update ingestion muslim

hadith-ingestion/argo/workflows/generate-embeddings.yaml

@@ -0,0 +1,31 @@
+apiVersion: argoproj.io/v1alpha1
+kind: Workflow
+metadata:
+  generateName: generate-embeddings-
+  namespace: ml
+spec:
+  entrypoint: generate
+  serviceAccountName: argo-workflow
+  
+  arguments:
+    parameters:
+    - name: batch-size
+      value: "32"
+  
+  templates:
+  - name: generate
+    container:
+      image: hadith-ingestion:latest
+      command: [python, /app/src/embeddings/generator.py]
+      args: ["--batch-size={{workflow.parameters.batch-size}}"]
+      env:
+        - name: DATABASE_HOST
+          value: "pg.betelgeusebytes.io"
+        - name: DATABASE_PASSWORD
+          valueFrom:
+            secretKeyRef:
+              name: hadith-db-secret
+              key: password
+      resources:
+        requests: {cpu: 2, memory: 4Gi}
+        limits: {cpu: 4, memory: 8Gi}

hadith-ingestion/argo/workflows/ingest-collection.yaml

@@ -59,8 +59,8 @@ spec:
     
     container:
       image: hadith-ingestion:latest
-      imagePullPolicy: IfNotPresent
-      command: [python, /app/src/main.py]
+      imagePullPolicy: Always
+      command: [python, /app/src/main_hadithapi.py]
       args:
         - "{{inputs.parameters.collection}}"
         - "--limit={{inputs.parameters.limit}}"
@@ -122,8 +122,8 @@ spec:
     
     container:
       image: hadith-embeddings:latest
-      imagePullPolicy: IfNotPresent
-      command: [python, /app/generate_embeddings.py]
+      imagePullPolicy: Always
+      command: [python, /app/src/embeddings/generator.py]
       args:
         - "--collection={{inputs.parameters.collection}}"
         - "--batch-size=32"
@@ -161,7 +161,7 @@ spec:
     
     container:
       image: hadith-qdrant-indexer:latest
-      imagePullPolicy: IfNotPresent
+      imagePullPolicy: Always
       command: [python, /app/index_qdrant.py]
       args:
         - "--collection={{inputs.parameters.collection}}"

hadith-ingestion/combine.sh

@@ -1,4 +1,4 @@
-find . -type f -name "*.txt" -o -name "production" -o -name "*.py" -o -name "*.yaml" -o -name "Dockerfile" -o -name "*.sh" -o -name "*.env" -o -name "*.md" ! -name "*.xls" ! -name "*.xlsx"| while read file; do
+find . -type f -name "*.txt" -o -name "production" -o -name "*.py" -o -name "*.yaml" -o -name "Dockerfile" -o -name "*.sh" -o -name "*.env" ! -name "*.md" ! -name "*.xls" ! -name "*.xlsx"| while read file; do
     echo "=== $file ===" >> combined.txt
     cat "$file" >> combined.txt
     echo "" >> combined.txt

hadith-ingestion/requirements.txt

@@ -40,3 +40,9 @@ pytest==7.4.3
 pytest-asyncio==0.21.1
 pytest-cov==4.1.0
 faker==21.0.0
+
+
+httpx==0.25.2
+qdrant-client==1.7.0
+tqdm==4.66.1
+asyncpg==0.29.0

hadith-ingestion/run-full-ingestion.sh

@@ -8,14 +8,14 @@ echo "=== Starting Full HadithAPI Ingestion ==="
 # Book slug to collection abbreviation mapping
 # Books to ingest (in order)
 BOOKS=(
-  "sahih-bukhari"
+  # "sahih-bukhari"
   "sahih-muslim"
-  "abu-dawood"
-  "al-tirmidhi"
-  "ibn-e-majah"
-  "sunan-nasai"
-  "musnad-ahmad"
-  "al-silsila-sahiha"
+  # "abu-dawood"
+  # "al-tirmidhi"
+  # "ibn-e-majah"
+  # "sunan-nasai"
+  # "musnad-ahmad"
+  # "al-silsila-sahiha"
 )
 
 for BOOK in "${BOOKS[@]}"; do
@@ -23,7 +23,7 @@ for BOOK in "${BOOKS[@]}"; do
   echo "Ingesting: $BOOK"
   echo "========================================="
   
-  argo submit -n argo argo/workflows/ingest-hadithapi.yaml \
+  argo submit -n ml argo/workflows/ingest-hadithapi.yaml \
     --parameter book-slug=$BOOK \
     --parameter limit=0 \
     --wait \

hadith-ingestion/src/api_clients/hadithapi_client.py

@@ -264,7 +264,11 @@ class HadithAPIClient(BaseAPIClient):
         
         # Process each chapter
         for chapter in chapters:
-            chapter_id = chapter.get('id')
+            # logger.warning("Processing chapter", chapter=chapter)
+            if book_slug == 'sahih-muslim':
+                chapter_id = chapter.get('chapterNumber')
+            else:
+                chapter_id = chapter.get('id')
             chapter_number = chapter.get('chapterNumber')
             
             logger.info(

hadith-ingestion/src/embeddings/generator.py

@@ -0,0 +1,148 @@
+# Update: src/embeddings/generator.py
+"""
+Embedding generation service for hadith texts
+"""
+import asyncio
+import httpx
+from typing import List, Tuple, Optional
+import psycopg2
+from qdrant_client import QdrantClient
+from qdrant_client.models import Distance, VectorParams, PointStruct
+import structlog
+from tqdm import tqdm
+import sys
+import argparse
+
+from config.settings import settings
+
+logger = structlog.get_logger()
+
+
+class EmbeddingGenerator:
+    def __init__(self, database_url: str, tei_url: str, qdrant_url: str, batch_size: int = 32):
+        self.database_url = database_url
+        self.tei_url = tei_url
+        self.qdrant_url = qdrant_url
+        self.batch_size = batch_size
+        self.http_client = httpx.AsyncClient(timeout=60.0)
+        self.qdrant = QdrantClient(url=qdrant_url)
+        
+    async def generate_embeddings_batch(self, texts: List[str]) -> List[List[float]]:
+        """Generate embeddings using TEI"""
+        response = await self.http_client.post(
+            f"{self.tei_url}/embed",
+            json={"inputs": texts}
+        )
+        response.raise_for_status()
+        return response.json()
+    
+    def create_collection(self, name: str = "hadith_embeddings"):
+        """Create Qdrant collection"""
+        try:
+            self.qdrant.get_collection(name)
+        except:
+            self.qdrant.create_collection(
+                collection_name=name,
+                vectors_config=VectorParams(size=1024, distance=Distance.COSINE)
+            )
+    
+    async def process_batch(self, conn, hadiths: List[Tuple], collection: str):
+        """Process batch: generate embeddings & store"""
+        texts = [f"{h[1]} {h[2] or ''}" for h in hadiths]  # arabic + english
+        embeddings = await self.generate_embeddings_batch(texts)
+        
+        points = [
+            PointStruct(
+                id=str(h[0]),
+                vector=emb,
+                payload={"hadith_id": str(h[0]), "collection_id": str(h[4])}
+            )
+            for h, emb in zip(hadiths, embeddings)
+        ]
+        
+        self.qdrant.upsert(collection_name=collection, points=points)
+        
+        # Mark completed
+        cursor = conn.cursor()
+        ids = [str(h[0]) for h in hadiths]
+        cursor.execute(
+            "UPDATE hadiths SET embedding_generated = TRUE, embedding_version = 'v1' WHERE id = ANY(%s)",
+            (ids,)
+        )
+        conn.commit()
+        cursor.close()
+        
+        return len(points)
+    
+    async def generate_all(self, collection: str = "hadith_embeddings"):
+        """Generate embeddings for all hadiths"""
+        self.create_collection(collection)
+        conn = psycopg2.connect(self.database_url)
+        cursor = conn.cursor()
+        
+        cursor.execute("SELECT COUNT(*) FROM hadiths WHERE embedding_generated = FALSE")
+        total = cursor.fetchone()[0]
+        cursor.close()
+        
+        if total == 0:
+            print("All hadiths already have embeddings!")
+            return
+        
+        print(f"Generating embeddings for {total} hadiths...")
+        processed = 0
+        
+        with tqdm(total=total) as pbar:
+            while True:
+                cursor = conn.cursor()
+                cursor.execute("""
+                    SELECT id, arabic_text, english_text, urdu_text, collection_id
+                    FROM hadiths
+                    WHERE embedding_generated = FALSE
+                    LIMIT 1000
+                """)
+                hadiths = cursor.fetchall()
+                cursor.close()
+                
+                if not hadiths:
+                    break
+                
+                for i in range(0, len(hadiths), self.batch_size):
+                    batch = hadiths[i:i+self.batch_size]
+                    try:
+                        count = await self.process_batch(conn, batch, collection)
+                        processed += count
+                        pbar.update(count)
+                    except Exception as e:
+                        logger.error("batch_failed", error=str(e))
+        
+        conn.close()
+        print(f"\nCompleted! Generated {processed} embeddings.")
+    
+    async def close(self):
+        await self.http_client.aclose()
+
+
+async def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--batch-size", type=int, default=32)
+    args = parser.parse_args()
+    
+    gen = EmbeddingGenerator(
+        database_url=settings.DATABASE_URL,
+        tei_url="http://tei.ml.svc.cluster.local",
+        qdrant_url="http://qdrant.vector.svc.cluster.local:6333",
+        batch_size=args.batch_size
+    )
+    
+    try:
+        await gen.generate_all()
+        return 0
+    except Exception as e:
+        logger.error("generation_failed", error=str(e))
+        return 1
+    finally:
+        await gen.close()
+
+
+if __name__ == "__main__":
+    sys.exit(asyncio.run(main()))

hadith-ingestion/src/main_hadithapi.py

@@ -2,10 +2,16 @@
 Main ingestion script for fetching hadiths from HadithAPI.com
 """
 import sys
+from pathlib import Path
 import argparse
 from typing import Optional, Dict, Any
 from uuid import UUID
 import structlog
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
 from config.settings import settings
 from src.api_clients.hadithapi_client import HadithAPIClient
 from src.database.repository import HadithRepository

hadith-ingestion/test-hadithapi-k8s.sh

@@ -19,7 +19,7 @@ echo "Building Docker image..."
 # 3. Submit test workflow (10 hadiths)
 echo "Submitting test workflow..."
 argo submit -n ml argo/workflows/ingest-hadithapi.yaml \
-  --parameter book-slug=sahih-bukhari \
+  --parameter book-slug=sahih-muslim \
   --parameter limit=10 \
   --wait \
   --log

hadith-ingestion/test-hadithapi-local.sh

@@ -19,7 +19,7 @@ python src/main_hadithapi.py --list-books
 
 # 4. Test ingestion (limited to 10 hadiths)
 echo -e "\nRunning test ingestion (10 hadiths from Sahih Bukhari)..."
-python src/main_hadithapi.py --book-slug sahih-bukhari --limit 10
+python src/main_hadithapi.py --book-slug sahih-muslim --limit 10
 
 # 5. Verify data
 echo -e "\nVerifying ingested data..."