Update app.py

app.py

@@ -1,385 +1,389 @@
-import os
-import json
-import re
-import hashlib
-from functools import partial
-from collections import defaultdict
-from pathlib import Path
-from typing import List, Dict, Any
-import numpy as np
-from dotenv import load_dotenv
-from rich.console import Console
-from rich.style import Style
-from langchain_core.runnables import RunnableLambda
-from langchain_nvidia_ai_endpoints import ChatNVIDIA
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate
-from langchain.schema.runnable.passthrough import RunnableAssign
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.docstore.document import Document
-from langchain.retrievers import BM25Retriever
-from langchain_openai import ChatOpenAI
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-
-dotenv_path = os.path.join(os.getcwd(), ".env")
-load_dotenv(dotenv_path)
-api_key = os.getenv("NVIDIA_API_KEY")
-os.environ["NVIDIA_API_KEY"] = api_key
-
-# Constants
-FAISS_PATH = "faiss_store/v30_600_150"
-CHUNKS_PATH = "all_chunks.json"
-KRISHNA_BIO = """Krishna Vamsi Dhulipalla is a graduate student in Computer Science at Virginia Tech (M.Eng, expected 2024), with over 3 years of experience across data engineering, machine learning research, and real-time analytics. He specializes in building scalable data systems and intelligent LLM-powered applications, with strong expertise in Python, PyTorch, Hugging Face Transformers, and end-to-end ML pipelines.
-
-He has led projects involving retrieval-augmented generation (RAG), feature selection for genomic classification, fine-tuning domain-specific LLMs (e.g., DNABERT, HyenaDNA), and real-time forecasting systems using Kafka, Spark, and Airflow. His cloud proficiency spans AWS (S3, SageMaker, ECS, CloudWatch), GCP (BigQuery, Cloud Composer), and DevOps tools like Docker, Kubernetes, and MLflow.
-
-Krishna’s academic focus areas include genomic sequence modeling, transformer optimization, MLOps automation, and cross-domain generalization. He has published research in bioinformatics and ML applications for circadian transcription prediction and transcription factor binding.
-
-He is certified in NVIDIA’s RAG Agents with LLMs, Google Cloud Data Engineering, AWS ML Specialization, and has a proven ability to blend research and engineering in real-world systems. Krishna is passionate about scalable LLM infra, data-centric AI, and domain-adaptive ML solutions."""
-
-def initialize_console():
-    console = Console()
-    base_style = Style(color="#76B900", bold=True)
-    return partial(console.print, style=base_style)
-
-pprint = initialize_console()
-
-def load_chunks_from_json(path: str = CHUNKS_PATH) -> List[Dict]:
-    with open(path, "r", encoding="utf-8") as f:
-        return json.load(f)
-
-def load_faiss(path: str = FAISS_PATH, 
-               model_name: str = "sentence-transformers/all-MiniLM-L6-v2") -> FAISS:
-    embeddings = HuggingFaceEmbeddings(model_name=model_name)
-    return FAISS.load_local(path, embeddings, allow_dangerous_deserialization=True)
-
-def initialize_resources():
-    vectorstore = load_faiss()
-    all_chunks = load_chunks_from_json()
-    all_texts = [chunk["text"] for chunk in all_chunks]
-    metadatas = [chunk["metadata"] for chunk in all_chunks]
-    return vectorstore, all_chunks, all_texts, metadatas
-
-vectorstore, all_chunks, all_texts, metadatas = initialize_resources()
-
-# LLMs
-repharser_llm = ChatNVIDIA(model="mistralai/mistral-7b-instruct-v0.3") | StrOutputParser()
-relevance_llm = ChatNVIDIA(model="meta/llama3-70b-instruct") | StrOutputParser()
-answer_llm = ChatOpenAI(
-    model="gpt-4-1106-preview",              
-    temperature=0.3,             
-    openai_api_key=os.getenv("OPENAI_API_KEY"),
-    streaming=True,
-    callbacks=[StreamingStdOutCallbackHandler()] 
-) | StrOutputParser()
-
-
-# Prompts
-repharser_prompt = ChatPromptTemplate.from_template(
-    "Rewrite the question below in 4 diverse ways to retrieve semantically similar information.Ensure diversity in phrasings across style, voice, and abstraction:\n\nQuestion: {query}\n\nRewrites:"
-)
-
-relevance_prompt = ChatPromptTemplate.from_template("""
-You are Krishna's personal AI assistant validator.
-Your job is to review a user's question and a list of retrieved document chunks.
-Identify which chunks (if any) directly help answer the question. Return **all relevant chunks**.
-
----
-⚠️ Do NOT select chunks just because they include keywords or technical terms.
-
-Exclude chunks that:
-- Mention universities, CGPA, or education history (they show qualifications, not skills)
-- List certifications or course names (they show credentials, not skills used)
-- Describe goals, future plans, or job aspirations
-- Contain tools mentioned in passing without describing actual usage
-
-Only include chunks if they contain **evidence of specific knowledge, tools used, skills applied, or experience demonstrated.**
-
----
-
-🔎 Examples:
-
-Q1: "What are Krishna's skills?"
-- Chunk A: Lists programming languages, ML tools, and projects → ✅
-- Chunk B: Talks about a Coursera certificate in ML → ❌
-- Chunk C: States a CGPA and master’s degree → ❌
-- Chunk D: Describes tools Krishna used in his work → ✅
-
-Output:
-{{
-  "valid_chunks": [A, D],
-  "is_out_of_scope": false,
-  "justification": "Chunks A and D describe tools and skills Krishna has actually used."
-}}
-
-Q2: "What is Krishna's favorite color?"
-- All chunks are about technical work or academic history → ❌
-
-Output:
-{{
-  "valid_chunks": [],
-  "is_out_of_scope": true,
-  "justification": "None of the chunks are related to the user's question about preferences or colors."
-}}
-
----
-
-Now your turn.
-
-User Question:
-"{query}"
-
-Chunks:
-{contents}
-
-Return only the JSON object. Think carefully before selecting any chunk.
-""")
-
-answer_prompt_relevant = ChatPromptTemplate.from_template(
-    "You are Krishna's personal AI assistant. Your job is to answer the user’s question clearly and professionally using the provided context.\n"
-    "Rather than copying sentences, synthesize relevant insights and explain them like a knowledgeable peer.\n\n"
-    "Krishna's Background:\n{profile}\n\n"
-    "Make your response rich and informative by:\n"
-    "- Combining relevant facts from multiple parts of the context\n"
-    "- Using natural, human-style language (not just bullet points)\n"
-    "- Expanding briefly on tools or skills when appropriate\n"
-    "- Avoiding repetition, filler, or hallucinations\n\n"
-    "Context:\n{context}\n\n"
-    "User Question:\n{query}\n\n"
-    "Answer:"
-)
-
-answer_prompt_fallback = ChatPromptTemplate.from_template(
-    "You are Krishna’s personal AI assistant. The user asked a question unrelated to Krishna’s background.\n"
-    "Gently let the user know, and then pivot to something Krishna is actually involved in to keep the conversation helpful.\n\n"
-    "Krishna's Background:\n{profile}\n\n"
-    "User Question:\n{query}\n\n"
-    "Your Answer:"
-)
-# Helper Functions
-def parse_rewrites(raw_response: str) -> list[str]:
-    lines = raw_response.strip().split("\n")
-    return [line.strip("0123456789. ").strip() for line in lines if line.strip()][:4]
-
-def hybrid_retrieve(inputs, exclude_terms=None):
-    # if exclude_terms is None:
-    #     exclude_terms = ["cgpa", "university", "b.tech", "m.s.", "certification", "coursera", "edx", "goal", "aspiration", "linkedin", "publication", "ieee", "doi", "degree"]
-
-    all_queries = inputs["all_queries"]
-    bm25_retriever = BM25Retriever.from_texts(texts=all_texts, metadatas=metadatas)
-    bm25_retriever.k = inputs["k_per_query"]
-    vectorstore = inputs["vectorstore"]
-    alpha = inputs["alpha"]
-    top_k = inputs.get("top_k", 15)
-
-    scored_chunks = defaultdict(lambda: {
-        "vector_scores": [],
-        "bm25_score": 0.0,
-        "content": None,
-        "metadata": None,
-    })
-
-    for subquery in all_queries:
-        vec_hits = vectorstore.similarity_search_with_score(subquery, k=inputs["k_per_query"])
-        for doc, score in vec_hits:
-            key = hashlib.md5(doc.page_content.encode("utf-8")).hexdigest()
-            scored_chunks[key]["vector_scores"].append(score)
-            scored_chunks[key]["content"] = doc.page_content
-            scored_chunks[key]["metadata"] = doc.metadata
-
-        bm_hits = bm25_retriever.invoke(subquery)
-        for rank, doc in enumerate(bm_hits):
-            key = hashlib.md5(doc.page_content.encode("utf-8")).hexdigest()
-            bm_score = 1.0 - (rank / inputs["k_per_query"])
-            scored_chunks[key]["bm25_score"] += bm_score
-            scored_chunks[key]["content"] = doc.page_content
-            scored_chunks[key]["metadata"] = doc.metadata
-
-    all_vec_means = [np.mean(v["vector_scores"]) for v in scored_chunks.values() if v["vector_scores"]]
-    max_vec = max(all_vec_means) if all_vec_means else 1
-    min_vec = min(all_vec_means) if all_vec_means else 0
-
-    final_results = []
-    for chunk in scored_chunks.values():
-        vec_score = np.mean(chunk["vector_scores"]) if chunk["vector_scores"] else 0.0
-        norm_vec = (vec_score - min_vec) / (max_vec - min_vec) if max_vec != min_vec else 1.0
-        bm25_score = chunk["bm25_score"] / len(all_queries)
-        final_score = alpha * norm_vec + (1 - alpha) * bm25_score
-
-        content = chunk["content"].lower()
-        # if any(term in content for term in exclude_terms):
-        #     continue
-        if final_score < 0.05 or len(content.strip()) < 100:
-            continue
-
-        final_results.append({
-            "content": chunk["content"],
-            "source": chunk["metadata"].get("source", ""),
-            "final_score": float(round(final_score, 4)),
-            "vector_score": float(round(vec_score, 4)),
-            "bm25_score": float(round(bm25_score, 4)),
-            "metadata": chunk["metadata"],
-            "summary": chunk["metadata"].get("summary", ""),
-            "synthetic_queries": chunk["metadata"].get("synthetic_queries", [])
-        })
-
-    final_results = sorted(final_results, key=lambda x: x["final_score"], reverse=True)
-
-    seen = set()
-    unique_chunks = []
-    for chunk in final_results:
-        clean_text = re.sub(r'\W+', '', chunk["content"].lower())[:300]
-        fingerprint = (chunk["source"], clean_text)
-        if fingerprint not in seen:
-            seen.add(fingerprint)
-            unique_chunks.append(chunk)
-
-    unique_chunks = unique_chunks[:top_k]
-
-    return {
-        "query": inputs["query"],
-        "chunks": unique_chunks
-    }
-    
-def safe_json_parse(s: str) -> Dict:
-    return json.loads(s) if isinstance(s, str) and "valid_chunks" in s else {
-        "valid_chunks": [], 
-        "is_out_of_scope": True, 
-        "justification": "Fallback due to invalid LLM output"
-    }
-
-# Rewrite generation
-rephraser_chain = (
-    repharser_prompt
-    | repharser_llm
-    | RunnableLambda(parse_rewrites)
-)
-
-generate_rewrites_chain = (
-    RunnableAssign({
-        "rewrites": lambda x: rephraser_chain.invoke({"query": x["query"]})
-    })
-    | RunnableAssign({
-        "all_queries": lambda x: [x["query"]] + x["rewrites"]
-    })
-)
-
-# Retrieval
-retrieve_chain = RunnableLambda(hybrid_retrieve)
-hybrid_chain = generate_rewrites_chain | retrieve_chain
-
-# Validation
-extract_validation_inputs = RunnableLambda(lambda x: {
-    "query": x["query"],
-    "contents": [c["content"] for c in x["chunks"]]
-})
-
-validation_chain = (
-    extract_validation_inputs
-    | relevance_prompt
-    | relevance_llm
-    | RunnableLambda(safe_json_parse)
-)
-
-# Answer Generation
-def prepare_answer_inputs(x: Dict) -> Dict:
-    context = KRISHNA_BIO if x["validation"]["is_out_of_scope"] else "\n\n".join(
-        [x["chunks"][i-1]["content"] for i in x["validation"]["valid_chunks"]])
-    
-    return {
-        "query": x["query"],
-        "profile": KRISHNA_BIO,
-        "context": context,
-        "use_fallback": x["validation"]["is_out_of_scope"]
-    }
-
-select_and_prompt = RunnableLambda(lambda x: 
-    answer_prompt_fallback.invoke(x) if x["use_fallback"]
-    else answer_prompt_relevant.invoke(x))
-
-answer_chain = (
-    prepare_answer_inputs
-    | select_and_prompt
-    | relevance_llm
-)
-
-# Full Pipeline
-full_pipeline = (
-    hybrid_chain
-    | RunnableAssign({"validation": validation_chain})
-    | RunnableAssign({"answer": answer_chain})
-)
-
-import gradio as gr
-
-def chat_interface(message, history):
-    inputs = {
-        "query": message,
-        "all_queries": [message],
-        "all_texts": all_chunks,
-        "k_per_query": 3,
-        "alpha": 0.7,
-        "vectorstore": vectorstore,
-        "full_document": "",
-    }
-    response = ""
-    for chunk in full_pipeline.stream(inputs):
-        if isinstance(chunk, str):
-            response += chunk
-            yield response
-        elif isinstance(chunk, dict) and "answer" in chunk:
-            response += chunk["answer"]
-            yield response
-
-with gr.Blocks(css="""
-     html, body, .gradio-container {
-        height: 100%;
-        margin: 0;
-        padding: 0;
-    }
-    .gradio-container {
-        width: 90%;
-        max-width: 1000px;
-        margin: 0 auto;
-        padding: 1rem;
-    }
-
-    .chatbox-container {
-        display: flex;
-        flex-direction: column;
-        height: 95%;
-    }
-
-    .chatbot {
-        flex: 1;
-        overflow-y: auto;
-        min-height: 500px;
-    }
-
-    .textbox {
-        margin-top: 1rem;
-    }
-    #component-523 {
-        height: 98%;
-    }
-""") as demo:
-    with gr.Column(elem_classes="chatbox-container"):
-        gr.Markdown("## 💬 Ask Krishna's AI Assistant")
-        gr.Markdown("💡 Ask anything about Krishna Vamsi Dhulipalla")
-        chatbot = gr.Chatbot(elem_classes="chatbot")
-        textbox = gr.Textbox(placeholder="Ask a question about Krishna...", elem_classes="textbox")
-
-        gr.ChatInterface(
-            fn=chat_interface,
-            chatbot=chatbot,
-            textbox=textbox,
-            examples=[
-                "What are Krishna's research interests?",
-                "Where did Krishna work?",
-                "What did he study at Virginia Tech?"
-            ],
-        )
-
+import os
+import json
+import re
+import hashlib
+from functools import partial
+from collections import defaultdict
+from pathlib import Path
+from typing import List, Dict, Any
+import numpy as np
+from dotenv import load_dotenv
+from rich.console import Console
+from rich.style import Style
+from langchain_core.runnables import RunnableLambda
+from langchain_nvidia_ai_endpoints import ChatNVIDIA
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.schema.runnable.passthrough import RunnableAssign
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.docstore.document import Document
+from langchain.retrievers import BM25Retriever
+from langchain_openai import ChatOpenAI
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
+#dotenv_path = os.path.join(os.getcwd(), ".env")
+#load_dotenv(dotenv_path)
+#api_key = os.getenv("NVIDIA_API_KEY")
+#os.environ["NVIDIA_API_KEY"] = api_key
+
+api_key = os.environ.get("NVIDIA_API_KEY")
+if not api_key:
+    raise RuntimeError("🚨 NVIDIA_API_KEY not found in environment! Please add it in Hugging Face Secrets.")
+
+# Constants
+FAISS_PATH = "faiss_store/v30_600_150"
+CHUNKS_PATH = "all_chunks.json"
+KRISHNA_BIO = """Krishna Vamsi Dhulipalla is a graduate student in Computer Science at Virginia Tech (M.Eng, expected 2024), with over 3 years of experience across data engineering, machine learning research, and real-time analytics. He specializes in building scalable data systems and intelligent LLM-powered applications, with strong expertise in Python, PyTorch, Hugging Face Transformers, and end-to-end ML pipelines.
+
+He has led projects involving retrieval-augmented generation (RAG), feature selection for genomic classification, fine-tuning domain-specific LLMs (e.g., DNABERT, HyenaDNA), and real-time forecasting systems using Kafka, Spark, and Airflow. His cloud proficiency spans AWS (S3, SageMaker, ECS, CloudWatch), GCP (BigQuery, Cloud Composer), and DevOps tools like Docker, Kubernetes, and MLflow.
+
+Krishna’s academic focus areas include genomic sequence modeling, transformer optimization, MLOps automation, and cross-domain generalization. He has published research in bioinformatics and ML applications for circadian transcription prediction and transcription factor binding.
+
+He is certified in NVIDIA’s RAG Agents with LLMs, Google Cloud Data Engineering, AWS ML Specialization, and has a proven ability to blend research and engineering in real-world systems. Krishna is passionate about scalable LLM infra, data-centric AI, and domain-adaptive ML solutions."""
+
+def initialize_console():
+    console = Console()
+    base_style = Style(color="#76B900", bold=True)
+    return partial(console.print, style=base_style)
+
+pprint = initialize_console()
+
+def load_chunks_from_json(path: str = CHUNKS_PATH) -> List[Dict]:
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+def load_faiss(path: str = FAISS_PATH, 
+               model_name: str = "sentence-transformers/all-MiniLM-L6-v2") -> FAISS:
+    embeddings = HuggingFaceEmbeddings(model_name=model_name)
+    return FAISS.load_local(path, embeddings, allow_dangerous_deserialization=True)
+
+def initialize_resources():
+    vectorstore = load_faiss()
+    all_chunks = load_chunks_from_json()
+    all_texts = [chunk["text"] for chunk in all_chunks]
+    metadatas = [chunk["metadata"] for chunk in all_chunks]
+    return vectorstore, all_chunks, all_texts, metadatas
+
+vectorstore, all_chunks, all_texts, metadatas = initialize_resources()
+
+# LLMs
+repharser_llm = ChatNVIDIA(model="mistralai/mistral-7b-instruct-v0.3") | StrOutputParser()
+relevance_llm = ChatNVIDIA(model="meta/llama3-70b-instruct") | StrOutputParser()
+answer_llm = ChatOpenAI(
+    model="gpt-4-1106-preview",              
+    temperature=0.3,             
+    openai_api_key=os.environ.get("OPENAI_API_KEY"),
+    streaming=True,
+    callbacks=[StreamingStdOutCallbackHandler()] 
+) | StrOutputParser()
+
+
+# Prompts
+repharser_prompt = ChatPromptTemplate.from_template(
+    "Rewrite the question below in 4 diverse ways to retrieve semantically similar information.Ensure diversity in phrasings across style, voice, and abstraction:\n\nQuestion: {query}\n\nRewrites:"
+)
+
+relevance_prompt = ChatPromptTemplate.from_template("""
+You are Krishna's personal AI assistant validator.
+Your job is to review a user's question and a list of retrieved document chunks.
+Identify which chunks (if any) directly help answer the question. Return **all relevant chunks**.
+
+---
+⚠️ Do NOT select chunks just because they include keywords or technical terms.
+
+Exclude chunks that:
+- Mention universities, CGPA, or education history (they show qualifications, not skills)
+- List certifications or course names (they show credentials, not skills used)
+- Describe goals, future plans, or job aspirations
+- Contain tools mentioned in passing without describing actual usage
+
+Only include chunks if they contain **evidence of specific knowledge, tools used, skills applied, or experience demonstrated.**
+
+---
+
+🔎 Examples:
+
+Q1: "What are Krishna's skills?"
+- Chunk A: Lists programming languages, ML tools, and projects → ✅
+- Chunk B: Talks about a Coursera certificate in ML → ❌
+- Chunk C: States a CGPA and master’s degree → ❌
+- Chunk D: Describes tools Krishna used in his work → ✅
+
+Output:
+{{
+  "valid_chunks": [A, D],
+  "is_out_of_scope": false,
+  "justification": "Chunks A and D describe tools and skills Krishna has actually used."
+}}
+
+Q2: "What is Krishna's favorite color?"
+- All chunks are about technical work or academic history → ❌
+
+Output:
+{{
+  "valid_chunks": [],
+  "is_out_of_scope": true,
+  "justification": "None of the chunks are related to the user's question about preferences or colors."
+}}
+
+---
+
+Now your turn.
+
+User Question:
+"{query}"
+
+Chunks:
+{contents}
+
+Return only the JSON object. Think carefully before selecting any chunk.
+""")
+
+answer_prompt_relevant = ChatPromptTemplate.from_template(
+    "You are Krishna's personal AI assistant. Your job is to answer the user’s question clearly and professionally using the provided context.\n"
+    "Rather than copying sentences, synthesize relevant insights and explain them like a knowledgeable peer.\n\n"
+    "Krishna's Background:\n{profile}\n\n"
+    "Make your response rich and informative by:\n"
+    "- Combining relevant facts from multiple parts of the context\n"
+    "- Using natural, human-style language (not just bullet points)\n"
+    "- Expanding briefly on tools or skills when appropriate\n"
+    "- Avoiding repetition, filler, or hallucinations\n\n"
+    "Context:\n{context}\n\n"
+    "User Question:\n{query}\n\n"
+    "Answer:"
+)
+
+answer_prompt_fallback = ChatPromptTemplate.from_template(
+    "You are Krishna’s personal AI assistant. The user asked a question unrelated to Krishna’s background.\n"
+    "Gently let the user know, and then pivot to something Krishna is actually involved in to keep the conversation helpful.\n\n"
+    "Krishna's Background:\n{profile}\n\n"
+    "User Question:\n{query}\n\n"
+    "Your Answer:"
+)
+# Helper Functions
+def parse_rewrites(raw_response: str) -> list[str]:
+    lines = raw_response.strip().split("\n")
+    return [line.strip("0123456789. ").strip() for line in lines if line.strip()][:4]
+
+def hybrid_retrieve(inputs, exclude_terms=None):
+    # if exclude_terms is None:
+    #     exclude_terms = ["cgpa", "university", "b.tech", "m.s.", "certification", "coursera", "edx", "goal", "aspiration", "linkedin", "publication", "ieee", "doi", "degree"]
+
+    all_queries = inputs["all_queries"]
+    bm25_retriever = BM25Retriever.from_texts(texts=all_texts, metadatas=metadatas)
+    bm25_retriever.k = inputs["k_per_query"]
+    vectorstore = inputs["vectorstore"]
+    alpha = inputs["alpha"]
+    top_k = inputs.get("top_k", 15)
+
+    scored_chunks = defaultdict(lambda: {
+        "vector_scores": [],
+        "bm25_score": 0.0,
+        "content": None,
+        "metadata": None,
+    })
+
+    for subquery in all_queries:
+        vec_hits = vectorstore.similarity_search_with_score(subquery, k=inputs["k_per_query"])
+        for doc, score in vec_hits:
+            key = hashlib.md5(doc.page_content.encode("utf-8")).hexdigest()
+            scored_chunks[key]["vector_scores"].append(score)
+            scored_chunks[key]["content"] = doc.page_content
+            scored_chunks[key]["metadata"] = doc.metadata
+
+        bm_hits = bm25_retriever.invoke(subquery)
+        for rank, doc in enumerate(bm_hits):
+            key = hashlib.md5(doc.page_content.encode("utf-8")).hexdigest()
+            bm_score = 1.0 - (rank / inputs["k_per_query"])
+            scored_chunks[key]["bm25_score"] += bm_score
+            scored_chunks[key]["content"] = doc.page_content
+            scored_chunks[key]["metadata"] = doc.metadata
+
+    all_vec_means = [np.mean(v["vector_scores"]) for v in scored_chunks.values() if v["vector_scores"]]
+    max_vec = max(all_vec_means) if all_vec_means else 1
+    min_vec = min(all_vec_means) if all_vec_means else 0
+
+    final_results = []
+    for chunk in scored_chunks.values():
+        vec_score = np.mean(chunk["vector_scores"]) if chunk["vector_scores"] else 0.0
+        norm_vec = (vec_score - min_vec) / (max_vec - min_vec) if max_vec != min_vec else 1.0
+        bm25_score = chunk["bm25_score"] / len(all_queries)
+        final_score = alpha * norm_vec + (1 - alpha) * bm25_score
+
+        content = chunk["content"].lower()
+        # if any(term in content for term in exclude_terms):
+        #     continue
+        if final_score < 0.05 or len(content.strip()) < 100:
+            continue
+
+        final_results.append({
+            "content": chunk["content"],
+            "source": chunk["metadata"].get("source", ""),
+            "final_score": float(round(final_score, 4)),
+            "vector_score": float(round(vec_score, 4)),
+            "bm25_score": float(round(bm25_score, 4)),
+            "metadata": chunk["metadata"],
+            "summary": chunk["metadata"].get("summary", ""),
+            "synthetic_queries": chunk["metadata"].get("synthetic_queries", [])
+        })
+
+    final_results = sorted(final_results, key=lambda x: x["final_score"], reverse=True)
+
+    seen = set()
+    unique_chunks = []
+    for chunk in final_results:
+        clean_text = re.sub(r'\W+', '', chunk["content"].lower())[:300]
+        fingerprint = (chunk["source"], clean_text)
+        if fingerprint not in seen:
+            seen.add(fingerprint)
+            unique_chunks.append(chunk)
+
+    unique_chunks = unique_chunks[:top_k]
+
+    return {
+        "query": inputs["query"],
+        "chunks": unique_chunks
+    }
+    
+def safe_json_parse(s: str) -> Dict:
+    return json.loads(s) if isinstance(s, str) and "valid_chunks" in s else {
+        "valid_chunks": [], 
+        "is_out_of_scope": True, 
+        "justification": "Fallback due to invalid LLM output"
+    }
+
+# Rewrite generation
+rephraser_chain = (
+    repharser_prompt
+    | repharser_llm
+    | RunnableLambda(parse_rewrites)
+)
+
+generate_rewrites_chain = (
+    RunnableAssign({
+        "rewrites": lambda x: rephraser_chain.invoke({"query": x["query"]})
+    })
+    | RunnableAssign({
+        "all_queries": lambda x: [x["query"]] + x["rewrites"]
+    })
+)
+
+# Retrieval
+retrieve_chain = RunnableLambda(hybrid_retrieve)
+hybrid_chain = generate_rewrites_chain | retrieve_chain
+
+# Validation
+extract_validation_inputs = RunnableLambda(lambda x: {
+    "query": x["query"],
+    "contents": [c["content"] for c in x["chunks"]]
+})
+
+validation_chain = (
+    extract_validation_inputs
+    | relevance_prompt
+    | relevance_llm
+    | RunnableLambda(safe_json_parse)
+)
+
+# Answer Generation
+def prepare_answer_inputs(x: Dict) -> Dict:
+    context = KRISHNA_BIO if x["validation"]["is_out_of_scope"] else "\n\n".join(
+        [x["chunks"][i-1]["content"] for i in x["validation"]["valid_chunks"]])
+    
+    return {
+        "query": x["query"],
+        "profile": KRISHNA_BIO,
+        "context": context,
+        "use_fallback": x["validation"]["is_out_of_scope"]
+    }
+
+select_and_prompt = RunnableLambda(lambda x: 
+    answer_prompt_fallback.invoke(x) if x["use_fallback"]
+    else answer_prompt_relevant.invoke(x))
+
+answer_chain = (
+    prepare_answer_inputs
+    | select_and_prompt
+    | relevance_llm
+)
+
+# Full Pipeline
+full_pipeline = (
+    hybrid_chain
+    | RunnableAssign({"validation": validation_chain})
+    | RunnableAssign({"answer": answer_chain})
+)
+
+import gradio as gr
+
+def chat_interface(message, history):
+    inputs = {
+        "query": message,
+        "all_queries": [message],
+        "all_texts": all_chunks,
+        "k_per_query": 3,
+        "alpha": 0.7,
+        "vectorstore": vectorstore,
+        "full_document": "",
+    }
+    response = ""
+    for chunk in full_pipeline.stream(inputs):
+        if isinstance(chunk, str):
+            response += chunk
+            yield response
+        elif isinstance(chunk, dict) and "answer" in chunk:
+            response += chunk["answer"]
+            yield response
+
+with gr.Blocks(css="""
+     html, body, .gradio-container {
+        height: 100%;
+        margin: 0;
+        padding: 0;
+    }
+    .gradio-container {
+        width: 90%;
+        max-width: 1000px;
+        margin: 0 auto;
+        padding: 1rem;
+    }
+
+    .chatbox-container {
+        display: flex;
+        flex-direction: column;
+        height: 95%;
+    }
+
+    .chatbot {
+        flex: 1;
+        overflow-y: auto;
+        min-height: 500px;
+    }
+
+    .textbox {
+        margin-top: 1rem;
+    }
+    #component-523 {
+        height: 98%;
+    }
+""") as demo:
+    with gr.Column(elem_classes="chatbox-container"):
+        gr.Markdown("## 💬 Ask Krishna's AI Assistant")
+        gr.Markdown("💡 Ask anything about Krishna Vamsi Dhulipalla")
+        chatbot = gr.Chatbot(elem_classes="chatbot")
+        textbox = gr.Textbox(placeholder="Ask a question about Krishna...", elem_classes="textbox")
+
+        gr.ChatInterface(
+            fn=chat_interface,
+            chatbot=chatbot,
+            textbox=textbox,
+            examples=[
+                "What are Krishna's research interests?",
+                "Where did Krishna work?",
+                "What did he study at Virginia Tech?"
+            ],
+        )
+
 demo.launch()