egutierrez
aef8791151
- Added Appshell component with responsive navbar and main content area - Integrated ColorSchemeToggle for light/dark mode switching - Created Welcome component with styled title and introductory text - Developed ChatPage for LLM interaction with WebSocket support - Implemented Biblioteca for managing notes with rich text editor - Added LoginPage for user authentication with error handling - Introduced MessageList and MessageBubble components for chat messages - Styled components with CSS modules for consistent design
63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
import requests
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from domains.Credenciales.ollama_credencial import OllamaCredencial
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class OllamaCliente:
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def __init__(self, credencial: OllamaCredencial):
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"""
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Inicializa el cliente de Ollama con una instancia de OllamaCredencial.
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"""
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self.credencial = credencial
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self.base_url = self.credencial.base_url
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# --- Chat Completions ---
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def chat_completion(self, model: str, messages: list, stream: bool = False, **kwargs):
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url = f"{self.base_url}/api/chat"
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payload = {
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"model": model,
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"messages": messages,
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"stream": stream,
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**kwargs
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}
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response = requests.post(url, json=payload, stream=stream)
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response.raise_for_status()
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return self._handle_stream(response) if stream else response.json()
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def _handle_stream(self, response):
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for line in response.iter_lines():
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if line:
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try:
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parsed = line.decode("utf-8")
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# Extraer contenido si está en JSON como {"message":{"content":"..."},...}
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if parsed.startswith("{"):
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import json
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data = json.loads(parsed)
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if "message" in data and "content" in data["message"]:
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yield data["message"]["content"]
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except Exception:
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continue
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# --- Text Completion (legacy) ---
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def completion(self, model: str, prompt: str, **kwargs):
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url = f"{self.base_url}/api/generate"
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payload = {
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"model": model,
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"prompt": prompt,
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**kwargs
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}
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response = requests.post(url, json=payload)
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response.raise_for_status()
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return response.json()
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# --- Embeddings ---
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def embedding(self, model: str, input: str | list[str], **kwargs):
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url = f"{self.base_url}/api/embeddings"
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payload = {
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"model": model,
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"prompt": input,
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**kwargs
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}
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response = requests.post(url, json=payload)
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response.raise_for_status()
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return response.json()
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