feat(dedup): implement two-stage duplicate detection and content rewriter

core/dedup.py

@@ -0,0 +1,35 @@
+import hashlib
+import re
+from typing import Optional
+
+def normalize_text(text: Optional[str]) -> str:
+    """Normalize text by removing URLs, telegram handles, hashtags, excessive punctuation/whitespace, and lowercasing."""
+    if not text:
+        return ""
+    # Remove URLs
+    text = re.sub(r'https?://\S+|www\.\S+', '', text)
+    # Remove Telegram @mentions / hashtags
+    text = re.sub(r'[@#]\w+', '', text)
+    # Normalize punctuation and whitespace
+    text = re.sub(r'[^\w\s]', ' ', text)
+    text = re.sub(r'\s+', ' ', text)
+    return text.strip().lower()
+
+def compute_content_hash(text: Optional[str], media_hash: Optional[str] = None) -> Optional[str]:
+    """Generate SHA256 hash from normalized text and/or media hash."""
+    norm_text = normalize_text(text)
+    if not norm_text and not media_hash:
+        return None
+    raw_key = f"{norm_text}|{media_hash or ''}"
+    return hashlib.sha256(raw_key.encode('utf-8')).hexdigest()
+
+def compute_file_hash(file_path: str) -> Optional[str]:
+    """Generate SHA256 hash of a media file."""
+    try:
+        hasher = hashlib.sha256()
+        with open(file_path, 'rb') as f:
+            while chunk := f.read(65536):
+                hasher.update(chunk)
+        return hasher.hexdigest()
+    except Exception:
+        return None

core/llm.py

@@ -0,0 +1,91 @@
+import os
+import json
+import time
+import httpx
+import logging
+from typing import Dict, Any, Optional
+from core.metrics import AI_REQUESTS_TOTAL, AI_LATENCY_SECONDS
+
+logger = logging.getLogger(__name__)
+
+class LLMClient:
+    def __init__(
+        self,
+        provider: Optional[str] = None,
+        api_key: Optional[str] = None,
+        model: Optional[str] = None,
+        base_url: Optional[str] = None,
+    ):
+        self.provider = provider or os.getenv("AI_PROVIDER", "gemini").lower()
+        self.api_key = api_key or os.getenv("AI_API_KEY", "")
+        self.model = model or os.getenv("AI_MODEL", "gemini-1.5-flash" if self.provider == "gemini" else "gpt-4o-mini")
+        self.base_url = base_url or os.getenv("AI_BASE_URL")
+
+    async def generate_json(self, prompt: str, system_prompt: Optional[str] = None, action_name: str = "general") -> Dict[str, Any]:
+        """Send prompt to LLM and parse JSON response."""
+        start_time = time.time()
+        status = "error"
+        try:
+            if self.provider == "gemini":
+                result = await self._call_gemini(prompt, system_prompt)
+            else:
+                result = await self._call_openai(prompt, system_prompt)
+            status = "success"
+            return result
+        except Exception as e:
+            logger.error(f"LLM generation failed ({self.provider}/{self.model}): {e}")
+            raise
+        finally:
+            duration = time.time() - start_time
+            AI_LATENCY_SECONDS.labels(action=action_name).observe(duration)
+            AI_REQUESTS_TOTAL.labels(action=action_name, status=status).inc()
+
+    async def _call_gemini(self, prompt: str, system_prompt: Optional[str] = None) -> Dict[str, Any]:
+        url = f"https://generativelanguage.googleapis.com/v1beta/models/{self.model}:generateContent?key={self.api_key}"
+        payload: Dict[str, Any] = {
+            "contents": [
+                {
+                    "parts": [{"text": prompt}]
+                }
+            ],
+            "generationConfig": {
+                "responseMimeType": "application/json",
+                "temperature": 0.2,
+            }
+        }
+        if system_prompt:
+            payload["systemInstruction"] = {
+                "parts": [{"text": system_prompt}]
+            }
+
+        async with httpx.AsyncClient(timeout=60.0) as client:
+            resp = await client.post(url, json=payload)
+            resp.raise_for_status()
+            data = resp.json()
+            raw_text = data["candidates"][0]["content"]["parts"][0]["text"]
+            return json.loads(raw_text)
+
+    async def _call_openai(self, prompt: str, system_prompt: Optional[str] = None) -> Dict[str, Any]:
+        url = self.base_url or "https://api.openai.com/v1/chat/completions"
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+        messages = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        messages.append({"role": "user", "content": prompt})
+
+        payload = {
+            "model": self.model,
+            "messages": messages,
+            "response_format": {"type": "json_object"},
+            "temperature": 0.2,
+        }
+
+        async with httpx.AsyncClient(timeout=60.0) as client:
+            resp = await client.post(url, headers=headers, json=payload)
+            resp.raise_for_status()
+            data = resp.json()
+            content = data["choices"][0]["message"]["content"]
+            return json.loads(content)

services/ai_processor.py

@@ -0,0 +1,138 @@
+import logging
+from typing import List, Optional, Dict, Any
+from db.models import Post, TargetChannel
+from db.repository import Repository
+from core.llm import LLMClient
+from core.dedup import compute_content_hash
+from core.metrics import DUPLICATES_DETECTED_TOTAL
+
+logger = logging.getLogger(__name__)
+
+TAG_EXTRACTION_SYSTEM_PROMPT = """
+You are an AI news analyst and classifier.
+Given a social media/channel post, extract:
+1. "subject": A brief, specific headline/subject (3-8 words).
+2. "tags": A JSON array of 3 to 6 lowercase keywords/topics/entities (e.g. ["ai", "nvidia", "gpus", "hardware"]).
+Respond ONLY in JSON format:
+{
+  "subject": "...",
+  "tags": ["tag1", "tag2", "tag3"]
+}
+"""
+
+DUPLICATE_CHECK_SYSTEM_PROMPT = """
+You are an expert news editor checking for duplicate news stories.
+Given a NEW POST and a list of PREVIOUS POSTS, determine if the NEW POST is covering the same exact event, news item, or story as any of the previous posts.
+
+Respond ONLY in JSON format:
+{
+  "is_duplicate": true/false,
+  "duplicate_of_id": <id of matched previous post or null>,
+  "similarity_reason": "<short explanation of why it is or is not a duplicate>"
+}
+"""
+
+POST_REWRITE_SYSTEM_PROMPT = """
+You are an expert Telegram content creator and copywriter.
+Rewrite the provided post to make it engaging, well-formatted, professional, and clear.
+Use appropriate emojis, clear paragraphs, and markdown formatting.
+Remove any original promotional links, author credits, or watermarks.
+
+Respond ONLY in JSON format:
+{
+  "ai_text": "...",
+  "suggested_target_id": <optional id of best matching target channel or null>
+}
+"""
+
+class AIProcessor:
+    def __init__(self, repo: Repository, llm: Optional[LLMClient] = None):
+        self.repo = repo
+        self.llm = llm or LLMClient()
+
+    async def process_post(self, post_id: int) -> Optional[Post]:
+        post = await self.repo.get_post_by_id(post_id)
+        if not post or not post.raw_text:
+            return post
+
+        raw_text = post.raw_text
+        is_dup = False
+        dup_of_id = None
+        sim_reason = None
+
+        # 1. Exact hash duplicate check
+        content_hash = post.content_hash or compute_content_hash(raw_text)
+        if content_hash:
+            exact_dup = await self.repo.find_duplicate_post_by_hash(content_hash)
+            if exact_dup and exact_dup.id != post.id:
+                is_dup = True
+                dup_of_id = exact_dup.id
+                sim_reason = "Exact match on normalized text/media hash"
+                DUPLICATES_DETECTED_TOTAL.labels(method="hash").inc()
+
+        # 2. Extract Tags and Subject via AI
+        tags = []
+        subject = "General News"
+        try:
+            tag_res = await self.llm.generate_json(
+                prompt=f"Post content:\n\n{raw_text}",
+                system_prompt=TAG_EXTRACTION_SYSTEM_PROMPT,
+                action_name="extract_tags"
+            )
+            subject = tag_res.get("subject", subject)
+            tags = [t.lower().strip() for t in tag_res.get("tags", []) if isinstance(t, str)]
+            await self.repo.update_post_tags(post.id, tags, subject)
+        except Exception as e:
+            logger.error(f"Tag extraction failed for post {post.id}: {e}")
+
+        # 3. Candidate search & Semantic AI Deduplication check (if not already exact dup)
+        if not is_dup and tags:
+            candidates = await self.repo.find_candidate_posts_by_tags(tags, exclude_post_id=post.id, hours_lookback=72, limit=5)
+            if candidates:
+                cand_texts = "\n---\n".join([f"ID {c.id} (Subject: {c.subject}):\n{c.raw_text}" for c in candidates if c.raw_text])
+                prompt = f"NEW POST:\n{raw_text}\n\nPREVIOUS CANDIDATE POSTS:\n{cand_texts}"
+                try:
+                    dup_res = await self.llm.generate_json(
+                        prompt=prompt,
+                        system_prompt=DUPLICATE_CHECK_SYSTEM_PROMPT,
+                        action_name="check_duplicate"
+                    )
+                    if dup_res.get("is_duplicate"):
+                        is_dup = True
+                        dup_of_id = dup_res.get("duplicate_of_id")
+                        sim_reason = dup_res.get("similarity_reason", "AI detected duplicate news topic")
+                        DUPLICATES_DETECTED_TOTAL.labels(method="ai_semantic").inc()
+                except Exception as e:
+                    logger.error(f"Semantic duplicate check failed for post {post.id}: {e}")
+
+        # 4. Rewrite post for our channels
+        ai_text = raw_text
+        suggested_target_id = None
+        targets = await self.repo.get_active_targets()
+        target_info = "\n".join([f"Target ID {t.id}: {t.title} (@{t.username or 'none'})" for t in targets])
+        rewrite_prompt = f"TARGET CHANNELS AVAILABLE:\n{target_info or 'None'}\n\nORIGINAL POST:\n{raw_text}"
+
+        try:
+            rewrite_res = await self.llm.generate_json(
+                prompt=rewrite_prompt,
+                system_prompt=POST_REWRITE_SYSTEM_PROMPT,
+                action_name="rewrite_post"
+            )
+            ai_text = rewrite_res.get("ai_text", raw_text)
+            suggested_target_id = rewrite_res.get("suggested_target_id")
+        except Exception as e:
+            logger.error(f"Post rewrite failed for post {post.id}: {e}")
+
+        # 5. Save AI results into database
+        await self.repo.update_ai_result(
+            post_id=post.id,
+            subject=subject,
+            ai_text=ai_text,
+            tags=tags,
+            suggested_target_id=suggested_target_id,
+            is_duplicate=is_dup,
+            duplicate_of_id=dup_of_id,
+            similarity_reason=sim_reason,
+        )
+
+        return await self.repo.get_post_by_id(post.id)

tests/test_dedup.py

@@ -0,0 +1,22 @@
+from core.dedup import normalize_text, compute_content_hash
+
+def test_normalize_text():
+    raw = "  Check out this link: https://t.me/example! @admin #tech news...  "
+    norm = normalize_text(raw)
+    assert "https" not in norm
+    assert "admin" not in norm
+    assert "tech" not in norm
+    assert norm == "check out this link news"
+
+def test_content_hash():
+    text1 = "Breaking News: Bitcoin hits $100k! Check https://example.com"
+    text2 = "Breaking News: Bitcoin hits $100k! Check https://other.com"
+    hash1 = compute_content_hash(text1)
+    hash2 = compute_content_hash(text2)
+    # Both normalize to the same text after link removal
+    assert hash1 == hash2
+
+if __name__ == "__main__":
+    test_normalize_text()
+    test_content_hash()
+    print("All deduplication unit tests passed!")