import gradio as gr
import os
from typing import List, Dict
import numpy as np
from datasets import load_dataset
from langchain.text_splitter import (
RecursiveCharacterTextSplitter,
CharacterTextSplitter,
TokenTextSplitter
)
from langchain_community.vectorstores import FAISS, Chroma
from langchain_community.document_loaders import PyPDFLoader
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceEndpoint
from langchain.memory import ConversationBufferMemory
from sentence_transformers import SentenceTransformer, util
import torch
from ragas import evaluate
from ragas.metrics import (
ContextRecall,
AnswerRelevancy,
Faithfulness,
ContextPrecision
)
import pandas as pd
# Constants and configurations
CHUNK_SIZES = {
"small": {"recursive": 512, "fixed": 512, "token": 256},
"medium": {"recursive": 1024, "fixed": 1024, "token": 512}
}
class RAGEvaluator:
def __init__(self):
self.datasets = {
"squad": "squad_v2",
"msmarco": "ms_marco"
}
self.current_dataset = None
self.test_samples = []
def load_dataset(self, dataset_name: str, num_samples: int = 50):
if dataset_name == "squad":
dataset = load_dataset("squad_v2", split="validation")
samples = dataset.select(range(num_samples))
self.test_samples = [
{
"question": sample["question"],
"ground_truth": sample["answers"]["text"][0] if sample["answers"]["text"] else "",
"context": sample["context"]
}
for sample in samples
if sample["answers"]["text"] # Filter out samples without answers
]
elif dataset_name == "msmarco":
dataset = load_dataset("ms_marco", "v2.1", split="train")
samples = dataset.select(range(num_samples))
self.test_samples = [
{
"question": sample["query"],
"ground_truth": sample["answers"][0] if sample["answers"] else "",
"context": sample["passages"]["passage_text"][0]
}
for sample in samples
if sample["answers"] # Filter out samples without answers
]
self.current_dataset = dataset_name
return self.test_samples
def evaluate_configuration(self,
vector_db,
qa_chain,
splitting_strategy: str,
chunk_size: str) -> Dict:
if not self.test_samples:
return {"error": "No dataset loaded"}
results = []
for sample in self.test_samples:
response = qa_chain.invoke({
"question": sample["question"],
"chat_history": []
})
results.append({
"question": sample["question"],
"answer": response["answer"],
"contexts": [doc.page_content for doc in response["source_documents"]],
"ground_truths": [sample["ground_truth"]]
})
# Convert to RAGAS dataset format
eval_dataset = Dataset.from_list(results)
# Calculate RAGAS metrics
metrics = [
ContextRecall(),
AnswerRelevancy(),
Faithfulness(),
ContextPrecision()
]
scores = evaluate(
eval_dataset,
metrics=metrics
)
return {
"configuration": f"{splitting_strategy}_{chunk_size}",
"context_recall": float(scores['context_recall']),
"answer_relevancy": float(scores['answer_relevancy']),
"faithfulness": float(scores['faithfulness']),
"context_precision": float(scores['context_precision']),
"average_score": float(np.mean([
scores['context_recall'],
scores['answer_relevancy'],
scores['faithfulness'],
scores['context_precision']
]))
}
def demo():
evaluator = RAGEvaluator()
with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo:
vector_db = gr.State()
qa_chain = gr.State()
gr.HTML("Enhanced RAG PDF Chatbot with Evaluation
")
with gr.Tabs():
# Custom PDF Tab
with gr.Tab("Custom PDF Chat"):
# Your existing UI components here
with gr.Row():
with gr.Column(scale=86):
gr.Markdown("Step 1 - Configure and Initialize RAG Pipeline")
with gr.Row():
document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
with gr.Row():
splitting_strategy = gr.Radio(
["recursive", "fixed", "token"],
label="Text Splitting Strategy",
value="recursive"
)
db_choice = gr.Dropdown(
["faiss", "chroma"],
label="Vector Database",
value="faiss"
)
chunk_size = gr.Radio(
["small", "medium"],
label="Chunk Size",
value="medium"
)
# Rest of your existing UI components...
# Evaluation Tab
with gr.Tab("RAG Evaluation"):
with gr.Row():
dataset_choice = gr.Dropdown(
choices=list(evaluator.datasets.keys()),
label="Select Evaluation Dataset",
value="squad"
)
load_dataset_btn = gr.Button("Load Dataset")
with gr.Row():
dataset_info = gr.JSON(label="Dataset Information")
with gr.Row():
eval_splitting_strategy = gr.Radio(
["recursive", "fixed", "token"],
label="Text Splitting Strategy",
value="recursive"
)
eval_chunk_size = gr.Radio(
["small", "medium"],
label="Chunk Size",
value="medium"
)
with gr.Row():
evaluate_btn = gr.Button("Run Evaluation")
evaluation_results = gr.DataFrame(label="Evaluation Results")
# Event handlers
def load_dataset_handler(dataset_name):
samples = evaluator.load_dataset(dataset_name)
return {
"dataset": dataset_name,
"num_samples": len(samples),
"sample_questions": [s["question"] for s in samples[:3]]
}
def run_evaluation(dataset_choice, splitting_strategy, chunk_size, vector_db, qa_chain):
if not evaluator.current_dataset:
return pd.DataFrame()
results = evaluator.evaluate_configuration(
vector_db=vector_db,
qa_chain=qa_chain,
splitting_strategy=splitting_strategy,
chunk_size=chunk_size
)
# Convert results to DataFrame
df = pd.DataFrame([results])
return df
# Connect event handlers
load_dataset_btn.click(
load_dataset_handler,
inputs=[dataset_choice],
outputs=[dataset_info]
)
evaluate_btn.click(
run_evaluation,
inputs=[
dataset_choice,
eval_splitting_strategy,
eval_chunk_size,
vector_db,
qa_chain
],
outputs=[evaluation_results]
)
qachain_btn.click(
initialize_llmchain, # Fixed function name here
inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db],
outputs=[qa_chain, llm_progress]
).then(
lambda: [None, "", 0, "", 0, "", 0],
inputs=None,
outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
queue=False
)
msg.submit(conversation,
inputs=[qa_chain, msg, chatbot],
outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
queue=False
)
submit_btn.click(conversation,
inputs=[qa_chain, msg, chatbot],
outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
queue=False
)
clear_btn.click(
lambda: [None, "", 0, "", 0, "", 0],
inputs=None,
outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
queue=False
)
demo.queue().launch(debug=True)
if __name__ == "__main__":
demo()