ingest_data.py

#!/usr/bin/env python3
"""

Ingestion script for Congressional Bioguide profiles.

Creates SQLite database and FAISS semantic search index.

"""

import json
import sqlite3
import os
import time
from pathlib import Path
from typing import Dict, List, Any
import faiss
import numpy as np
import pickle
from sentence_transformers import SentenceTransformer


class BioguideIngester:
    def __init__(self, data_dir: str = "BioguideProfiles", db_path: str = "congress.db"):
        self.data_dir = Path(data_dir)
        self.db_path = db_path
        self.model = None  # Load model only when needed for FAISS indexing

    def create_database_schema(self):
        """Create SQLite database schema for Congressional profiles."""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()

        # Main members table
        cursor.execute("""

        CREATE TABLE IF NOT EXISTS members (

            bio_id TEXT PRIMARY KEY,

            family_name TEXT,

            given_name TEXT,

            middle_name TEXT,

            honorific_prefix TEXT,

            unaccented_family_name TEXT,

            unaccented_given_name TEXT,

            unaccented_middle_name TEXT,

            birth_date TEXT,

            birth_circa INTEGER,

            death_date TEXT,

            death_circa INTEGER,

            profile_text TEXT,

            full_name TEXT GENERATED ALWAYS AS (

                COALESCE(honorific_prefix || ' ', '') ||

                COALESCE(given_name, '') || ' ' ||

                COALESCE(middle_name || ' ', '') ||

                COALESCE(family_name, '')

            ) STORED

        )

        """)

        # Images table
        cursor.execute("""

        CREATE TABLE IF NOT EXISTS images (

            id INTEGER PRIMARY KEY AUTOINCREMENT,

            bio_id TEXT,

            content_url TEXT,

            caption TEXT,

            FOREIGN KEY (bio_id) REFERENCES members(bio_id)

        )

        """)

        # Job positions table
        cursor.execute("""

        CREATE TABLE IF NOT EXISTS job_positions (

            id INTEGER PRIMARY KEY AUTOINCREMENT,

            bio_id TEXT,

            job_name TEXT,

            job_type TEXT,

            start_date TEXT,

            start_circa INTEGER,

            end_date TEXT,

            end_circa INTEGER,

            congress_number INTEGER,

            congress_name TEXT,

            party TEXT,

            caucus TEXT,

            region_type TEXT,

            region_code TEXT,

            note TEXT,

            FOREIGN KEY (bio_id) REFERENCES members(bio_id)

        )

        """)

        # Relationships table
        cursor.execute("""

        CREATE TABLE IF NOT EXISTS relationships (

            id INTEGER PRIMARY KEY AUTOINCREMENT,

            bio_id TEXT,

            related_bio_id TEXT,

            relationship_type TEXT,

            FOREIGN KEY (bio_id) REFERENCES members(bio_id),

            FOREIGN KEY (related_bio_id) REFERENCES members(bio_id)

        )

        """)

        # Creative works table
        cursor.execute("""

        CREATE TABLE IF NOT EXISTS creative_works (

            id INTEGER PRIMARY KEY AUTOINCREMENT,

            bio_id TEXT,

            citation_text TEXT,

            FOREIGN KEY (bio_id) REFERENCES members(bio_id)

        )

        """)

        # Assets table
        cursor.execute("""

        CREATE TABLE IF NOT EXISTS assets (

            id INTEGER PRIMARY KEY AUTOINCREMENT,

            bio_id TEXT,

            name TEXT,

            asset_type TEXT,

            content_url TEXT,

            credit_line TEXT,

            accession_number TEXT,

            upload_date TEXT,

            FOREIGN KEY (bio_id) REFERENCES members(bio_id)

        )

        """)

        # Create indexes for common queries
        cursor.execute("CREATE INDEX IF NOT EXISTS idx_family_name ON members(unaccented_family_name)")
        cursor.execute("CREATE INDEX IF NOT EXISTS idx_given_name ON members(unaccented_given_name)")
        cursor.execute("CREATE INDEX IF NOT EXISTS idx_birth_date ON members(birth_date)")
        cursor.execute("CREATE INDEX IF NOT EXISTS idx_death_date ON members(death_date)")
        cursor.execute("CREATE INDEX IF NOT EXISTS idx_job_congress ON job_positions(congress_number)")
        cursor.execute("CREATE INDEX IF NOT EXISTS idx_job_party ON job_positions(party)")
        cursor.execute("CREATE INDEX IF NOT EXISTS idx_job_region ON job_positions(region_code)")
        cursor.execute("CREATE INDEX IF NOT EXISTS idx_job_type ON job_positions(job_name)")

        conn.commit()
        conn.close()
        print("✓ Database schema created")

    def extract_data_field(self, data: Dict[str, Any], key: str, default=None):
        """Safely extract data from nested 'data' field if it exists."""
        if 'data' in data:
            return data['data'].get(key, default)
        return data.get(key, default)

    def ingest_profiles(self):
        """Ingest all JSON profiles into SQLite database."""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()

        profile_files = list(self.data_dir.glob("*.json"))
        total = len(profile_files)

        print(f"Ingesting {total} profiles...")

        for idx, profile_file in enumerate(profile_files, 1):
            if idx % 1000 == 0:
                print(f"  Processed {idx}/{total} profiles...")

            try:
                with open(profile_file, 'r', encoding='utf-8') as f:
                    data = json.load(f)

                # Handle nested 'data' structure
                bio_id = self.extract_data_field(data, 'usCongressBioId')
                if not bio_id:
                    print(f"  Skipping {profile_file}: no bio_id found")
                    continue

                # Insert member data
                cursor.execute("""

                INSERT OR REPLACE INTO members (

                    bio_id, family_name, given_name, middle_name, honorific_prefix,

                    unaccented_family_name, unaccented_given_name, unaccented_middle_name,

                    birth_date, birth_circa, death_date, death_circa, profile_text

                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)

                """, (
                    bio_id,
                    self.extract_data_field(data, 'familyName'),
                    self.extract_data_field(data, 'givenName'),
                    self.extract_data_field(data, 'middleName'),
                    self.extract_data_field(data, 'honorificPrefix'),
                    self.extract_data_field(data, 'unaccentedFamilyName'),
                    self.extract_data_field(data, 'unaccentedGivenName'),
                    self.extract_data_field(data, 'unaccentedMiddleName'),
                    self.extract_data_field(data, 'birthDate'),
                    1 if self.extract_data_field(data, 'birthCirca') else 0,
                    self.extract_data_field(data, 'deathDate'),
                    1 if self.extract_data_field(data, 'deathCirca') else 0,
                    self.extract_data_field(data, 'profileText')
                ))

                # Insert images
                images = self.extract_data_field(data, 'image', [])
                for img in images:
                    cursor.execute("""

                    INSERT INTO images (bio_id, content_url, caption)

                    VALUES (?, ?, ?)

                    """, (bio_id, img.get('contentUrl'), img.get('caption')))

                # Insert job positions
                job_positions = self.extract_data_field(data, 'jobPositions', [])
                for job_pos in job_positions:
                    job = job_pos.get('job', {})
                    congress_aff = job_pos.get('congressAffiliation', {})
                    congress = congress_aff.get('congress', {})
                    party_list = congress_aff.get('partyAffiliation', [])
                    caucus_list = congress_aff.get('caucusAffiliation', [])
                    represents = congress_aff.get('represents', {})
                    notes = congress_aff.get('note', [])
                    note_text = notes[0].get('content') if notes else None

                    cursor.execute("""

                    INSERT INTO job_positions (

                        bio_id, job_name, job_type, start_date, start_circa,

                        end_date, end_circa, congress_number, congress_name,

                        party, caucus, region_type, region_code, note

                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)

                    """, (
                        bio_id,
                        job.get('name'),
                        job.get('jobType'),
                        job_pos.get('startDate'),
                        1 if job_pos.get('startCirca') else 0,
                        job_pos.get('endDate'),
                        1 if job_pos.get('endCirca') else 0,
                        congress.get('congressNumber'),
                        congress.get('name'),
                        party_list[0].get('party', {}).get('name') if party_list else None,
                        caucus_list[0].get('party', {}).get('name') if caucus_list else None,
                        represents.get('regionType'),
                        represents.get('regionCode'),
                        note_text
                    ))

                # Insert relationships
                relationships = self.extract_data_field(data, 'relationship', [])
                for rel in relationships:
                    related = rel.get('relatedTo', {})
                    cursor.execute("""

                    INSERT INTO relationships (bio_id, related_bio_id, relationship_type)

                    VALUES (?, ?, ?)

                    """, (bio_id, related.get('usCongressBioId'), rel.get('relationshipType')))

                # Insert creative works
                creative_works = self.extract_data_field(data, 'creativeWork', [])
                for work in creative_works:
                    cursor.execute("""

                    INSERT INTO creative_works (bio_id, citation_text)

                    VALUES (?, ?)

                    """, (bio_id, work.get('freeFormCitationText')))

                # Insert assets
                assets = self.extract_data_field(data, 'asset', [])
                for asset in assets:
                    cursor.execute("""

                    INSERT INTO assets (

                        bio_id, name, asset_type, content_url, credit_line,

                        accession_number, upload_date

                    ) VALUES (?, ?, ?, ?, ?, ?, ?)

                    """, (
                        bio_id,
                        asset.get('name'),
                        asset.get('assetType'),
                        asset.get('contentUrl'),
                        asset.get('creditLine'),
                        asset.get('accessionNumber'),
                        asset.get('uploadDate')
                    ))

            except Exception as e:
                print(f"  Error processing {profile_file}: {e}")
                continue

        conn.commit()
        conn.close()
        print(f"✓ Ingested {total} profiles into database")

    def build_faiss_index(self):
        """Build FAISS index for semantic search on profile biographies."""
        print("\n" + "=" * 60)
        print("BUILDING FAISS INDEX FOR SEMANTIC SEARCH")
        print("=" * 60)

        try:
            # Load model
            print("\n1. Loading sentence transformer model...")
            start_time = time.time()

            # Disable all parallelism to avoid Python 3.14 issues
            os.environ['TOKENIZERS_PARALLELISM'] = 'false'
            os.environ['OMP_NUM_THREADS'] = '1'
            os.environ['MKL_NUM_THREADS'] = '1'
            os.environ['OPENBLAS_NUM_THREADS'] = '1'

            import torch
            torch.set_num_threads(1)

            self.model = SentenceTransformer('all-MiniLM-L6-v2')
            print(f"   ✓ Model loaded in {time.time() - start_time:.3f}s")

            # Load biographies from database
            print("\n2. Loading biographies from database...")
            start_time = time.time()
            conn = sqlite3.connect(self.db_path)
            cursor = conn.cursor()
            cursor.execute("SELECT bio_id, profile_text FROM members WHERE profile_text IS NOT NULL")
            profiles = cursor.fetchall()
            conn.close()
            print(f"   ✓ Loaded {len(profiles):,} biographies in {time.time() - start_time:.3f}s")

            if len(profiles) == 0:
                print("\n❌ ERROR: No profiles with text found in database!")
                return False

            # Prepare data
            print("\n3. Preparing data for encoding...")
            start_time = time.time()
            bio_ids = [p[0] for p in profiles]
            texts = [p[1] if p[1] else "" for p in profiles]
            print(f"   ✓ Prepared {len(bio_ids):,} texts")
            print(f"   ✓ Time: {time.time() - start_time:.3f}s")

            # Generate embeddings in batches
            print("\n4. Generating embeddings...")
            start_time = time.time()
            batch_size = 32
            embeddings = []

            for i in range(0, len(texts), batch_size):
                batch = texts[i:i + batch_size]
                batch_embeddings = self.model.encode(
                    batch,
                    show_progress_bar=False,
                    convert_to_numpy=True,
                    normalize_embeddings=False,
                    device='cpu'  # Explicit CPU to avoid GPU issues
                )
                embeddings.extend(batch_embeddings)

                # Progress update every 100 batches
                if (i // batch_size + 1) % 100 == 0:
                    elapsed = time.time() - start_time
                    rate = (i + len(batch)) / elapsed
                    print(f"   Encoded {i + len(batch):,}/{len(texts):,} ({rate:.0f} texts/sec)")

            embeddings = np.array(embeddings, dtype=np.float32)
            elapsed = time.time() - start_time
            print(f"   ✓ Generated {len(embeddings):,} embeddings in {elapsed:.3f}s")
            print(f"   ✓ Shape: {embeddings.shape}")

            # Build FAISS index
            print("\n5. Building FAISS index...")
            start_time = time.time()
            dimension = embeddings.shape[1]
            print(f"   Dimension: {dimension}")

            # Use IndexFlatIP for exact cosine similarity search
            index = faiss.IndexFlatIP(dimension)

            # Normalize embeddings for cosine similarity
            faiss.normalize_L2(embeddings)

            # Add to index
            index.add(embeddings)
            print(f"   ✓ Index built in {time.time() - start_time:.3f}s")
            print(f"   ✓ Total vectors in index: {index.ntotal:,}")

            # Save FAISS index
            print("\n6. Saving FAISS index to disk...")
            start_time = time.time()
            faiss.write_index(index, "congress_faiss.index")
            print(f"   ✓ Index saved to: congress_faiss.index")
            print(f"   ✓ Time: {time.time() - start_time:.3f}s")

            # Save note ID mapping
            print("\n7. Saving bio ID mapping...")
            start_time = time.time()
            with open("congress_bio_ids.pkl", "wb") as f:
                pickle.dump(bio_ids, f)
            print(f"   ✓ Mapping saved to: congress_bio_ids.pkl")
            print(f"   ✓ Time: {time.time() - start_time:.3f}s")

            # Get file sizes
            from pathlib import Path
            index_size_mb = Path("congress_faiss.index").stat().st_size / (1024**2)
            mapping_size_mb = Path("congress_bio_ids.pkl").stat().st_size / (1024**2)

            print("\n" + "=" * 60)
            print("FAISS INDEX BUILD COMPLETE")
            print("=" * 60)
            print(f"Total embeddings indexed: {len(bio_ids):,}")
            print(f"Index file size: {index_size_mb:.2f} MB")
            print(f"Mapping file size: {mapping_size_mb:.2f} MB")
            print(f"Total size: {index_size_mb + mapping_size_mb:.2f} MB")
            print("\nThe MCP server will load this index on startup for fast searches.")

            return True

        except Exception as e:
            print(f"\n❌ ERROR building FAISS index: {e}")
            print(f"   This may be due to Python 3.14 compatibility issues.")
            print(f"   The database is still usable, but semantic search will not work.")
            print(f"   Consider using Python 3.11 or 3.12 for full functionality.")
            import traceback
            traceback.print_exc()
            return False

    def run(self):
        """Run the complete ingestion pipeline."""
        print("Starting Congressional Bioguide ingestion...")
        print("=" * 60)

        try:
            self.create_database_schema()
            self.ingest_profiles()
            faiss_success = self.build_faiss_index()

            print("\n" + "=" * 60)
            print("INGESTION COMPLETE")
            print("=" * 60)
            print(f"Database: {self.db_path}")

            if faiss_success:
                print(f"FAISS index: congress_faiss.index ✓")
                print(f"ID mapping: congress_bio_ids.pkl ✓")
                print("\nAll features available, including semantic search!")
            else:
                print(f"FAISS index: ❌ (failed to build)")
                print("\nDatabase is ready, but semantic search is unavailable.")
                print("All other MCP tools will work normally.")

            return faiss_success

        except Exception as e:
            print(f"\n❌ FATAL ERROR: {e}")
            import traceback
            traceback.print_exc()
            return False


def main():
    ingester = BioguideIngester()
    ingester.run()


if __name__ == "__main__":
    main()