Initial commit — kb-app RAG server

2026-05-10 23:07:43 +00:00
commit 89331c1fa5
13 changed files with 1055 additions and 0 deletions
@@ -0,0 +1,66 @@
+"""Extract topic labels for chunks using a local LLM."""
+import requests
+import os
+from .config import CONFIG
+
+# Use a fast model — chosen at config time
+TOPIC_MODEL = os.getenv("KB_TOPIC_MODEL", "qwen2.5-coder:7b")
+
+
+def extract_topic(chunk_text, doc_title=None, doc_category=None):
+    """Generate a brief topic label for a chunk.
+    
+    Returns a single-line description of what specific entities, 
+    concepts, and topics this chunk discusses.
+    """
+    # Truncate very long chunks for the LLM
+    if len(chunk_text) > 3000:
+        chunk_text = chunk_text[:3000] + "..."
+    
+    context_hint = ""
+    if doc_title:
+        context_hint += f"\nThe broader document is titled: {doc_title}"
+    if doc_category:
+        context_hint += f"\nThe document category: {doc_category}"
+    
+    prompt = (
+        f"<|im_start|>system\n"
+        f"You extract topic labels for retrieval. Given a passage, identify the specific "
+        f"entities, concepts, names, and topics it discusses. Output a single concise line "
+        f"of 10-30 words listing what this passage is specifically about. Include proper "
+        f"nouns, technical terms, and metric values. Do not summarize the content; just "
+        f"label what it covers.<|im_end|>\n"
+        f"<|im_start|>user\n"
+        f"Passage:{context_hint}\n\n{chunk_text}<|im_end|>\n"
+        f"<|im_start|>assistant\n"
+    )
+    
+    try:
+        resp = requests.post(
+            f"{CONFIG.ollama_url}/api/generate",
+            json={
+                "model": TOPIC_MODEL,
+                "prompt": prompt,
+                "stream": False,
+                "options": {
+                    "num_predict": 60,
+                    "temperature": 0.0,
+                    "top_p": 1.0,
+                },
+                "raw": True,
+            },
+            timeout=60,
+        )
+        resp.raise_for_status()
+        text = resp.json().get("response", "").strip()
+        
+        # Take only the first line, drop any "Labels:" prefix
+        first_line = text.split("\n")[0].strip()
+        for prefix in ("Topics:", "Topic:", "Labels:", "Label:", "Subject:"):
+            if first_line.lower().startswith(prefix.lower()):
+                first_line = first_line[len(prefix):].strip()
+        
+        return first_line[:300]  # cap length
+    except Exception as e:
+        print(f"Topic extraction error: {e}", flush=True)
+        return ""