Implement Nodes to compute text embeddings

- Implements Dockerized Version of Scraper
- Atomized tags and categories columns

.gitignore

@@ -1,3 +1,5 @@
 data/
 venv/
+experiment/
 .DS_STORE
+.env

Dockerfile

@@ -1,15 +0,0 @@
-FROM python:slim
-
-RUN mkdir /app
-RUN mkdir /data
-
-WORKDIR /app
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-RUN apt update -y
-RUN apt install -y cron
-COPY crontab .
-RUN crontab crontab
-
-COPY main.py .

Makefile

@@ -1,2 +1,12 @@
-build:
-	docker build -t knack-scraper .
+volume:
+	docker volume create knack_data
+
+stop:
+	docker stop knack-scraper || true
+	docker rm knack-scraper || true
+
+up:
+	docker compose up -d
+
+down:
+	docker compose down

README.md

@@ -0,0 +1,18 @@
+Knack-Scraper does exacly what its name suggests it does.
+Knack-Scraper scrapes knack.news and writes to an sqlite
+database for later usage.
+
+## Example for .env
+
+```
+NUM_THREADS=8
+NUM_SCRAPES=100
+DATABASE_LOCATION='./data/knack.sqlite'
+```
+
+## Run once
+
+```
+python main.py
+```
+

crontab

@@ -1 +0,0 @@
-5 4 * * * python /app/main.py

docker-compose.yml

@@ -0,0 +1,43 @@
+services:
+  scraper:
+    build:
+      context: ./scrape
+      dockerfile: Dockerfile
+    image: knack-scraper
+    container_name: knack-scraper
+    env_file:
+      - scrape/.env
+    volumes:
+      - knack_data:/data
+    restart: unless-stopped
+
+  transform:
+    build:
+      context: ./transform
+      dockerfile: Dockerfile
+    image: knack-transform
+    container_name: knack-transform
+    env_file:
+      - transform/.env
+    volumes:
+      - knack_data:/data
+      - models:/models
+    restart: unless-stopped
+
+  sqlitebrowser:
+    image: lscr.io/linuxserver/sqlitebrowser:latest
+    container_name: sqlitebrowser
+    environment:
+      - PUID=1000
+      - PGID=1000
+      - TZ=Etc/UTC
+    volumes:
+      - knack_data:/data
+    ports:
+      - "3000:3000" # noVNC web UI
+      - "3001:3001" # VNC server
+    restart: unless-stopped
+
+volumes:
+  knack_data:
+  models:

main.py

@@ -1,167 +0,0 @@
-#! python3
-import locale
-import logging
-import os
-import sqlite3
-import sys
-import time
-from concurrent.futures import ThreadPoolExecutor
-from datetime import datetime
-
-import pandas as pd
-import requests
-import tqdm
-from bs4 import BeautifulSoup
-
-logger = logging.getLogger("knack-scraper")
-# ch = logging.StreamHandler()
-# formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
-# ch.setFormatter(formatter)
-# ch.setLevel(logging.INFO)
-# logger.addHandler(ch)
-
-
-def table_exists(tablename: str, con: sqlite3.Connection):
-    query = "SELECT 1 FROM sqlite_master WHERE type='table' AND name=? LIMIT 1"
-    return len(con.execute(query, [tablename]).fetchall()) > 0
-
-
-def download(id: int):
-    if id == 0:
-        return
-    base_url = "https://knack.news/"
-    url = f"{base_url}{id}"
-    res = requests.get(url)
-
-    # make sure we don't dos knack
-    time.sleep(2)
-
-    if not (200 <= res.status_code <= 300):
-        return
-
-    logger.info("Found promising page with id %d!", id)
-
-    content = res.content
-    soup = BeautifulSoup(content, "html.parser")
-    date_format = "%d. %B %Y"
-
-    # TODO FIXME: this fails inside the docker container
-    locale.setlocale(locale.LC_TIME, "de_DE")
-    pC = soup.find("div", {"class": "postContent"})
-
-    if pC is None:
-        # not a normal post
-        logger.info(
-            "Page with id %d does not have a .pageContent-div. Skipping for now.", id
-        )
-        return
-
-    # every post has these fields
-    title = pC.find("h3", {"class": "postTitle"}).text
-    postText = pC.find("div", {"class": "postText"})
-
-    # these fields are possible but not required
-    # TODO: cleanup
-    try:
-        date_string = pC.find("span", {"class": "singledate"}).text
-        parsed_date = datetime.strptime(date_string, date_format)
-    except AttributeError:
-        parsed_date = None
-
-    try:
-        author = pC.find("span", {"class": "author"}).text
-    except AttributeError:
-        author = None
-
-    try:
-        category = pC.find("span", {"class": "categoryInfo"}).find_all()
-        category = [c.text for c in category]
-        category = ";".join(category)
-    except AttributeError:
-        category = None
-
-    try:
-        tags = [x.text for x in pC.find("div", {"class": "tagsInfo"}).find_all("a")]
-        tags = ";".join(tags)
-    except AttributeError:
-        tags = None
-
-    img = pC.find("img", {"class": "postImage"})
-    if img is not None:
-        img = img["src"]
-
-    res_dict = {
-        "id": id,
-        "title": title,
-        "author": author,
-        "date": parsed_date,
-        "category": category,
-        "url": url,
-        "img_link": img,
-        "tags": tags,
-        "text": postText.text,
-        "html": str(postText),
-        "scraped_at": datetime.now(),
-    }
-
-    return res_dict
-
-
-def run_downloads(min_id: int, max_id: int, num_threads: int = 8):
-    res = []
-
-    logger.info(
-        "Started parallel scrape of posts from id %d to id %d using %d threads.",
-        min_id,
-        max_id - 1,
-        num_threads,
-    )
-    with ThreadPoolExecutor(max_workers=num_threads) as executor:
-        # Use a list comprehension to create a list of futures
-        futures = [executor.submit(download, i) for i in range(min_id, max_id)]
-
-        for future in tqdm.tqdm(
-            futures, total=max_id - min_id
-        ):  # tqdm to track progress
-            post = future.result()
-            if post is not None:
-                res.append(post)
-
-    # sqlite can't handle lists so let's convert them to a single row csv
-    # TODO: make sure our database is properly normalized
-    df = pd.DataFrame(res)
-
-    return df
-
-
-def main():
-    num_threads = int(os.environ.get("NUM_THREADS", 8))
-    n_scrapes = int(os.environ.get("NUM_SCRAPES", 100))
-    database_location = os.environ.get("DATABASE_LOCATION", "/data/knack.sqlite")
-
-    con = sqlite3.connect(database_location)
-    with con:
-        post_table_exists = table_exists("posts", con)
-
-        if post_table_exists:
-            logger.info("found posts retrieved earlier")
-            # retrieve max post id from db so
-            # we can skip retrieving known posts
-            max_id_in_db = con.execute("SELECT MAX(id) FROM posts").fetchone()[0]
-            logger.info("Got max id %d!", max_id_in_db)
-        else:
-            logger.info("no posts scraped so far - starting from 0")
-            # retrieve from 0 onwards
-            max_id_in_db = -1
-
-    con = sqlite3.connect(database_location)
-    df = run_downloads(
-        min_id=max_id_in_db + 1,
-        max_id=max_id_in_db + n_scrapes,
-        num_threads=num_threads,
-    )
-    df.to_sql("posts", con, if_exists="append")
-
-
-if __name__ == "__main__":
-    main()

requirements.txt

@@ -1,14 +0,0 @@
-beautifulsoup4==4.12.2
-certifi==2023.7.22
-charset-normalizer==3.3.0
-idna==3.4
-numpy==1.26.1
-pandas==2.1.1
-python-dateutil==2.8.2
-pytz==2023.3.post1
-requests==2.31.0
-six==1.16.0
-soupsieve==2.5
-tqdm==4.66.1
-tzdata==2023.3
-urllib3==2.0.7

scrape/Dockerfile

@@ -0,0 +1,29 @@
+FROM python:slim
+
+RUN mkdir /app
+RUN mkdir /data
+
+#COPY /data/knack.sqlite /data
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY .env .
+
+RUN apt update -y
+RUN apt install -y cron locales
+
+COPY main.py .
+
+ENV PYTHONUNBUFFERED=1
+ENV LANG=de_DE.UTF-8
+ENV LC_ALL=de_DE.UTF-8
+
+# Create cron job that runs every 15 minutes with environment variables
+RUN echo "5 4 * * * cd /app && /usr/local/bin/python main.py >> /proc/1/fd/1 2>&1" > /etc/cron.d/knack-scraper
+RUN chmod 0644 /etc/cron.d/knack-scraper
+RUN crontab /etc/cron.d/knack-scraper
+
+# Start cron in foreground
+CMD ["cron", "-f"]

scrape/main.py

@@ -0,0 +1,262 @@
+#! python3
+import logging
+import os
+import sqlite3
+import time
+from concurrent.futures import ThreadPoolExecutor
+from datetime import datetime
+import sys
+
+from dotenv import load_dotenv
+import pandas as pd
+import requests
+from bs4 import BeautifulSoup
+
+load_dotenv()
+
+if (os.environ.get('LOGGING_LEVEL', 'INFO') == 'INFO'):
+    logging_level = logging.INFO
+else:
+    logging_level = logging.DEBUG
+
+logging.basicConfig(
+    level=logging_level,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler("app.log"),
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+logger = logging.getLogger("knack-scraper")
+
+def table_exists(tablename: str, con: sqlite3.Connection):
+    query = "SELECT 1 FROM sqlite_master WHERE type='table' AND name=? LIMIT 1"
+    return len(con.execute(query, [tablename]).fetchall()) > 0
+
+
+def split_semicolon_list(value: str):
+    if pd.isna(value):
+        return []
+    return [item.strip() for item in str(value).split(';') if item.strip()]
+
+
+def build_dimension_and_mapping(postdf: pd.DataFrame, field_name: str, dim_col: str):
+    """Extract unique dimension values and post-to-dimension mappings from a column."""
+    if postdf.empty or field_name not in postdf.columns:
+        return None, None
+
+    values = set()
+    mapping_rows = []
+
+    for post_id, raw in zip(postdf['id'], postdf[field_name]):
+        items = split_semicolon_list(raw)
+        for item in items:
+            values.add(item)
+            mapping_rows.append({'post_id': post_id, dim_col: item})
+
+    if not values:
+        return None, None
+
+    dim_df = pd.DataFrame({
+        'id': range(len(values)),
+        dim_col: sorted(values),
+    })
+    map_df = pd.DataFrame(mapping_rows)
+    return dim_df, map_df
+
+
+def store_dimension_and_mapping(
+    con: sqlite3.Connection,
+    dim_df: pd.DataFrame | None,
+    map_df: pd.DataFrame | None,
+    table_name: str,
+    dim_col: str,
+    mapping_table: str,
+    mapping_id_col: str,
+):
+    """Persist a dimension table and its mapping table, merging with existing values."""
+    if dim_df is None or dim_df.empty:
+        return
+
+    if table_exists(table_name, con):
+        existing = pd.read_sql(f"SELECT id, {dim_col} FROM {table_name}", con)
+        merged = pd.concat([existing, dim_df], ignore_index=True)
+        merged = merged.drop_duplicates(subset=[dim_col], keep='first').reset_index(drop=True)
+        merged['id'] = range(len(merged))
+    else:
+        merged = dim_df.copy()
+
+    # Replace table with merged content
+    merged.to_sql(table_name, con, if_exists="replace", index=False)
+
+    if map_df is None or map_df.empty:
+        return
+
+    value_to_id = dict(zip(merged[dim_col], merged['id']))
+    map_df = map_df.copy()
+    map_df[mapping_id_col] = map_df[dim_col].map(value_to_id)
+    map_df = map_df[['post_id', mapping_id_col]].dropna()
+    map_df.to_sql(mapping_table, con, if_exists="append", index=False)
+
+
+def download(id: int):
+    if id == 0:
+        return
+    base_url = "https://knack.news/"
+    url = f"{base_url}{id}"
+    res = requests.get(url)
+
+    # make sure we don't dos knack
+    time.sleep(2)
+
+    if not (200 <= res.status_code <= 300):
+        return
+
+    logger.debug("Found promising page with id %d!", id)
+
+    content = res.content
+    soup = BeautifulSoup(content, "html.parser")
+
+    pC = soup.find("div", {"class": "postContent"})
+
+    if pC is None:
+        # not a normal post
+        logger.debug(
+            "Page with id %d does not have a .pageContent-div. Skipping for now.", id
+        )
+        return
+
+    # every post has these fields
+    title = pC.find("h3", {"class": "postTitle"}).text
+    postText = pC.find("div", {"class": "postText"})
+
+    # these fields are possible but not required
+    # TODO: cleanup
+    try:
+        date_parts = pC.find("span", {"class": "singledate"}).text.split(' ')
+        day = int(date_parts[0][:-1])
+        months = {'Januar': 1, 'Februar': 2, 'März': 3, 'April': 4, 'Mai': 5, 'Juni': 6, 'Juli': 7, 'August': 8, 'September': 9, 'Oktober': 10, 'November': 11, 'Dezember': 12}
+        month = months[date_parts[1]]
+        year = int(date_parts[2])
+        parsed_date = datetime(year, month, day)
+    except Exception:
+        parsed_date = None
+
+    try:
+        author = pC.find("span", {"class": "author"}).text
+    except AttributeError:
+        author = None
+
+    try:
+        category = pC.find("span", {"class": "categoryInfo"}).find_all()
+        category = [c.text for c in category if c.text != 'Alle Artikel']
+        category = ";".join(category)
+    except AttributeError:
+        category = None
+
+    try:
+        tags = [x.text for x in pC.find("div", {"class": "tagsInfo"}).find_all("a")]
+        tags = ";".join(tags)
+    except AttributeError:
+        tags = None
+
+    img = pC.find("img", {"class": "postImage"})
+    if img is not None:
+        img = img["src"]
+
+    res_dict = {
+        "id": id,
+        "title": title,
+        "author": author,
+        "date": parsed_date,
+        "category": category,
+        "url": url,
+        "img_link": img,
+        "tags": tags,
+        "text": postText.text,
+        "html": str(postText),
+        "scraped_at": datetime.now(),
+        "is_cleaned": False
+    }
+
+    return res_dict
+
+
+def run_downloads(min_id: int, max_id: int, num_threads: int = 8):
+    res = []
+
+    logger.info(
+        "Started parallel scrape of posts from id %d to id %d using %d threads.",
+        min_id,
+        max_id - 1,
+        num_threads,
+    )
+    with ThreadPoolExecutor(max_workers=num_threads) as executor:
+        # Use a list comprehension to create a list of futures
+        futures = [executor.submit(download, i) for i in range(min_id, max_id)]
+
+        for future in futures:
+            post = future.result()
+            if post is not None:
+                res.append(post)
+
+    postdf = pd.DataFrame(res)
+    return postdf
+
+
+def main():
+    num_threads = int(os.environ.get("NUM_THREADS", 8))
+    n_scrapes = int(os.environ.get("NUM_SCRAPES", 100))
+    database_location = os.environ.get("DATABASE_LOCATION", "../data/knack.sqlite")
+
+    logger.debug(f"Started Knack Scraper: \nNUM_THREADS: {num_threads}\nN_SCRAPES: {n_scrapes}\nDATABASE_LOCATION: {database_location}")
+
+    con = sqlite3.connect(database_location)
+    with con:
+        if table_exists("posts", con):
+            logger.info("found posts retrieved earlier")
+            max_id_in_db = con.execute("SELECT MAX(id) FROM posts").fetchone()[0]
+            logger.info("Got max id %d!", max_id_in_db)
+        else:
+            logger.info("no posts scraped so far - starting from 0")
+            max_id_in_db = -1
+
+        postdf = run_downloads(
+            min_id=max_id_in_db + 1,
+            max_id=max_id_in_db + n_scrapes,
+            num_threads=num_threads,
+        )
+
+        # Drop category and tags columns as they're stored in separate tables
+        postdf = postdf.drop(columns=['category', 'tags'])
+        postdf.to_sql("posts", con, if_exists="append", index=False)
+
+        # Tags
+        tag_dim, tag_map = build_dimension_and_mapping(postdf, 'tags', 'tag')
+        store_dimension_and_mapping(
+            con,
+            tag_dim,
+            tag_map,
+            table_name="tags",
+            dim_col="tag",
+            mapping_table="posttags",
+            mapping_id_col="tag_id",
+        )
+
+        # Categories
+        category_dim, category_map = build_dimension_and_mapping(postdf, 'category', 'category')
+        store_dimension_and_mapping(
+            con,
+            category_dim,
+            category_map,
+            table_name="categories",
+            dim_col="category",
+            mapping_table="postcategories",
+            mapping_id_col="category_id",
+        )
+
+        logger.info(f"scraped new entries. number of new posts: {len(postdf.index)}")
+
+
+if __name__ == "__main__":
+    main()

scrape/requirements.txt

@@ -0,0 +1,4 @@
+pandas
+requests
+bs4
+dotenv

transform/.env.example

@@ -0,0 +1,4 @@
+LOGGING_LEVEL=INFO
+DB_PATH=/data/knack.sqlite
+MAX_CLEANED_POSTS=1000
+COMPUTE_DEVICE=mps

transform/Dockerfile

@@ -0,0 +1,51 @@
+FROM python:3.12-slim
+
+RUN mkdir -p /app /data /models
+
+# Install build dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    g++ \
+    gfortran \
+    libopenblas-dev \
+    liblapack-dev \
+    pkg-config \
+    curl \
+    jq \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV GLINER_MODEL_ID=urchade/gliner_multi-v2.1
+ENV GLINER_MODEL_PATH=/models/gliner_multi-v2.1
+
+ENV MINILM_MODEL_ID=sentence-transformers/all-MiniLM-L6-v2
+ENV MINILM_MODEL_PATH=/models/all-MiniLM-L6-v2
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY .env .
+
+RUN apt update -y
+RUN apt install -y cron locales
+
+# Ensure GLiNER helper scripts are available
+COPY ensure_gliner_model.sh /usr/local/bin/ensure_gliner_model.sh
+# Ensure MiniLM helper scripts are available
+COPY ensure_minilm_model.sh /usr/local/bin/ensure_minilm_model.sh
+COPY entrypoint.sh /usr/local/bin/entrypoint.sh
+RUN chmod +x /usr/local/bin/ensure_gliner_model.sh /usr/local/bin/ensure_minilm_model.sh /usr/local/bin/entrypoint.sh
+
+COPY *.py .
+
+# Create cron job that runs every weekend (Sunday at 3 AM) 0 3 * * 0
+# Testing every 30 Minutes */30 * * * *
+RUN echo "*/30 * * * * cd /app && /usr/local/bin/python main.py >> /proc/1/fd/1 2>&1" > /etc/cron.d/knack-transform
+RUN chmod 0644 /etc/cron.d/knack-transform
+RUN crontab /etc/cron.d/knack-transform
+
+# Persist models between container runs
+VOLUME /models
+
+CMD ["/usr/local/bin/entrypoint.sh"]
+#CMD ["python", "main.py"]

transform/README.md

@@ -0,0 +1,67 @@
+# Knack Transform
+
+Data transformation pipeline for the Knack scraper project.
+
+## Overview
+
+This folder contains the transformation logic that processes data from the SQLite database. It runs on a scheduled basis (every weekend) via cron.
+
+The pipeline supports **parallel execution** of independent transform nodes, allowing you to leverage multi-core processors for faster data transformation.
+
+## Structure
+
+- `base.py` - Abstract base class for transform nodes
+- `pipeline.py` - Parallel pipeline orchestration system
+- `main.py` - Main entry point and pipeline execution
+- `author_node.py` - NER-based author classification node
+- `example_node.py` - Template for creating new nodes
+- `Dockerfile` - Docker image configuration with cron setup
+- `requirements.txt` - Python dependencies
+
+## Transform Nodes
+
+Transform nodes inherit from `TransformNode` and implement the `run` method:
+
+```python
+from base import TransformNode, TransformContext
+import sqlite3
+
+class MyTransform(TransformNode):
+    def run(self, con: sqlite3.Connection, context: TransformContext) -> TransformContext:
+        df = context.get_dataframe()
+        
+        # Transform logic here
+        transformed_df = df.copy()
+        # ... your transformations ...
+        
+        # Optionally write back to database
+        transformed_df.to_sql("my_table", con, if_exists="replace", index=False)
+        
+        return TransformContext(transformed_df)
+```
+
+## Configuration
+
+Copy `.env.example` to `.env` and configure:
+
+- `LOGGING_LEVEL` - Log level (INFO or DEBUG)
+- `DB_PATH` - Path to SQLite database
+
+## Running
+
+### With Docker
+
+```bash
+docker build -t knack-transform .
+docker run -v $(pwd)/data:/data knack-transform
+```
+
+### Locally
+
+```bash
+python main.py
+```
+
+## Cron Schedule
+
+The Docker container runs the transform pipeline every Sunday at 3 AM.

transform/author_node.py

@@ -0,0 +1,420 @@
+"""Author classification transform node using NER."""
+import os
+import sqlite3
+import pandas as pd
+import logging
+import fuzzysearch
+from concurrent.futures import ThreadPoolExecutor
+
+from pipeline import TransformContext
+from transform_node import TransformNode
+
+logger = logging.getLogger("knack-transform")
+
+try:
+    from gliner import GLiNER
+    import torch
+    GLINER_AVAILABLE = True
+except ImportError:
+    GLINER_AVAILABLE = False
+    logging.warning("GLiNER not available. Install with: pip install gliner")
+
+class NerAuthorNode(TransformNode):
+    """Transform node that extracts and classifies authors using NER.
+    
+    Creates two tables:
+    - authors: stores unique authors with their type (Person, Organisation, etc.)
+    - post_authors: maps posts to their authors
+    """
+    
+    def __init__(self, model_name: str = "urchade/gliner_multi-v2.1", 
+                 model_path: str = None,
+                 threshold: float = 0.5, 
+                 max_workers: int = 64,
+                 device: str = "cpu"):
+        """Initialize the AuthorNode.
+        
+        Args:
+            model_name: GLiNER model to use
+            model_path: Optional local path to a downloaded GLiNER model
+            threshold: Confidence threshold for entity predictions
+            max_workers: Number of parallel workers for prediction
+            device: Device to run model on ('cpu', 'cuda', 'mps')
+        """
+        self.model_name = model_name
+        self.model_path = model_path or os.environ.get('GLINER_MODEL_PATH')
+        self.threshold = threshold
+        self.max_workers = max_workers
+        self.device = device
+        self.model = None
+        self.labels = ["Person", "Organisation", "Email", "Newspaper", "NGO"]
+    
+    def _setup_model(self):
+        """Initialize the NER model."""
+        if not GLINER_AVAILABLE:
+            raise ImportError("GLiNER is required for AuthorNode. Install with: pip install gliner")
+        
+        model_source = None
+        if self.model_path:
+            if os.path.exists(self.model_path):
+                model_source = self.model_path
+                logger.info(f"Loading GLiNER model from local path: {self.model_path}")
+            else:
+                logger.warning(f"GLINER_MODEL_PATH '{self.model_path}' not found; falling back to hub model {self.model_name}")
+
+        if model_source is None:
+            model_source = self.model_name
+            logger.info(f"Loading GLiNER model from hub: {self.model_name}")
+        
+        if self.device == "cuda" and torch.cuda.is_available():
+            self.model = GLiNER.from_pretrained(
+                model_source, 
+                max_length=255
+            ).to('cuda', dtype=torch.float16)
+        elif self.device == "mps" and torch.backends.mps.is_available():
+            self.model = GLiNER.from_pretrained(
+                model_source, 
+                max_length=255
+            ).to('mps', dtype=torch.float16)
+        else:
+            self.model = GLiNER.from_pretrained(
+                model_source, 
+                max_length=255
+            )
+        
+        logger.info("Model loaded successfully")
+    
+    def _predict(self, text_data: dict):
+        """Predict entities for a single author text.
+        
+        Args:
+            text_data: Dict with 'author' and 'id' keys
+            
+        Returns:
+            Tuple of (predictions, post_id) or None
+        """
+        if text_data is None or text_data.get('author') is None:
+            return None
+        
+        predictions = self.model.predict_entities(
+            text_data['author'], 
+            self.labels, 
+            threshold=self.threshold
+        )
+        return predictions, text_data['id']
+    
+    def _classify_authors(self, posts_df: pd.DataFrame):
+        """Classify all authors in the posts dataframe.
+        
+        Args:
+            posts_df: DataFrame with 'id' and 'author' columns
+            
+        Returns:
+            List of dicts with 'text', 'label', 'id' keys
+        """
+        if self.model is None:
+            self._setup_model()
+        
+        # Prepare input data
+        authors_data = []
+        for idx, row in posts_df.iterrows():
+            if pd.notna(row['author']):
+                authors_data.append({
+                    'author': row['author'],
+                    'id': row['id']
+                })
+        
+        logger.info(f"Classifying {len(authors_data)} authors")
+        
+        results = []
+        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
+            futures = [executor.submit(self._predict, data) for data in authors_data]
+            
+            for future in futures:
+                result = future.result()
+                if result is not None:
+                    predictions, post_id = result
+                    for pred in predictions:
+                        results.append({
+                            'text': pred['text'],
+                            'label': pred['label'],
+                            'id': post_id
+                        })
+        
+        logger.info(f"Classification complete. Found {len(results)} author entities")
+        return results
+    
+    def _create_tables(self, con: sqlite3.Connection):
+        """Create authors and post_authors tables if they don't exist."""
+        logger.info("Creating authors tables")
+        
+        con.execute("""
+            CREATE TABLE IF NOT EXISTS authors (
+                id INTEGER PRIMARY KEY,
+                name TEXT,
+                type TEXT,
+                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+            )
+        """)
+        
+        con.execute("""
+            CREATE TABLE IF NOT EXISTS post_authors (
+                post_id INTEGER,
+                author_id INTEGER,
+                PRIMARY KEY (post_id, author_id),
+                FOREIGN KEY (post_id) REFERENCES posts(id),
+                FOREIGN KEY (author_id) REFERENCES authors(id)
+            )
+        """)
+        
+        con.commit()
+    
+    def _store_authors(self, con: sqlite3.Connection, results: list):
+        """Store classified authors and their mappings.
+        
+        Args:
+            con: Database connection
+            results: List of classification results
+        """
+        if not results:
+            logger.info("No authors to store")
+            return
+        
+        # Convert results to DataFrame
+        results_df = pd.DataFrame(results)
+        
+        # Get unique authors with their types
+        unique_authors = results_df[['text', 'label']].drop_duplicates()
+        unique_authors.columns = ['name', 'type']
+        
+        # Get existing authors
+        existing_authors = pd.read_sql("SELECT id, name FROM authors", con)
+        
+        # Find new authors to insert
+        if not existing_authors.empty:
+            new_authors = unique_authors[~unique_authors['name'].isin(existing_authors['name'])]
+        else:
+            new_authors = unique_authors
+        
+        if not new_authors.empty:
+            logger.info(f"Inserting {len(new_authors)} new authors")
+            new_authors.to_sql('authors', con, if_exists='append', index=False)
+        
+        # Get all authors with their IDs
+        all_authors = pd.read_sql("SELECT id, name FROM authors", con)
+        name_to_id = dict(zip(all_authors['name'], all_authors['id']))
+        
+        # Create post_authors mappings
+        mappings = []
+        for _, row in results_df.iterrows():
+            author_id = name_to_id.get(row['text'])
+            if author_id:
+                mappings.append({
+                    'post_id': row['id'],
+                    'author_id': author_id
+                })
+        
+        if mappings:
+            mappings_df = pd.DataFrame(mappings).drop_duplicates()
+            
+            # Clear existing mappings for these posts (optional, depends on your strategy)
+            # post_ids = tuple(mappings_df['post_id'].unique())
+            # con.execute(f"DELETE FROM post_authors WHERE post_id IN ({','.join('?' * len(post_ids))})", post_ids)
+            
+            logger.info(f"Creating {len(mappings_df)} post-author mappings")
+            mappings_df.to_sql('post_authors', con, if_exists='append', index=False)
+        
+        con.commit()
+        logger.info("Authors and mappings stored successfully")
+    
+    def run(self, con: sqlite3.Connection, context: TransformContext) -> TransformContext:
+        """Execute the author classification transformation.
+        
+        Args:
+            con: SQLite database connection
+            context: TransformContext containing posts dataframe
+            
+        Returns:
+            TransformContext with classified authors dataframe
+        """
+        logger.info("Starting AuthorNode transformation")
+        
+        posts_df = context.get_dataframe()
+        
+        # Ensure required columns exist
+        if 'author' not in posts_df.columns:
+            logger.warning("No 'author' column in dataframe. Skipping AuthorNode.")
+            return context
+        
+        # Create tables
+        self._create_tables(con)
+        
+        # Classify authors
+        results = self._classify_authors(posts_df)
+        
+        # Store results
+        self._store_authors(con, results)
+        
+        # Return context with results
+        logger.info("AuthorNode transformation complete")
+        
+        return TransformContext(posts_df)
+
+
+class FuzzyAuthorNode(TransformNode):
+    """FuzzyAuthorNode
+
+    This Node takes in data and rules of authornames that have been classified already
+    and uses those 'rule' to find more similar fields.
+    """
+    
+    def __init__(self, 
+                 max_l_dist: int = 1,):
+        """Initialize FuzzyAuthorNode.
+        
+        Args:
+            max_l_dist: The number of 'errors' that are allowed by the fuzzy search algorithm
+        """
+        self.max_l_dist = max_l_dist
+        logger.info(f"Initialized FuzzyAuthorNode with max_l_dist={max_l_dist}")
+    
+    def _process_data(self, con: sqlite3.Connection, df: pd.DataFrame) -> pd.DataFrame:
+        """Process the input dataframe.
+        
+        This is where your main transformation logic goes.
+        
+        Args:
+            con: Database connection
+            df: Input dataframe from context
+            
+        Returns:
+            Processed dataframe
+        """
+        logger.info(f"Processing {len(df)} rows")
+
+        # Retrieve all known authors from the authors table as 'rules'
+        authors_df = pd.read_sql("SELECT id, name FROM authors", con)
+        
+        if authors_df.empty:
+            logger.warning("No authors found in database for fuzzy matching")
+            return pd.DataFrame(columns=['post_id', 'author_id'])
+        
+        # Get existing post-author mappings to avoid duplicates
+        existing_mappings = pd.read_sql(
+            "SELECT post_id, author_id FROM post_authors", con
+        )
+        existing_post_ids = set(existing_mappings['post_id'].unique())
+        
+        logger.info(f"Found {len(authors_df)} known authors for fuzzy matching")
+        logger.info(f"Found {len(existing_post_ids)} posts with existing author mappings")
+    
+        # Filter to posts without author mappings and with non-null author field
+        if 'author' not in df.columns or 'id' not in df.columns:
+            logger.warning("Missing 'author' or 'id' column in input dataframe")
+            return pd.DataFrame(columns=['post_id', 'author_id'])
+        
+        posts_to_process = df[
+            (df['id'].notna()) & 
+            (df['author'].notna()) & 
+            (~df['id'].isin(existing_post_ids))
+        ]
+        
+        logger.info(f"Processing {len(posts_to_process)} posts for fuzzy matching")
+        
+        # Perform fuzzy matching
+        mappings = []
+        for _, post_row in posts_to_process.iterrows():
+            post_id = post_row['id']
+            post_author = str(post_row['author'])
+            
+            # Try to find matches against all known author names
+            for _, author_row in authors_df.iterrows():
+                author_id = author_row['id']
+                author_name = str(author_row['name'])
+                # for author names < than 2 characters I want a fault tolerance of 0!
+                l_dist = self.max_l_dist if len(author_name) > 2 else 0
+                
+                # Use fuzzysearch to find matches with allowed errors
+                matches = fuzzysearch.find_near_matches(
+                    author_name,
+                    post_author,
+                    max_l_dist=l_dist, 
+                )
+                
+                if matches:
+                    logger.debug(f"Found fuzzy match: '{author_name}' in '{post_author}' for post {post_id}")
+                    mappings.append({
+                        'post_id': post_id,
+                        'author_id': author_id
+                    })
+                    # Only take the first match per post to avoid multiple mappings
+                    break
+        
+        # Create result dataframe
+        result_df = pd.DataFrame(mappings, columns=['post_id', 'author_id']) if mappings else pd.DataFrame(columns=['post_id', 'author_id'])
+        
+        logger.info(f"Processing complete. Found {len(result_df)} fuzzy matches")
+        return result_df
+    
+    def _store_results(self, con: sqlite3.Connection, df: pd.DataFrame):
+        """Store results back to the database.
+        
+        Uses INSERT OR IGNORE to avoid inserting duplicates.
+        
+        Args:
+            con: Database connection
+            df: Processed dataframe to store
+        """
+        if df.empty:
+            logger.info("No results to store")
+            return
+        
+        logger.info(f"Storing {len(df)} results")
+        
+        # Use INSERT OR IGNORE to handle duplicates (respects PRIMARY KEY constraint)
+        cursor = con.cursor()
+        inserted_count = 0
+        
+        for _, row in df.iterrows():
+            cursor.execute(
+                "INSERT OR IGNORE INTO post_authors (post_id, author_id) VALUES (?, ?)",
+                (int(row['post_id']), int(row['author_id']))
+            )
+            if cursor.rowcount > 0:
+                inserted_count += 1
+        
+        con.commit()
+        logger.info(f"Results stored successfully. Inserted {inserted_count} new mappings, skipped {len(df) - inserted_count} duplicates")
+    
+    def run(self, con: sqlite3.Connection, context: TransformContext) -> TransformContext:
+        """Execute the transformation.
+        
+        This is the main entry point called by the pipeline.
+        
+        Args:
+            con: SQLite database connection
+            context: TransformContext containing input dataframe
+            
+        Returns:
+            TransformContext with processed dataframe
+        """
+        logger.info("Starting FuzzyAuthorNode transformation")
+        
+        # Get input dataframe from context
+        input_df = context.get_dataframe()
+        
+        # Validate input
+        if input_df.empty:
+            logger.warning("Empty dataframe provided to FuzzyAuthorNode")
+            return context
+        
+        # Process the data
+        result_df = self._process_data(con, input_df)
+        
+        # Store results
+        self._store_results(con, result_df)
+        
+        logger.info("FuzzyAuthorNode transformation complete")
+        
+        # Return new context with results
+        return TransformContext(input_df)

transform/embeddings_node.py

@@ -0,0 +1,445 @@
+"""Classes of Transformernodes that have to do with
+text processing.
+
+- TextEmbeddingNode calculates text embeddings
+- UmapNode calculates xy coordinates on those vector embeddings
+- SimilarityNode calculates top n similar posts based on those embeddings
+    using the spectral distance.
+"""
+from pipeline import TransformContext
+from transform_node import TransformNode
+import sqlite3
+import pandas as pd
+import logging
+import os
+import numpy as np
+
+logger = logging.getLogger("knack-transform")
+
+try: 
+    from sentence_transformers import SentenceTransformer
+    import torch
+    MINILM_AVAILABLE = True
+except ImportError:
+    MINILM_AVAILABLE = False
+    logging.warning("MiniLM not available. Install with pip!")
+
+try:
+    import umap
+    UMAP_AVAILABLE = True
+except ImportError:
+    UMAP_AVAILABLE = False
+    logging.warning("UMAP not available. Install with pip install umap-learn!")
+
+class TextEmbeddingNode(TransformNode):
+    """Calculates vector embeddings based on a dataframe
+    of posts.
+    """
+    def __init__(self, 
+                 model_name: str = "sentence-transformers/all-MiniLM-L6-v2",
+                 model_path: str = None,
+                 device: str = "cpu"):
+        """Initialize the ExampleNode.
+        
+        Args:
+            model_name: Name of the ML Model to calculate text embeddings
+            model_path: Optional local path to a downloaded embedding model
+            device: Device to use for computations ('cpu', 'cuda', 'mps')
+        """
+        self.model_name = model_name
+        self.model_path = model_path or os.environ.get('MINILM_MODEL_PATH')
+        self.device = device
+        self.model = None
+        logger.info(f"Initialized TextEmbeddingNode with model_name={model_name}, model_path={model_path}, device={device}")
+    
+    def _setup_model(self):
+        """Init the Text Embedding Model."""
+        if not MINILM_AVAILABLE:
+            raise ImportError("MiniLM is required for TextEmbeddingNode. Please install.")
+        
+        model_source = None
+        if self.model_path:
+            if os.path.exists(self.model_path):
+                model_source = self.model_path
+                logger.info(f"Loading MiniLM model from local path: {self.model_path}")
+            else:
+                logger.warning(f"MiniLM_MODEL_PATH '{self.model_path}' not found; Falling back to hub model {self.model_name}")
+
+        if model_source is None:
+            model_source = self.model_name
+            logger.info(f"Loading MiniLM model from the hub: {self.model_name}")
+
+        if self.device == "cuda" and torch.cuda.is_available():
+            self.model = SentenceTransformer(model_source).to('cuda', dtype=torch.float16)
+        elif self.device == "mps" and torch.backends.mps.is_available():
+            self.model = SentenceTransformer(model_source).to('mps', dtype=torch.float16)
+        else:
+            self.model = SentenceTransformer(model_source)
+
+    def _process_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Process the input dataframe.
+        
+        Calculates an embedding as a np.array. 
+        Also pickles that array to prepare it to
+        storage in the database.
+        
+        Args:
+            df: Input dataframe from context
+            
+        Returns:
+            Processed dataframe
+        """
+        logger.info(f"Processing {len(df)} rows")
+
+        if self.model is None:
+            self._setup_model()
+        
+        # Example: Add a new column based on existing data
+        result_df = df.copy()
+
+        df['embedding'] = df['text'].apply(lambda x: self.model.encode(x, convert_to_numpy=True))
+        
+        logger.info("Processing complete")
+        return result_df
+    
+    def _store_results(self, con: sqlite3.Connection, df: pd.DataFrame):
+        """Store results back to the database using batch updates."""
+        if df.empty:
+            logger.info("No results to store")
+            return
+        
+        logger.info(f"Storing {len(df)} results")
+        
+        # Convert numpy arrays to bytes for BLOB storage
+        # Use tobytes() to serialize numpy arrays efficiently
+        updates = [(row['embedding'].tobytes(), row['id']) for _, row in df.iterrows()]
+        con.executemany(
+            "UPDATE posts SET embedding = ? WHERE id = ?",
+            updates
+        )
+        
+        con.commit()
+        logger.info("Results stored successfully")
+    
+    def run(self, con: sqlite3.Connection, context: TransformContext) -> TransformContext:
+        """Execute the transformation.
+        
+        This is the main entry point called by the pipeline.
+        
+        Args:
+            con: SQLite database connection
+            context: TransformContext containing input dataframe
+            
+        Returns:
+            TransformContext with processed dataframe
+        """
+        logger.info("Starting TextEmbeddingNode transformation")
+        
+        # Get input dataframe from context
+        input_df = context.get_dataframe()
+        
+        # Validate input
+        if input_df.empty:
+            logger.warning("Empty dataframe provided to TextEmbeddingNdode")
+            return context
+        
+        if 'text' not in input_df.columns:
+            logger.warning("No 'text' column in context dataframe. Skipping TextEmbeddingNode")
+            return context
+
+        # Process the data
+        result_df = self._process_data(input_df)
+        
+        # Store results (optional)
+        self._store_results(con, result_df)
+        
+        logger.info("TextEmbeddingNode transformation complete")
+        
+        # Return new context with results
+        return TransformContext(result_df)
+
+
+class UmapNode(TransformNode):
+    """Calculates 2D coordinates from embeddings using UMAP dimensionality reduction.
+    
+    This node takes text embeddings and reduces them to 2D coordinates
+    for visualization purposes.
+    """
+    
+    def __init__(self, 
+                 n_neighbors: int = 15,
+                 min_dist: float = 0.1,
+                 n_components: int = 2,
+                 metric: str = "cosine",
+                 random_state: int = 42):
+        """Initialize the UmapNode.
+        
+        Args:
+            n_neighbors: Number of neighbors to consider for UMAP (default: 15)
+            min_dist: Minimum distance between points in low-dimensional space (default: 0.1)
+            n_components: Number of dimensions to reduce to (default: 2)
+            metric: Distance metric to use (default: 'cosine')
+            random_state: Random seed for reproducibility (default: 42)
+        """
+        self.n_neighbors = n_neighbors
+        self.min_dist = min_dist
+        self.n_components = n_components
+        self.metric = metric
+        self.random_state = random_state
+        self.reducer = None
+        logger.info(f"Initialized UmapNode with n_neighbors={n_neighbors}, min_dist={min_dist}, "
+                   f"n_components={n_components}, metric={metric}, random_state={random_state}")
+    
+    def _process_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Process the input dataframe.
+        
+        Retrieves embeddings from BLOB storage, converts them back to numpy arrays,
+        and applies UMAP dimensionality reduction to create 2D coordinates.
+        
+        Args:
+            df: Input dataframe from context
+            
+        Returns:
+            Processed dataframe with umap_x and umap_y columns
+        """
+        logger.info(f"Processing {len(df)} rows")
+        
+        if not UMAP_AVAILABLE:
+            raise ImportError("UMAP is required for UmapNode. Install with: pip install umap-learn")
+        
+        result_df = df.copy()
+        
+        # Convert BLOB embeddings back to numpy arrays
+        if 'embedding' not in result_df.columns:
+            logger.error("No 'embedding' column found in dataframe")
+            raise ValueError("Input dataframe must contain 'embedding' column")
+        
+        logger.info("Converting embeddings from BLOB to numpy arrays")
+        result_df['embedding'] = result_df['embedding'].apply(
+            lambda x: np.frombuffer(x, dtype=np.float32) if x is not None else None
+        )
+        
+        # Filter out rows with None embeddings
+        valid_rows = result_df['embedding'].notna()
+        if not valid_rows.any():
+            logger.error("No valid embeddings found in dataframe")
+            raise ValueError("No valid embeddings to process")
+        
+        logger.info(f"Found {valid_rows.sum()} valid embeddings out of {len(result_df)} rows")
+        
+        # Stack embeddings into a matrix
+        embeddings_matrix = np.vstack(result_df.loc[valid_rows, 'embedding'].values)
+        logger.info(f"Embeddings matrix shape: {embeddings_matrix.shape}")
+        
+        # Apply UMAP
+        logger.info("Fitting UMAP reducer...")
+        self.reducer = umap.UMAP(
+            n_neighbors=self.n_neighbors,
+            min_dist=self.min_dist,
+            n_components=self.n_components,
+            metric=self.metric,
+            random_state=self.random_state
+        )
+        
+        umap_coords = self.reducer.fit_transform(embeddings_matrix)
+        logger.info(f"UMAP transformation complete. Output shape: {umap_coords.shape}")
+        
+        # Add UMAP coordinates to dataframe
+        result_df.loc[valid_rows, 'umap_x'] = umap_coords[:, 0]
+        result_df.loc[valid_rows, 'umap_y'] = umap_coords[:, 1]
+        
+        # Fill NaN for invalid rows
+        result_df['umap_x'] = result_df['umap_x'].fillna(None)
+        result_df['umap_y'] = result_df['umap_y'].fillna(None)
+        
+        logger.info("Processing complete")
+        return result_df
+    
+    def _store_results(self, con: sqlite3.Connection, df: pd.DataFrame):
+        """Store UMAP coordinates back to the database.
+        
+        Args:
+            con: Database connection
+            df: Processed dataframe with umap_x and umap_y columns
+        """
+        if df.empty:
+            logger.info("No results to store")
+            return
+        
+        logger.info(f"Storing {len(df)} results")
+        
+        # Batch update UMAP coordinates
+        updates = [
+            (row['umap_x'], row['umap_y'], row['id']) 
+            for _, row in df.iterrows()
+            if pd.notna(row.get('umap_x')) and pd.notna(row.get('umap_y'))
+        ]
+        
+        if updates:
+            con.executemany(
+                "UPDATE posts SET umap_x = ?, umap_y = ? WHERE id = ?",
+                updates
+            )
+            con.commit()
+            logger.info(f"Stored {len(updates)} UMAP coordinate pairs successfully")
+        else:
+            logger.warning("No valid UMAP coordinates to store")
+    
+    def run(self, con: sqlite3.Connection, context: TransformContext) -> TransformContext:
+        """Execute the transformation.
+        
+        This is the main entry point called by the pipeline.
+        
+        Args:
+            con: SQLite database connection
+            context: TransformContext containing input dataframe
+            
+        Returns:
+            TransformContext with processed dataframe
+        """
+        logger.info("Starting ExampleNode transformation")
+        
+        # Get input dataframe from context
+        input_df = context.get_dataframe()
+        
+        # Validate input
+        if input_df.empty:
+            logger.warning("Empty dataframe provided to ExampleNode")
+            return context
+        
+        # Process the data
+        result_df = self._process_data(input_df)
+        
+        # Store results (optional)
+        self._store_results(con, result_df)
+        
+        logger.info("ExampleNode transformation complete")
+        
+        # Return new context with results
+        return TransformContext(result_df)
+
+
+class SimilarityNode(TransformNode):
+    """Example transform node template.
+    
+    This node demonstrates the basic structure for creating
+    new transformation nodes in the pipeline.
+    """
+    
+    def __init__(self, 
+                 param1: str = "default_value",
+                 param2: int = 42,
+                 device: str = "cpu"):
+        """Initialize the ExampleNode.
+        
+        Args:
+            param1: Example string parameter
+            param2: Example integer parameter
+            device: Device to use for computations ('cpu', 'cuda', 'mps')
+        """
+        self.param1 = param1
+        self.param2 = param2
+        self.device = device
+        logger.info(f"Initialized ExampleNode with param1={param1}, param2={param2}")
+    
+    def _create_tables(self, con: sqlite3.Connection):
+        """Create any necessary tables in the database.
+        
+        This is optional - only needed if your node creates new tables.
+        """
+        logger.info("Creating example tables")
+        
+        con.execute("""
+            CREATE TABLE IF NOT EXISTS example_results (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                post_id INTEGER,
+                result_value TEXT,
+                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                FOREIGN KEY (post_id) REFERENCES posts(id)
+            )
+        """)
+        
+        con.commit()
+    
+    def _process_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Process the input dataframe.
+        
+        This is where your main transformation logic goes.
+        
+        Args:
+            df: Input dataframe from context
+            
+        Returns:
+            Processed dataframe
+        """
+        logger.info(f"Processing {len(df)} rows")
+        
+        # Example: Add a new column based on existing data
+        result_df = df.copy()
+        result_df['processed'] = True
+        result_df['example_value'] = result_df['id'].apply(lambda x: f"{self.param1}_{x}")
+        
+        logger.info("Processing complete")
+        return result_df
+    
+    def _store_results(self, con: sqlite3.Connection, df: pd.DataFrame):
+        """Store results back to the database.
+        
+        This is optional - only needed if you want to persist results.
+        
+        Args:
+            con: Database connection
+            df: Processed dataframe to store
+        """
+        if df.empty:
+            logger.info("No results to store")
+            return
+        
+        logger.info(f"Storing {len(df)} results")
+        
+        # Example: Store to database
+        # df[['post_id', 'result_value']].to_sql(
+        #     'example_results', 
+        #     con, 
+        #     if_exists='append', 
+        #     index=False
+        # )
+        
+        con.commit()
+        logger.info("Results stored successfully")
+    
+    def run(self, con: sqlite3.Connection, context: TransformContext) -> TransformContext:
+        """Execute the transformation.
+        
+        This is the main entry point called by the pipeline.
+        
+        Args:
+            con: SQLite database connection
+            context: TransformContext containing input dataframe
+            
+        Returns:
+            TransformContext with processed dataframe
+        """
+        logger.info("Starting ExampleNode transformation")
+        
+        # Get input dataframe from context
+        input_df = context.get_dataframe()
+        
+        # Validate input
+        if input_df.empty:
+            logger.warning("Empty dataframe provided to ExampleNode")
+            return context
+        
+        # Create any necessary tables
+        self._create_tables(con)
+        
+        # Process the data
+        result_df = self._process_data(input_df)
+        
+        # Store results (optional)
+        self._store_results(con, result_df)
+        
+        logger.info("ExampleNode transformation complete")
+        
+        # Return new context with results
+        return TransformContext(result_df)

transform/ensure_gliner_model.sh

@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+if [ -d "$GLINER_MODEL_PATH" ] && find "$GLINER_MODEL_PATH" -type f | grep -q .; then
+    echo "GLiNER model already present at $GLINER_MODEL_PATH"
+    exit 0
+fi
+
+echo "Downloading GLiNER model to $GLINER_MODEL_PATH"
+mkdir -p "$GLINER_MODEL_PATH"
+curl -sL "https://huggingface.co/api/models/${GLINER_MODEL_ID}" | jq -r '.siblings[].rfilename' | while read -r file; do
+    target="${GLINER_MODEL_PATH}/${file}"
+    mkdir -p "$(dirname "$target")"
+    echo "Downloading ${file}"
+    curl -sL "https://huggingface.co/${GLINER_MODEL_ID}/resolve/main/${file}" -o "$target"
+done

transform/ensure_minilm_model.sh

@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+if [ -d "$MINILM_MODEL_PATH" ] && find "$MINILM_MODEL_PATH" -type f | grep -q .; then
+    echo "MiniLM model already present at $MINILM_MODEL_PATH"
+    exit 0
+fi
+
+echo "Downloading MiniLM model to $MINILM_MODEL_PATH"
+mkdir -p "$MINILM_MODEL_PATH"
+curl -sL "https://huggingface.co/api/models/${MINILM_MODEL_ID}" | jq -r '.siblings[].rfilename' | while read -r file; do
+    target="${MINILM_MODEL_PATH}/${file}"
+    mkdir -p "$(dirname "$target")"
+    echo "Downloading ${file}"
+    curl -sL "https://huggingface.co/${MINILM_MODEL_ID}/resolve/main/${file}" -o "$target"
+done

transform/entrypoint.sh

@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+# Run model download with output to stdout/stderr
+/usr/local/bin/ensure_minilm_model.sh 2>&1
+/usr/local/bin/ensure_gliner_model.sh 2>&1
+
+# Start cron in foreground with logging
+exec cron -f -L 2
+# cd /app && /usr/local/bin/python main.py >> /proc/1/fd/1 2>&1

transform/example_node.py

@@ -0,0 +1,170 @@
+"""Example template node for the transform pipeline.
+
+This is a template showing how to create new transform nodes.
+Copy this file and modify it for your specific transformation needs.
+"""
+from pipeline import TransformContext
+from transform_node import TransformNode
+import sqlite3
+import pandas as pd
+import logging
+
+logger = logging.getLogger("knack-transform")
+
+
+class ExampleNode(TransformNode):
+    """Example transform node template.
+    
+    This node demonstrates the basic structure for creating
+    new transformation nodes in the pipeline.
+    """
+    
+    def __init__(self, 
+                 param1: str = "default_value",
+                 param2: int = 42,
+                 device: str = "cpu"):
+        """Initialize the ExampleNode.
+        
+        Args:
+            param1: Example string parameter
+            param2: Example integer parameter
+            device: Device to use for computations ('cpu', 'cuda', 'mps')
+        """
+        self.param1 = param1
+        self.param2 = param2
+        self.device = device
+        logger.info(f"Initialized ExampleNode with param1={param1}, param2={param2}")
+    
+    def _create_tables(self, con: sqlite3.Connection):
+        """Create any necessary tables in the database.
+        
+        This is optional - only needed if your node creates new tables.
+        """
+        logger.info("Creating example tables")
+        
+        con.execute("""
+            CREATE TABLE IF NOT EXISTS example_results (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                post_id INTEGER,
+                result_value TEXT,
+                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                FOREIGN KEY (post_id) REFERENCES posts(id)
+            )
+        """)
+        
+        con.commit()
+    
+    def _process_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Process the input dataframe.
+        
+        This is where your main transformation logic goes.
+        
+        Args:
+            df: Input dataframe from context
+            
+        Returns:
+            Processed dataframe
+        """
+        logger.info(f"Processing {len(df)} rows")
+        
+        # Example: Add a new column based on existing data
+        result_df = df.copy()
+        result_df['processed'] = True
+        result_df['example_value'] = result_df['id'].apply(lambda x: f"{self.param1}_{x}")
+        
+        logger.info("Processing complete")
+        return result_df
+    
+    def _store_results(self, con: sqlite3.Connection, df: pd.DataFrame):
+        """Store results back to the database.
+        
+        This is optional - only needed if you want to persist results.
+        
+        Args:
+            con: Database connection
+            df: Processed dataframe to store
+        """
+        if df.empty:
+            logger.info("No results to store")
+            return
+        
+        logger.info(f"Storing {len(df)} results")
+        
+        # Example: Store to database
+        # df[['post_id', 'result_value']].to_sql(
+        #     'example_results', 
+        #     con, 
+        #     if_exists='append', 
+        #     index=False
+        # )
+        
+        con.commit()
+        logger.info("Results stored successfully")
+    
+    def run(self, con: sqlite3.Connection, context: TransformContext) -> TransformContext:
+        """Execute the transformation.
+        
+        This is the main entry point called by the pipeline.
+        
+        Args:
+            con: SQLite database connection
+            context: TransformContext containing input dataframe
+            
+        Returns:
+            TransformContext with processed dataframe
+        """
+        logger.info("Starting ExampleNode transformation")
+        
+        # Get input dataframe from context
+        input_df = context.get_dataframe()
+        
+        # Validate input
+        if input_df.empty:
+            logger.warning("Empty dataframe provided to ExampleNode")
+            return context
+        
+        # Create any necessary tables
+        self._create_tables(con)
+        
+        # Process the data
+        result_df = self._process_data(input_df)
+        
+        # Store results (optional)
+        self._store_results(con, result_df)
+        
+        logger.info("ExampleNode transformation complete")
+        
+        # Return new context with results
+        return TransformContext(result_df)
+
+
+# Example usage:
+if __name__ == "__main__":
+    # This allows you to test your node independently
+    import os
+    os.chdir('/Users/linussilberstein/Documents/Knack-Scraper/transform')
+    
+    from pipeline import TransformContext
+    import sqlite3
+    
+    # Create test data
+    test_df = pd.DataFrame({
+        'id': [1, 2, 3],
+        'author': ['Test Author 1', 'Test Author 2', 'Test Author 3']
+    })
+    
+    # Create test database connection
+    test_con = sqlite3.connect(':memory:')
+    
+    # Create and run node
+    node = ExampleNode(param1="test", param2=100)
+    context = TransformContext(test_df)
+    result_context = node.run(test_con, context)
+    
+    # Check results
+    result_df = result_context.get_dataframe()
+    print("\nResult DataFrame:")
+    print(result_df)
+    
+    test_con.close()
+    print("\n✓ ExampleNode test completed successfully!")

transform/main.py

@@ -0,0 +1,102 @@
+#! python3
+import logging
+import os
+import sqlite3
+import sys
+from dotenv import load_dotenv
+
+load_dotenv()
+
+if (os.environ.get('LOGGING_LEVEL', 'INFO') == 'INFO'):
+    logging_level = logging.INFO
+else:
+    logging_level = logging.DEBUG
+
+logging.basicConfig(
+    level=logging_level,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler("app.log"),
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+logger = logging.getLogger("knack-transform")
+
+
+def setup_database_connection():
+    """Create connection to the SQLite database."""
+    db_path = os.environ.get('DB_PATH', '/data/knack.sqlite')
+    logger.info(f"Connecting to database: {db_path}")
+    return sqlite3.connect(db_path)
+
+
+def table_exists(tablename: str, con: sqlite3.Connection):
+    """Check if a table exists in the database."""
+    query = "SELECT 1 FROM sqlite_master WHERE type='table' AND name=? LIMIT 1"
+    return len(con.execute(query, [tablename]).fetchall()) > 0
+
+
+def main():
+    """Main entry point for the transform pipeline."""
+    logger.info("Starting transform pipeline")
+    
+    try:
+        con = setup_database_connection()
+        logger.info("Database connection established")
+        
+        # Check if posts table exists
+        if not table_exists('posts', con):
+            logger.warning("Posts table does not exist yet. Please run the scraper first to populate the database.")
+            logger.info("Transform pipeline skipped - no data available")
+            return
+        
+        # Import transform components
+        from pipeline import create_default_pipeline, TransformContext
+        import pandas as pd
+        
+        # Load posts data
+        logger.info("Loading posts from database")
+        sql = "SELECT * FROM posts WHERE author IS NOT NULL AND (is_cleaned IS NULL OR is_cleaned = 0)"
+        # MAX_CLEANED_POSTS = os.environ.get("MAX_CLEANED_POSTS", 100)
+        df = pd.read_sql(sql, con)
+        logger.info(f"Loaded {len(df)} uncleaned posts with authors")
+        
+        if df.empty:
+            logger.info("No uncleaned posts found. Transform pipeline skipped.")
+            return
+        
+        # Create initial context
+        context = TransformContext(df)
+        
+        # Create and run parallel pipeline
+        device = os.environ.get('COMPUTE_DEVICE', 'cpu')
+        max_workers = int(os.environ.get('MAX_WORKERS', 4))
+        
+        pipeline = create_default_pipeline(device=device, max_workers=max_workers)
+        results = pipeline.run(
+            db_path=os.environ.get('DB_PATH', '/data/knack.sqlite'),
+            initial_context=context,
+            fail_fast=False  # Continue even if some nodes fail
+        )
+        
+        logger.info(f"Pipeline completed. Processed {len(results)} node(s)")
+        
+        # Mark all processed posts as cleaned
+        post_ids = df['id'].tolist()
+        if post_ids:
+            placeholders = ','.join('?' * len(post_ids))
+            con.execute(f"UPDATE posts SET is_cleaned = 1 WHERE id IN ({placeholders})", post_ids)
+            con.commit()
+            logger.info(f"Marked {len(post_ids)} posts as cleaned")
+        
+    except Exception as e:
+        logger.error(f"Error in transform pipeline: {e}", exc_info=True)
+        sys.exit(1)
+    finally:
+        if 'con' in locals():
+            con.close()
+            logger.info("Database connection closed")
+
+
+if __name__ == "__main__":
+    main()

transform/pipeline.py

@@ -0,0 +1,266 @@
+"""Parallel pipeline orchestration for transform nodes."""
+import logging
+import os
+import sqlite3
+from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
+from typing import List, Dict, Optional
+
+import pandas as pd
+import multiprocessing as mp
+
+logger = logging.getLogger("knack-transform")
+
+class TransformContext:
+    """Context object containing the dataframe for transformation."""
+    # Possibly add a dict for the context to give more Information
+    
+    def __init__(self, df: pd.DataFrame):
+        self.df = df
+    
+    def get_dataframe(self) -> pd.DataFrame:
+        """Get the pandas dataframe from the context."""
+        return self.df
+
+class NodeConfig:
+    """Configuration for a transform node."""
+    
+    def __init__(self, 
+                 node_class: type,
+                 node_kwargs: Dict = None,
+                 dependencies: List[str] = None,
+                 name: str = None):
+        """Initialize node configuration.
+        
+        Args:
+            node_class: The TransformNode class to instantiate
+            node_kwargs: Keyword arguments to pass to node constructor
+            dependencies: List of node names that must complete before this one
+            name: Optional name for the node (defaults to class name)
+        """
+        self.node_class = node_class
+        self.node_kwargs = node_kwargs or {}
+        self.dependencies = dependencies or []
+        self.name = name or node_class.__name__
+
+class ParallelPipeline:
+    """Pipeline for executing transform nodes in parallel where possible.
+    
+    The pipeline analyzes dependencies between nodes and executes
+    independent nodes concurrently using multiprocessing or threading.
+    """
+    
+    def __init__(self, 
+                 max_workers: Optional[int] = None,
+                 use_processes: bool = False):
+        """Initialize the parallel pipeline.
+        
+        Args:
+            max_workers: Maximum number of parallel workers (defaults to CPU count)
+            use_processes: If True, use ProcessPoolExecutor; if False, use ThreadPoolExecutor
+        """
+        self.max_workers = max_workers or mp.cpu_count()
+        self.use_processes = use_processes
+        self.nodes: Dict[str, NodeConfig] = {}
+        logger.info(f"Initialized ParallelPipeline with {self.max_workers} workers "
+                   f"({'processes' if use_processes else 'threads'})")
+    
+    def add_node(self, config: NodeConfig):
+        """Add a node to the pipeline.
+        
+        Args:
+            config: NodeConfig with node details and dependencies
+        """
+        self.nodes[config.name] = config
+        logger.info(f"Added node '{config.name}' with dependencies: {config.dependencies}")
+    
+    def _get_execution_stages(self) -> List[List[str]]:
+        """Determine execution stages based on dependencies.
+        
+        Returns:
+            List of stages, where each stage contains node names that can run in parallel
+        """
+        stages = []
+        completed = set()
+        remaining = set(self.nodes.keys())
+        
+        while remaining:
+            # Find nodes whose dependencies are all completed
+            ready = []
+            for node_name in remaining:
+                config = self.nodes[node_name]
+                if all(dep in completed for dep in config.dependencies):
+                    ready.append(node_name)
+            
+            if not ready:
+                # Circular dependency or missing dependency
+                raise ValueError(f"Cannot resolve dependencies. Remaining nodes: {remaining}")
+            
+            stages.append(ready)
+            completed.update(ready)
+            remaining -= set(ready)
+        
+        return stages
+    
+    def _execute_node(self, 
+                      node_name: str, 
+                      db_path: str,
+                      context: TransformContext) -> tuple:
+        """Execute a single node.
+        
+        Args:
+            node_name: Name of the node to execute
+            db_path: Path to the SQLite database
+            context: TransformContext for the node
+            
+        Returns:
+            Tuple of (node_name, result_context, error)
+        """
+        try:
+            # Create fresh database connection (not shared across processes/threads)
+            con = sqlite3.connect(db_path)
+            
+            config = self.nodes[node_name]
+            node = config.node_class(**config.node_kwargs)
+            
+            logger.info(f"Executing node: {node_name}")
+            result_context = node.run(con, context)
+            
+            con.close()
+            logger.info(f"Node '{node_name}' completed successfully")
+            
+            return node_name, result_context, None
+            
+        except Exception as e:
+            logger.error(f"Error executing node '{node_name}': {e}", exc_info=True)
+            return node_name, None, str(e)
+    
+    def run(self, 
+            db_path: str,
+            initial_context: TransformContext,
+            fail_fast: bool = False) -> Dict[str, TransformContext]:
+        """Execute the pipeline.
+        
+        Args:
+            db_path: Path to the SQLite database
+            initial_context: Initial TransformContext for the pipeline
+            fail_fast: If True, stop execution on first error
+            
+        Returns:
+            Dict mapping node names to their output TransformContext
+        """
+        logger.info("Starting parallel pipeline execution")
+        
+        stages = self._get_execution_stages()
+        logger.info(f"Pipeline has {len(stages)} execution stage(s)")
+        
+        results = {}
+        errors = []
+        
+        ExecutorClass = ProcessPoolExecutor if self.use_processes else ThreadPoolExecutor
+        
+        for stage_num, stage_nodes in enumerate(stages, 1):
+            logger.info(f"Stage {stage_num}/{len(stages)}: Executing {len(stage_nodes)} node(s) in parallel: {stage_nodes}")
+            
+            # For nodes in this stage, use the context from their dependencies
+            # If multiple dependencies, we'll use the most recent one (or could merge)
+            stage_futures = {}
+            
+            with ExecutorClass(max_workers=min(self.max_workers, len(stage_nodes))) as executor:
+                for node_name in stage_nodes:
+                    config = self.nodes[node_name]
+                    
+                    # Get context from dependencies (use the last dependency's output)
+                    if config.dependencies:
+                        context = results.get(config.dependencies[-1], initial_context)
+                    else:
+                        context = initial_context
+                    
+                    future = executor.submit(self._execute_node, node_name, db_path, context)
+                    stage_futures[future] = node_name
+                
+                # Wait for all nodes in this stage to complete
+                for future in as_completed(stage_futures):
+                    node_name = stage_futures[future]
+                    name, result_context, error = future.result()
+                    
+                    if error:
+                        errors.append((name, error))
+                        if fail_fast:
+                            logger.error(f"Pipeline failed at node '{name}': {error}")
+                            raise RuntimeError(f"Node '{name}' failed: {error}")
+                    else:
+                        results[name] = result_context
+        
+        if errors:
+            logger.warning(f"Pipeline completed with {len(errors)} error(s)")
+            for name, error in errors:
+                logger.error(f"  - {name}: {error}")
+        else:
+            logger.info("Pipeline completed successfully")
+        
+        return results
+
+
+def create_default_pipeline(device: str = "cpu", 
+                            max_workers: Optional[int] = None) -> ParallelPipeline:
+    """Create a pipeline with default transform nodes.
+    
+    Args:
+        device: Device to use for compute-intensive nodes ('cpu', 'cuda', 'mps')
+        max_workers: Maximum number of parallel workers
+        
+    Returns:
+        Configured ParallelPipeline
+    """
+    from author_node import NerAuthorNode, FuzzyAuthorNode
+    from embeddings_node import TextEmbeddingNode, UmapNode
+    
+    pipeline = ParallelPipeline(max_workers=max_workers, use_processes=False)
+    
+    # Add AuthorNode (no dependencies)
+    pipeline.add_node(NodeConfig(
+        node_class=NerAuthorNode,
+        node_kwargs={
+            'device': device,
+            'model_path': os.environ.get('GLINER_MODEL_PATH')
+        },
+        dependencies=[],
+        name='AuthorNode'
+    ))
+
+    pipeline.add_node(NodeConfig(
+        node_class=FuzzyAuthorNode,
+        node_kwargs={
+            'max_l_dist': 1
+        },
+        dependencies=['AuthorNode'],
+        name='FuzzyAuthorNode'
+    ))
+
+    pipeline.add_node(NodeConfig(
+        node_class=TextEmbeddingNode,
+        node_kwargs={
+            'device': device,
+            'model_path': os.environ.get('MINILM_MODEL_PATH')
+        },
+        dependencies=[],
+        name='TextEmbeddingNode'
+    ))
+
+    pipeline.add_node(NodeConfig(
+        node_class=UmapNode,
+        node_kwargs={},
+        dependencies=['TextEmbeddingNode'],
+        name='UmapNode'
+    ))
+
+    # TODO: Create Node to compute Text Embeddings and UMAP. 
+    
+    # pipeline.add_node(NodeConfig(
+    #     node_class=UMAPNode,
+    #     node_kwargs={'device': device},
+    #     dependencies=['EmbeddingNode'],  # Runs after EmbeddingNode
+    #     name='UMAPNode'
+    # ))
+    
+    return pipeline

transform/requirements.txt

@@ -0,0 +1,7 @@
+pandas
+python-dotenv
+gliner
+torch
+fuzzysearch
+sentence_transformers
+umap-learn

transform/transform_node.py

@@ -0,0 +1,26 @@
+"""Base transform node for data pipeline."""
+from abc import ABC, abstractmethod
+import sqlite3
+
+from pipeline import TransformContext
+
+class TransformNode(ABC):
+    """Abstract base class for transformation nodes.
+    
+    Each transform node implements a single transformation step
+    that takes data from the database, transforms it, and 
+    potentially writes results back.
+    """
+    
+    @abstractmethod
+    def run(self, con: sqlite3.Connection, context: TransformContext) -> TransformContext:
+        """Execute the transformation.
+        
+        Args:
+            con: SQLite database connection
+            context: TransformContext containing the input dataframe
+            
+        Returns:
+            TransformContext with the transformed dataframe
+        """
+        pass