Adds TransformNode to FuzzyFind Author Names

docker-compose.yml

@@ -21,7 +21,23 @@ services:
       - 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:

transform/Dockerfile

@@ -1,7 +1,6 @@
 FROM python:3.12-slim
 
-RUN mkdir /app
-RUN mkdir /data
+RUN mkdir -p /app /data /models
 
 # Install build dependencies
 RUN apt-get update && apt-get install -y --no-install-recommends \
@@ -11,9 +10,12 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     libopenblas-dev \
     liblapack-dev \
     pkg-config \
+    curl \
+    jq \
     && rm -rf /var/lib/apt/lists/*
 
-#COPY /data/knack.sqlite /data
+ENV GLINER_MODEL_ID=urchade/gliner_multi-v2.1
+ENV GLINER_MODEL_PATH=/models/gliner_multi-v2.1
 
 WORKDIR /app
 COPY requirements.txt .
@@ -24,18 +26,21 @@ COPY .env .
 RUN apt update -y
 RUN apt install -y cron locales
 
-COPY *.py .
+# Ensure GLiNER helper scripts are available
+COPY ensure_gliner_model.sh /usr/local/bin/ensure_gliner_model.sh
+COPY entrypoint.sh /usr/local/bin/entrypoint.sh
+RUN chmod +x /usr/local/bin/ensure_gliner_model.sh /usr/local/bin/entrypoint.sh
 
-ENV PYTHONUNBUFFERED=1
-ENV LANG=de_DE.UTF-8
-ENV LC_ALL=de_DE.UTF-8
+COPY *.py .
 
 # Create cron job that runs every weekend (Sunday at 3 AM) 0 3 * * 0
 # Testing every 30 Minutes */30 * * * *
-RUN echo "0 3 * * 0 cd /app && /usr/local/bin/python main.py >> /proc/1/fd/1 2>&1" > /etc/cron.d/knack-transform
+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
 
-# Start cron in foreground
-CMD ["cron", "-f"]
+# Persist models between container runs
+VOLUME /models
+
+CMD ["/usr/local/bin/entrypoint.sh"]
 #CMD ["python", "main.py"]

transform/README.md

@@ -6,10 +6,15 @@ Data transformation pipeline for the Knack scraper project.
 
 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
-- `main.py` - Main entry point and pipeline orchestration
+- `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/author_node.py

@@ -1,10 +1,13 @@
 """Author classification transform node using NER."""
-from base import TransformNode, TransformContext
+import os
 import sqlite3
 import pandas as pd
 import logging
+import fuzzysearch
 from concurrent.futures import ThreadPoolExecutor
-from datetime import datetime
+
+from pipeline import TransformContext
+from transform_node import TransformNode
 
 try:
     from gliner import GLiNER
@@ -17,7 +20,7 @@ except ImportError:
 logger = logging.getLogger("knack-transform")
 
 
-class AuthorNode(TransformNode):
+class NerAuthorNode(TransformNode):
     """Transform node that extracts and classifies authors using NER.
     
     Creates two tables:
@@ -25,7 +28,8 @@ class AuthorNode(TransformNode):
     - post_authors: maps posts to their authors
     """
     
-    def __init__(self, model_name: str = "urchade/gliner_medium-v2.1", 
+    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"):
@@ -33,11 +37,13 @@ class AuthorNode(TransformNode):
         
         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
@@ -49,21 +55,31 @@ class AuthorNode(TransformNode):
         if not GLINER_AVAILABLE:
             raise ImportError("GLiNER is required for AuthorNode. Install with: pip install gliner")
         
-        logger.info(f"Loading GLiNER model: {self.model_name}")
+        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(
-                self.model_name, 
+                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(
-                self.model_name, 
+                model_source, 
                 max_length=255
             ).to('mps', dtype=torch.float16)
         else:
             self.model = GLiNER.from_pretrained(
-                self.model_name, 
+                model_source, 
                 max_length=255
             )
         
@@ -209,13 +225,6 @@ class AuthorNode(TransformNode):
             logger.info(f"Creating {len(mappings_df)} post-author mappings")
             mappings_df.to_sql('post_authors', con, if_exists='append', index=False)
         
-            # Mark posts as cleaned
-            processed_post_ids = mappings_df['post_id'].unique().tolist()
-            if processed_post_ids:
-                placeholders = ','.join('?' * len(processed_post_ids))
-                con.execute(f"UPDATE posts SET is_cleaned = 1 WHERE id IN ({placeholders})", processed_post_ids)
-                logger.info(f"Marked {len(processed_post_ids)} posts as cleaned")
-        
         con.commit()
         logger.info("Authors and mappings stored successfully")
     
@@ -247,17 +256,165 @@ class AuthorNode(TransformNode):
         # Store results
         self._store_authors(con, results)
         
-        # Mark posts without author entities as cleaned too (no authors found)
-        processed_ids = set([r['id'] for r in results]) if results else set()
-        unprocessed_ids = [pid for pid in posts_df['id'].tolist() if pid not in processed_ids]
-        if unprocessed_ids:
-            placeholders = ','.join('?' * len(unprocessed_ids))
-            con.execute(f"UPDATE posts SET is_cleaned = 1 WHERE id IN ({placeholders})", unprocessed_ids)
-            con.commit()
-            logger.info(f"Marked {len(unprocessed_ids)} posts without author entities as cleaned")
-        
         # Return context with results
         results_df = pd.DataFrame(results) if results else pd.DataFrame()
         logger.info("AuthorNode transformation complete")
         
         return TransformContext(results_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'])
+                
+                # Use fuzzysearch to find matches with allowed errors
+                matches = fuzzysearch.find_near_matches(
+                    author_name,
+                    post_author,
+                    max_l_dist=self.max_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(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/entrypoint.sh

@@ -0,0 +1,8 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+# Run model download with output to stdout/stderr
+/usr/local/bin/ensure_gliner_model.sh 2>&1
+
+# Start cron in foreground with logging
+exec cron -f -L 2

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

@@ -50,15 +50,14 @@ def main():
             logger.info("Transform pipeline skipped - no data available")
             return
         
-        # Import transform nodes
-        from author_node import AuthorNode
-        from base import TransformContext
+        # 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 id, author FROM posts WHERE author IS NOT NULL AND (is_cleaned IS NULL OR is_cleaned = 0) LIMIT ?"
-        MAX_CLEANED_POSTS = os.environ.get("MAX_CLEANED_POSTS", 500)
+        MAX_CLEANED_POSTS = os.environ.get("MAX_CLEANED_POSTS", 100)
         df = pd.read_sql(sql, con, params=[MAX_CLEANED_POSTS])
         logger.info(f"Loaded {len(df)} uncleaned posts with authors")
         
@@ -66,15 +65,29 @@ def main():
             logger.info("No uncleaned posts found. Transform pipeline skipped.")
             return
         
-        # Create context and run author classification
+        # Create initial context
         context = TransformContext(df)
-        author_transform = AuthorNode(device=os.environ.get('COMPUTE_DEVICE', 'cpu'))  # Change to "cuda" or "mps" if available
-        result_context = author_transform.run(con, context)
         
-        # TODO: Create Node to compute Text Embeddings and UMAP. 
-        # TODO: Create Node to pre-compute data based on visuals to reduce load time.
+        # Create and run parallel pipeline
+        device = os.environ.get('COMPUTE_DEVICE', 'cpu')
+        max_workers = int(os.environ.get('MAX_WORKERS', 4))
         
-        logger.info("Transform pipeline completed successfully")
+        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)

transform/pipeline.py

@@ -0,0 +1,258 @@
+"""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."""
+    
+    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 = {}
+        contexts = {None: initial_context}  # Track contexts from each node
+        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
+    
+    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'
+    ))
+
+    # TODO: Create Node to compute Text Embeddings and UMAP. 
+    # TODO: Create Node to pre-compute data based on visuals to reduce load time.
+    
+    # TODO: Add more nodes here as they are implemented
+    # Example:
+    # pipeline.add_node(NodeConfig(
+    #     node_class=EmbeddingNode,
+    #     node_kwargs={'device': device},
+    #     dependencies=[],  # Runs after AuthorNode
+    #     name='EmbeddingNode'
+    # ))    
+    
+    # pipeline.add_node(NodeConfig(
+    #     node_class=UMAPNode,
+    #     node_kwargs={'device': device},
+    #     dependencies=['EmbeddingNode'],  # Runs after EmbeddingNode
+    #     name='UMAPNode'
+    # ))
+    
+    return pipeline

transform/requirements.txt

@@ -2,3 +2,4 @@ pandas
 python-dotenv
 gliner
 torch
+fuzzysearch

transform/transform_node.py

@@ -1,19 +1,8 @@
 """Base transform node for data pipeline."""
 from abc import ABC, abstractmethod
 import sqlite3
-import pandas as pd
-
-
-class TransformContext:
-    """Context object containing the dataframe for transformation."""
-    
-    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
 
+from pipeline import TransformContext
 
 class TransformNode(ABC):
     """Abstract base class for transformation nodes.