new file: .dockerignore

new file:   .env.example
	new file:   Dockerfile
	new file:   app.py
	new file:   blueprints/__init__.py
	new file:   blueprints/auth.py
	new file:   blueprints/chat.py
	new file:   blueprints/context.py
	new file:   blueprints/documents.py
	new file:   blueprints/main.py
	new file:   config.py
	new file:   docker-compose.yml
	new file:   models/__init__.py
	new file:   models/chat_session.py
	new file:   models/document.py
	new file:   models/user.py
	new file:   requirements.txt
	new file:   services/__init__.py
	new file:   services/document_parser.py
	new file:   services/llm_service.py
	new file:   services/rag_service.py
	new file:   services/url_scraper.py
	new file:   static/css/style.css
	new file:   static/js/chat.js
	new file:   static/js/inline_chat.js
	new file:   static/js/main.js
	new file:   templates/base.html
	new file:   templates/document_view.html
	new file:   templates/index.html
	new file:   templates/login.html
	new file:   templates/register.html

services/llm_service.py

@@ -0,0 +1,109 @@
+"""
+Abstracted LLM service.
+Supports AI_PROVIDER=lmstudio (default) or AI_PROVIDER=openai.
+Both use the openai Python client — only base_url / api_key differ.
+"""
+
+from typing import Optional
+from flask import current_app
+
+_client = None
+
+
+def _get_client():
+    global _client
+    if _client is not None:
+        return _client
+
+    import openai
+
+    provider = current_app.config.get("AI_PROVIDER", "lmstudio").lower()
+
+    if provider == "openai":
+        api_key = current_app.config.get("OPENAI_API_KEY", "")
+        _client = openai.OpenAI(api_key=api_key)
+    else:
+        # LM Studio or any OpenAI-compatible endpoint
+        base_url = current_app.config.get("LM_STUDIO_URL", "http://localhost:1234")
+        _client = openai.OpenAI(
+            base_url=f"{base_url.rstrip('/')}/v1",
+            api_key="lm-studio",
+        )
+
+    return _client
+
+
+def _get_model() -> str:
+    provider = current_app.config.get("AI_PROVIDER", "lmstudio").lower()
+    if provider == "openai":
+        return current_app.config.get("OPENAI_MODEL", "gpt-4o")
+    return current_app.config.get("LM_STUDIO_MODEL", "local-model")
+
+
+def ask(
+    user_message: str,
+    context_chunks: Optional[list[str]] = None,
+    history: Optional[list[dict]] = None,
+    system_extra: Optional[str] = None,
+) -> str:
+    """
+    Send a message to the LLM with optional RAG context and chat history.
+    Returns the assistant reply as a string.
+    """
+    client = _get_client()
+    model = _get_model()
+
+    system_parts = [
+        "You are a helpful AI assistant. Answer questions accurately based on the provided context.",
+        "If the context does not contain enough information, say so clearly.",
+    ]
+
+    if context_chunks:
+        context_text = "\n\n---\n\n".join(context_chunks)
+        system_parts.append(f"\n\n## Context\n\n{context_text}")
+
+    if system_extra:
+        system_parts.append(system_extra)
+
+    messages = [{"role": "system", "content": "\n".join(system_parts)}]
+
+    if history:
+        messages.extend(history)
+
+    messages.append({"role": "user", "content": user_message})
+
+    response = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        temperature=0.7,
+    )
+
+    return response.choices[0].message.content.strip()
+
+
+def ask_inline(selected_text: str, question: str) -> str:
+    """
+    Inline chat: use selected_text directly as context — no RAG lookup.
+    """
+    system = (
+        "You are a helpful AI assistant. The user has selected the following text "
+        "and has a question about it. Answer specifically about the selected content."
+    )
+    messages = [
+        {"role": "system", "content": system},
+        {
+            "role": "user",
+            "content": f"## Selected text\n\n{selected_text}\n\n## Question\n\n{question}",
+        },
+    ]
+
+    client = _get_client()
+    model = _get_model()
+
+    response = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        temperature=0.7,
+    )
+
+    return response.choices[0].message.content.strip()