in Windsurf for collaborative tasks. - **Feedback Loop**: Incorporate feedback from the Ollama model into Windsurf's workflows for iterative tasks. - **Custom Prompts**: Preload complex templates into the `prompt` for advanced model queries. ### Troubleshooting - **Connection Issues**: Verify the Ollama server is running (`curl http://localhost:11434/health`). - **Model Errors**: Ensure the model is properly configured and compatible with Ollama. This setup lets you leverage the flexibility of Windsurf with the local power of Ollama, ideal for applications requiring agent-based LLM orchestration.

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To use a **local Ollama model** with **Windsurf**, follow these precise steps without external lookups: --- ### **1. Prerequisites** Ensure you have: - **Ollama Local Model**: Installed and running. - **Windsurf**: Installed in your Python environment. - **Python Environment**: Ensure libraries like `requests` and Windsurf are installed. Use: ```bash pip install requests windsurf ``` --- ### **2. Set Up the Ollama Local Model** Run the Ollama server locally by executing: ```bash ollama serve --model-path /path/to/your/model ``` - By default, Ollama serves requests at `http://localhost:11434`. --- ### **3. Create an Ollama Agent in Windsurf** Define a custom agent class in Python that interacts with the Ollama server. ```python from windsurf import Agent import requests class OllamaAgent(Agent): def __init__(self, base_url="http://localhost:11434"): super().__init__() self.base_url = base_url def generate(self, prompt): """Send a prompt to the local Ollama server and receive a response.""" try: response = requests.post( f"{self.base_url}/api/generate", json={"prompt": prompt} ) response.raise_for_status() return response.json().get("response", "") except requests.RequestException as e: return f"Error: {e}" ``` --- ### **4. Orchestrate Ollama with Windsurf** Windsurf allows for agent interactions. Integrate the Ollama agent into your orchestration logic: ```python from windsurf import Orchestrator # Create an orchestrator orchestrator = Orchestrator() # Add the Ollama agent ollama_agent = OllamaAgent() orchestrator.add_agent("ollama", ollama_agent) # Define a task using the Ollama agent def task(): prompt = "Explain the significance of the Renaissance in world history." result = orchestrator.get_agent("ollama").generate(prompt) print("Ollama Response:", result) # Run the task if __name__ == "__main__": task() ``` --- ### **5. Run the Setup** Save the script and execute it in your Python environment: ```bash python ollama_windsurf_integration.py ``` --- ### **6. Advanced Integration** - **Feedback Loops**: Use Windsurf’s capabilities to iteratively improve prompts. - **Multiple Agents**: Add other agents, like a summarizer or sentiment analyzer, to process outputs collaboratively. - **Custom Prompts**: Implement templating for more complex interactions. --- ### **7. Example Output** Running the task may yield: ``` Ollama Response: The Renaissance was a period of great cultural, artistic, and intellectual flourishing in Europe... ``` This implementation lets you seamlessly connect Windsurf's agentic framework with the local Ollama server.

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To integrate a local Ollama model with **Windsurf**, follow these steps: 1. **Install Windsurf**: - Ensure Windsurf is installed in your development environment. - You can download Windsurf from the official website: - citeturn0search1 2. **Set Up the Ollama Model Server**: - Start your local Ollama model server. - Ensure it's accessible, typically at `http://localhost:11434`. 3. **Create an Ollama Agent in Windsurf**: - Define a custom agent in Windsurf to interact with the Ollama server. - Here's an example implementation: ```python from windsurf import Agent import requests class OllamaAgent(Agent): def __init__(self, base_url="http://localhost:11434"): super().__init__() self.base_url = base_url def generate(self, prompt): response = requests.post( f"{self.base_url}/api/generate", json={"prompt": prompt} ) response.raise_for_status() return response.json().get("response", "") ``` 4. **Integrate the Agent into Windsurf's Workflow**: - Incorporate the `OllamaAgent` into your Windsurf orchestration logic. - For example: ```python from windsurf import Orchestrator orchestrator = Orchestrator() ollama_agent = OllamaAgent() orchestrator.add_agent("ollama", ollama_agent) def task(): result = orchestrator.get_agent("ollama").generate("Your prompt here") print(result) task() ``` 5. **Test the Integration**: - Run your script to ensure the Ollama model processes requests correctly through Windsurf. For more detailed information, refer to the Windsurf documentation: - citeturn0search0 Additionally, you might find this tutorial helpful: - videoHow To Use Windsurf Editor For Beginnersturn0search7

write a guide on how to set up continue.dev to work with ollama using any model using the following as guidance : Got Qwen2.5 up and running with Aider with browser UI ``` python -m pip install -U aider-chat export OPENAI_API_KEY=<key> # Mac/Linux setx OPENAI_API_KEY <key> # Windows, restart shell after setx aider --browser ``` ``` # Pull the model ollama pull <model> # Start your ollama server ollama serve # In another terminal window... python -m pip install -U aider-chat export OLLAMA_API_BASE=http://127.0.0.1:11434 # Mac/Linux setx OLLAMA_API_BASE http://127.0.0.1:11434 # Windows, restart shell after setx aider --model ollama_chat/<model> ``` `http://localhost:11434/api/generate` working config: { "models": [ { "title": "Mistral Medium", "model": "mistral-medium", "apiKey": "boYuhDuiWCiTZnmfALaRCgBLjIHj6oZL", "completionOptions": {}, "provider": "mistral" }, { "title": "Qwen 2.5 Coder 32b", "model": "qwen2.5-coder-32b", "provider": "ollama", "apiBase": "http://localhost:11434/api/generate" } ], "slashCommands": [ { "name": "edit", "description": "Edit selected code" }, { "name": "comment", "description": "Write comments for the selected code" }, { "name": "share", "description": "Download and share this session" }, { "name": "cmd", "description": "Generate a shell command" } ], "customCommands": [ { "name": "test", "prompt": "Write a comprehensive set of unit tests for the selected code. It should setup, run tests that check for correctness including important edge cases, and teardown. Ensure that the tests are complete and sophisticated. Give the tests just as chat output, don't edit any file.", "description": "Write unit tests for highlighted code" } ], "contextProviders": [ { "name": "diff", "params": {} }, { "name": "open", "params": {} }, { "name": "terminal", "params": {} }, { "name": "problems", "params": {} } ], "embeddingsProvider": { "provider": "ollama", "apiBase": "http://localhost:11434/api/generate", "model": "qwen2.5-coder:1.5b", }, "reranker": { "name": "free-trial" }, "tabAutocompleteModel": { "title": "Qwen2.5-Coder 1.5B", "provider": "ollama", "model": "qwen2.5-coder:1.5b", "apiBase": "http://localhost:11434/api/generate" } }

### **Guide to Set Up Continue.dev with Ollama and Custom Models** This guide will walk you through configuring **Continue.dev** to work with **Ollama** using any local LLM model. We'll use the provided guidance and integrate Ollama models seamlessly into the Continue.dev workflow. --- ### **Prerequisites** 1. **Installed Software**: - Python 3.8+ with `pip`. - Ollama installed and configured locally. - Continue.dev installed (`pip install continue`). 2. **Model Availability**: - Ensure the desired Ollama model is pulled and available on your machine. --- ### **Step 1: Install and Configure Continue.dev** 1. Install the latest version of Continue.dev: ```bash python -m pip install -U continue ``` 2. Set your API Key (if required): ```bash export CONTINUE_API_KEY=<your_api_key> # Mac/Linux setx CONTINUE_API_KEY <your_api_key> # Windows (restart your shell afterward) ``` --- ### **Step 2: Set Up the Ollama Local Model Server** 1. Pull the desired model: ```bash ollama pull <model> ``` 2. Start the Ollama server: ```bash ollama serve ``` The server will be accessible at `http://127.0.0.1:11434` by default. 3. Confirm the server is running: ```bash curl http://127.0.0.1:11434/health ``` --- ### **Step 3: Configure Ollama as a Provider in Continue.dev** Modify your **Continue.dev configuration** file to add Ollama as a model provider. Locate the config file at `~/.continue/config.json` or create one if it doesn’t exist. Here’s an example configuration: ```json { "models": [ { "title": "Qwen 2.5 Coder 32b", "model": "qwen2.5-coder:1.5b", "provider": "ollama", "apiBase": "http://127.0.0.1:11434/api/generate" } ], "contextProviders": [ { "name": "diff", "params": {} }, { "name": "open", "params": {} }, { "name": "terminal", "params": {} }, { "name": "problems", "params": {} } ], "customCommands": [ { "name": "test", "prompt": "Write a comprehensive set of unit tests for the selected code. Ensure the tests cover edge cases and are well-structured.", "description": "Write unit tests for selected code." } ], "embeddingsProvider": { "provider": "ollama", "apiBase": "http://127.0.0.1:11434/api/generate", "model": "qwen2.5-coder:1.5b" }, "tabAutocompleteModel": { "title": "Qwen 2.5 Coder Autocomplete", "provider": "ollama", "model": "qwen2.5-coder:1.5b", "apiBase": "http://127.0.0.1:11434/api/generate" } } ``` --- ### **Step 4: Start Continue.dev with Ollama** 1. Launch Continue.dev with the Ollama model: ```bash continue --model ollama_chat/<model> ``` 2. Verify the integration by entering a prompt or running any commands within the Continue.dev interface. --- ### **Step 5: Optional Customizations** - **Slash Commands**: Extend Continue.dev with custom slash commands for specific tasks. Add these to your configuration: ```json { "slashCommands": [ { "name": "edit", "description": "Edit the selected code" }, { "name": "comment", "description": "Generate comments for the selected code" } ] } ``` - **Embedded Features**: - Use embeddings for context-aware suggestions or ranking tasks. - Add advanced rerankers as needed. --- ### **Step 6: Test the Setup** 1. Use a terminal or the Continue.dev interface to test: ```bash curl -X POST http://127.0.0.1:11434/api/generate -d '{"prompt":"Hello, world!"}' ``` 2. Validate that the response matches the expected output from your model. --- ### **Troubleshooting** - **Connection Issues**: - Ensure the Ollama server is running and reachable at `http://127.0.0.1:11434`. - **Model Errors**: - Verify the model is correctly pulled and loaded. --- With this setup, you can leverage Continue.dev's interactive environment powered by your locally hosted Ollama models.