Retrieval working

self_hosted_env/data-handler/Dockerfile

+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+#just keep the container running without doing anything
+CMD ["sh", "-c", "while :; do sleep 10; done"]

self_hosted_env/data-handler/insert_csv_embeddings.py

+import os
+import psycopg2
+import pandas as pd
+import json
+from dotenv import load_dotenv
+
+# Import the pgvector adapter for psycopg2
+from pgvector.psycopg2 import register_vector
+
+load_dotenv()
+
+def get_db_connection():
+    conn = psycopg2.connect(
+        dbname=os.getenv("POSTGRES_DB", "embeddings_db"),
+        user=os.getenv("POSTGRES_USER", "db_admin"),
+        password=os.getenv("POSTGRES_PASSWORD"),
+        host=os.getenv("POSTGRES_HOST", "localhost"),
+        port=os.getenv("POSTGRES_PORT", 5432)
+    )
+    # Register the vector type with the connection
+    register_vector(conn)
+    return conn
+
+def create_schema_and_table():
+    create_extension = "CREATE EXTENSION IF NOT EXISTS vector;"
+    drop_table = "DROP TABLE IF EXISTS educational_chunks;"
+    create_table = """
+    CREATE TABLE IF NOT EXISTS educational_chunks (
+      id SERIAL PRIMARY KEY,
+      grade TEXT NOT NULL,
+      subject TEXT,
+      unit TEXT,
+      concept TEXT,
+      lesson TEXT,
+      from_page INT,
+      to_page INT,
+      chunk_index INT,
+      chunk_text TEXT NOT NULL,
+      is_arabic BOOLEAN NOT NULL,
+      embedding VECTOR(1536) NOT NULL
+    );
+    """
+    create_indexes = [
+        "CREATE INDEX IF NOT EXISTS idx_embedding ON educational_chunks USING hnsw (embedding vector_cosine_ops);",
+        "CREATE INDEX IF NOT EXISTS idx_grade ON educational_chunks (grade);",
+        "CREATE INDEX IF NOT EXISTS idx_is_arabic ON educational_chunks (is_arabic);",
+        "CREATE INDEX IF NOT EXISTS idx_subject ON educational_chunks (subject);",
+        "CREATE INDEX IF NOT EXISTS idx_grade_is_arabic ON educational_chunks (grade, is_arabic);"
+    ]
+
+    conn = get_db_connection()
+    cur = conn.cursor()
+    cur.execute(create_extension)
+    print("CREATE EXTENSION vector operation fine.")
+    cur.execute(drop_table)
+    print("DROP TABLE educational_chunks operation fine.")
+    cur.execute(create_table)
+    print("CREATE TABLE educational_chunks operation fine.")
+    for idx_query in create_indexes:
+        cur.execute(idx_query)
+        print(f"CREATE INDEX operation fine for: {idx_query}")
+    conn.commit()
+    cur.close()
+    conn.close()
+
+def insert_chunks_from_csv(csv_file: str):
+    df = pd.read_csv(csv_file)
+
+    required_cols = [
+        "Grade", "Subject", "Unit", "Concept", "Lesson",
+        "From page", "To page", "Chunk index", "Chunk text",
+        "Is Arabic", "Embedding"
+    ]
+
+    for col in required_cols:
+        if col not in df.columns:
+            raise ValueError(f"Missing required column in CSV: {col}")
+
+    conn = get_db_connection()
+    cur = conn.cursor()
+
+    insert_query = """
+        INSERT INTO educational_chunks
+        (grade, subject, unit, concept, lesson,
+         from_page, to_page, chunk_index, chunk_text,
+         is_arabic, embedding)
+        VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
+    """
+
+    batch_size = 50
+    buffer = []
+
+    for idx, row in df.iterrows():
+        try:
+            embedding = json.loads(row["Embedding"])
+            buffer.append((
+                str(row["Grade"]),
+                row["Subject"],
+                row.get("Unit"),
+                row.get("Concept"),
+                row.get("Lesson"),
+                int(row["From page"]) if not pd.isna(row["From page"]) else None,
+                int(row["To page"]) if not pd.isna(row["To page"]) else None,
+                int(row["Chunk index"]),
+                row["Chunk text"],
+                bool(row["Is Arabic"]),
+                embedding
+            ))
+        except Exception as e:
+            print(f"Skipping row {idx} due to error: {e}")
+            continue
+
+        if len(buffer) >= batch_size:
+            cur.executemany(insert_query, buffer)
+            conn.commit()
+            print(f"Inserted {len(buffer)} rows. Operation fine.")
+            buffer = []
+
+    if buffer:
+        cur.executemany(insert_query, buffer)
+        conn.commit()
+        print(f"Inserted final {len(buffer)} rows. Operation fine.")
+
+    cur.close()
+    conn.close()
+    print("All data inserted successfully.")
+
+if __name__ == "__main__":
+    create_schema_and_table()
+    csv_files = ["prime4_ar_embeddings.csv", "Prime5_en_chunked_with_embeddings.csv", "prime6_ar_embeddings.csv", "Prime6_en_chunked_with_embeddings.csv"]
+    for file in csv_files:
+        if os.path.exists(file):
+            print(f"Inserting data from {file}...")
+            insert_chunks_from_csv(file)
+        else:
+            print(f"File not found: {file}")
\ No newline at end of file

self_hosted_env/data-handler/requirements.txt

+psycopg2-binary
+pandas
+python-dotenv
+pgvector

self_hosted_env/docker-compose.yml

@@ -86,6 +86,28 @@ services:
 
 
 
+  data-handler:
+    build: ./data-handler    # path to your Dockerfile folder
+    container_name: data-handler
+    restart: always
+    environment:
+      MINIO_ENDPOINT: "http://minio:9000"
+      MINIO_ACCESS_KEY: "${MINIO_ROOT_USER}"
+      MINIO_SECRET_KEY: "${MINIO_ROOT_PASSWORD}"
+      N8N_WEBHOOK_URL: "${N8N_WEBHOOK_URL}"
+      OPENAI_API_KEY: "${OPENAI_API_KEY}"
+      MINIO_BUCKET: "${MINIO_BUCKET}"
+      POSTGRES_HOST: "postgres"
+      POSTGRES_USER: "${POSTGRES_USER}"
+      POSTGRES_PASSWORD: "${POSTGRES_PASSWORD}"
+      POSTGRES_DB: "${POSTGRES_DB}"
+
+    depends_on:
+      - minio
+      - postgres
+
+
+
 volumes:
   pgdata:
   miniodata:

self_hosted_env/voice_agent/requirements.txt

@@ -6,4 +6,5 @@ fastapi
 uvicorn[standard]
 python-multipart
 openai
-psycopg2-binary
+psycopg2-binary
\ No newline at end of file
+pgvector
\ No newline at end of file

self_hosted_env/voice_agent/services/agent_service.py

@@ -48,7 +48,7 @@ SYSTEM_PROMPTS: Dict[StudentNationality, str] = {
 class AgentService:
     """Service class for handling AI agent conversations using database memory"""
 
-    def __init__(self, use_pgvector: bool = False):
+    def __init__(self, use_pgvector: bool = True):
         self.openai_service = OpenAIService()
         if not self.openai_service.is_available():
             logger.warning("Warning: OPENAI_API_KEY not found. Agent service will be disabled.")
@@ -106,6 +106,13 @@ class AgentService:
             if not student_info:
                 raise HTTPException(status_code=404, detail=f"Student with ID {student_id} not found")
             
+            # Print student information
+            print("----------------- Student Info Retrieved -----------------")
+            print(f"Student ID: {student_id}")
+            for key, value in student_info.items():
+                print(f"{key.capitalize()}: {value}")
+            print("---------------------------------------------------------")
+
             logger.info(f"Retrieved student info from DB: {student_info} for student: {student_id}")
             
             # Convert nationality string to StudentNationality enum
@@ -163,6 +170,8 @@ class AgentService:
                     )
 
                     if neighbors:
+                        # Print retrieval results
+                        print("\n----------------- Retrieval Results -----------------")
                         context_message = f"معلومات من المنهج لمادة {subject} للصف {student_info['grade']}:\n"
                         for i, n in enumerate(neighbors, 1):
                             unit_info = f" - الوحدة: {n['unit']}" if n['unit'] else ""
@@ -172,10 +181,22 @@ class AgentService:
                             context_message += f"\n{i}. {unit_info}{concept_info}{lesson_info}\n"
                             context_message += f"المحتوى: {n['chunk_text'][:200]}...\n"
                             context_message += f"(درجة التشابه: {n['distance']:.3f})\n"
+
+                            print(f"Result {i}:")
+                            print(f"  Unit: {n['unit']}")
+                            print(f"  Concept: {n['concept']}")
+                            print(f"  Lesson: {n['lesson']}")
+                            print(f"  Chunk Text: {n['chunk_text']}...")
+                            print(f"  Distance: {n['distance']:.3f}")
+                            print("-" * 20)
+                        print("-----------------------------------------------------")
                         
                         messages.append({"role": "system", "content": context_message})
                         logger.info(f"Added {len(neighbors)} filtered knowledge base results for subject: {subject}")
                     else:
+                        print("\n----------------- Retrieval Results -----------------")
+                        print(f"No relevant content found for subject: {subject}, grade: {student_info['grade']}, Arabic: {student_info['is_arabic']}")
+                        print("-----------------------------------------------------")
                         logger.info(f"No relevant content found for subject: {subject}, grade: {student_info['grade']}, Arabic: {student_info['is_arabic']}")
                         
                 except Exception as e:
@@ -187,6 +208,7 @@ class AgentService:
                 messages=messages,
                 temperature=temperature
             )
+            
 
             ai_response = response.choices[0].message.content.strip()
             if not ai_response:

self_hosted_env/voice_agent/services/pgvector_service.py

@@ -2,6 +2,8 @@ import os
 import psycopg2
 from psycopg2.extras import RealDictCursor
 from typing import List, Optional
+# Import the pgvector adapter
+from pgvector.psycopg2 import register_vector
 
 
 class PGVectorService:
@@ -14,6 +16,8 @@ class PGVectorService:
             password=os.getenv("POSTGRES_PASSWORD"),
             dbname=os.getenv("POSTGRES_DB"),
         )
+        # Register the vector type with the connection
+        register_vector(self.conn)
 
     def insert_embedding(self, id: int, embedding: list):
         """Insert or update an embedding"""
@@ -55,12 +59,12 @@ class PGVectorService:
             cur.execute(
                 """
                 SELECT id, grade, subject, unit, concept, lesson, chunk_text, 
-                       is_arabic, embedding <-> %s AS distance
+                       is_arabic, embedding <-> %s::vector AS distance
                 FROM educational_chunks
                 WHERE grade = %s 
                   AND subject ILIKE %s 
                   AND is_arabic = %s
-                ORDER BY embedding <-> %s
+                ORDER BY embedding <-> %s::vector
                 LIMIT %s;
                 """,
                 (query_embedding, grade, f"%{subject}%", is_arabic, query_embedding, limit),
@@ -103,10 +107,10 @@ class PGVectorService:
             cur.execute(
                 f"""
                 SELECT id, grade, subject, unit, concept, lesson, chunk_text, 
-                       is_arabic, embedding <-> %s AS distance
+                       is_arabic, embedding <-> %s::vector AS distance
                 FROM educational_chunks
                 {where_clause}
-                ORDER BY embedding <-> %s
+                ORDER BY embedding <-> %s::vector
                 LIMIT %s;
                 """,
                 params