RAG

Author: TejasGupta-27

RAG/README

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+Get GEMINI API KEY from google console 
+For Database query you can alter the prompts according to your schema 

RAG/VectorDB/__pycache__/client.cpython-312.pyc

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+import chromadb 
+from chromadb.utils import embedding_functions
+def get_chroma_client():
+    client = chromadb.PersistentClient(path="/media/tejas/b25dc664-2aec-424c-8f6c-f895bbec7e5d/Ericsson_RAG/data")
+    return client
+
+def get_or_create_collections(client, collection_name, model_name='all-mpnet-base-v2'):
+    sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=model_name)
+    collections = client.list_collections()
+    collection_names = [c.name for c in collections]
+    
+    if collection_name not in collection_names:
+        collection = client.create_collection(name=collection_name)
+    else:
+        collection = client.get_collection(name=collection_name)
+    return collection
+
+def delete_all_collections():
+    client = get_chroma_client()
+    collections = client.list_collections()
+    for collection in collections:
+        client.delete_collection(collection.name)
+        print(f"Deleted collection: {collection.name}")
+
+if __name__ == '__main__':
+    delete_all_collections()

RAG/VectorDB/client.py

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+# database_handler.py
+import os
+import sqlite3
+import google.generativeai as genai
+from dotenv import load_dotenv
+load_dotenv()
+api_key=os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=api_key)
+model = genai.GenerativeModel('gemini-1.5-flash')
+
+# System prompt for the Gemini model
+system_prompt = """
+I have an sqlite database with the following tables and columns:
+
+Table name: RatePlan
+Columns:
+RatePlanId INTEGER PRIMARY KEY
+Name VARCHAR(255)
+MonthlyFee FLOAT
+CallRate FLOAT
+SmsRate FLOAT
+DataRate FLOAT
+
+
+Table name: Customer
+Columns:
+CustomerId INTEGER PRIMARY KEY
+FirstName VARCHAR(255)
+LastName VARCHAR(255)
+Address VARCHAR(255)
+City VARCHAR(255)
+State VARCHAR(255)
+Country VARCHAR(255)
+PostalCode VARCHAR(255)
+Phone VARCHAR(255)
+Email VARCHAR(255)
+RatePlanId INT
+ContractStart DATE
+ContractEnd DATE
+
+Foreign Keys:
+Foreign key: RatePlanId references RatePlanId(NO ACTION)
+
+Table name: Phone
+Columns:
+PhoneId INTEGER PRIMARY KEY
+Brand VARCHAR(255)
+Model VARCHAR(255)
+OS VARCHAR(255)
+Price FLOAT
+
+Table name: CustomerPhone
+Columns:
+CustomerPhoneId INTEGER PRIMARY KEY
+CustomerId INT
+PhoneId INT
+PhoneAcquisitionDate DATE
+
+Foreign Keys:
+Foreign key: PhoneId references PhoneId(NO ACTION)
+Foreign key: CustomerId references CustomerId(NO ACTION)
+
+Table name: CDR
+Columns:
+CdrId INTEGER PRIMARY KEY
+CustomerId INT
+PhoneNumber VARCHAR(255)
+CallDateTime DATETIME
+CallType VARCHAR(255)
+DurationInSeconds INT
+DataUsageKb INT
+SmsCount INT
+
+Foreign Keys:
+Foreign key: CustomerId references CustomerId(NO ACTION)
+
+I will need you to help me generate SQL queries to get data from my database.
+Please respond only with the query in simple text format. Do not provide any explanations or additional text.
+
+If the user tries to modify the database respond with 'ERROR: cannot modify db'
+"""
+
+# Initialize chat with system prompt
+model = genai.GenerativeModel('gemini-1.5-flash')
+chat = model.start_chat(history=[])
+chat.send_message(system_prompt)
+
+def generate_sql_query(prompt):
+    response = chat.send_message(prompt)
+    sql_query = response.text.strip()
+    if(sql_query=="ERROR: cannot modify db"):return "ERROR: cannot modify db"
+    sql_query = sql_query.replace('```sql', '').replace('```', '').strip()
+    print(sql_query)
+    return sql_query
+
+def fetch_data_from_db(sql_query, db_path='/media/tejas/b25dc664-2aec-424c-8f6c-f895bbec7e5d/Ericsson_RAG/call_db.sqlite'):
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+
+
+    cursor.execute(sql_query)
+    results = cursor.fetchall()
+    columns = [description[0] for description in cursor.description]
+    conn.close()
+    return columns, results
+
+def format_results_as_table(columns, results):
+    table = [columns]
+    table.extend(results)
+    return table
+
+

RAG/__pycache__/database_handler.cpython-312.pyc

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+import nltk
+from nltk.tokenize import sent_tokenize
+from PyPDF2 import PdfReader
+from io import BytesIO
+from docx import Document
+
+nltk.download('punkt')
+
+def extract_text_from_pdf(pdf_file):
+    reader = PdfReader(pdf_file)
+    text = ''
+    for page in reader.pages:
+        text += page.extract_text() + '\n'
+    return text
+
+def extract_text_from_txt(txt_file_path):
+    with open(txt_file_path, 'r', encoding='utf-8') as f:
+        text = f.read()
+    return text
+
+def extract_text_from_docx(docx_file):
+    doc = Document(docx_file)
+    paragraphs = [paragraph.text for paragraph in doc.paragraphs]
+    text = '\n'.join(paragraphs)
+    return text
+
+def chunk_text(text, chunk_size=5):
+    sentences = sent_tokenize(text)
+    return [' '.join(sentences[i:i + chunk_size]) for i in range(0, len(sentences), chunk_size)]

RAG/__pycache__/preprocessing.cpython-312.pyc

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+from VectorDB.client import get_chroma_client
+import os 
+import google.generativeai as genai
+from chromadb.utils import embedding_functions
+import uuid
+from preprocessing import extract_text_from_pdf, chunk_text,extract_text_from_docx,extract_text_from_txt
+from VectorDB.client import get_chroma_client, get_or_create_collections
+from dotenv import load_dotenv
+load_dotenv()
+api_key=os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=api_key)
+model = genai.GenerativeModel('gemini-1.5-flash')
+chat = model.start_chat(history=[])
+
+def store_document_embeddings(document_path, collection_name='document', chunk_size=5, model_name='all-mpnet-base-v2'):
+    client = get_chroma_client()
+    
+    collection = get_or_create_collections(client, collection_name, model_name)
+    sentence_trans_ef=embedding_functions.SentenceTransformerEmbeddingFunction(model_name='all-mpnet-base-v2')
+    _, file_extension = os.path.splitext(document_path)
+    if file_extension == '.pdf':
+        text = extract_text_from_pdf(document_path)
+    elif file_extension == '.docx':
+        text = extract_text_from_docx(document_path)
+    elif file_extension == '.txt':
+        text= extract_text_from_txt(document_path)
+    else:
+        raise ValueError(f"Unsupported file type: {file_extension}. Supported types are pdf, docx, txt.")
+    
+    chunks = chunk_text(text, chunk_size)
+    ids = [str(uuid.uuid4()) for _ in range(len(chunks))]
+    metadata = [{"document": document_path, "chunk": i} for i in range(len(chunks))]
+    
+    collection.add(ids=ids, documents=chunks, metadatas=metadata)
+
+
+def retrieve_documents(query, collection):
+    
+    sentence_trans_ef=embedding_functions.SentenceTransformerEmbeddingFunction(model_name='all-mpnet-base-v2')
+    
+    results = collection.query(query_texts=[query], n_results=5,)
+    return results
+
+def generate_response(query, collection_name='document'):
+    client = get_chroma_client()
+    collections=client.list_collections()
+    expansion_prompt = f"Expand or transform the following query to include related keywords and phrases that will improve the chances of finding relevant text in a document database:\n\nQuery: {query}\n\nExpanded query:"
+    expanded_query = model.generate_content(expansion_prompt).text.strip()
+    context = ""
+    for collection in collections:
+        documents = retrieve_documents(expanded_query,collection)
+        for document in documents["documents"]:
+            for i in document :
+                context+=i
+  
+    
+    prompt = f"User query: {query}\n\nRelevant information:\n{context}\n\nBased on the above information, please provide a detailed response.Dont add on your own anything just stick to the info given and answer the query without any suggestions or recommendations"
+    
+    chats= chat.send_message(prompt)
+    # Generate the response in streaming mode
+    response = chats.text
+    
+    print(type(chat.history[1]))
+    return response

RAG/__pycache__/rag_pipeline.cpython-312.pyc

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+import sqlite3
+
+def get_database_schema(db_path):
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+
+    # Get all table names
+    cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
+    tables = cursor.fetchall()
+
+    schema_details = []
+
+    for table in tables:
+        table_name = table[0]
+        cursor.execute(f"PRAGMA table_info({table_name});")
+        columns = cursor.fetchall()
+
+        column_details = []
+        for col in columns:
+            column_info = f"{col[1]} {col[2]}"
+            if col[5]:  # Check if the column is a primary key
+                column_info += " PRIMARY KEY"
+            column_details.append(column_info)
+        
+        # Get foreign key information
+        cursor.execute(f"PRAGMA foreign_key_list({table_name});")
+        foreign_keys = cursor.fetchall()
+        foreign_key_details = []
+        for fk in foreign_keys:
+            foreign_key_details.append(
+                f"Foreign key: {fk[3]} references {fk[4]}({fk[5]})"
+            )
+
+        schema_details.append(f"Table name: {table_name}\nColumns:\n" + "\n".join(column_details))
+        if foreign_key_details:
+            schema_details.append("Foreign Keys:\n" + "\n".join(foreign_key_details))
+
+    conn.close()
+
+    return "\n\n".join(schema_details)
+
+def generate_system_prompt(db_path):
+    schema_details = get_database_schema(db_path)
+    system_prompt = f"""I have an sqlite database with the following tables and columns:
+
+{schema_details}
+
+I will need you to help me generate SQL queries to get data from my database.
+Please respond only with the query. Do not provide any explanations or additional text.
+
+If the user tries to modify the database respond with 'ERROR: cannot modify db'
+"""
+    return system_prompt
+
+# Example usage
+db_path = 'call_db.sqlite'  # Replace with your actual database path
+system_prompt = generate_system_prompt(db_path)
+print(system_prompt)