removed ingest.py and updates this file code in direclty in rag file and removed filed preocessor

backend/Rag.py

@@ -0,0 +1,107 @@
+import os
+from dotenv import load_dotenv
+from langchain_chroma import Chroma
+from dotenv import load_dotenv
+load_dotenv()
+from langchain_community.document_loaders import DirectoryLoader, TextLoader
+from langchain_huggingface import HuggingFaceEndpointEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+embedding_model=HuggingFaceEndpointEmbeddings(model="sentence-transformers/all-MiniLM-L6-v2")
+load_dotenv()
+
+# 2. Use same HuggingFace embeddings
+# Note: Switched to HuggingFaceEmbeddings for local model execution
+embedding_model = HuggingFaceEndpointEmbeddings(model="sentence-transformers/all-MiniLM-L6-v2")
+#path configuration
+
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+#DATA_DIR="./knowedge_base"
+#CHROMA_DIR="./croma_db"
+
+DATA_DIR = os.path.join(PROJECT_ROOT, "knowedge_base")
+CHROMA_DIR= os.path.join(PROJECT_ROOT, "croma_db")
+
+
+print("dirrrrrrrrrrrrrrrrrrrrrrrrrr",DATA_DIR)
+print("cromammmmmmmmmmmmmmmm",CHROMA_DIR)
+
+# text loading
+def cunking_docs():
+    print("loading documnet fom knowedgebase:")
+    loader = DirectoryLoader(DATA_DIR, glob="**/*.txt", loader_cls=TextLoader)
+    # documents = loader.load()
+    # if not documents:
+    #     print(" Add documnet to knowedge base")
+    #     return
+    # print(f"Loaded doc length of doc: {len(documents)}")
+    # # text chunking 
+    # print( "creating splitters:")
+    # text_splitter=RecursiveCharacterTextSplitter(chunk_size=500,chunk_overlap=40)
+    # chunks=text_splitter.split_documents(documents)
+    # print(f"Split into lenght: {len(chunks)} chunks.")
+
+    # # storing chunks in vector db by creating embedding
+    # print("embedding the chunks and storing in CromaDB: ")
+    # vectorstore=Chroma.from_documents(
+    #     documents=chunks,
+    #     embedding=embedding_model,
+    #     persist_directory=CHROMA_DIR
+    # )
+    # print(f"stroing verctor at{CHROMA_DIR}: ")
+    documents = loader.load()
+
+    print("Documents loaded:", len(documents))
+
+    for doc in documents:
+     print("File:", doc.metadata)
+
+    if not documents:
+     print("No documents found!")
+
+   # text chunking 
+    print( "creating splitters:")
+    text_splitter=RecursiveCharacterTextSplitter(chunk_size=500,chunk_overlap=40)
+    chunks=text_splitter.split_documents(documents)
+    print(f"Split into lenght: {len(chunks)} chunks.")
+
+    chunks = text_splitter.split_documents(documents)
+
+    print("Chunks created:", len(chunks))
+
+    vectorstore = Chroma.from_documents(
+    documents=chunks,
+    embedding=embedding_model,
+    persist_directory=CHROMA_DIR
+    )
+
+    print("Stored documents:", vectorstore._collection.count())
+
+# 1. Force the absolute path to the root folder
+
+
+# 4. Expose a function: get_retriever()
+def get_retriever():
+    print(f"Loading ChromaDB from {CHROMA_DIR} and creating retriever...")
+    
+    vectorstore = Chroma(
+        embedding_function=embedding_model,
+        persist_directory=CHROMA_DIR
+    )
+    
+    # 3. Create retriever (k=3 most relevant chunks)
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
+    
+    return retriever
+
+if __name__ == "__main__":
+    cunking_docs()  # Create embeddings and store in Chroma
+
+    print("Testing the RAG retriever...")
+    test_retriever = get_retriever()
+
+    test_query = "What is your return policy?"
+    results = test_retriever.invoke(test_query)
+
+    print("Results found:", len(results))