app.py

import os
import time
import pdfplumber
import docx
import nltk
import gradio as gr
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.embeddings import (
    #HuggingFaceEmbeddings,
    OpenAIEmbeddings,
    CohereEmbeddings,
)
from langchain_openai import OpenAIEmbeddings

from langchain_community.vectorstores import FAISS, Chroma
from langchain_text_splitters import (
    RecursiveCharacterTextSplitter,
    TokenTextSplitter,
)
#from langchain.retrievers import (
#    VectorStoreRetriever,
#    ContextualCompressionRetriever,
#)
from langchain.retrievers.document_compressors import LLMChainExtractor
from langchain_community.llms import OpenAI
from typing import List, Dict, Any
import pandas as pd

# Ensure nltk sentence tokenizer is downloaded
nltk.download('punkt', quiet=True)

FILES_DIR = './files'

# Supported embedding models
MODELS = {
    'HuggingFace': {
        'e5-base': "danielheinz/e5-base-sts-en-de",
        'multilingual-e5-base': "multilingual-e5-base",
        'paraphrase-miniLM': "paraphrase-multilingual-MiniLM-L12-v2",
        'paraphrase-mpnet': "paraphrase-multilingual-mpnet-base-v2",
        'gte-large': "gte-large",
        'gbert-base': "gbert-base"
    },
    'OpenAI': {
        'text-embedding-ada-002': "text-embedding-ada-002"
    },
    'Cohere': {
        'embed-multilingual-v2.0': "embed-multilingual-v2.0"
    }
}

class FileHandler:
    @staticmethod
    def extract_text(file_path):
        ext = os.path.splitext(file_path)[-1].lower()
        if ext == '.pdf':
            return FileHandler._extract_from_pdf(file_path)
        elif ext == '.docx':
            return FileHandler._extract_from_docx(file_path)
        elif ext == '.txt':
            return FileHandler._extract_from_txt(file_path)
        else:
            raise ValueError(f"Unsupported file type: {ext}")

    @staticmethod
    def _extract_from_pdf(file_path):
        with pdfplumber.open(file_path) as pdf:
            return ' '.join([page.extract_text() for page in pdf.pages])

    @staticmethod
    def _extract_from_docx(file_path):
        doc = docx.Document(file_path)
        return ' '.join([para.text for para in doc.paragraphs])

    @staticmethod
    def _extract_from_txt(file_path):
        with open(file_path, 'r', encoding='utf-8') as f:
            return f.read()

def get_embedding_model(model_type, model_name):
    if model_type == 'HuggingFace':
        return HuggingFaceEmbeddings(model_name=MODELS[model_type][model_name])
    elif model_type == 'OpenAI':
        return OpenAIEmbeddings(model=MODELS[model_type][model_name])
    elif model_type == 'Cohere':
        return CohereEmbeddings(model=MODELS[model_type][model_name])
    else:
        raise ValueError(f"Unsupported model type: {model_type}")

def get_text_splitter(split_strategy, chunk_size, overlap_size, custom_separators=None):
    if split_strategy == 'token':
        return TokenTextSplitter(chunk_size=chunk_size, chunk_overlap=overlap_size)
    elif split_strategy == 'recursive':
        return RecursiveCharacterTextSplitter(
            chunk_size=chunk_size,
            chunk_overlap=overlap_size,
            separators=custom_separators or ["\n\n", "\n", " ", ""]
        )
    else:
        raise ValueError(f"Unsupported split strategy: {split_strategy}")

def get_vector_store(store_type, texts, embedding_model):
    if store_type == 'FAISS':
        return FAISS.from_texts(texts, embedding_model)
    elif store_type == 'Chroma':
        return Chroma.from_texts(texts, embedding_model)
    else:
        raise ValueError(f"Unsupported vector store type: {store_type}")

def get_retriever(vector_store, search_type, search_kwargs=None):
    if search_type == 'similarity':
        return vector_store.as_retriever(search_type="similarity", search_kwargs=search_kwargs)
    elif search_type == 'mmr':
        return vector_store.as_retriever(search_type="mmr", search_kwargs=search_kwargs)
    else:
        raise ValueError(f"Unsupported search type: {search_type}")

def process_files(file_path, model_type, model_name, split_strategy, chunk_size, overlap_size, custom_separators):
    # File processing
    if file_path:
        text = FileHandler.extract_text(file_path)
    else:
        text = ""
        for file in os.listdir(FILES_DIR):
            file_path = os.path.join(FILES_DIR, file)
            text += FileHandler.extract_text(file_path)

    # Split text into chunks
    text_splitter = get_text_splitter(split_strategy, chunk_size, overlap_size, custom_separators)
    chunks = text_splitter.split_text(text)

    # Get embedding model
    embedding_model = get_embedding_model(model_type, model_name)

    return chunks, embedding_model

def search_embeddings(chunks, embedding_model, vector_store_type, search_type, query, top_k):
    # Create vector store
    vector_store = get_vector_store(vector_store_type, chunks, embedding_model)

    # Get retriever
    retriever = get_retriever(vector_store, search_type, {"k": top_k})

    # Perform search
    start_time = time.time()
    results = retriever.get_relevant_documents(query)
    end_time = time.time()

    return results, end_time - start_time

def calculate_statistics(results, search_time):
    return {
        "num_results": len(results),
        "avg_content_length": sum(len(doc.page_content) for doc in results) / len(results),
        "search_time": search_time
    }

import gradio as gr
import pandas as pd

def compare_embeddings(file, query, model_types, model_names, split_strategy, chunk_size, overlap_size, custom_separators, vector_store_type, search_type, top_k):
    all_results = []
    all_stats = []

    for model_type, model_name in zip(model_types, model_names):
        chunks, embedding_model = process_files(
            file.name if file else None,
            model_type,
            model_name,
            split_strategy,
            chunk_size,
            overlap_size,
            custom_separators.split(',') if custom_separators else None
        )

        results, search_time = search_embeddings(
            chunks,
            embedding_model,
            vector_store_type,
            search_type,
            query,
            top_k
        )

        stats = calculate_statistics(results, search_time)
        stats["model"] = f"{model_type} - {model_name}"

        formatted_results, formatted_stats = format_results(results, stats)
        all_results.append(formatted_results)
        all_stats.append(formatted_stats)

    return all_results + all_stats

def format_results(results, stats):
    # List to store the processed document data
    data = []

    # Extracting content and metadata from each document
    for doc in results:
        # Ensure metadata is a dictionary (if it's a custom object, convert it)
        metadata_dict = dict(doc.metadata)

        # Create a combined dictionary with 'Content' and all metadata fields
        doc_data = {"Content": doc.page_content}
        doc_data.update(metadata_dict)  # Add all metadata key-value pairs

        # Append the processed document data to the list
        data.append(doc_data)

    # Convert the list of document data into a DataFrame
    df = pd.DataFrame(data)

    # Formatting stats as a DataFrame
    formatted_stats = pd.DataFrame([stats])
    
    return df, formatted_stats


# Gradio interface
iface = gr.Interface(
    fn=compare_embeddings,
    inputs=[
        gr.File(label="Upload File (Optional)"),
        gr.Textbox(label="Search Query"),
        gr.CheckboxGroup(choices=list(MODELS.keys()), label="Embedding Model Types", value=["HuggingFace"]),
        gr.CheckboxGroup(choices=[model for models in MODELS.values() for model in models], label="Embedding Models", value=["e5-base"]),
        gr.Radio(choices=["token", "recursive"], label="Split Strategy", value="recursive"),
        gr.Slider(100, 1000, step=100, value=500, label="Chunk Size"),
        gr.Slider(0, 100, step=10, value=50, label="Overlap Size"),
        gr.Textbox(label="Custom Split Separators (comma-separated, optional)"),
        gr.Radio(choices=["FAISS", "Chroma"], label="Vector Store Type", value="FAISS"),
        gr.Radio(choices=["similarity", "mmr"], label="Search Type", value="similarity"),
        gr.Slider(1, 10, step=1, value=5, label="Top K")
    ],
    outputs=[
        gr.Dataframe(label="Results"),
        gr.Dataframe(label="Statistics")
    ],
    title="Embedding Comparison Tool",
    description="Compare different embedding models and retrieval strategies"
)

iface.launch()