RAG_AI_V2

Running

File size: 19,758 Bytes

from langchain_community.document_loaders import DirectoryLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Document
from langchain_core.documents import Document
from langchain_community.vectorstores import Chroma
import os
import shutil
import asyncio
from unstructured.partition.pdf import partition_pdf
from unstructured.partition.auto import partition
import pytesseract
import os
import re
import uuid
from langchain.schema import Document
from collections import defaultdict
pytesseract.pytesseract.tesseract_cmd = (r'/usr/bin/tesseract')
pytesseract.pytesseract.tesseract_cmd = (r'/usr/bin/tesseract')

# Configurations
UPLOAD_FOLDER = "./uploads"
VECTOR_DB_FOLDER = "./VectorDB"
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(VECTOR_DB_FOLDER, exist_ok=True)

########################################################################################################################################################
####--------------------------------------------------------------  Documnet Loader  ---------------------------------------------------------------####
########################################################################################################################################################
# Loaders for loading Document text, tables and images from any file format.
#data_path=r"H:\DEV PATEL\2025\RAG Project\test_data\google data"
def load_document(data_path):
    processed_documents = []
    #element_content = []
    table_document = []
    #having different process for the pdf
    for root, _, files in os.walk(data_path):
        for file in files:
            file_path = os.path.join(root, file)
            doc_id = str(uuid.uuid4())  # Generate a unique ID for the document

            print(f"Processing document ID: {doc_id}, Path: {file_path}")

            try:
                # Determine the file type based on extension
                filename, file_extension = os.path.splitext(file.lower())
                image_output = f"H:/DEV PATEL/2025/RAG Project/Images/{filename}/"
                # Use specific partition techniques based on file extension
                if file_extension == ".pdf":
                    elements = partition_pdf(
                        filename=file_path,
                        strategy="hi_res",  # Use layout detection
                        infer_table_structure=True,
                        hi_res_model_name="yolox",
                        extract_images_in_pdf=True,
                        extract_image_block_types=["Image","Table"],
                        extract_image_block_output_dir=image_output,
                        show_progress=True,
                        #chunking_strategy="by_title",
                    )
                else:
                    # Default to auto partition if no specific handler is found
                    elements = partition(
                        filename=file_path,
                        strategy="hi_res",
                        infer_table_structure=True,
                        show_progress=True,
                        #chunking_strategy="by_title"
                    )
            except Exception as e:
                print(f"Failed to process document {file_path}: {e}")
                continue
            categorized_content = {                
                "tables": {"content": [], "Metadata": []},
                "images": {"content": [], "Metadata": []},
                "text": {"content": [], "Metadata": []},                                         
            }
            #element_content.append(elements)
            CNT=1
            for chunk in elements:
                # Safely extract metadata and text
                chunk_type = str(type(chunk))
                chunk_metadata = chunk.metadata.to_dict() if chunk.metadata else {}                
                chunk_text = getattr(chunk, "text", None)

                # Separate content into categories
                #if "Table" in chunk_type:
                if any(
                    keyword in chunk_type
                    for keyword in [
                        "Table",
                        "TableChunk"]):                    
                    categorized_content["tables"]["content"].append(chunk_text)
                    categorized_content["tables"]["Metadata"].append(chunk_metadata)
                    
                    #test1
                    TABLE_DATA=f"Table number {CNT} "+chunk_metadata.get("text_as_html", "")+" "
                    CNT+=1
                    categorized_content["text"]["content"].append(TABLE_DATA)
                    categorized_content["text"]["Metadata"].append(chunk_metadata) 

                elif "Image" in chunk_type:
                    categorized_content["images"]["content"].append(chunk_text)
                    categorized_content["images"]["Metadata"].append(chunk_metadata)
                elif any(
                    keyword in chunk_type
                    for keyword in [
                        "CompositeElement",
                        "Text",
                        "NarrativeText",
                        "Title",
                        "Header",
                        "Footer",
                        "FigureCaption",
                        "ListItem",
                        "UncategorizedText",
                        "Formula",
                        "CodeSnippet",
                        "Address",
                        "EmailAddress",
                        "PageBreak",
                    ]
                ):
                    categorized_content["text"]["content"].append(chunk_text)
                    categorized_content["text"]["Metadata"].append(chunk_metadata)                     
                                
                else:
                    continue
            # Append processed document
            processed_documents.append({
                "doc_id": doc_id,
                "source": file_path,
                **categorized_content,
            })
            
    # Loop over tables and match text from the same document and page
    
    for doc in processed_documents:
        cnt=1 # count for storing number of the table
        for table_metadata in doc.get("tables", {}).get("Metadata", []):
            page_number = table_metadata.get("page_number")
            source = doc.get("source")
            page_content = ""
    
            for text_metadata, text_content in zip(
                doc.get("text", {}).get("Metadata", []),
                doc.get("text", {}).get("content", [])
            ):
                page_number2 = text_metadata.get("page_number")
                source2 = doc.get("source")
            
                if source == source2 and page_number == page_number2:
                    print(f"Matching text found for source: {source}, page: {page_number}")
                    page_content += f"{text_content} "  # Concatenate text with a space
        
            # Add the matched content to the table metadata 
            table_metadata["page_content"] =f"Table number {cnt} "+table_metadata.get("text_as_html", "")+" "+page_content.strip()  # Remove trailing spaces and have the content proper here 
            table_metadata["text_as_html"] = table_metadata.get("text_as_html", "") # we are also storing it seperatly
            table_metadata["Table_number"] = cnt  # addiing the table number it will be use in retrival
            cnt+=1
        
            # Custom loader of document which will store the table along with the text on that page specifically
            # making document of each table with its content
            unique_id = str(uuid.uuid4())
            table_document.append(
                Document(
                    
                    id =unique_id, # Add doc_id directly
                    page_content=table_metadata.get("page_content", ""),  # Get page_content from metadata, default to empty string if missing
                    metadata={
                        "source": doc["source"],
                        "text_as_html": table_metadata.get("text_as_html", ""),
                        "filetype": table_metadata.get("filetype", ""),
                        "page_number": str(table_metadata.get("page_number", 0)),  # Default to 0 if missing
                        "image_path": table_metadata.get("image_path", ""),
                        "file_directory": table_metadata.get("file_directory", ""),
                        "filename": table_metadata.get("filename", ""),
                        "Table_number": str(table_metadata.get("Table_number", 0))  # Default to 0 if missing
                    }
                )
            )

    # Initialize a structure to group content by doc_id
    grouped_by_doc_id = defaultdict(lambda: {
        "text_content": [],
        "metadata": None,  # Metadata will only be set once per doc_id
    })

    for doc in processed_documents:
        doc_id = doc.get("doc_id")
        source = doc.get("source")
        text_content = doc.get("text", {}).get("content", [])
        metadata_list = doc.get("text", {}).get("Metadata", [])

        # Merge text content
        grouped_by_doc_id[doc_id]["text_content"].extend(text_content)

        # Set metadata (if not already set)
        if grouped_by_doc_id[doc_id]["metadata"] is None and metadata_list:
            metadata = metadata_list[0]  # Assuming metadata is consistent
            grouped_by_doc_id[doc_id]["metadata"] = {
                "source": source,
                #"filetype": metadata.get("filetype"),
                "file_directory": metadata.get("file_directory"),
                "filename": metadata.get("filename"),
                #"languages": str(metadata.get("languages")),
            }

    # Convert grouped content into Document objects
    grouped_documents = []
    for doc_id, data in grouped_by_doc_id.items():
        grouped_documents.append(
            Document(
                id=doc_id,
                page_content=" ".join(data["text_content"]).strip(),
                metadata=data["metadata"],
            )
        )

    # Output the grouped documents
    # for document in grouped_documents:
    #     print(document)


    #Dirctory loader for loading the text data only to specific db
    loader = DirectoryLoader(data_path, glob="*.*")
    documents = loader.load()
    
    # update the metadata adding filname to the met
    for doc in documents:   
        unique_id = str(uuid.uuid4()) 
        doc.id = unique_id  
        path=doc.metadata.get("source")
        match = re.search(r'([^\\]+\.[^\\]+)$', path)
        doc.metadata.update({"filename":match.group(1)})
    
    return grouped_documents,documents,table_document
#grouped_documents = load_document(data_path)
#documents,processed_documents,table_document = load_document(data_path)


########################################################################################################################################################
####-------------------------------------------------------------- Chunking the Text  --------------------------------------------------------------####
########################################################################################################################################################

def split_text(documents: list[Document]):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=2000,
        chunk_overlap=600,
        length_function=len,
        add_start_index=True,
    )
    chunks = text_splitter.split_documents(documents) # splitting the document into chunks 
    for index in chunks:
        index.metadata["start_index"]=str(index.metadata["start_index"]) # the converstion of int metadata to str was done to store it in sqlite3 
    print(f"Split {len(documents)} documents into {len(chunks)} chunks.")
    return chunks

########################################################################################################################################################
####---------------------------------------------------- Creating and Storeing Data in Vector DB  --------------------------------------------------####
########################################################################################################################################################

#def save_to_chroma(chunks: list[Document], name: str, tables: list[Document]):
async def save_to_chroma(chunks: list[Document], name: str, tables: list[Document]):
    CHROMA_PATH = f"./VectorDB/chroma_{name}"
    TABLE_PATH = f"./TableDB/chroma_{name}"
    if os.path.exists(CHROMA_PATH):
        shutil.rmtree(CHROMA_PATH)
    if os.path.exists(TABLE_PATH):
        shutil.rmtree(TABLE_PATH)

    try:
        # Load the embedding model
        embedding_function = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2",show_progress=True)
        #embedding_function = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
        # Create Chroma DB for documents using from_documents [NOTE: Some of the data is converted to string because int and float show null if added]
        print("Creating document vector database...")
        db =Chroma.from_documents(
                                            documents=chunks,
                                            embedding=embedding_function,
                                            persist_directory=CHROMA_PATH,
                                            
                                        )
      
        print("Persisting the document database...")
        db.persist()
        print("Document database successfully saved.")
        
        # Create Chroma DB for tables if available [NOTE: Some of the data is converted to string because int and float show null if added]
        if tables !=[]:
            print("Creating table vector database...")
            tdb =Chroma.from_documents(
                documents=tables,
                embedding=embedding_function,
                persist_directory=TABLE_PATH,
            )
            print("Persisting the table database...")
            db.persist()
            print("Table database successfully saved.")
        else:
            tdb = None

        return db, tdb
        #return db
    
    except Exception as e:
        print("Error while saving to Chroma:", e)
        return None

# def get_unique_sources(chroma_path):
#     db = Chroma(persist_directory=chroma_path)
#     metadata_list = db.get()["metadatas"]
#     unique_sources = {metadata["source"] for metadata in metadata_list if "source" in metadata}
#     return list(unique_sources)

########################################################################################################################################################
####----------------------------------------------------------- Updating Existing Data in Vector DB  -----------------------------------------------####
########################################################################################################################################################

# def add_document_to_existing_db(new_documents: list[Document], db_name: str):
#     CHROMA_PATH = f"./VectorDB/chroma_{db_name}"

#     if not os.path.exists(CHROMA_PATH):
#         print(f"Database '{db_name}' does not exist. Please create it first.")
#         return

#     try:
#         embedding_function = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
#         #embedding_function = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
#         db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)

#         print("Adding new documents to the existing database...")
#         chunks = split_text(new_documents)
#         db.add_documents(chunks)
#         db.persist()
#         print("New documents added and database updated successfully.")
#     except Exception as e:
#         print("Error while adding documents to existing database:", e)

# def delete_chunks_by_source(chroma_path, source_to_delete):
#     if not os.path.exists(chroma_path):
#         print(f"Database at path '{chroma_path}' does not exist.")
#         return

#     try:
#         #embedding_function = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
#         embedding_function = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
#         db = Chroma(persist_directory=chroma_path, embedding_function=embedding_function)

#         print(f"Retrieving all metadata to identify chunks with source '{source_to_delete}'...")
#         metadata_list = db.get()["metadatas"]

#         # Identify indices of chunks to delete
#         indices_to_delete = [
#             idx for idx, metadata in enumerate(metadata_list) if metadata.get("source") == source_to_delete
#         ]

#         if not indices_to_delete:
#             print(f"No chunks found with source '{source_to_delete}'.")
#             return

#         print(f"Deleting {len(indices_to_delete)} chunks with source '{source_to_delete}'...")
#         db.delete(indices=indices_to_delete)
#         db.persist()
#         print("Chunks deleted and database updated successfully.")
#     except Exception as e:
#         print(f"Error while deleting chunks by source: {e}")
        
# # update a data store        
# def update_data_store(file_path, db_name):
#     CHROMA_PATH = f"./VectorDB/chroma_{db_name}"
#     print(f"Filepath ===> {file_path}  DB Name ====> {db_name}")

#     try:
#         documents,table_document = load_document(file_path)
#         print("Documents loaded successfully.")
#     except Exception as e:
#         print(f"Error loading documents: {e}")
#         return

#     try:
#         chunks = split_text(documents)
#         print(f"Text split into {len(chunks)} chunks.")
#     except Exception as e:
#         print(f"Error splitting text: {e}")
#         return

#     try:
#         asyncio.run(save_to_chroma(save_to_chroma(chunks, db_name, table_document)))
#         print(f"Data saved to Chroma for database {db_name}.")
#     except Exception as e:
#         print(f"Error saving to Chroma: {e}")
#         return

########################################################################################################################################################
####------------------------------------------------------- Combine Process of Load, Chunk and Store  ----------------------------------------------####
########################################################################################################################################################

async def generate_data_store(file_path, db_name):
    CHROMA_PATH = f"./VectorDB/chroma_{db_name}"
    print(f"Filepath ===> {file_path}  DB Name ====> {db_name}")

    try:
        documents,processed_documents,table_document = load_document(file_path)
        #grouped_document,document = load_document(file_path)
        print("Documents loaded successfully.")
    except Exception as e:
        print(f"Error loading documents: {e}")
        return

    try:
        chunks = split_text(documents)
        print(f"Text split into {len(chunks)} chunks.")
    except Exception as e:
        print(f"Error splitting text: {e}")
        return

    try:
        await save_to_chroma(chunks, db_name, table_document)
        #await asyncio.run(save_to_chroma(chunks, db_name,table_document))
        print(f"Data saved to Chroma for database {db_name}.")
    except Exception as e:
        print(f"Error saving to Chroma: {e}")
        return