Implement LLM abstraction layer

core/llm.py

@@ -0,0 +1,57 @@
+import requests
+import openai
+from utils.config import config
+
+class LLMClient:
+    def __init__(self, provider="ollama", model_name=None):
+        self.provider = provider
+        self.model_name = model_name or config.local_model_name
+
+        # Set up OpenAI client for Hugging Face endpoint
+        self.hf_client = openai.OpenAI(
+            base_url=config.hf_api_url,
+            api_key=config.hf_token
+        )
+
+    def generate(self, prompt, max_tokens=8192, stream=True):
+        if self.provider == "ollama":
+            return self._generate_ollama(prompt, max_tokens, stream)
+        elif self.provider == "huggingface":
+            return self._generate_hf(prompt, max_tokens, stream)
+        else:
+            raise ValueError(f"Unsupported provider: {self.provider}")
+
+    def _generate_ollama(self, prompt, max_tokens, stream):
+        url = f"{config.ollama_host}/api/generate"
+        payload = {
+            "model": self.model_name,
+            "prompt": prompt,
+            "stream": stream
+        }
+
+        try:
+            with requests.post(url, json=payload, stream=stream) as response:
+                if response.status_code != 200:
+                    raise Exception(f"Ollama API error: {response.text}")
+
+                if stream:
+                    return (chunk.decode("utf-8") for chunk in response.iter_content())
+                else:
+                    return response.json()["response"]
+        except Exception as e:
+            raise Exception(f"Ollama request failed: {e}")
+
+    def _generate_hf(self, prompt, max_tokens, stream):
+        try:
+            response = self.hf_client.chat.completions.create(
+                model=self.model_name,
+                messages=[{"role": "user", "content": prompt}],
+                max_tokens=max_tokens,
+                stream=stream
+            )
+            if stream:
+                return (chunk.choices[0].delta.content or "" for chunk in response)
+            else:
+                return response.choices[0].text
+        except Exception as e:
+            raise Exception(f"Hugging Face API error: {e}")