Few changes for making it production ready

config.py

@@ -4,13 +4,11 @@ import os
 
 class Settings(BaseSettings):
     groq_api_key: str
-    neo4j_uri: str
-    neo4j_username: str
-    neo4j_password: str
-    neo4j_database: str
-    aura_instanceid: str
-    aura_instancename: str
     model_name: str
+    postgres_user: str
+    postgres_password: str
+    postgres_db: str
+    database_url: str
 
     class Config:
         env_file = '.env'

docker-compose.yml

@@ -0,0 +1,16 @@
+services:
+  db:
+    image: postgres:15-alpine
+    container_name: anime_postgres
+    restart: always
+    environment:
+      POSTGRES_USER: ${POSTGRES_USER}
+      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
+      POSTGRES_DB: ${POSTGRES_DB}
+    ports:
+      - "5433:5432"
+    volumes:
+      - postgres_data:/var/lib/postgresql/data
+
+volumes:
+  postgres_data:

pyproject.toml

@@ -10,11 +10,12 @@ dependencies = [
     "gradio>=6.2.0",
     "langchain>=1.1.3",
     "langchain-groq>=1.1.1",
-    "neo4j>=6.1.0",
     "pandas>=2.3.3",
+    "psycopg2-binary>=2.9.11",
     "python-dotenv>=1.2.1",
     "requests>=2.32.5",
     "sentence-transformers>=5.2.0",
+    "sqlmodel>=0.0.37",
     "torch>=2.9.1",
     "torchvision>=0.24.1",
     "uvicorn>=0.38.0",

scraper.log

@@ -0,0 +1,52 @@
+Critical error on page 41: 504 Server Error: Gateway Time-out for url: https://api.jikan.moe/v4/anime?page=41&limit=25&order_by=popularity&sort=asc
+Saved 1000 anime to data/raw/raw_anime.json
+
+Sample anime: 
+{
+  "mal_id": 16498,
+  "url": "https://myanimelist.net/anime/16498/Shingeki_no_Kyojin",
+  "images": {
+    "jpg": {
+      "image_url": "https://cdn.myanimelist.net/images/anime/10/47347.jpg",
+      "small_image_url": "https://cdn.myanimelist.net/images/anime/10/47347t.jpg",
+      "large_image_url": "https://cdn.myanimelist.net/images/anime/10/47347l.jpg"
+    },
+    "webp": {
+      "image_url": "https://cdn.myanimelist.net/images/anime/10/47347.webp",
+      "small_image_url": "https://cdn.myanimelist.net/images/anime/10/47347t.webp",
+      "large_image_url": "https://cdn.myanimelist.net/images/anime/10/47347l.webp"
+    }
+  },
+  "title": "Shingeki no Kyojin",
+  "title_english": "Attack on Titan",
+  "synopsis": "Centuries ago, mankind was slaughtered to near extinction by monstrous humanoid creatures called Titans, forcing humans to hide in fear behind enormous concentric walls. What makes these giants truly terrifying is that their taste for human flesh is not born out of hunger but what appears to be out of pleasure. To ensure their survival, the remnants of humanity began living within defensive barriers, resulting in one hundred years without a single titan encounter. However, that fragile calm is soon shattered when a colossal Titan manages to breach the supposedly impregnable outer wall, reigniting the fight for survival against the man-eating abominations.\n\nAfter witnessing a horrific personal loss at the hands of the invading creatures, Eren Yeager dedicates his life to their eradication by enlisting into the Survey Corps, an elite military unit that combats the merciless humanoids outside the protection of the walls. Eren, his adopted sister Mikasa Ackerman, and his childhood friend Armin Arlert join the brutal war against the Titans and race to discover a way of defeating them before the last walls are breached.\n\n[Written by MAL Rewrite]",
+  "genres": [
+    "Action",
+    "Award Winning",
+    "Drama",
+    "Suspense"
+  ],
+  "studios": [
+    "Wit Studio"
+  ],
+  "themes": [
+    "Gore",
+    "Military",
+    "Survival"
+  ],
+  "demographics": [
+    "Shounen"
+  ],
+  "type": "TV",
+  "episodes": 25,
+  "score": 8.57,
+  "scored_by": 3036922,
+  "rank": 120,
+  "popularity": 1,
+  "year": 2013,
+  "rating": "R - 17+ (violence & profanity)",
+  "season": "spring",
+  "aired_from": "2013-04-07T00:00:00+00:00",
+  "aired_to": "2013-09-29T00:00:00+00:00",
+  "favorites": 187237
+}

src/data_ingestion/clean_data.py

@@ -3,6 +3,7 @@ import pandas as pd
 import logging
 
 logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
 
 
 class AnimeDataCleaner:
@@ -89,19 +90,33 @@ class AnimeDataCleaner:
         for anime in anime_list:
             record = {
                 "mal_id": anime["mal_id"],
-                "title": anime["title"],
-                "searchable_text": AnimeDataCleaner.create_searchable_text(anime),
-                # Rest is metadata for filtering and display
-                "genres": ", ".join(anime.get("genres", [])),
-                "score": anime.get("score"),
-                "episodes": anime.get("episodes"),
+                "url": anime.get("url"),
+                "title": anime.get("title"),
+                "title_english": anime.get("title_english"),
+                "synopsis": AnimeDataCleaner.clean_synopsis(anime.get("synopsis", "")),
+
+                # Keep these as native dicts/lists for Postgres JSONB!
+                "images": anime.get("images", {}),
+                "genres": anime.get("genres", []),
+                "studios": anime.get("studios", []),
+                "themes": anime.get("themes", []),
+                "demographics": anime.get("demographics", []),
+
                 "type": anime.get("type"),
+                "episodes": anime.get("episodes"),
+                "score": anime.get("score"),
+                "scored_by": anime.get("scored_by"),
+                "rank": anime.get("rank"),
+                "popularity": anime.get("popularity"),
                 "year": anime.get("year"),
-                "synopsis": AnimeDataCleaner.clean_synopsis(anime.get("synopsis", "")),
-                "aired_from": anime.get("aired_from", ""),
-                "aired_to": anime.get("aired_to", ""),
+                "season": anime.get("season"),
                 "rating": anime.get("rating"),
-                "scored_by": anime.get("scored_by"),
+                "aired_from": anime.get("aired_from"),
+                "aired_to": anime.get("aired_to"),
+                "favorites": anime.get("favorites"),
+
+                # And our custom RAG field
+                "searchable_text": AnimeDataCleaner.create_searchable_text(anime)
             }
 
             records.append(record)
@@ -125,14 +140,15 @@ if __name__ == "__main__":
     cleaner = AnimeDataCleaner()
 
     print("Loading raw data....")
-    raw_animes = cleaner.load_raw_data()
+    raw_animes = cleaner.load_raw_data("data/raw/raw_anime.json")
 
     valid_animes = cleaner.filter_valid_anime(raw_animes)
 
     print("\nPreparing data for embedding...")
     df = cleaner.prepare_for_embedding(valid_animes)
 
-    cleaner.save_processed_data(df)
+    cleaner.save_processed_data(
+        df, filepath="data/processed/anime_clean.csv")
 
     print("\nSample searchable text:")
     print(df.iloc[0]["searchable_text"][:500])

src/data_ingestion/fetch_anime.py

@@ -11,6 +11,8 @@ def convert_datetime(dt: str | None):
     return datetime.fromisoformat(dt)
 
 
+logging.basicConfig(level=logging.INFO,
+                    format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
 
@@ -22,55 +24,94 @@ class AnimeDataFetcher:
     def __init__(self):
         self.session = requests.Session()
 
-    def fetch_top_anime(self, limit: int = 100) -> list[dict]:
+    def fetch_bulk_anime(self, total_limit: int = 10000, filename: str = "raw_anime.json"):
         """
-        Fetches top 'limit' animes from MyAnimeLists
-        Args:
-            limit: Number of anime to fetch(max ~500 with pagination)
-        Returns:
-            List of anime dictionaries
+        Fetches anime in bulk with Resume and Retry capabilities.
         """
-
+        filepath = f"data/raw/{filename}"
         all_animes = []
-        page = 1
-        per_page = 25
-
-        while len(all_animes) < limit:
-            try:
-                response = self.session.get(
-                    f"{self.BASE_URL}top/anime",
-                    params={"page": page, "limit": per_page}
-                )
-                response.raise_for_status()
-                logger.info(
-                    f"Response received successfully: {response.status_code} !!")
-
-                data = response.json()
-                anime_list = data.get("data", [])
-                if not anime_list:
-                    break
-
-                all_animes.extend(anime_list)
-                logger.info(
-                    f"Fetched page {page}: {len(anime_list)} animes. Total: {len(all_animes)}")
-
-                page += 1
-                time.sleep(1.5)
-
-            except Exception as e:
-                logger.error(f"Error fetching page {page}: {e}")
+
+        # --- RESUME LOGIC ---
+        try:
+            with open(filepath, "r", encoding="utf-8") as f:
+                all_animes = json.load(f)
+            logger.info(
+                f"Found existing data. Resuming from record {len(all_animes)}.")
+        except (FileNotFoundError, json.JSONDecodeError):
+            logger.info("No existing data found. Starting fresh.")
+
+        # Calculate the next page to fetch (25 items per page)
+        page = (len(all_animes) // 25) + 1
+        max_retries = 5
+
+        while len(all_animes) < total_limit:
+            retries = 0
+            success = False
+
+            while retries < max_retries and not success:
+                try:
+                    logger.info(
+                        f"🚀 Fetching page {page} (Progress: {len(all_animes)}/{total_limit})...")
+                    response = self.session.get(
+                        f"{self.BASE_URL}anime",
+                        params={
+                            "page": page,
+                            "limit": 25,
+                            "order_by": "popularity",
+                            "sort": "asc"
+                        },
+                        timeout=30
+                    )
+
+                    if response.status_code == 429:
+                        wait = 60 + (retries * 30)  # Increasing wait time
+                        logger.warning(f"⚠️ Rate limit! Sleeping {wait}s...")
+                        time.sleep(wait)
+                        retries += 1
+                        continue
+
+                    response.raise_for_status()
+                    data = response.json()
+                    anime_list = data.get("data", [])
+
+                    if not anime_list:
+                        logger.info("🏁 No more anime found in API.")
+                        return all_animes
+
+                    all_animes.extend(anime_list)
+
+                    # --- AUTO-SAVE EVERY PAGE ---
+                    # In production, we save often so we never lose more than 1 page of work
+                    self.save_raw_data(all_animes, filename=filename)
+
+                    success = True
+                    page += 1
+                    time.sleep(1.2)  # Polite delay
+
+                except Exception as e:
+                    retries += 1
+                    logger.error(
+                        f"❌ Error on page {page}: {e}. Retry {retries}/{max_retries}")
+                    time.sleep(10 * retries)
+
+            if not success:
+                logger.critical(
+                    f"🛑 Giving up on page {page}. Run script again later to resume.")
                 break
 
-        return all_animes[:limit]
+        return all_animes[:total_limit]
 
     def extract_relevant_fields(self, anime: dict) -> dict:
         """Extract only fields we need for RAG"""
         return {
             "mal_id": anime.get("mal_id"),
+            "url": anime.get("url", ""),
+            "images": anime.get("images", {}),
             "title": anime.get("title"),
             "title_english": anime.get("title_english"),
             "synopsis": anime.get("synopsis"),
             "genres": [g["name"] for g in anime.get("genres", [])],
+            "studios": [s["name"] for s in anime.get("studios", [])],
             "themes": [t["name"] for t in anime.get("themes", [])],
             "demographics": [d["name"] for d in anime.get("demographics", [])],
             "type": anime.get("type"),
@@ -98,12 +139,13 @@ class AnimeDataFetcher:
 if __name__ == "__main__":
     fetcher = AnimeDataFetcher()
 
-    logger.info("Fetching top anime from MyAnimeList...")
-    raw_anime = fetcher.fetch_top_anime(limit=1000)
+    logger.info("Fetching top 10000 anime from MyAnimeList...")
+    raw_anime = fetcher.fetch_bulk_anime(total_limit=10000)
 
     processed_anime = [fetcher.extract_relevant_fields(a) for a in raw_anime]
 
-    fetcher.save_raw_data(processed_anime)
+    fetcher.save_raw_data(processed_anime,
+                          filename="raw_anime.json")
 
     print("\nSample anime: ")
     print(json.dumps(processed_anime[0], indent=2, ensure_ascii=False))

src/data_ingestion/load_to_neo4j.py

@@ -1,24 +0,0 @@
-from neo4j import GraphDatabase
-import pandas as pd
-from src.utils.neo4j_client import Neo4j_Client
-
-class AnimeGraphLoader:
-    """Loads anime into Neo4j Graph"""
-
-    def __init__(self):
-        self.client = Neo4j_Client()
-
-    def clear_database(self):
-        """Delete all nodes (use carefully !)"""
-        query = "MATCH (n) DETACH DELETE n"
-        self.client.run_query(query)
-    
-    def create_constraints(self):
-        """Create uniqueness constraints for performance"""
-        queries = [
-            "CREATE CONSTRAINT anime_id IF NOT EXISTS FOR (a: Anime) REQUIRE a.mal_id IS UNIQUE",
-            "CREATE CONSTRAINT genre_name IF NOT EXISTS FOR (g:Genre) REQUIRE g.name IS UNIQUE",
-            "CREATE CONSTRAINT theme_name IF NOT EXISTS FOR (t:Theme) REQUIRE t.name IS UNIQUE",
-            "CREATE CONSTRAINT demographic_name IF NOT EXISTS FOR (d:Demographic) REQUIRE d.name IS UNIQUE",
-            "CREAE CONSTRAINT content_type IF NOT EXISTS FOR (t: Type) REQUIRE t.type IS UNIUE"
-        ]

src/data_ingestion/load_to_postgres.py

@@ -0,0 +1,53 @@
+import json
+import logging
+from sqlmodel import Session, select
+from src.database.session import engine
+from src.database.models import Animes
+from src.database import init_db
+
+init_db()
+
+logging.basicConfig(level=logging.WARNING)
+logger = logging.getLogger(__name__)
+
+
+def load_json_data(filepath: str = "data/processed/anime_clean.json") -> list[dict]:
+    """Reads the cleaned JSON file."""
+    with open(filepath, 'r', encoding='utf-8') as f:
+        return json.load(f)
+
+
+def insert_animes_to_db(anime_list: list[dict]):
+    """
+    Inject a list of anime dictionaries into PostgreSQL safely
+    """
+    inserted_count = 0
+    skipped_count = 0
+    with Session(engine) as session:
+        for data in anime_list:
+            try:
+                existing = session.exec(
+                    select(Animes).where(Animes.mal_id == data["mal_id"])
+                ).first()
+
+                if not existing:
+                    new_anime = Animes(**data)
+                    session.add(new_anime)
+                    inserted_count += 1
+                    print(f"Inserted_count: {inserted_count}/{len(anime_list)} animes")
+                else:
+                    skipped_count += 1
+
+            except Exception as e:
+                logger.error(
+                    f"Error processing anime ID: {data.get("mal_id")}: {e}")
+
+        session.commit()
+    logger.info(
+        f"Injection complete! Inserted: {inserted_count} | Skipped (Duplicates): {skipped_count}")
+
+
+if __name__ == "__main__":
+    anime_data = load_json_data(filepath="data/processed/anime_clean.json")
+
+    insert_animes_to_db(anime_data)

src/database/__init__.py

@@ -0,0 +1,17 @@
+import logging
+from sqlmodel import SQLModel
+from src.database.session import engine
+from src.database.models import Animes, User
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def init_db():
+    """Connects to PostgreSQL DB and creates all tables defined in SQLModel classes"""
+    logger.info("Started table creation....")
+    try:
+        SQLModel.metadata.create_all(engine)
+        logger.info("Database Tables created successfully")
+    except Exception as e:
+        logger.error(f"Failed to create tables: {e}")

src/database/models.py

@@ -0,0 +1,56 @@
+from sqlmodel import SQLModel, Field, TIMESTAMP, text
+from sqlalchemy import Column
+from sqlalchemy.dialects.postgresql import JSONB
+from datetime import datetime, timezone
+
+
+class Animes(SQLModel, table=True):
+    id: int | None = Field(default=None, primary_key=True)
+
+    mal_id: int = Field(index=True, unique=True)
+    url: str | None = Field(default=None)
+
+    title: str = Field(index=True)
+    title_english: str | None = Field(default=None)
+    searchable_text: str | None = Field(default=None)
+
+    synopsis: str | None = Field(default=None)
+
+    # Store nested JSON
+    images: dict = Field(sa_column=Column(JSONB))
+
+    # Arrays → Postgres ARRAY or JSON
+    genres: list[str] = Field(sa_column=Column(JSONB))
+    studios: list[str] = Field(sa_column=Column(JSONB))
+    themes: list[str] = Field(sa_column=Column(JSONB))
+    demographics: list[str] = Field(sa_column=Column(JSONB))
+
+    type: str | None = None
+    episodes: int | None = None
+
+    score: float | None = Field(default=None, index=True)
+    scored_by: int | None = None
+
+    rank: int | None = Field(default=None, index=True)
+    popularity: int | None = None
+
+    year: int | None = Field(default=None, index=True)
+    season: str | None = None
+
+    rating: str | None = None
+
+    aired_from: str | None = None
+    aired_to: str | None = None
+
+    favorites: int | None = None
+
+
+class User(SQLModel, table=True):
+    id: int | None = Field(default=None, primary_key=True)
+    email: str = Field(unique=True, index=True)
+    hashed_password: str
+
+    created_at: datetime | None = Field(sa_column=Column(TIMESTAMP(timezone=True),
+                                                         nullable=True,
+                                                         server_default=text("NOW()")))
+    is_active: bool = Field(default=True)

src/database/session.py

@@ -0,0 +1,5 @@
+from sqlmodel import create_engine
+import os
+from config import settings
+
+engine = create_engine(settings.database_url, echo=True)

src/utils/neo4j_client.py

@@ -1,49 +0,0 @@
-from dotenv import load_dotenv
-from neo4j import GraphDatabase
-
-from config import settings
-
-
-class Neo4j_Client:
-    """Neo4j database client"""
-
-    def __init__(self):
-        uri = settings.neo4j_uri
-        user = settings.neo4j_username
-        password = settings.neo4j_password
-        try:
-            self.driver = GraphDatabase.driver(
-                uri, auth=(user, password))  # type: ignore
-            self.driver.verify_connectivity()
-
-        except Exception as e:
-            raise e
-
-    def close(self):
-        self.driver.close()
-
-    def verify_connectivity(self):
-        """Test connection"""
-        with self.driver.session() as session:
-            result = session.run("RETURN 'Connection Successful !' AS message")
-            record = result.single()
-            if record['message']: #type: ignore
-                return record["message"]  # type: ignore
-            else:
-                return "Connection failed"
-
-    def run_query(self, query, parameters=None):
-        with self.driver.session() as session:
-            result = session.run(query, parameters or {})
-            return [record.data() for record in result]
-
-
-if __name__ == '__main__':
-    client = Neo4j_Client()
-
-    message = client.verify_connectivity()
-    print(f"Message: {message}")
-
-    result = client.run_query("RETURN 1 + 1 AS result")
-    print(f"Test query result: {result}")
-    client.close()

uv.lock

@@ -32,11 +32,12 @@ dependencies = [
     { name = "gradio" },
     { name = "langchain" },
     { name = "langchain-groq" },
-    { name = "neo4j" },
     { name = "pandas" },
+    { name = "psycopg2-binary" },
     { name = "python-dotenv" },
     { name = "requests" },
     { name = "sentence-transformers" },
+    { name = "sqlmodel" },
     { name = "torch", version = "2.9.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
     { name = "torch", version = "2.9.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
     { name = "torchvision", version = "0.24.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux') or sys_platform == 'darwin'" },
@@ -58,11 +59,12 @@ requires-dist = [
     { name = "gradio", specifier = ">=6.2.0" },
     { name = "langchain", specifier = ">=1.1.3" },
     { name = "langchain-groq", specifier = ">=1.1.1" },
-    { name = "neo4j", specifier = ">=6.1.0" },
     { name = "pandas", specifier = ">=2.3.3" },
+    { name = "psycopg2-binary", specifier = ">=2.9.11" },
     { name = "python-dotenv", specifier = ">=1.2.1" },
     { name = "requests", specifier = ">=2.32.5" },
     { name = "sentence-transformers", specifier = ">=5.2.0" },
+    { name = "sqlmodel", specifier = ">=0.0.37" },
     { name = "torch", specifier = ">=2.9.1", index = "https://download.pytorch.org/whl/cpu" },
     { name = "torchvision", specifier = ">=0.24.1", index = "https://download.pytorch.org/whl/cpu" },
     { name = "uvicorn", specifier = ">=0.38.0" },
@@ -950,6 +952,38 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a7/a2/a3497afea984202f481de3464b3bfbcb3de1cd83cbbf3714933d40dd7106/gradio_client-2.0.2-py3-none-any.whl", hash = "sha256:46a7f63eaa7758fe2e38be7f78f26a1fff48a7b526ebdd87141b050e08556622", size = 55566, upload-time = "2025-12-19T18:29:47.508Z" },
 ]
 
+[[package]]
+name = "greenlet"
+version = "3.3.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a3/51/1664f6b78fc6ebbd98019a1fd730e83fa78f2db7058f72b1463d3612b8db/greenlet-3.3.2.tar.gz", hash = "sha256:2eaf067fc6d886931c7962e8c6bede15d2f01965560f3359b27c80bde2d151f2", size = 188267, upload-time = "2026-02-20T20:54:15.531Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f3/47/16400cb42d18d7a6bb46f0626852c1718612e35dcb0dffa16bbaffdf5dd2/greenlet-3.3.2-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:c56692189a7d1c7606cb794be0a8381470d95c57ce5be03fb3d0ef57c7853b86", size = 278890, upload-time = "2026-02-20T20:19:39.263Z" },
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