Updated UI & personal data

.gitignore

@@ -6,4 +6,5 @@ ui/node_modules/
 .DS_Store
 *.log
 .env
-/env
+/env
+/.venv

Vector_storing.py

@@ -3,11 +3,16 @@ import re
 import json
 import hashlib
 from pathlib import Path
+import sys
 
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
+# Ensure we can import from backend if running from root
+sys.path.append(str(Path(__file__).resolve().parent))
 
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_openai import OpenAIEmbeddings
+from backend import config
 
 # === UTILS ===
 def hash_text(text: str) -> str:
@@ -16,11 +21,11 @@ def hash_text(text: str) -> str:
 
 # === MAIN FUNCTION ===
 def create_faiss_store(
-    md_dir: str = "./personal_data",
+    md_dir: str = str(config.PERSONAL_DATA_DIR),
     chunk_size: int = 1000,
     chunk_overlap: int = 250,
-    persist_dir: str = "./backend/data/faiss_store",
-    chunk_save_path: str = "./backend/data/all_chunks.json",
+    persist_dir: str = str(config.FAISS_PATH.parent), # Save to parent of specific version
+    chunk_save_path: str = str(config.CHUNKS_PATH),
     min_chunk_chars: int = 50,
 ):
     """
@@ -114,11 +119,19 @@ def create_faiss_store(
     os.makedirs(save_path, exist_ok=True)
 
     # Embeddings + FAISS
-    embeddings = HuggingFaceEmbeddings(
-        model_name="sentence-transformers/all-MiniLM-L6-v2",
-        model_kwargs={"device": "cpu"},
-        encode_kwargs={"normalize_embeddings": True},
-    )
+    if config.USE_OPENAI_EMBEDDING:
+        print(f"🔹 Using OpenAI Embeddings: {config.EMBEDDING_MODEL_NAME}")
+        embeddings = OpenAIEmbeddings(
+            model=config.EMBEDDING_MODEL_NAME,
+            openai_api_key=config.OPENAI_API_KEY
+        )
+    else:
+        print(f"🔹 Using HuggingFace Embeddings: {config.EMBEDDING_MODEL_NAME}")
+        embeddings = HuggingFaceEmbeddings(
+            model_name=config.EMBEDDING_MODEL_NAME,
+            model_kwargs={"device": "cpu"},
+            encode_kwargs={"normalize_embeddings": True},
+        )
 
     vector_store = FAISS.from_texts(
         texts=[c["text"] for c in all_chunks],
@@ -132,17 +145,11 @@ def create_faiss_store(
     print(f"📊 Stats → Chunks: {len(all_chunks)} | Avg length: {avg_len:.1f} characters")
 
     if failed_chunks:
-        with open("failed_chunks.txt", "w", encoding="utf-8") as f:
+        with open(config.FAILED_CHUNKS_PATH, "w", encoding="utf-8") as f:
             for line in failed_chunks:
                 f.write(line + "\n")
         print("📝 Failed chunk IDs saved to failed_chunks.txt")
 
 
 if __name__ == "__main__":
-    create_faiss_store(
-        md_dir="./personal_data",
-        chunk_size=1000,
-        chunk_overlap=250,
-        persist_dir="./backend/data/faiss_store",
-        chunk_save_path="./backend/data/all_chunks.json",
-    )
+    create_faiss_store()

backend/agent.py

@@ -18,11 +18,13 @@ from langchain_core.tools import tool
 from langchain_openai import ChatOpenAI
 from langchain_core.output_parsers import StrOutputParser
 # from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import OpenAIEmbeddings
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_community.vectorstores import FAISS
 from langchain_community.retrievers import BM25Retriever
-from langchain_nvidia_ai_endpoints import ChatNVIDIA
+# from langchain_nvidia_ai_endpoints import ChatNVIDIA (removed)
 from langgraph.graph.message import add_messages
+
 from langgraph.graph import StateGraph, START, END
 from langgraph.prebuilt import ToolNode
 from langgraph.checkpoint.memory import MemorySaver
@@ -40,22 +42,22 @@ tool_log = logging.getLogger("tools")
 
 # import function from api.py
 from .g_cal import get_gcal_service
-
-api_key = os.environ.get("NVIDIA_API_KEY")
-if not api_key:
-    raise RuntimeError("🚨 NVIDIA_API_KEY not found in environment!")
+from . import config
 
 # Constants
-FAISS_PATH = "backend/data/faiss_store/v41_1000-250"
-CHUNKS_PATH = "backend/data/all_chunks.json"
+FAISS_PATH = str(config.FAISS_PATH)
+CHUNKS_PATH = str(config.CHUNKS_PATH)
 
-# Validate files
+# Validate files (simplified check)
 if not Path(FAISS_PATH).exists():
-    raise FileNotFoundError(f"FAISS index not found at {FAISS_PATH}")
-if not Path(CHUNKS_PATH).exists():
-    raise FileNotFoundError(f"Chunks file not found at {CHUNKS_PATH}")
+    print(f"⚠️ FAISS index not found at {FAISS_PATH}. Agent may fail if not initialized.")
+
+def load_bio():
+    if config.BIO_PATH.exists():
+        return config.BIO_PATH.read_text(encoding="utf-8")
+    return "Krishna's bio is currently unavailable."
 
-KRISHNA_BIO = """Krishna Vamsi Dhulipalla completed masters in Computer Science at Virginia Tech, awarded degree in december 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 agents, with strong expertise in Python, PyTorch,Langgraph, autogen Hugging Face Transformers, and end-to-end ML pipelines."""
+KRISHNA_BIO = load_bio()
 
 # Load resources
 def load_chunks(path=CHUNKS_PATH) -> List[Dict]:
@@ -63,7 +65,10 @@ def load_chunks(path=CHUNKS_PATH) -> List[Dict]:
         return json.load(f)
 
 def load_faiss(path=FAISS_PATH, model_name="sentence-transformers/all-MiniLM-L6-v2") -> FAISS:
-    embeddings = HuggingFaceEmbeddings(model_name=model_name)
+    if config.USE_OPENAI_EMBEDDING:
+        embeddings = OpenAIEmbeddings(model=config.EMBEDDING_MODEL_NAME, openai_api_key=config.OPENAI_API_KEY)
+    else:
+        embeddings = HuggingFaceEmbeddings(model_name=model_name)
     return FAISS.load_local(path, embeddings, allow_dangerous_deserialization=True)
 
 vectorstore = load_faiss()
@@ -72,19 +77,25 @@ all_texts = [chunk["text"] for chunk in all_chunks]
 metadatas = [chunk["metadata"] for chunk in all_chunks]
 bm25_retriever = BM25Retriever.from_texts(texts=all_texts, metadatas=metadatas)
 
-K_PER_QUERY    = 10         # how many from each retriever
-TOP_K          = 8         # final results to return
-RRF_K          = 60        # reciprocal-rank-fusion constant
-RERANK_TOP_N   = 50        # rerank this many fused hits
-MMR_LAMBDA     = 0.7       # 0..1 (higher favors query relevance; lower favors diversity)
-CE_MODEL       = "cross-encoder/ms-marco-MiniLM-L-6-v2"
+K_PER_QUERY    = config.K_PER_QUERY
+TOP_K          = config.TOP_K
+RRF_K          = config.RRF_K
+RERANK_TOP_N   = config.RERANK_TOP_N
+MMR_LAMBDA     = config.MMR_LAMBDA
+CE_MODEL       = config.CROSS_ENCODER_MODEL
 ALPHA = 0.7
 
-from sentence_transformers import CrossEncoder
-_cross_encoder = CrossEncoder(CE_MODEL)
+# from sentence_transformers import CrossEncoder
+_cross_encoder = None # CrossEncoder(CE_MODEL)
 
-embeddings = HuggingFaceEmbeddings(
-        model_name="sentence-transformers/all-MiniLM-L6-v2",
+if config.USE_OPENAI_EMBEDDING:
+    embeddings = OpenAIEmbeddings(
+        model=config.EMBEDDING_MODEL_NAME,
+        openai_api_key=config.OPENAI_API_KEY
+    )
+else:
+    embeddings = HuggingFaceEmbeddings(
+        model_name=config.EMBEDDING_MODEL_NAME,
         model_kwargs={"device": "cpu"},
         encode_kwargs={"normalize_embeddings": True},
     )
@@ -220,12 +231,12 @@ def retriever(query: str) -> list[str]:
     return results
 
 # --- memory globals ---
-MEM_FAISS_PATH = os.getenv("MEM_FAISS_PATH", "/data/memory_faiss")
+MEM_FAISS_PATH = config.MEM_FAISS_PATH
 mem_embeddings = embeddings
 memory_vs = None
 memory_dirty = False
 memory_write_count = 0
-MEM_AUTOSAVE_EVERY = 20  
+MEM_AUTOSAVE_EVERY = config.MEM_AUTOSAVE_EVERY
 
 def _ensure_memory_vs():
     global memory_vs
@@ -479,7 +490,7 @@ def download_resume() -> str:
     """
     Return a direct download link to Krishna's latest resume PDF.
     """
-    BASE_URL = os.getenv("PUBLIC_BASE_URL", "http://localhost:8080")
+    BASE_URL = config.PUBLIC_BASE_URL
     url = f"{BASE_URL}/resume/download"
     return (
         f"Here is Krishna’s latest resume:\n\n"
@@ -501,12 +512,12 @@ def get_portfolio_links() -> str:
         "- LinkedIn: https://www.linkedin.com/in/krishnavamsidhulipalla\n"
         "\n"
         "## 📦 Highlight Repos\n"
-        "- LangGraph ChatBot: https://github.com/krishna-dhulipalla/LangGraph_ChatBot"
+        "- LangGraph ChatBot: https://github.com/krishna-dhulipalla/LangGraph_ChatBot\n"
         "- Android World agent: https://github.com/krishna-dhulipalla/android_world\n"
-        "- Gene Co-expression tootl: https://github.com/krishna-dhulipalla/gene_co-expression_tool"
+        "- Gene Co-expression tool: https://github.com/krishna-dhulipalla/gene_co-expression_tool\n"
         "- ProxyTuNER (cross-domain NER): https://github.com/krishna-creator/ProxytuNER\n"
         "- IntelliMeet (decentralized video conf): https://github.com/krishna-creator/SE-Project---IntelliMeet\n"
-        "- More repos: https://github.com/krishna-dhulipalla?tab=repositories"
+        "- More repos: https://github.com/krishna-dhulipalla?tab=repositories\n"
         "\n"
         "## 📚 Publications\n"
         "- BIBM 2024 paper: https://www.researchgate.net/publication/387924249_Leveraging_Machine_Learning_for_Predicting_Circadian_Transcription_in_mRNAs_and_lncRNAs\n"

backend/api.py

@@ -13,7 +13,8 @@ from pathlib import Path
 import json, secrets, urllib.parse, os
 from google_auth_oauthlib.flow import Flow
 from .g_cal import get_gcal_service
-from .g_cal import SCOPES, TOKEN_FILE 
+from .g_cal import SCOPES, TOKEN_FILE
+from . import config
 
 # logging + helpers
 import logging, os, time
@@ -27,10 +28,9 @@ from .agent import app as lg_app
 
 api = FastAPI(title="LangGraph Chat API")
 
-CLIENT_ID = os.getenv("GOOGLE_CLIENT_ID")
-CLIENT_SECRET = os.getenv("GOOGLE_CLIENT_SECRET")
-BASE_URL_RAW = os.getenv("PUBLIC_BASE_URL", "http://localhost:8000")
-BASE_URL = BASE_URL_RAW.rstrip("/")                    # no trailing slash
+CLIENT_ID = config.GOOGLE_CLIENT_ID
+CLIENT_SECRET = config.GOOGLE_CLIENT_SECRET
+BASE_URL = config.PUBLIC_BASE_URL.rstrip("/")
 REDIRECT_URI = f"{BASE_URL}/oauth/google/callback" 
 
 # CORS (handy during dev; tighten in prod)
@@ -76,7 +76,7 @@ def list_routes():
 @api.get("/api/debug/oauth")
 def debug_oauth():
     return {
-        "base_url_env": BASE_URL_RAW,
+        "base_url_env": config.PUBLIC_BASE_URL,
         "base_url_effective": BASE_URL,
         "redirect_uri_built": REDIRECT_URI,
     }
@@ -106,9 +106,9 @@ def google_reset():
 @api.get("/debug/env")
 def debug_env():
     return {
-        "public_base_url": os.getenv("PUBLIC_BASE_URL"),
-        "has_google_client_id": bool(os.getenv("GOOGLE_CLIENT_ID")),
-        "has_google_client_secret": bool(os.getenv("GOOGLE_CLIENT_SECRET")),
+        "public_base_url": config.PUBLIC_BASE_URL,
+        "has_google_client_id": bool(config.GOOGLE_CLIENT_ID),
+        "has_google_client_secret": bool(config.GOOGLE_CLIENT_SECRET),
         "ui_dist_exists": UI_DIST.is_dir(),
         "resume_exists": RESUME_PATH.is_file(),
     }
@@ -163,11 +163,8 @@ async def _event_stream_with_config(thread_id: str, message: str, request: Reque
         yield {"event": "done", "data": "1"}
 
 # --- Serve built React UI (ui/dist) under the same origin ---
-# repo_root = <project>/  ; this file is <project>/backend/api.py
-REPO_ROOT = Path(__file__).resolve().parents[1]
-UI_DIST = REPO_ROOT / "ui" / "dist"
-
-RESUME_PATH = REPO_ROOT / "backend" / "assets" / "KrishnaVamsiDhulipalla.pdf"
+UI_DIST = config.UI_DIST
+RESUME_PATH = config.RESUME_PATH
 
 @api.get("/resume/download")
 def resume_download():

backend/config.py

@@ -0,0 +1,42 @@
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Base Paths
+BACKEND_DIR = Path(__file__).resolve().parent
+PROJECT_ROOT = BACKEND_DIR.parent
+PERSONAL_DATA_DIR = PROJECT_ROOT / "personal_data"
+ASSETS_DIR = BACKEND_DIR / "assests" # Copied spelling from existing folder structure
+DATA_DIR = BACKEND_DIR / "data"
+
+# Data Paths
+FAISS_PATH = DATA_DIR / "faiss_store" / "v30_1000-250" # Make this dynamic if needed?
+CHUNKS_PATH = DATA_DIR / "all_chunks.json"
+FAILED_CHUNKS_PATH = PROJECT_ROOT / "failed_chunks.txt"
+BIO_PATH = PERSONAL_DATA_DIR / "bio.md"
+RESUME_PATH = ASSETS_DIR / "KrishnaVamsiDhulipalla.pdf"
+UI_DIST = PROJECT_ROOT / "ui" / "dist"
+
+# API Keys
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+GOOGLE_CLIENT_ID = os.getenv("GOOGLE_CLIENT_ID")
+GOOGLE_CLIENT_SECRET = os.getenv("GOOGLE_CLIENT_SECRET")
+PUBLIC_BASE_URL = os.getenv("PUBLIC_BASE_URL", "http://localhost:8000")
+
+# Embedding Config
+EMBEDDING_MODEL_NAME = "text-embedding-3-small"
+USE_OPENAI_EMBEDDING = True
+CROSS_ENCODER_MODEL = "cross-encoder/ms-marco-MiniLM-L-6-v2"
+
+# Retriever Config
+K_PER_QUERY = 6
+TOP_K = 8
+RRF_K = 60
+RERANK_TOP_N = 20
+MMR_LAMBDA = 0.7
+
+# Memory
+MEM_FAISS_PATH = os.getenv("MEM_FAISS_PATH", str(DATA_DIR / "memory_faiss"))
+MEM_AUTOSAVE_EVERY = 20

backend/data/all_chunks.json

@@ -1,262 +1,112 @@
 [
   {
-    "text": "[HEADER] # 👋 Hello, I'm Krishna Vamsi Dhulipalla\n\n# 👋 Hello, I'm Krishna Vamsi Dhulipalla\n\nI’m a **Machine Learning Engineer** with over **3 years of experience** designing and deploying intelligent AI systems, integrating backend infrastructure, and building real-time data workflows. I specialize in **LLM-powered agents**, **semantic search**, **bioinformatics AI models**, and **cloud-native ML infrastructure**.\n\nI earned my **M.S. in Computer Science** from **Virginia Tech** in December 2024 with a 3.95/4.0 GPA, focusing on large language models, intelligent agents, and scalable data systems. My work spans the full ML lifecycle—from research and fine-tuning transformer architectures to deploying production-ready applications on AWS and GCP.\n\nI’m passionate about **LLM-driven systems**, **multi-agent orchestration**, and **domain-adaptive ML**, particularly in **genomic data analysis** and **real-time analytics**.\n\n---",
+    "text": "[HEADER] Krishna Vamsi Dhulipalla is a Software Engineer specializing in generic workflows and AI platforms. He currently works at **Cloud Systems LLC**, where he architects LangGraph-based agents to automate data auditing. Previously, he served as a Machine Learning Engineer at **Virginia Tech**, optimizing genomic models with LoRA/soft prompting, and as a Software Engineer at **UJR Technologies**, building ML SDKs and CI/CD pipelines. He holds an M.S. in Computer Science from Virginia Tech (Dec 2024) and has significant expertise in **LangGraph**, **Kubernetes**, **PyTorch**, and **MLOps**.\n\nKrishna Vamsi Dhulipalla is a Software Engineer specializing in generic workflows and AI platforms. He currently works at **Cloud Systems LLC**, where he architects LangGraph-based agents to automate data auditing. Previously, he served as a Machine Learning Engineer at **Virginia Tech**, optimizing genomic models with LoRA/soft prompting, and as a Software Engineer at **UJR Technologies**, building ML SDKs and CI/CD pipelines. He holds an M.S. in Computer Science from Virginia Tech (Dec 2024) and has significant expertise in **LangGraph**, **Kubernetes**, **PyTorch**, and **MLOps**.",
     "metadata": {
-      "source": "aprofile.md",
-      "header": "# 👋 Hello, I'm Krishna Vamsi Dhulipalla",
-      "chunk_id": "aprofile.md_#0_c786e01b",
-      "has_header": true,
-      "word_count": 113
-    }
-  },
-  {
-    "text": "[HEADER] # 👋 Hello, I'm Krishna Vamsi Dhulipalla\n\n# # 🎯 Career Summary\n\n- 👨‍💻 3+ years of experience in **ML systems design**, **LLM-powered applications**, and **data engineering**\n- 🧬 Proven expertise in **transformer fine-tuning** (LoRA, soft prompting) for genomic classification\n- 🤖 Skilled in **LangChain**, **LangGraph**, **AutoGen**, and **CrewAI** for intelligent agent workflows\n- ☁️ Deep knowledge of **AWS** (S3, Glue, Lambda, SageMaker, ECS, CloudWatch) and **GCP** (BigQuery, Dataflow, Composer)\n- ⚡ Experienced in **real-time data pipelines** using **Apache Kafka**, **Spark**, **Airflow**, and **dbt**\n- 📊 Strong foundation in **synthetic data generation**, **domain adaptation**, and **cross-domain NER**",
-    "metadata": {
-      "source": "aprofile.md",
-      "header": "# 👋 Hello, I'm Krishna Vamsi Dhulipalla",
-      "chunk_id": "aprofile.md_#1_0ba34e9a",
-      "has_header": true,
-      "word_count": 90
-    }
-  },
-  {
-    "text": "[HEADER] # 👋 Hello, I'm Krishna Vamsi Dhulipalla\n\n# # 🔭 Areas of Current Focus\n\n- Developing **LLM-powered mobile automation agents** for UI task execution\n- Architecting **retrieval-augmented generation (RAG)** systems with hybrid retrieval and cross-encoder reranking\n- Fine-tuning **DNA foundation models** like DNABERT & HyenaDNA for plant genomics\n- Building **real-time analytics pipelines** integrating Kafka, Spark, Airflow, and cloud services\n\n---\n\n# # 🎓 Education\n\n## # Virginia Tech — M.S. in Computer Science\n\n📍 Blacksburg, VA | Jan 2023 – Dec 2024  \n**GPA:** 3.95 / 4.0  \nRelevant Coursework: Distributed Systems, Machine Learning Optimization, Genomics, LLMs & Transformer Architectures\n\n## # Anna University — B.Tech in Computer Science and Engineering\n\n📍 Chennai, India | Jun 2018 – May 2022  \n**GPA:** 8.24 / 10  \nSpecialization: Real-Time Analytics, Cloud Systems, Software Engineering Principles\n\n---",
-    "metadata": {
-      "source": "aprofile.md",
-      "header": "# 👋 Hello, I'm Krishna Vamsi Dhulipalla",
-      "chunk_id": "aprofile.md_#2_5b08eda4",
-      "has_header": true,
-      "word_count": 125
-    }
-  },
-  {
-    "text": "[HEADER] # 👋 Hello, I'm Krishna Vamsi Dhulipalla\n\n# # 🛠️ Technical Skills\n\n**Programming:** Python, R, SQL, JavaScript, TypeScript, Node.js, FastAPI, MongoDB  \n**ML Frameworks:** PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers  \n**LLM & Agents:** LangChain, LangGraph, AutoGen, CrewAI, Prompt Engineering, RAG, LoRA, GANs  \n**ML Techniques:** Self-Supervised Learning, Cross-Domain Adaptation, Hyperparameter Optimization, A/B Testing  \n**Data Engineering:** Apache Spark, Kafka, dbt, Airflow, ETL Pipelines, Delta Lake, Snowflake  \n**Cloud & Infra:** AWS (S3, Glue, Lambda, Redshift, ECS, SageMaker, CloudWatch), GCP (GCS, BigQuery, Dataflow, Composer)  \n**DevOps/MLOps:** Docker, Kubernetes, MLflow, CI/CD, Weights & Biases  \n**Visualization:** Tableau, Shiny (R), Plotly, Matplotlib  \n**Other Tools:** Pandas, NumPy, Git, LangSmith, LangFlow, Linux\n\n---",
-    "metadata": {
-      "source": "aprofile.md",
-      "header": "# 👋 Hello, I'm Krishna Vamsi Dhulipalla",
-      "chunk_id": "aprofile.md_#3_2035a7b6",
-      "has_header": true,
-      "word_count": 95
-    }
-  },
-  {
-    "text": "[HEADER] # 👋 Hello, I'm Krishna Vamsi Dhulipalla\n\n# # 💼 Professional Experience\n\n## # Cloud Systems LLC — ML Research Engineer (Current role)\n\n📍 Remote | Jul 2024 – Present\n\n- Designed and optimized **SQL-based data retrieval** and **batch + real-time pipelines**\n- Built automated **ETL workflows** integrating multiple data sources\n\n## # Virginia Tech — ML Research Engineer\n\n📍 Blacksburg, VA | Sep 2024 – Jul 2024\n\n- Developed **DNA sequence classification pipelines** using DNABERT & HyenaDNA with LoRA & soft prompting (94%+ accuracy)\n- Automated preprocessing of **1M+ genomic sequences** with Biopython & Airflow, reducing runtime by 40%\n- Built **LangChain-based semantic search** for genomics literature\n- Deployed fine-tuned LLMs using Docker, MLflow, and optionally SageMaker",
-    "metadata": {
-      "source": "aprofile.md",
-      "header": "# 👋 Hello, I'm Krishna Vamsi Dhulipalla",
-      "chunk_id": "aprofile.md_#4_6b27454e",
-      "has_header": true,
-      "word_count": 111
-    }
-  },
-  {
-    "text": "[HEADER] # 👋 Hello, I'm Krishna Vamsi Dhulipalla\n\n## # Virginia Tech — Research Assistant\n\n📍 Blacksburg, VA | Jun 2023 – May 2024\n\n- Built **genomic ETL pipelines** (Airflow + AWS Glue) improving research data availability by 50%\n- Automated retraining workflows via CI/CD, reducing manual workload by 40%\n- Benchmarked compute cluster performance to cut runtime costs by 15%\n\n## # UJR Technologies Pvt Ltd — Data Engineer\n\n📍 Hyderabad, India | Jul 2021 – Dec 2022\n\n- Migrated **batch ETL to real-time streaming** with Kafka & Spark (↓ latency 30%)\n- Deployed Dockerized microservices to AWS ECS, improving deployment speed by 25%\n- Optimized Snowflake schemas to improve query performance by 40%\n\n---",
-    "metadata": {
-      "source": "aprofile.md",
-      "header": "# 👋 Hello, I'm Krishna Vamsi Dhulipalla",
-      "chunk_id": "aprofile.md_#5_0002cd5a",
-      "has_header": true,
-      "word_count": 108
-    }
-  },
-  {
-    "text": "[HEADER] # 👋 Hello, I'm Krishna Vamsi Dhulipalla\n\n# # 📊 Highlight Projects\n\n- **LLM-Based Android Agent** – Multi-step UI automation with memory, self-reflection, and context recovery (80%+ accuracy)\n\n## # Real-Time IoT-Based Temperature Forecasting\n\n- Kafka-based pipeline for 10K+ sensor readings with LLaMA 2-based time series model (91% accuracy)\n- Airflow + Looker dashboards (↓ manual reporting by 30%)\n- S3 lifecycle policies saved 40% storage cost with versioned backups  \n  🔗 [GitHub](https://github.com/krishna-creator/Real-Time-IoT-Based-Temperature-Analytics-and-Forecasting)\n\n## # Proxy TuNER: Cross-Domain NER\n\n- Developed a proxy tuning method for domain-agnostic BERT\n- 15% generalization gain using gradient reversal + feature alignment\n- 70% cost reduction via logit-level ensembling  \n  🔗 [GitHub](https://github.com/krishna-creator/ProxytuNER)",
-    "metadata": {
-      "source": "aprofile.md",
-      "header": "# 👋 Hello, I'm Krishna Vamsi Dhulipalla",
-      "chunk_id": "aprofile.md_#6_48d0bbf0",
-      "has_header": true,
-      "word_count": 99
-    }
-  },
-  {
-    "text": "[HEADER] # 👋 Hello, I'm Krishna Vamsi Dhulipalla\n\n## # IntelliMeet: AI-Powered Conferencing\n\n- Federated learning, end-to-end encrypted platform\n- Live attention detection using RetinaFace (<200ms latency)\n- Summarization with Transformer-based speech-to-text  \n  🔗 [GitHub](https://github.com/krishna-creator/SE-Project---IntelliMeet)\n\n## # Automated Drone Image Analysis\n\n- Real-time crop disease detection using drone imagery\n- Used OpenCV, RAG, and GANs for synthetic data generation\n- Improved detection accuracy by 15% and reduced processing latency by 70%\n\n---",
-    "metadata": {
-      "source": "aprofile.md",
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-    "text": "[HEADER] # 👋 Hello, I'm Krishna Vamsi Dhulipalla\n\n# # 📜 Certifications\n\n- 🏆 NVIDIA – Building RAG Agents with LLMs\n- 🏆 Google Cloud – Data Engineering Foundations\n- 🏆 AWS – Machine Learning Specialty\n- 🏆 Microsoft – MERN Stack Development\n- 🏆 Snowflake – End-to-End Data Engineering\n- 🏆 Coursera – Machine Learning Specialization  \n  🔗 [View All Credentials](https://www.linkedin.com/in/krishnavamsidhulipalla/)\n\n---\n\n# # 📚 Research Publications\n\n- **IEEE BIBM 2024** – “Leveraging ML for Predicting Circadian Transcription in mRNAs and lncRNAs”  \n  [DOI: 10.1109/BIBM62325.2024.10822684](https://doi.org/10.1109/BIBM62325.2024.10822684)\n\n- **MLCB** – “Harnessing DNA Foundation Models for TF Binding Prediction in Plants”\n\n---",
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-    "text": "[HEADER] # 👋 Hello, I'm Krishna Vamsi Dhulipalla\n\n# # 🔗 External Links / Contact details\n\n- 🌐 [Personal Portfolio/ personal website](http://krishna-dhulipalla.github.io)\n- 🧪 [GitHub](https://github.com/Krishna-dhulipalla)\n- 💼 [LinkedIn](https://www.linkedin.com/in/krishnavamsidhulipalla)\n- 📬 dhulipallakrishnavamsi@gmail.com\n- 🤖 [Personal Chatbot](https://huggingface.co/spaces/krishnadhulipalla/Personal_ChatBot)",
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-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\nThis document outlines the technical architecture and modular design of Krishna Vamsi Dhulipalla’s personal AI chatbot system, implemented using **LangChain**, **OpenAI**, **NVIDIA NIMs**, and **Gradio**. The assistant is built for intelligent, retriever-augmented, memory-aware interaction tailored to Krishna’s background and user context.\n\n---",
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-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n| Purpose                             | Model Name                               | Role Description                                                 |\n| ----------------------------------- | ---------------------------------------- | ---------------------------------------------------------------- |\n| **Rephraser LLM**                   | `microsoft/phi-3-mini-4k-instruct`       | Rewrites vague/short queries into detailed, keyword-rich queries |\n| **Relevance Classifier + Reranker** | `mistralai/mixtral-8x22b-instruct-v0.1`  | Classifies query relevance to KB and reranks retrieved chunks    |\n| **Answer Generator**                | `nvidia/llama-3.1-nemotron-70b-instruct` | Provides rich, structured answers (replacing GPT-4o for testing) |\n| **Fallback Humor Model**            | `mistralai/mixtral-8x22b-instruct-v0.1`  | Responds humorously and redirects when out-of-scope              |",
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+      "header": "Krishna Vamsi Dhulipalla is a Software Engineer specializing in generic workflows and AI platforms. He currently works at **Cloud Systems LLC**, where he architects LangGraph-based agents to automate data auditing. Previously, he served as a Machine Learning Engineer at **Virginia Tech**, optimizing genomic models with LoRA/soft prompting, and as a Software Engineer at **UJR Technologies**, building ML SDKs and CI/CD pipelines. He holds an M.S. in Computer Science from Virginia Tech (Dec 2024) and has significant expertise in **LangGraph**, **Kubernetes**, **PyTorch**, and **MLOps**.",
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-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n| **Fallback Humor Model**            | `mistralai/mixtral-8x22b-instruct-v0.1`  | Responds humorously and redirects when out-of-scope              |\n| **KnowledgeBase Updater**           | `mistralai/mistral-7b-instruct-v0.3`     | Extracts and updates structured memory about the user            |",
+    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant\n\n# 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant\n\nThis document details the architecture of **Krishna Vamsi Dhulipalla’s** personal AI assistant, implemented with **LangGraph** for orchestrated state management and tool execution. The system is designed for **retrieval-augmented, memory-grounded, and multi-turn conversational intelligence**, integrating **OpenAI GPT-4o**, **Hugging Face embeddings**, and **cross-encoder reranking**.\n\n---",
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-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\nAll models are integrated via **LangChain RunnableChains**, supporting both streaming and structured execution.\n\n---",
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-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# # 🔍 Retrieval Architecture\n\n## # ✅ **Hybrid Retrieval System**\n\nThe assistant combines:\n\n- **BM25Retriever**: Lexical keyword match\n- **FAISS Vector Search**: Dense embeddings from `sentence-transformers/all-MiniLM-L6-v2`\n\n## # 🧠 Rephrasing for Retrieval\n\n- The **user's query** is expanded using the Rephraser LLM, with awareness of `last_followups` and memory\n- **Rewritten query** is used throughout retrieval, validation, and reranking\n\n## # 📊 Scoring & Ranking\n\n- Each subquery is run through both BM25 and FAISS\n- Results are merged via weighted formula:  \n  `final_score = α * vector_score + (1 - α) * bm25_score`\n- Deduplication via fingerprinting\n- Top-k (default: 15) results are passed forward\n\n---",
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-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# # 🔎 Validation + Chunk Reranking\n\n## # 🔍 Relevance Classification\n\n- LLM2 evaluates:\n  - Whether the query (or rewritten query) is **in-scope**\n  - If so, returns a **reranked list of chunk indices**\n- Memory (`last_input`, `last_output`, `last_followups`) and `rewritten_query` are included for better context\n\n## # ❌ If Out-of-Scope\n\n- Chunks are discarded\n- Response is generated using fallback LLM with humor and redirection\n\n---\n\n# # 🧠 Memory + Personalization\n\n## # 📘 KnowledgeBase Model\n\nTracks structured user data:\n\n- `user_name`, `company`, `last_input`, `last_output`\n- `summary_history`, `recent_interests`, `last_followups`, `tone`\n\n## # 🔄 Memory Updates\n\n- After every response, assistant extracts and updates memory\n- Handled via `RExtract` pipeline using `PydanticOutputParser` and KB LLM\n\n---",
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-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# # 🧭 Orchestration Flow\n\n```text\nUser Input\n   ↓\nRephraser LLM (phi-3-mini)\n   ↓\nHybrid Retrieval (BM25 + FAISS)\n   ↓\nValidation + Reranking (mixtral-8x22b)\n   ↓\n ┌──────────────┐     ┌────────────────────┐\n │ In-Scope     │     │ Out-of-Scope Query │\n │ (Top-k Chunks)│     │ (Memory-based only)│\n └────┬─────────┘     └─────────────┬──────┘\n      ↓                                  ↓\n Answer LLM (nemotron-70b)       Fallback Humor LLM\n```\n\n---\n\n# # 💬 Frontend Interface (Gradio)\n\n- Built using **Gradio ChatInterface + Blocks**\n- Features:\n  - Responsive design\n  - Custom CSS\n  - Streaming markdown responses\n  - Preloaded examples and auto-scroll\n\n---",
+    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant\n\n# # 🧱 Core Components\n\n## # 1. **Models & Their Roles**\n\n| Purpose                    | Model Name                               | Role Description                                 |\n| -------------------------- | ---------------------------------------- | ------------------------------------------------ |\n| **Main Chat Model**        | `gpt-4o`                                 | Handles conversation, tool calls, and reasoning  |\n| **Retriever Embeddings**   | `sentence-transformers/all-MiniLM-L6-v2` | Embedding generation for FAISS vector search     |\n| **Cross-Encoder Reranker** | `cross-encoder/ms-marco-MiniLM-L-6-v2`   | Reranks retrieval results for semantic relevance |\n| **BM25 Retriever**         | (LangChain BM25Retriever)                | Keyword-based search complementing vector search |\n\nAll models are bound to LangGraph **StateGraph** nodes for structured execution.\n\n---",
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-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# # 💬 Frontend Interface (Gradio)\n\n- Built using **Gradio ChatInterface + Blocks**\n- Features:\n  - Responsive design\n  - Custom CSS\n  - Streaming markdown responses\n  - Preloaded examples and auto-scroll\n\n---\n\n# # 🧩 Additional Design Highlights\n\n- **Streaming**: Nemotron-70B used via LangChain streaming\n- **Prompt Engineering**: Answer prompts use markdown formatting, section headers, bullet points, and personalized sign-offs\n- **Memory-Aware Rewriting**: Handles vague replies like `\"yes\"` or `\"A\"` by mapping them to `last_followups`\n- **Knowledge Chunk Enrichment**: Each FAISS chunk includes synthetic summary and 3 QA-style synthetic queries\n\n---",
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-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# # 🚀 Future Enhancements\n\n- Tool calling for tasks like calendar access or Google search\n- Multi-model reranking agents\n- Memory summarization agents for long dialogs\n- Topic planners to group conversations\n- Retrieval filtering based on user interest and session\n\n---\n\nThis architecture is modular, extensible, and designed to simulate a memory-grounded, expert-aware personal assistant tailored to Krishna’s evolving knowledge and conversational goals.\n\n# 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (LangGraph Version) (New and current one)\n\nThis document details the updated architecture of **Krishna Vamsi Dhulipalla’s** personal AI assistant, now fully implemented with **LangGraph** for orchestrated state management and tool execution. The system is designed for **retrieval-augmented, memory-grounded, and multi-turn conversational intelligence**, integrating **OpenAI GPT-4o**, **Hugging Face embeddings**, and **cross-encoder reranking**.\n\n---",
+    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant\n\n# # 🔍 Retrieval System\n\n## # ✅ **Hybrid Retrieval**\n\n- **FAISS Vector Search** with normalized embeddings\n- **BM25Retriever** for lexical keyword matching\n- Combined using **Reciprocal Rank Fusion (RRF)**\n\n## # 📊 **Reranking & Diversity**\n\n1. Initial retrieval with FAISS & BM25 (top-K per retriever)\n2. Fusion via RRF scoring\n3. **Cross-Encoder reranking** (top-N candidates)\n4. **Maximal Marginal Relevance (MMR)** selection for diversity\n\n## # 🔎 Retriever Tool (`@tool retriever`)\n\n- Returns top passages with minimal duplication\n- Used in-system prompt to fetch accurate facts about Krishna\n\n---",
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-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# # 🧱 Core Components\n\n## # 1. **Models & Their Roles**\n\n| Purpose                    | Model Name                               | Role Description                                 |\n| -------------------------- | ---------------------------------------- | ------------------------------------------------ |\n| **Main Chat Model**        | `gpt-4o`                                 | Handles conversation, tool calls, and reasoning  |\n| **Retriever Embeddings**   | `sentence-transformers/all-MiniLM-L6-v2` | Embedding generation for FAISS vector search     |\n| **Cross-Encoder Reranker** | `cross-encoder/ms-marco-MiniLM-L-6-v2`   | Reranks retrieval results for semantic relevance |\n| **BM25 Retriever**         | (LangChain BM25Retriever)                | Keyword-based search complementing vector search |\n\nAll models are bound to LangGraph **StateGraph** nodes for structured execution.\n\n---",
+    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant\n\n# # 🧠 Memory System\n\n## # Long-Term Memory\n\n- **FAISS-based memory vector store** stored at `backend/data/memory_faiss`\n- Stores conversation summaries per thread ID\n\n## # Memory Search Tool (`@tool memory_search`)\n\n- Retrieves relevant conversation snippets by semantic similarity\n- Supports **thread-scoped** search for contextual continuity\n\n## # Memory Write Node\n\n- After each AI response, stores `[Q]: ... [A]: ...` summary\n- Autosaves after every `MEM_AUTOSAVE_EVERY` turns or on thread end\n\n---",
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-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# # 🔍 Retrieval System\n\n## # ✅ **Hybrid Retrieval**\n\n- **FAISS Vector Search** with normalized embeddings\n- **BM25Retriever** for lexical keyword matching\n- Combined using **Reciprocal Rank Fusion (RRF)**\n\n## # 📊 **Reranking & Diversity**\n\n1. Initial retrieval with FAISS & BM25 (top-K per retriever)\n2. Fusion via RRF scoring\n3. **Cross-Encoder reranking** (top-N candidates)\n4. **Maximal Marginal Relevance (MMR)** selection for diversity\n\n## # 🔎 Retriever Tool (`@tool retriever`)\n\n- Returns top passages with minimal duplication\n- Used in-system prompt to fetch accurate facts about Krishna\n\n---",
+    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant\n\n# # 🧭 Orchestration Flow (LangGraph)\n\n```mermaid\ngraph TD\n    A[START] --> B[agent node]\n    B -->|tool call| C[tools node]\n    B -->|no tool| D[memory_write]\n    C --> B\n    D --> E[END]\n```\n\n## # **Nodes**:\n\n- **agent**: Calls main LLM with conversation window + system prompt\n- **tools**: Executes retriever or memory search tools\n- **memory_write**: Persists summaries to long-term memory\n\n## # **Conditional Edges**:\n\n- From **agent** → `tools` if tool call detected\n- From **agent** → `memory_write` if no tool call\n\n---\n\n# # 💬 System Prompt\n\nThe assistant:\n\n- Uses retriever and memory search tools to gather facts about Krishna\n- Avoids fabrication and requests clarification when needed\n- Responds humorously when off-topic but steers back to Krishna’s expertise\n- Formats with Markdown, headings, and bullet points\n\nEmbedded **Krishna’s Bio** provides static grounding context.\n\n---",
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-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# # 🧠 Memory System\n\n## # Long-Term Memory\n\n- **FAISS-based memory vector store** stored at `backend/data/memory_faiss`\n- Stores conversation summaries per thread ID\n\n## # Memory Search Tool (`@tool memory_search`)\n\n- Retrieves relevant conversation snippets by semantic similarity\n- Supports **thread-scoped** search for contextual continuity\n\n## # Memory Write Node\n\n- After each AI response, stores `[Q]: ... [A]: ...` summary\n- Autosaves after every `MEM_AUTOSAVE_EVERY` turns or on thread end\n\n---",
+    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant\n\n# # 🌐 API & Streaming\n\n- **Backend**: FastAPI (`backend/api.py`)\n  - `/chat` SSE endpoint streams tokens in real-time\n  - Passes `thread_id` & `is_final` to LangGraph for stateful conversations\n- **Frontend**: React + Tailwind (custom chat UI)\n  - Threaded conversation storage in browser `localStorage`\n  - Real-time token rendering via `EventSource`\n  - Features: new chat, clear chat, delete thread, suggestions\n\n---\n\n# # 🧩 Design Improvements\n\n- **LangGraph StateGraph** ensures explicit control of message flow\n- **Thread-scoped memory** enables multi-session personalization\n- **Hybrid RRF + Cross-Encoder + MMR** retrieval pipeline improves relevance & diversity\n- **SSE streaming** for low-latency feedback\n- **Decoupled retrieval** and **memory** as separate tools for modularity",
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   {
-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# # 🧭 Orchestration Flow (LangGraph)\n\n```mermaid\ngraph TD\n    A[START] --> B[agent node]\n    B -->|tool call| C[tools node]\n    B -->|no tool| D[memory_write]\n    C --> B\n    D --> E[END]\n```\n\n## # **Nodes**:\n\n- **agent**: Calls main LLM with conversation window + system prompt\n- **tools**: Executes retriever or memory search tools\n- **memory_write**: Persists summaries to long-term memory\n\n## # **Conditional Edges**:\n\n- From **agent** → `tools` if tool call detected\n- From **agent** → `memory_write` if no tool call\n\n---\n\n# # 💬 System Prompt\n\nThe assistant:\n\n- Uses retriever and memory search tools to gather facts about Krishna\n- Avoids fabrication and requests clarification when needed\n- Responds humorously when off-topic but steers back to Krishna’s expertise\n- Formats with Markdown, headings, and bullet points\n\nEmbedded **Krishna’s Bio** provides static grounding context.\n\n---",
+    "text": "[HEADER] # Education\n\n# Education\n\n# # Virginia Tech | Master of Science, Computer Science\n\n**Dec 2024**\n\n- **GPA**: 3.9/4.0\n\n# # Vel Tech University | Bachelor of Technology, Computer Science and Engineering\n\n- **GPA**: 8.24/10",
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+      "source": "education.md",
+      "header": "# Education",
+      "chunk_id": "education.md_#0_ac6a482e",
       "has_header": true,
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+      "word_count": 33
     }
   },
   {
-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# # 🌐 API & Streaming\n\n- **Backend**: FastAPI (`backend/api.py`)\n  - `/chat` SSE endpoint streams tokens in real-time\n  - Passes `thread_id` & `is_final` to LangGraph for stateful conversations\n- **Frontend**: React + Tailwind (custom chat UI)\n  - Threaded conversation storage in browser `localStorage`\n  - Real-time token rendering via `EventSource`\n  - Features: new chat, clear chat, delete thread, suggestions\n\n---\n\n# # 🖥️ Frontend Highlights\n\n- Dark theme ChatGPT-style UI\n- Sidebar for thread management\n- Live streaming responses with Markdown rendering\n- Suggestion prompts for quick interactions\n- Message actions: copy, edit, regenerate\n\n---",
+    "text": "[HEADER] # Professional Experience\n\n# # Software Engineer - AI Platform | Cloud Systems LLC | US Remote\n\n**Jul 2025 - Present**\n\n- **Agentic Workflow Automation**: Architected an agentic workflow using **LangGraph** and **ReAct** to automate SQL generation. This system automated **~65%** of ad-hoc data-auditing requests from internal stakeholders, reducing the average response time from **4 hours to under 2 minutes**.\n- **ETL Optimization**: Optimized data ingestion performance by rebuilding ETL pipelines with batched I/O, incremental refresh logic, and dependency pruning, cutting daily execution runtime by **25%**.\n- **Infrastructure & Reliability**: Improved production reliability by shipping the agent service on **Kubernetes** with autoscaling and rolling deploys, adding alerts and rollback steps for failed releases.\n- **Contract Testing**: Improved cross-service reliability by implementing **Pydantic** schema validation and contract tests, preventing multiple breaking changes from reaching production.",
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+      "source": "experience.md",
+      "header": "# Professional Experience",
+      "chunk_id": "experience.md_#1_33df1d29",
       "has_header": true,
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+      "word_count": 131
     }
   },
   {
-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# # 🖥️ Frontend Highlights\n\n- Dark theme ChatGPT-style UI\n- Sidebar for thread management\n- Live streaming responses with Markdown rendering\n- Suggestion prompts for quick interactions\n- Message actions: copy, edit, regenerate\n\n---\n\n# # 🧩 Design Improvements Over Previous Version\n\n- **LangGraph StateGraph** ensures explicit control of message flow\n- **Thread-scoped memory** enables multi-session personalization\n- **Hybrid RRF + Cross-Encoder + MMR** retrieval pipeline improves relevance & diversity\n- **SSE streaming** for low-latency feedback\n- Decoupled **retrieval** and **memory** as separate tools for modularity\n\n---",
+    "text": "[HEADER] # Professional Experience\n\n# # Machine Learning Engineer | Virginia Tech, Dept. of Plant Sciences | Blacksburg, VA\n\n**Aug 2024 - Jul 2025**\n\n- **Model Optimization**: Increased genomics sequence classification throughput by **32%** by applying **LoRA** and **soft prompting** methods. Packaged repeatable **PyTorch** pipelines that cut per-experiment training time by **4.5 hours**.\n- **HPC Orchestration**: Developed an ML orchestration layer for distributed GPU training on HPC clusters. Engineered checkpoint-resume logic that handled preemptive node shutdowns, optimizing resource utilization and reducing compute waste by **15%**.\n- **MLOps**: Reduced research environment setup time from hours to minutes by containerizing fine-tuned models with **Docker** and managing the experimental lifecycle (versions, hyperparameters, and weights) via **MLflow**.",
     "metadata": {
-      "source": "Chatbot_Architecture_Notes.md",
-      "header": "# 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)",
-      "chunk_id": "Chatbot_Architecture_Notes.md_#16_0247e9ee",
+      "source": "experience.md",
+      "header": "# Professional Experience",
+      "chunk_id": "experience.md_#2_a1069896",
       "has_header": true,
-      "word_count": 88
+      "word_count": 109
     }
   },
   {
-    "text": "[HEADER] # 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)\n\n# # 🚀 Future Enhancements\n\n- Integrate **tool calling** for external APIs (calendar, search)\n- Summarization agents for condensing memory store\n- Interest-based retrieval filtering\n- Multi-agent orchestration for complex tasks\n\n---\n\nThis LangGraph-powered architecture delivers a **stateful, retrieval-augmented, memory-aware personal assistant** optimized for Krishna’s profile and designed for **extensibility, performance, and precision**.",
+    "text": "[HEADER] # Professional Experience\n\n# # Software Engineer | UJR Technologies Pvt Ltd | Hyderabad, India\n\n**Jul 2021 - Dec 2022**\n\n- **API & SDK Development**: Designed and maintained standardized **REST APIs** and **Python-based SDKs** to streamline the ML development lifecycle, reducing cross-team integration defects by **40%**.\n- **Model Serving**: Engineered model-serving endpoints with automated input validation and deployment health checks, lowering prediction-related failures by **30%** for ML-driven features.\n- **CI/CD Pipeline**: Automated CI/CD pipelines via **GitHub Actions** with comprehensive test coverage and scripted rollback procedures, decreasing release failures by **20%** across production environments.",
     "metadata": {
-      "source": "Chatbot_Architecture_Notes.md",
-      "header": "# 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)",
-      "chunk_id": "Chatbot_Architecture_Notes.md_#17_328ca9e7",
+      "source": "experience.md",
+      "header": "# Professional Experience",
+      "chunk_id": "experience.md_#3_f6eb60f5",
       "has_header": true,
-      "word_count": 53
+      "word_count": 90
     }
   },
   {
@@ -379,6 +229,46 @@
       "word_count": 121
     }
   },
+  {
+    "text": "[HEADER] # Projects\n\n# Projects\n\n# # Autonomous Multi-Agent Web UI Automation System\n\n- **Overview**: Developed a multi-agent system using **LangGraph** and **Playwright** to navigate non-deterministic UI changes across 5 high-complexity SaaS platforms.\n- **Impact**: Increased task completion success rate from **68% to 94%** by implementing a two-stage verification loop with step-level assertions and exponential backoff for dynamic DOM states.\n- **Observability**: Integrated **LangSmith** traces for observability, reducing the mean-time-to-debug for broken selectors by **14 minutes per incident**.",
+    "metadata": {
+      "source": "projects.md",
+      "header": "# Projects",
+      "chunk_id": "projects.md_#0_89043f3a",
+      "has_header": true,
+      "word_count": 75
+    }
+  },
+  {
+    "text": "[HEADER] # Projects\n\n# # Proxy TuNER: Advancing Cross-Domain Named Entity Recognition through Proxy Tuning\n\n- **Overview**: Improved cross-domain NER F1-score by **8%** by implementing a proxy-tuning approach for **LLaMA 2 models** (7B, 7B-Chat, 13B) using logit ensembling and gradient reversal.\n- **Optimization**: Optimized inference performance by **30%** and reduced training costs by **70%** through distributed execution and model path optimizations in **PyTorch**.\n\n# # IntelliMeet: AI-Enabled Decentralized Video Conferencing App\n\n- **Overview**: Architected a secure, decentralized video platform using **WebRTC** and **federated learning** to maintain data privacy while sharing only aggregated model updates.\n- **Reliability**: Reduced call dropouts by **25%** by engineering network recovery logic and on-device **RetinaFace** attention detection for client-side quality adaptation.",
+    "metadata": {
+      "source": "projects.md",
+      "header": "# Projects",
+      "chunk_id": "projects.md_#1_48eb2f1e",
+      "has_header": true,
+      "word_count": 112
+    }
+  },
+  {
+    "text": "[HEADER] # Publications\n\n# Publications\n\n- **Predicting Circadian Transcription in mRNAs and lncRNAs**, IEEE BIBM 2024\n- **DNA Foundation Models for Cross-Species TF Binding Prediction**, NeurIPS ML in CompBio 2025\n- **Multi-omics atlas of the plant nuclear envelope**, Science Advances (under review) 2025, University of California, Berkeley",
+    "metadata": {
+      "source": "publications.md",
+      "header": "# Publications",
+      "chunk_id": "publications.md_#0_3ab998c3",
+      "has_header": true,
+      "word_count": 44
+    }
+  },
+  {
+    "text": "[HEADER] # Technical Skills\n\n# Technical Skills\n\n# # Languages\n\n- Python, SQL, TypeScript, JavaScript, MongoDB\n\n# # ML & AI Frameworks\n\n- PyTorch, Transformers, LangChain, LangGraph, LoRA, RAG, NLP, SKLearn, XGBoost\n\n# # Data & Infrastructure\n\n- Docker, Kubernetes, Apache Airflow, MLflow, Redis, FAISS, AWS, GCP, Git\n\n# # Tools & Observability\n\n- LangSmith, Grafana, CI/CD (GitHub Actions/Jenkins), Weights & Biases, Linux",
+    "metadata": {
+      "source": "skills.md",
+      "header": "# Technical Skills",
+      "chunk_id": "skills.md_#0_8e74dc40",
+      "has_header": true,
+      "word_count": 59
+    }
+  },
   {
     "text": "[HEADER] ## 🧗‍♂️ Hobbies & Passions\n\n## 🧗‍♂️ Hobbies & Passions\n\nHere’s what keeps me energized and curious outside of work:\n\n- **🥾 Hiking & Outdoor Adventures** — Nothing clears my mind like a good hike.\n- **🎬 Marvel Fan for Life** — I’ve seen every Marvel movie, and I’d probably give my life for the MCU (Team Iron Man, always).\n- **🏏 Cricket Enthusiast** — Whether it's IPL or gully cricket, I'm all in.\n- **🚀 Space Exploration Buff** — Obsessed with rockets, Mars missions, and the future of interplanetary travel.\n- **🍳 Cooking Explorer** — I enjoy experimenting with recipes, especially fusion dishes.\n- **🕹️ Gaming & Reverse Engineering** — I love diving into game logic and breaking things down just to rebuild them better.\n- **🧑‍🤝‍🧑 Time with Friends** — Deep conversations, spontaneous trips, or chill evenings—friends keep me grounded.\n\n---",
     "metadata": {

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personal_data/Chatbot_Architecture_Notes.md

@@ -1,137 +1,6 @@
-# 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (old and intial one)
+# 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant
 
-This document outlines the technical architecture and modular design of Krishna Vamsi Dhulipalla’s personal AI chatbot system, implemented using **LangChain**, **OpenAI**, **NVIDIA NIMs**, and **Gradio**. The assistant is built for intelligent, retriever-augmented, memory-aware interaction tailored to Krishna’s background and user context.
-
----
-
-## 🧱 Core Components
-
-### 1. **LLMs Used and Their Roles**
-
-| Purpose                             | Model Name                               | Role Description                                                 |
-| ----------------------------------- | ---------------------------------------- | ---------------------------------------------------------------- |
-| **Rephraser LLM**                   | `microsoft/phi-3-mini-4k-instruct`       | Rewrites vague/short queries into detailed, keyword-rich queries |
-| **Relevance Classifier + Reranker** | `mistralai/mixtral-8x22b-instruct-v0.1`  | Classifies query relevance to KB and reranks retrieved chunks    |
-| **Answer Generator**                | `nvidia/llama-3.1-nemotron-70b-instruct` | Provides rich, structured answers (replacing GPT-4o for testing) |
-| **Fallback Humor Model**            | `mistralai/mixtral-8x22b-instruct-v0.1`  | Responds humorously and redirects when out-of-scope              |
-| **KnowledgeBase Updater**           | `mistralai/mistral-7b-instruct-v0.3`     | Extracts and updates structured memory about the user            |
-
-All models are integrated via **LangChain RunnableChains**, supporting both streaming and structured execution.
-
----
-
-## 🔍 Retrieval Architecture
-
-### ✅ **Hybrid Retrieval System**
-
-The assistant combines:
-
-- **BM25Retriever**: Lexical keyword match
-- **FAISS Vector Search**: Dense embeddings from `sentence-transformers/all-MiniLM-L6-v2`
-
-### 🧠 Rephrasing for Retrieval
-
-- The **user's query** is expanded using the Rephraser LLM, with awareness of `last_followups` and memory
-- **Rewritten query** is used throughout retrieval, validation, and reranking
-
-### 📊 Scoring & Ranking
-
-- Each subquery is run through both BM25 and FAISS
-- Results are merged via weighted formula:  
-  `final_score = α * vector_score + (1 - α) * bm25_score`
-- Deduplication via fingerprinting
-- Top-k (default: 15) results are passed forward
-
----
-
-## 🔎 Validation + Chunk Reranking
-
-### 🔍 Relevance Classification
-
-- LLM2 evaluates:
-  - Whether the query (or rewritten query) is **in-scope**
-  - If so, returns a **reranked list of chunk indices**
-- Memory (`last_input`, `last_output`, `last_followups`) and `rewritten_query` are included for better context
-
-### ❌ If Out-of-Scope
-
-- Chunks are discarded
-- Response is generated using fallback LLM with humor and redirection
-
----
-
-## 🧠 Memory + Personalization
-
-### 📘 KnowledgeBase Model
-
-Tracks structured user data:
-
-- `user_name`, `company`, `last_input`, `last_output`
-- `summary_history`, `recent_interests`, `last_followups`, `tone`
-
-### 🔄 Memory Updates
-
-- After every response, assistant extracts and updates memory
-- Handled via `RExtract` pipeline using `PydanticOutputParser` and KB LLM
-
----
-
-## 🧭 Orchestration Flow
-
-```text
-User Input
-   ↓
-Rephraser LLM (phi-3-mini)
-   ↓
-Hybrid Retrieval (BM25 + FAISS)
-   ↓
-Validation + Reranking (mixtral-8x22b)
-   ↓
- ┌──────────────┐     ┌────────────────────┐
- │ In-Scope     │     │ Out-of-Scope Query │
- │ (Top-k Chunks)│     │ (Memory-based only)│
- └────┬─────────┘     └─────────────┬──────┘
-      ↓                                  ↓
- Answer LLM (nemotron-70b)       Fallback Humor LLM
-```
-
----
-
-## 💬 Frontend Interface (Gradio)
-
-- Built using **Gradio ChatInterface + Blocks**
-- Features:
-  - Responsive design
-  - Custom CSS
-  - Streaming markdown responses
-  - Preloaded examples and auto-scroll
-
----
-
-## 🧩 Additional Design Highlights
-
-- **Streaming**: Nemotron-70B used via LangChain streaming
-- **Prompt Engineering**: Answer prompts use markdown formatting, section headers, bullet points, and personalized sign-offs
-- **Memory-Aware Rewriting**: Handles vague replies like `"yes"` or `"A"` by mapping them to `last_followups`
-- **Knowledge Chunk Enrichment**: Each FAISS chunk includes synthetic summary and 3 QA-style synthetic queries
-
----
-
-## 🚀 Future Enhancements
-
-- Tool calling for tasks like calendar access or Google search
-- Multi-model reranking agents
-- Memory summarization agents for long dialogs
-- Topic planners to group conversations
-- Retrieval filtering based on user interest and session
-
----
-
-This architecture is modular, extensible, and designed to simulate a memory-grounded, expert-aware personal assistant tailored to Krishna’s evolving knowledge and conversational goals.
-
-# 🤖 Chatbot Architecture Overview: Krishna's Personal AI Assistant (LangGraph Version) (New and current one)
-
-This document details the updated architecture of **Krishna Vamsi Dhulipalla’s** personal AI assistant, now fully implemented with **LangGraph** for orchestrated state management and tool execution. The system is designed for **retrieval-augmented, memory-grounded, and multi-turn conversational intelligence**, integrating **OpenAI GPT-4o**, **Hugging Face embeddings**, and **cross-encoder reranking**.
+This document details the architecture of **Krishna Vamsi Dhulipalla’s** personal AI assistant, implemented with **LangGraph** for orchestrated state management and tool execution. The system is designed for **retrieval-augmented, memory-grounded, and multi-turn conversational intelligence**, integrating **OpenAI GPT-4o**, **Hugging Face embeddings**, and **cross-encoder reranking**.
 
 ---
 
@@ -240,33 +109,10 @@ Embedded **Krishna’s Bio** provides static grounding context.
 
 ---
 
-## 🖥️ Frontend Highlights
-
-- Dark theme ChatGPT-style UI
-- Sidebar for thread management
-- Live streaming responses with Markdown rendering
-- Suggestion prompts for quick interactions
-- Message actions: copy, edit, regenerate
-
----
-
-## 🧩 Design Improvements Over Previous Version
+## 🧩 Design Improvements
 
 - **LangGraph StateGraph** ensures explicit control of message flow
 - **Thread-scoped memory** enables multi-session personalization
 - **Hybrid RRF + Cross-Encoder + MMR** retrieval pipeline improves relevance & diversity
 - **SSE streaming** for low-latency feedback
-- Decoupled **retrieval** and **memory** as separate tools for modularity
-
----
-
-## 🚀 Future Enhancements
-
-- Integrate **tool calling** for external APIs (calendar, search)
-- Summarization agents for condensing memory store
-- Interest-based retrieval filtering
-- Multi-agent orchestration for complex tasks
-
----
-
-This LangGraph-powered architecture delivers a **stateful, retrieval-augmented, memory-aware personal assistant** optimized for Krishna’s profile and designed for **extensibility, performance, and precision**.
+- **Decoupled retrieval** and **memory** as separate tools for modularity

personal_data/aprofile.md

@@ -1,155 +0,0 @@
-# 👋 Hello, I'm Krishna Vamsi Dhulipalla
-
-I’m a **Machine Learning Engineer** with over **3 years of experience** designing and deploying intelligent AI systems, integrating backend infrastructure, and building real-time data workflows. I specialize in **LLM-powered agents**, **semantic search**, **bioinformatics AI models**, and **cloud-native ML infrastructure**.
-
-I earned my **M.S. in Computer Science** from **Virginia Tech** in December 2024 with a 3.95/4.0 GPA, focusing on large language models, intelligent agents, and scalable data systems. My work spans the full ML lifecycle—from research and fine-tuning transformer architectures to deploying production-ready applications on AWS and GCP.
-
-I’m passionate about **LLM-driven systems**, **multi-agent orchestration**, and **domain-adaptive ML**, particularly in **genomic data analysis** and **real-time analytics**.
-
----
-
-## 🎯 Career Summary
-
-- 👨‍💻 3+ years of experience in **ML systems design**, **LLM-powered applications**, and **data engineering**
-- 🧬 Proven expertise in **transformer fine-tuning** (LoRA, soft prompting) for genomic classification
-- 🤖 Skilled in **LangChain**, **LangGraph**, **AutoGen**, and **CrewAI** for intelligent agent workflows
-- ☁️ Deep knowledge of **AWS** (S3, Glue, Lambda, SageMaker, ECS, CloudWatch) and **GCP** (BigQuery, Dataflow, Composer)
-- ⚡ Experienced in **real-time data pipelines** using **Apache Kafka**, **Spark**, **Airflow**, and **dbt**
-- 📊 Strong foundation in **synthetic data generation**, **domain adaptation**, and **cross-domain NER**
-
-## 🔭 Areas of Current Focus
-
-- Developing **LLM-powered mobile automation agents** for UI task execution
-- Architecting **retrieval-augmented generation (RAG)** systems with hybrid retrieval and cross-encoder reranking
-- Fine-tuning **DNA foundation models** like DNABERT & HyenaDNA for plant genomics
-- Building **real-time analytics pipelines** integrating Kafka, Spark, Airflow, and cloud services
-
----
-
-## 🎓 Education
-
-### Virginia Tech — M.S. in Computer Science
-
-📍 Blacksburg, VA | Jan 2023 – Dec 2024  
-**GPA:** 3.95 / 4.0  
-Relevant Coursework: Distributed Systems, Machine Learning Optimization, Genomics, LLMs & Transformer Architectures
-
-### Anna University — B.Tech in Computer Science and Engineering
-
-📍 Chennai, India | Jun 2018 – May 2022  
-**GPA:** 8.24 / 10  
-Specialization: Real-Time Analytics, Cloud Systems, Software Engineering Principles
-
----
-
-## 🛠️ Technical Skills
-
-**Programming:** Python, R, SQL, JavaScript, TypeScript, Node.js, FastAPI, MongoDB  
-**ML Frameworks:** PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers  
-**LLM & Agents:** LangChain, LangGraph, AutoGen, CrewAI, Prompt Engineering, RAG, LoRA, GANs  
-**ML Techniques:** Self-Supervised Learning, Cross-Domain Adaptation, Hyperparameter Optimization, A/B Testing  
-**Data Engineering:** Apache Spark, Kafka, dbt, Airflow, ETL Pipelines, Delta Lake, Snowflake  
-**Cloud & Infra:** AWS (S3, Glue, Lambda, Redshift, ECS, SageMaker, CloudWatch), GCP (GCS, BigQuery, Dataflow, Composer)  
-**DevOps/MLOps:** Docker, Kubernetes, MLflow, CI/CD, Weights & Biases  
-**Visualization:** Tableau, Shiny (R), Plotly, Matplotlib  
-**Other Tools:** Pandas, NumPy, Git, LangSmith, LangFlow, Linux
-
----
-
-## 💼 Professional Experience
-
-### Cloud Systems LLC — ML Research Engineer (Current role)
-
-📍 Remote | Jul 2024 – Present
-
-- Designed and optimized **SQL-based data retrieval** and **batch + real-time pipelines**
-- Built automated **ETL workflows** integrating multiple data sources
-
-### Virginia Tech — ML Research Engineer
-
-📍 Blacksburg, VA | Sep 2024 – Jul 2024
-
-- Developed **DNA sequence classification pipelines** using DNABERT & HyenaDNA with LoRA & soft prompting (94%+ accuracy)
-- Automated preprocessing of **1M+ genomic sequences** with Biopython & Airflow, reducing runtime by 40%
-- Built **LangChain-based semantic search** for genomics literature
-- Deployed fine-tuned LLMs using Docker, MLflow, and optionally SageMaker
-
-### Virginia Tech — Research Assistant
-
-📍 Blacksburg, VA | Jun 2023 – May 2024
-
-- Built **genomic ETL pipelines** (Airflow + AWS Glue) improving research data availability by 50%
-- Automated retraining workflows via CI/CD, reducing manual workload by 40%
-- Benchmarked compute cluster performance to cut runtime costs by 15%
-
-### UJR Technologies Pvt Ltd — Data Engineer
-
-📍 Hyderabad, India | Jul 2021 – Dec 2022
-
-- Migrated **batch ETL to real-time streaming** with Kafka & Spark (↓ latency 30%)
-- Deployed Dockerized microservices to AWS ECS, improving deployment speed by 25%
-- Optimized Snowflake schemas to improve query performance by 40%
-
----
-
-## 📊 Highlight Projects
-
-- **LLM-Based Android Agent** – Multi-step UI automation with memory, self-reflection, and context recovery (80%+ accuracy)
-
-### Real-Time IoT-Based Temperature Forecasting
-
-- Kafka-based pipeline for 10K+ sensor readings with LLaMA 2-based time series model (91% accuracy)
-- Airflow + Looker dashboards (↓ manual reporting by 30%)
-- S3 lifecycle policies saved 40% storage cost with versioned backups  
-  🔗 [GitHub](https://github.com/krishna-creator/Real-Time-IoT-Based-Temperature-Analytics-and-Forecasting)
-
-### Proxy TuNER: Cross-Domain NER
-
-- Developed a proxy tuning method for domain-agnostic BERT
-- 15% generalization gain using gradient reversal + feature alignment
-- 70% cost reduction via logit-level ensembling  
-  🔗 [GitHub](https://github.com/krishna-creator/ProxytuNER)
-
-### IntelliMeet: AI-Powered Conferencing
-
-- Federated learning, end-to-end encrypted platform
-- Live attention detection using RetinaFace (<200ms latency)
-- Summarization with Transformer-based speech-to-text  
-  🔗 [GitHub](https://github.com/krishna-creator/SE-Project---IntelliMeet)
-
-### Automated Drone Image Analysis
-
-- Real-time crop disease detection using drone imagery
-- Used OpenCV, RAG, and GANs for synthetic data generation
-- Improved detection accuracy by 15% and reduced processing latency by 70%
-
----
-
-## 📜 Certifications
-
-- 🏆 NVIDIA – Building RAG Agents with LLMs
-- 🏆 Google Cloud – Data Engineering Foundations
-- 🏆 AWS – Machine Learning Specialty
-- 🏆 Microsoft – MERN Stack Development
-- 🏆 Snowflake – End-to-End Data Engineering
-- 🏆 Coursera – Machine Learning Specialization  
-  🔗 [View All Credentials](https://www.linkedin.com/in/krishnavamsidhulipalla/)
-
----
-
-## 📚 Research Publications
-
-- **IEEE BIBM 2024** – “Leveraging ML for Predicting Circadian Transcription in mRNAs and lncRNAs”  
-  [DOI: 10.1109/BIBM62325.2024.10822684](https://doi.org/10.1109/BIBM62325.2024.10822684)
-
-- **MLCB** – “Harnessing DNA Foundation Models for TF Binding Prediction in Plants”
-
----
-
-## 🔗 External Links / Contact details
-
-- 🌐 [Personal Portfolio/ personal website](http://krishna-dhulipalla.github.io)
-- 🧪 [GitHub](https://github.com/Krishna-dhulipalla)
-- 💼 [LinkedIn](https://www.linkedin.com/in/krishnavamsidhulipalla)
-- 📬 dhulipallakrishnavamsi@gmail.com
-- 🤖 [Personal Chatbot](https://huggingface.co/spaces/krishnadhulipalla/Personal_ChatBot)

personal_data/bio.md

@@ -0,0 +1 @@
+Krishna Vamsi Dhulipalla is a Software Engineer specializing in generic workflows and AI platforms. He currently works at **Cloud Systems LLC**, where he architects LangGraph-based agents to automate data auditing. Previously, he served as a Machine Learning Engineer at **Virginia Tech**, optimizing genomic models with LoRA/soft prompting, and as a Software Engineer at **UJR Technologies**, building ML SDKs and CI/CD pipelines. He holds an M.S. in Computer Science from Virginia Tech (Dec 2024) and has significant expertise in **LangGraph**, **Kubernetes**, **PyTorch**, and **MLOps**.

personal_data/education.md

@@ -0,0 +1,11 @@
+# Education
+
+## Virginia Tech | Master of Science, Computer Science
+
+**Dec 2024**
+
+- **GPA**: 3.9/4.0
+
+## Vel Tech University | Bachelor of Technology, Computer Science and Engineering
+
+- **GPA**: 8.24/10

personal_data/experience.md

@@ -0,0 +1,26 @@
+# Professional Experience
+
+## Software Engineer - AI Platform | Cloud Systems LLC | US Remote
+
+**Jul 2025 - Present**
+
+- **Agentic Workflow Automation**: Architected an agentic workflow using **LangGraph** and **ReAct** to automate SQL generation. This system automated **~65%** of ad-hoc data-auditing requests from internal stakeholders, reducing the average response time from **4 hours to under 2 minutes**.
+- **ETL Optimization**: Optimized data ingestion performance by rebuilding ETL pipelines with batched I/O, incremental refresh logic, and dependency pruning, cutting daily execution runtime by **25%**.
+- **Infrastructure & Reliability**: Improved production reliability by shipping the agent service on **Kubernetes** with autoscaling and rolling deploys, adding alerts and rollback steps for failed releases.
+- **Contract Testing**: Improved cross-service reliability by implementing **Pydantic** schema validation and contract tests, preventing multiple breaking changes from reaching production.
+
+## Machine Learning Engineer | Virginia Tech, Dept. of Plant Sciences | Blacksburg, VA
+
+**Aug 2024 - Jul 2025**
+
+- **Model Optimization**: Increased genomics sequence classification throughput by **32%** by applying **LoRA** and **soft prompting** methods. Packaged repeatable **PyTorch** pipelines that cut per-experiment training time by **4.5 hours**.
+- **HPC Orchestration**: Developed an ML orchestration layer for distributed GPU training on HPC clusters. Engineered checkpoint-resume logic that handled preemptive node shutdowns, optimizing resource utilization and reducing compute waste by **15%**.
+- **MLOps**: Reduced research environment setup time from hours to minutes by containerizing fine-tuned models with **Docker** and managing the experimental lifecycle (versions, hyperparameters, and weights) via **MLflow**.
+
+## Software Engineer | UJR Technologies Pvt Ltd | Hyderabad, India
+
+**Jul 2021 - Dec 2022**
+
+- **API & SDK Development**: Designed and maintained standardized **REST APIs** and **Python-based SDKs** to streamline the ML development lifecycle, reducing cross-team integration defects by **40%**.
+- **Model Serving**: Engineered model-serving endpoints with automated input validation and deployment health checks, lowering prediction-related failures by **30%** for ML-driven features.
+- **CI/CD Pipeline**: Automated CI/CD pipelines via **GitHub Actions** with comprehensive test coverage and scripted rollback procedures, decreasing release failures by **20%** across production environments.

personal_data/projects.md

@@ -0,0 +1,17 @@
+# Projects
+
+## Autonomous Multi-Agent Web UI Automation System
+
+- **Overview**: Developed a multi-agent system using **LangGraph** and **Playwright** to navigate non-deterministic UI changes across 5 high-complexity SaaS platforms.
+- **Impact**: Increased task completion success rate from **68% to 94%** by implementing a two-stage verification loop with step-level assertions and exponential backoff for dynamic DOM states.
+- **Observability**: Integrated **LangSmith** traces for observability, reducing the mean-time-to-debug for broken selectors by **14 minutes per incident**.
+
+## Proxy TuNER: Advancing Cross-Domain Named Entity Recognition through Proxy Tuning
+
+- **Overview**: Improved cross-domain NER F1-score by **8%** by implementing a proxy-tuning approach for **LLaMA 2 models** (7B, 7B-Chat, 13B) using logit ensembling and gradient reversal.
+- **Optimization**: Optimized inference performance by **30%** and reduced training costs by **70%** through distributed execution and model path optimizations in **PyTorch**.
+
+## IntelliMeet: AI-Enabled Decentralized Video Conferencing App
+
+- **Overview**: Architected a secure, decentralized video platform using **WebRTC** and **federated learning** to maintain data privacy while sharing only aggregated model updates.
+- **Reliability**: Reduced call dropouts by **25%** by engineering network recovery logic and on-device **RetinaFace** attention detection for client-side quality adaptation.

personal_data/publications.md

@@ -0,0 +1,5 @@
+# Publications
+
+- **Predicting Circadian Transcription in mRNAs and lncRNAs**, IEEE BIBM 2024
+- **DNA Foundation Models for Cross-Species TF Binding Prediction**, NeurIPS ML in CompBio 2025
+- **Multi-omics atlas of the plant nuclear envelope**, Science Advances (under review) 2025, University of California, Berkeley

personal_data/skills.md

@@ -0,0 +1,17 @@
+# Technical Skills
+
+## Languages
+
+- Python, SQL, TypeScript, JavaScript, MongoDB
+
+## ML & AI Frameworks
+
+- PyTorch, Transformers, LangChain, LangGraph, LoRA, RAG, NLP, SKLearn, XGBoost
+
+## Data & Infrastructure
+
+- Docker, Kubernetes, Apache Airflow, MLflow, Redis, FAISS, AWS, GCP, Git
+
+## Tools & Observability
+
+- LangSmith, Grafana, CI/CD (GitHub Actions/Jenkins), Weights & Biases, Linux

ui/package-lock.json

@@ -17,6 +17,7 @@
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         "@tailwindcss/cli": "^4.1.11",
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         "@vitejs/plugin-react": "^4.7.0",
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       "version": "3.1.2",
       "resolved": "https://registry.npmjs.org/@jridgewell/resolve-uri/-/resolve-uri-3.1.2.tgz",
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+        "url": "https://opencollective.com/parcel"
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+      "dev": true,
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+        "url": "https://opencollective.com/parcel"
+      }
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+    "node_modules/@tailwindcss/vite/node_modules/lightningcss-linux-arm64-musl": {
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+        "url": "https://opencollective.com/parcel"
+      }
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+        "url": "https://opencollective.com/parcel"
+      }
+    },
+    "node_modules/@tailwindcss/vite/node_modules/lightningcss-win32-arm64-msvc": {
+      "version": "1.30.2",
+      "resolved": "https://registry.npmjs.org/lightningcss-win32-arm64-msvc/-/lightningcss-win32-arm64-msvc-1.30.2.tgz",
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+      "cpu": [
+        "arm64"
+      ],
+      "dev": true,
+      "license": "MPL-2.0",
+      "optional": true,
+      "os": [
+        "win32"
+      ],
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+      },
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+        "url": "https://opencollective.com/parcel"
+      }
+    },
+    "node_modules/@tailwindcss/vite/node_modules/lightningcss-win32-x64-msvc": {
+      "version": "1.30.2",
+      "resolved": "https://registry.npmjs.org/lightningcss-win32-x64-msvc/-/lightningcss-win32-x64-msvc-1.30.2.tgz",
+      "integrity": "sha512-5g1yc73p+iAkid5phb4oVFMB45417DkRevRbt/El/gKXJk4jid+vPFF/AXbxn05Aky8PapwzZrdJShv5C0avjw==",
+      "cpu": [
+        "x64"
+      ],
+      "dev": true,
+      "license": "MPL-2.0",
+      "optional": true,
+      "os": [
+        "win32"
+      ],
+      "engines": {
+        "node": ">= 12.0.0"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/parcel"
+      }
+    },
+    "node_modules/@tailwindcss/vite/node_modules/tailwindcss": {
+      "version": "4.1.18",
+      "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-4.1.18.tgz",
+      "integrity": "sha512-4+Z+0yiYyEtUVCScyfHCxOYP06L5Ne+JiHhY2IjR2KWMIWhJOYZKLSGZaP5HkZ8+bY0cxfzwDE5uOmzFXyIwxw==",
+      "dev": true,
+      "license": "MIT"
+    },
     "node_modules/@types/babel__core": {
       "version": "7.20.5",
       "resolved": "https://registry.npmjs.org/@types/babel__core/-/babel__core-7.20.5.tgz",
@@ -3503,9 +4044,9 @@
       "license": "ISC"
     },
     "node_modules/jiti": {
-      "version": "2.5.1",
-      "resolved": "https://registry.npmjs.org/jiti/-/jiti-2.5.1.tgz",
-      "integrity": "sha512-twQoecYPiVA5K/h6SxtORw/Bs3ar+mLUtoPSc7iMXzQzK8d7eJ/R09wmTwAjiamETn1cXYPGfNnu7DMoHgu12w==",
+      "version": "2.6.1",
+      "resolved": "https://registry.npmjs.org/jiti/-/jiti-2.6.1.tgz",
+      "integrity": "sha512-ekilCSN1jwRvIbgeg/57YFh8qQDNbwDb9xT/qu2DAHbFFZUicIl4ygVaAvzveMhMVr3LnpSKTNnwt8PoOfmKhQ==",
       "dev": true,
       "license": "MIT",
       "bin": {
@@ -3632,6 +4173,27 @@
         "lightningcss-win32-x64-msvc": "1.30.1"
       }
     },
+    "node_modules/lightningcss-android-arm64": {
+      "version": "1.30.2",
+      "resolved": "https://registry.npmjs.org/lightningcss-android-arm64/-/lightningcss-android-arm64-1.30.2.tgz",
+      "integrity": "sha512-BH9sEdOCahSgmkVhBLeU7Hc9DWeZ1Eb6wNS6Da8igvUwAe0sqROHddIlvU06q3WyXVEOYDZ6ykBZQnjTbmo4+A==",
+      "cpu": [
+        "arm64"
+      ],
+      "dev": true,
+      "license": "MPL-2.0",
+      "optional": true,
+      "os": [
+        "android"
+      ],
+      "engines": {
+        "node": ">= 12.0.0"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/parcel"
+      }
+    },
     "node_modules/lightningcss-darwin-arm64": {
       "version": "1.30.1",
       "resolved": "https://registry.npmjs.org/lightningcss-darwin-arm64/-/lightningcss-darwin-arm64-1.30.1.tgz",
@@ -3896,13 +4458,13 @@
       }
     },
     "node_modules/magic-string": {
-      "version": "0.30.17",
-      "resolved": "https://registry.npmjs.org/magic-string/-/magic-string-0.30.17.tgz",
-      "integrity": "sha512-sNPKHvyjVf7gyjwS4xGTaW/mCnF8wnjtifKBEhxfZ7E/S8tQ0rssrwGNn6q8JH/ohItJfSQp9mBtQYuTlH5QnA==",
+      "version": "0.30.21",
+      "resolved": "https://registry.npmjs.org/magic-string/-/magic-string-0.30.21.tgz",
+      "integrity": "sha512-vd2F4YUyEXKGcLHoq+TEyCjxueSeHnFxyyjNp80yg0XV4vUhnDer/lvvlqM/arB5bXQN5K2/3oinyCRyx8T2CQ==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@jridgewell/sourcemap-codec": "^1.5.0"
+        "@jridgewell/sourcemap-codec": "^1.5.5"
       }
     },
     "node_modules/markdown-table": {

ui/package.json

@@ -21,6 +21,7 @@
   "devDependencies": {
     "@eslint/js": "^9.32.0",
     "@tailwindcss/cli": "^4.1.11",
+    "@tailwindcss/vite": "^4.1.18",
     "@types/react": "^19.1.9",
     "@types/react-dom": "^19.1.7",
     "@vitejs/plugin-react": "^4.7.0",

ui/src/App.tsx

@@ -5,49 +5,116 @@ import React, {
   useRef,
   useState,
 } from "react";
-import { BookmarkIcon } from "@heroicons/react/24/outline";
-import { TrashIcon } from "@heroicons/react/24/outline";
+import {
+  BookmarkIcon,
+  TrashIcon,
+  SparklesIcon,
+  PaperClipIcon,
+  MicrophoneIcon,
+  PaperAirplaneIcon,
+  ArrowPathIcon,
+  DocumentDuplicateIcon,
+  PencilSquareIcon,
+  ChatBubbleLeftRightIcon,
+  XMarkIcon,
+  CommandLineIcon,
+} from "@heroicons/react/24/outline";
 import ReactMarkdown from "react-markdown";
 import remarkGfm from "remark-gfm";
-import { SparklesIcon } from "@heroicons/react/24/outline";
-
-// const API_PATH = "/chat";
-// const THREAD_KEY = "lg_thread_id";
 
+// Internal hooks/types
 import { useChat } from "./useChat";
 import type { ThreadMeta } from "./threads";
 
+// --- Constants & Config ---
+const APP_TITLE = "Krishna's Digital Twin";
+const BOT_AVATAR =
+  "https://api.dicebear.com/9.x/bottts-neutral/svg?seed=Krishna1&backgroundColor=6366f1"; // Cleaner 3D-ish Robot
+
+const DID_YOU_KNOW = [
+  "Krishna achieved a 3.95 GPA during his M.S. at Virginia Tech.",
+  "Krishna built an agent that automates Android UI tasks with 80%+ accuracy.",
+  "Krishna optimized genomic ETL pipelines reducing runtime by 70%.",
+  "Krishna specializes in building autonomous agents and RAG systems.",
+];
+
 const SUGGESTIONS = [
   {
-    title: "Quick intro to Krishna",
+    title: "Summarize Experience",
     text: "Give me a 90-second intro to Krishna Vamsi Dhulipalla—recent work, top strengths, and impact.",
+    icon: "✨",
   },
   {
-    title: "Get Krishna’s resume",
+    title: "Download Resume",
     text: "Share Krishna’s latest resume and provide a download link.",
+    icon: "📄",
   },
   {
-    title: "What this agent can do",
-    text: "What tools and actions can you perform for me? Show examples and how to use them.",
+    title: "Capabilities",
+    text: "What tools and actions can you perform for me?",
+    icon: "🛠️",
   },
   {
-    title: "Schedule/modify a meeting",
-    text: "Schedule a 30-minute meeting with Krishna next week and show how I can reschedule or cancel.",
+    title: "Schedule Meeting",
+    text: "Schedule a 30-minute meeting with Krishna next week.",
+    icon: "📅",
   },
 ];
 
-// --- Helpers for message actions ---
-const copyToClipboard = async (text: string) => {
-  try {
-    await navigator.clipboard.writeText(text);
-  } catch {
-    console.error("Failed to copy text to clipboard");
-  }
-};
+function DidYouKnowRotator() {
+  const [index, setIndex] = useState(0);
+  useEffect(() => {
+    const interval = setInterval(() => {
+      setIndex((prev) => (prev + 1) % DID_YOU_KNOW.length);
+    }, 5000); // Rotate every 5 seconds
+    return () => clearInterval(interval);
+  }, []);
+
+  return (
+    <div className="mt-8 p-4 rounded-xl bg-white/5 border border-white/5 max-w-lg mx-auto text-center animate-in fade-in slide-in-from-bottom-2 duration-700">
+      <p className="text-[10px] uppercase tracking-widest text-zinc-500 font-semibold mb-2">
+        Did you know?
+      </p>
+      <p
+        key={index}
+        className="text-sm text-zinc-300 italic min-h-[20px] transition-all duration-500"
+      >
+        "{DID_YOU_KNOW[index]}"
+      </p>
+    </div>
+  );
+}
+
+// --- Helper: Date Grouping for Sidebar ---
+function groupThreadsByDate(threads: ThreadMeta[]) {
+  const today = new Date();
+  const yesterday = new Date();
+  yesterday.setDate(yesterday.getDate() - 1);
+
+  const groups: Record<string, ThreadMeta[]> = {
+    Today: [],
+    Yesterday: [],
+    "Previous 7 Days": [],
+    Older: [],
+  };
+
+  threads.forEach((t) => {
+    const d = new Date(t.lastAt);
+    if (d.toDateString() === today.toDateString()) {
+      groups["Today"].push(t);
+    } else if (d.toDateString() === yesterday.toDateString()) {
+      groups["Yesterday"].push(t);
+    } else if (d.getTime() > today.getTime() - 7 * 24 * 60 * 60 * 1000) {
+      groups["Previous 7 Days"].push(t);
+    } else {
+      groups["Older"].push(t);
+    }
+  });
 
-const getLastUserMessage = (msgs: { role: string; content: string }[]) =>
-  [...msgs].reverse().find((m) => m.role === "user") || null;
+  return groups;
+}
 
+// --- Main Component ---
 export default function App() {
   const {
     threads,
@@ -64,46 +131,65 @@ export default function App() {
 
   const [input, setInput] = useState("");
   const bottomRef = useRef<HTMLDivElement | null>(null);
-
   const inputRef = useRef<HTMLTextAreaElement | null>(null);
   const prevThreadId = useRef<string | null>(null);
 
-  // Scroll on message changes
+  // --- Voice Input Logic ---
+  const [isListening, setIsListening] = useState(false);
+  const recognitionRef = useRef<any>(null);
+
   useEffect(() => {
-    const currentThreadId = active?.id ?? null;
+    const SpeechRecognition =
+      (window as any).SpeechRecognition ||
+      (window as any).webkitSpeechRecognition;
+    if (SpeechRecognition) {
+      recognitionRef.current = new SpeechRecognition();
+      recognitionRef.current.continuous = false;
+      recognitionRef.current.interimResults = false;
+      recognitionRef.current.lang = "en-US";
+
+      recognitionRef.current.onresult = (event: any) => {
+        const transcript = event.results[0][0].transcript;
+        setInput((prev) => (prev ? prev + " " + transcript : transcript));
+        setIsListening(false);
+      };
+
+      recognitionRef.current.onerror = (event: any) => {
+        console.error("Speech recognition error", event.error);
+        setIsListening(false);
+      };
+
+      recognitionRef.current.onend = () => {
+        setIsListening(false);
+      };
+    }
+  }, []);
+
+  const toggleListening = useCallback(() => {
+    if (!recognitionRef.current) {
+      alert("Browser does not support Speech Recognition");
+      return;
+    }
+    if (isListening) {
+      recognitionRef.current.stop();
+      setIsListening(false);
+    } else {
+      recognitionRef.current.start();
+      setIsListening(true);
+    }
+  }, [isListening]);
 
-    // If the thread changed, scroll instantly to bottom
+  // Auto-scroll logic
+  useEffect(() => {
+    const currentThreadId = active?.id ?? null;
     if (currentThreadId !== prevThreadId.current) {
       prevThreadId.current = currentThreadId;
-      bottomRef.current?.scrollIntoView({ behavior: "auto" }); // instant scroll
+      bottomRef.current?.scrollIntoView({ behavior: "auto" });
     } else {
-      // If same thread but messages changed, smooth scroll
       bottomRef.current?.scrollIntoView({ behavior: "smooth" });
     }
   }, [messages, active?.id]);
 
-  const handleShare = async () => {
-    const url = window.location.href;
-    const title = document.title || "My Chat";
-    try {
-      if (navigator.share) {
-        await navigator.share({ title, url });
-      } else {
-        await navigator.clipboard.writeText(url);
-        // optionally toast: "Link copied"
-      }
-    } catch {
-      // ignored
-    }
-  };
-
-  const handleBookmark = () => {
-    // Browsers don't allow programmatic bookmarks; show the right shortcut.
-    const isMac = navigator.platform.toUpperCase().includes("MAC");
-    const combo = isMac ? "⌘ + D" : "Ctrl + D";
-    alert(`Press ${combo} to bookmark this page.`);
-  };
-
   const sendMessage = useCallback(() => {
     const text = input.trim();
     if (!text || isStreaming) return;
@@ -111,21 +197,7 @@ export default function App() {
     setInput("");
   }, [input, isStreaming, send]);
 
-  const selectSuggestion = useCallback((text: string) => {
-    setInput(text);
-    requestAnimationFrame(() => inputRef.current?.focus());
-  }, []);
-
-  const sendSuggestion = useCallback(
-    (text: string) => {
-      if (isStreaming) return;
-      setInput(text);
-      setTimeout(() => sendMessage(), 0);
-    },
-    [isStreaming, sendMessage]
-  );
-
-  const onKeyDown = useCallback(
+  const handleKeyDown = useCallback(
     (e: React.KeyboardEvent<HTMLTextAreaElement>) => {
       if (e.key === "Enter" && !e.shiftKey) {
         e.preventDefault();
@@ -135,423 +207,248 @@ export default function App() {
     [sendMessage]
   );
 
-  const REGEN_PREFIX = "Regenerate this response with a different angle:\n\n";
+  const groupedThreads = useMemo(() => groupThreadsByDate(threads), [threads]);
 
-  const sendPrefixed = useCallback(
-    (prefix: string, text: string) => {
-      if (!text || isStreaming) return;
-      // your hook’s `send` already appends messages & streams
-      send(`${prefix}${text}`);
-      setInput("");
-    },
-    [send, isStreaming]
-  );
+  return (
+    <div className="flex h-screen w-screen bg-[#05050A] text-zinc-100 font-sans selection:bg-indigo-500/30">
+      {/* Background Ambience */}
+      <div className="fixed inset-0 z-0 pointer-events-none">
+        <div className="absolute top-[-10%] right-[-5%] w-[500px] h-[500px] bg-indigo-900/20 rounded-full blur-[128px]" />
+        <div className="absolute bottom-[-10%] left-[-10%] w-[600px] h-[600px] bg-purple-900/10 rounded-full blur-[128px]" />
+      </div>
 
-  // Sidebar
-  const Sidebar = useMemo(
-    () => (
-      <aside className="hidden md:flex w-64 shrink-0 flex-col bg-zinc-950/80 border-r border-zinc-800/60">
-        <div className="p-4 border-b border-zinc-800/60">
-          <h1 className="flex items-center text-zinc-100 font-semibold tracking-tight">
-            <SparklesIcon className="h-4 w-4 text-zinc-300 mr-2" />
-            ChatK
-          </h1>
-          <p className="text-xs text-zinc-400 mt-1">
-            Chatbot ID:{" "}
-            <span className="font-mono">
-              {active?.id ? active.id.slice(0, 8) : "…"}{" "}
-            </span>
-          </p>
-        </div>
-        <div className="p-3 space-y-2">
+      {/* --- Sidebar --- */}
+      <aside className="hidden md:flex flex-col w-[280px] z-10 bg-zinc-950/40 backdrop-blur-xl border-r border-white/5 transition-all">
+        {/* Header */}
+        <div className="p-4 border-b border-white/5">
           <button
-            className="w-full rounded-xl bg-emerald-600 text-white hover:bg-emerald-500 px-3 py-2 text-sm"
             onClick={newChat}
-            title="Start a new session"
+            className="group flex items-center gap-3 w-full px-3 py-2.5 rounded-xl bg-white/5 hover:bg-white/10 border border-white/5 transition-all duration-200"
           >
-            New Chat
-          </button>
-          <button
-            className="w-full rounded-xl bg-zinc-800 text-zinc-200 hover:bg-zinc-700 px-3 py-2 text-sm"
-            onClick={clearChat}
-            title="Clear current messages"
-          >
-            Clear Chat
+            <div className="p-1.5 rounded-lg bg-indigo-500/10 text-indigo-400 group-hover:text-indigo-300">
+              <SparklesIcon className="w-5 h-5" />
+            </div>
+            <span className="text-sm font-medium text-zinc-200">New Chat</span>
           </button>
-          {/* View Source on GitHub */}
-          <a
-            href="https://github.com/krishna-dhulipalla/LangGraph_ChatBot"
-            target="_blank"
-            rel="noopener noreferrer"
-            className="w-full flex items-center justify-center gap-2 rounded-xl border border-zinc-800 bg-zinc-900 hover:bg-zinc-800 px-3 py-2 text-sm text-zinc-300"
-            title="View the source code on GitHub"
-          >
-            {/* GitHub Icon */}
-            <svg
-              xmlns="http://www.w3.org/2000/svg"
-              viewBox="0 0 24 24"
-              fill="currentColor"
-              className="h-4 w-4"
-            >
-              <path
-                fillRule="evenodd"
-                d="M12 0C5.37 0 0 5.37 0 
-          12c0 5.3 3.438 9.8 8.205 
-          11.387.6.113.82-.262.82-.58 
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-          1.205.085 1.84 1.238 1.84 1.238 
-          1.07 1.835 2.807 1.305 3.492.997.107-.775.418-1.305.762-1.605-2.665-.3-5.466-1.334-5.466-5.93 
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-          0 0 1.008-.322 3.3 1.23a11.5 11.5 
-          0 013.003-.404c1.018.005 2.045.138 
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-          1.102.823 2.222 0 1.606-.015 2.898-.015 3.293 
-          0 .32.218.698.825.58C20.565 21.796 24 
-          17.297 24 12c0-6.63-5.37-12-12-12z"
-                clipRule="evenodd"
-              />
-            </svg>
-            View Source
-          </a>
         </div>
 
-        {/* Thread list */}
-        <div className="px-3 pb-3 space-y-1 overflow-y-auto">
-          {threads.map((t: ThreadMeta) => (
-            <div
-              key={t.id}
-              className={`group w-full flex items-center gap-2 px-3 py-2 rounded-lg hover:bg-zinc-800 cursor-pointer ${
-                t.id === active?.id ? "bg-zinc-800" : ""
-              }`}
-              onClick={() => setActiveThread(t)}
-              title={t.id}
-            >
-              <div className="flex-1 min-w-0 p-1 hover:bg-gray-100">
-                <div className="text-sm">
-                  {t.title && t.title.length > 20
-                    ? t.title.slice(0, 20) + "..."
-                    : t.title || "Untitled"}
-                </div>
-                <div
-                  className="text-zinc-500 truncate"
-                  style={{ fontSize: "10px", fontStyle: "italic" }}
-                >
-                  {new Date(t.lastAt).toLocaleString()}
+        {/* History List */}
+        <div className="flex-1 overflow-y-auto p-3 space-y-6 scrollbar-thin scrollbar-thumb-zinc-800">
+          {Object.entries(groupedThreads).map(([label, group]) => {
+            if (group.length === 0) return null;
+            return (
+              <div key={label}>
+                <h3 className="px-3 mb-2 text-[11px] font-semibold uppercase tracking-wider text-zinc-500">
+                  {label}
+                </h3>
+                <div className="space-y-0.5">
+                  {group.map((t) => (
+                    <div
+                      key={t.id}
+                      onClick={() => setActiveThread(t)}
+                      className={`group relative flex items-center gap-2 px-3 py-2 rounded-lg cursor-pointer transition-colors ${
+                        active?.id === t.id
+                          ? "bg-white/10 text-zinc-100"
+                          : "text-zinc-400 hover:bg-white/5 hover:text-zinc-200"
+                      }`}
+                    >
+                      <span
+                        className="text-sm flex-1 line-clamp-1 break-all"
+                        title={t.title || "New Conversation"}
+                      >
+                        {t.title || "New Conversation"}
+                      </span>
+                      {/* Delete Action */}
+                      <button
+                        onClick={(e) => {
+                          e.stopPropagation();
+                          if (window.confirm("Delete thread?"))
+                            deleteThread(t.id);
+                        }}
+                        className="opacity-0 group-hover:opacity-100 p-1 rounded hover:bg-red-500/20 text-zinc-500 hover:text-red-400 transition-all"
+                      >
+                        <TrashIcon className="w-3.5 h-3.5" />
+                      </button>
+                    </div>
+                  ))}
                 </div>
               </div>
-
-              {/* Delete button (shows on hover) */}
-              <button
-                type="button"
-                className="opacity-0 group-hover:opacity-100 shrink-0 rounded-md p-1 border border-zinc-700/60 bg-zinc-900/60 hover:bg-zinc-800/80"
-                title="Delete thread"
-                aria-label="Delete thread"
-                onClick={(e) => {
-                  e.stopPropagation(); // don't switch threads
-                  if (
-                    window.confirm("Delete this thread? This cannot be undone.")
-                  ) {
-                    deleteThread(t.id);
-                  }
-                }}
-              >
-                <TrashIcon className="h-4 w-4 text-zinc-300" />
-              </button>
-            </div>
-          ))}
+            );
+          })}
         </div>
 
-        <div className="mt-auto p-3 text-xs text-zinc-500">
-          Tip: Press <kbd className="px-1 bg-zinc-800 rounded">Enter</kbd> to
-          send,
-          <span className="mx-1" />{" "}
-          <kbd className="px-1 bg-zinc-800 rounded">Shift+Enter</kbd> for
-          newline.
+        {/* Sidebar Footer */}
+        <div className="p-4 border-t border-white/5">
+          <a
+            href="https://github.com/krishna-dhulipalla/LangGraph_ChatBot"
+            target="_blank"
+            rel="noreferrer"
+            className="flex items-center gap-2 text-xs text-zinc-500 hover:text-zinc-300 transition-colors"
+          >
+            <CommandLineIcon className="w-4 h-4" />
+            <span>View Source Code</span>
+          </a>
         </div>
       </aside>
-    ),
-    [active?.id, clearChat, newChat, setActiveThread, deleteThread, threads]
-  );
 
-  return (
-    <div className="h-screen w-screen bg-[#0b0b0f] text-zinc-100 flex">
-      {Sidebar}
+      {/* --- Main Chat Area --- */}
+      <main className="relative z-10 flex-1 flex flex-col h-full overflow-hidden">
+        {/* Top Navigation */}
+        <header className="h-16 flex items-center justify-between px-6 border-b border-white/5 bg-zinc-950/20 backdrop-blur-sm">
+          <div className="flex items-center gap-3">
+            <div className="relative">
+              <img
+                src={BOT_AVATAR}
+                alt="Bot"
+                className="w-9 h-9 rounded-full ring-2 ring-indigo-500/20 shadow-lg shadow-indigo-500/10"
+              />
+              <span className="absolute bottom-0 right-0 w-2.5 h-2.5 bg-emerald-500 border-2 border-[#05050A] rounded-full"></span>
+            </div>
+            <div>
+              <h1 className="text-sm font-semibold text-zinc-100">
+                {APP_TITLE}
+              </h1>
+              <p className="text-[10px] text-zinc-400 flex items-center gap-1">
+                <span className="inline-block w-1 h-1 rounded-full bg-indigo-500 animate-pulse" />
+                Online & Ready
+              </p>
+            </div>
+          </div>
 
-      {/* Main column */}
-      <main className="flex-1 flex flex-col">
-        {/* Header minimal */}
-        <div className="h-12 shrink-0 flex items-center justify-between px-3 md:px-6 border-b border-zinc-800/60 bg-zinc-950/60 backdrop-blur">
           <div className="flex items-center gap-2">
-            <span className="h-2.5 w-2.5 rounded-full bg-emerald-500 animate-pulse" />
-            <span className="font-medium">Krishna’s Assistant</span>
-          </div>
-          <div className="text-xs text-zinc-400 md:hidden">
-            ID: {active?.id ? active.id.slice(0, 8) : "…"}
-          </div>
-          <div className="ml-auto flex items-center gap-2">
-            {/* LinkedIn */}
-            <a
-              href="https://www.linkedin.com/in/krishnavamsidhulipalla/"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="rounded-xl px-3 py-1.5 text-sm border border-zinc-800 bg-zinc-900 hover:bg-zinc-800"
-              title="LinkedIn"
-            >
-              <svg
-                xmlns="http://www.w3.org/2000/svg"
-                viewBox="0 0 24 24"
-                fill="currentColor"
-                className="h-5 w-5 text-zinc-400 hover:text-zinc-200"
-              >
-                <path
-                  d="M19 0h-14c-2.76 0-5 2.24-5 5v14c0 
-        2.76 2.24 5 5 5h14c2.76 0 5-2.24 
-        5-5v-14c0-2.76-2.24-5-5-5zm-11 
-        19h-3v-10h3v10zm-1.5-11.27c-.97 
-        0-1.75-.79-1.75-1.76s.78-1.76 
-        1.75-1.76 1.75.79 
-        1.75 1.76-.78 1.76-1.75 
-        1.76zm13.5 11.27h-3v-5.5c0-1.31-.02-3-1.83-3-1.83 
-        0-2.12 1.43-2.12 2.9v5.6h-3v-10h2.88v1.36h.04c.4-.75 
-        1.38-1.54 2.85-1.54 3.05 0 3.61 
-        2.01 3.61 4.63v5.55z"
-                />
-              </svg>
-            </a>
-
-            {/* GitHub */}
-            <a
-              href="https://github.com/krishna-dhulipalla"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="rounded-xl px-3 py-1.5 text-sm border border-zinc-800 bg-zinc-900 hover:bg-zinc-800"
-              title="GitHub"
-            >
-              <svg
-                xmlns="http://www.w3.org/2000/svg"
-                viewBox="0 0 24 24"
-                fill="currentColor"
-                className="h-5 w-5 text-zinc-400 hover:text-zinc-200"
-              >
-                <path
-                  fillRule="evenodd"
-                  d="M12 0C5.37 0 0 5.37 0 
-          12c0 5.3 3.438 9.8 8.205 
-          11.387.6.113.82-.262.82-.58 
-          0-.287-.01-1.045-.015-2.05-3.338.724-4.042-1.61-4.042-1.61-.546-1.385-1.333-1.754-1.333-1.754-1.09-.745.083-.73.083-.73 
-          1.205.085 1.84 1.238 1.84 1.238 
-          1.07 1.835 2.807 1.305 3.492.997.107-.775.418-1.305.762-1.605-2.665-.3-5.466-1.334-5.466-5.93 
-          0-1.31.468-2.38 1.235-3.22-.124-.303-.536-1.523.117-3.176 
-          0 0 1.008-.322 3.3 1.23a11.5 11.5 
-          0 013.003-.404c1.018.005 2.045.138 
-          3.003.404 2.29-1.552 3.297-1.23 
-          3.297-1.23.655 1.653.243 2.873.12 
-          3.176.77.84 1.233 1.91 1.233 3.22 
-          0 4.61-2.803 5.625-5.475 5.92.43.372.823 
-          1.102.823 2.222 0 1.606-.015 2.898-.015 3.293 
-          0 .32.218.698.825.58C20.565 21.796 24 
-          17.297 24 12c0-6.63-5.37-12-12-12z"
-                  clipRule="evenodd"
-                />
-              </svg>
-            </a>
-            <button
-              onClick={handleShare}
-              className="rounded-xl px-3 py-1.5 text-sm border border-zinc-800 bg-zinc-900 hover:bg-zinc-800"
-              title="Share"
-            >
-              Share
-            </button>
             <button
-              onClick={handleBookmark}
-              className="rounded-xl px-3 py-1.5 text-sm border border-zinc-800 bg-zinc-900 hover:bg-zinc-800"
-              title="Bookmark"
+              onClick={clearChat}
+              className="p-2 text-zinc-400 hover:text-zinc-200 hover:bg-white/5 rounded-full transition-all"
+              title="Clear Chat"
             >
-              <BookmarkIcon className="h-5 w-5 text-zinc-400 hover:text-zinc-200" />
+              <ArrowPathIcon className="w-5 h-5" />
             </button>
           </div>
-        </div>
+        </header>
 
-        {/* Messages */}
-        <div className="flex-1 overflow-y-auto px-3 md:px-6 py-4">
+        {/* Chat Stream */}
+        <div className="flex-1 overflow-y-auto p-4 md:p-8 scroll-smooth">
           {messages.length === 0 ? (
-            <EmptyState onSelect={selectSuggestion} onSend={sendSuggestion} />
-          ) : (
-            <div className="mx-auto max-w-3xl space-y-3 relative">
-              {messages.map((m, idx) => {
-                const isAssistant = m.role === "assistant";
-                const emptyAssistant =
-                  isAssistant && (!m.content || m.content.trim() === "");
-                if (emptyAssistant) return null; // hide blank bubble
-                const key = m.id ?? `m-${idx}`; // NEW stable key
-                return (
-                  <div key={key} className={isAssistant ? "group" : undefined}>
-                    {/* bubble row */}
-                    <div
-                      className={`flex ${
-                        isAssistant ? "justify-start" : "justify-end"
-                      }`}
-                    >
-                      <div
-                        className={`max-w-[85%] md:max-w-[75%] leading-relaxed tracking-tight rounded-2xl px-4 py-3 shadow-sm ${
-                          isAssistant
-                            ? "bg-zinc-900/80 text-zinc-100 border border-zinc-800/60"
-                            : "bg-emerald-600/90 text-white"
-                        }`}
-                      >
-                        {isAssistant ? (
-                          <>
-                            <ReactMarkdown
-                              remarkPlugins={[remarkGfm]}
-                              components={{
-                                a: (props) => (
-                                  <a
-                                    {...props}
-                                    target="_blank"
-                                    rel="noreferrer"
-                                    className="underline text-blue-400 hover:text-blue-600"
-                                  />
-                                ),
-                                p: (props) => (
-                                  <p className="mb-2 last:mb-0" {...props} />
-                                ),
-                                ul: (props) => (
-                                  <ul
-                                    className="list-disc list-inside mb-2 last:mb-0"
-                                    {...props}
-                                  />
-                                ),
-                                ol: (props) => (
-                                  <ol
-                                    className="list-decimal list-inside mb-2 last:mb-0"
-                                    {...props}
-                                  />
-                                ),
-                                li: (props) => (
-                                  <li className="ml-4 mb-1" {...props} />
-                                ),
-                                code: (
-                                  props: React.HTMLAttributes<HTMLElement> & {
-                                    inline?: boolean;
-                                  }
-                                ) => {
-                                  // react-markdown v8+ passes 'node', 'inline', etc. in props, but types may not include 'inline'
-                                  const {
-                                    className,
-                                    children,
-                                    inline,
-                                    ...rest
-                                  } = props;
-                                  const isInline = inline;
-                                  return isInline ? (
-                                    <code
-                                      className="bg-zinc-800/80 px-1 py-0.5 rounded"
-                                      {...rest}
-                                    >
-                                      {children}
-                                    </code>
-                                  ) : (
-                                    <pre className="overflow-x-auto rounded-xl border border-zinc-800/60 bg-zinc-950/80 p-3 mb-2">
-                                      <code className={className} {...rest}>
-                                        {children}
-                                      </code>
-                                    </pre>
-                                  );
-                                },
-                              }}
-                            >
-                              {m.content}
-                            </ReactMarkdown>
-                          </>
-                        ) : (
-                          m.content
-                        )}
+            <div className="h-full flex flex-col items-center justify-center max-w-2xl mx-auto animate-in fade-in duration-500">
+              <div className="mb-8 p-4 rounded-full bg-white/5 ring-1 ring-white/10 shadow-2xl shadow-indigo-500/10">
+                <ChatBubbleLeftRightIcon className="w-10 h-10 text-indigo-400" />
+              </div>
+              <h2 className="text-2xl font-semibold text-transparent bg-clip-text bg-gradient-to-br from-zinc-100 to-zinc-500 mb-2">
+                How can I help you today?
+              </h2>
+              <p className="text-zinc-400 text-sm mb-6 max-w-md text-center">
+                I'm Krishna's digital twin. Ask me about his architecture
+                skills, recent projects, or schedule a meeting.
+              </p>
+
+              {/* <DidYouKnowRotator /> */}
+              {/* <div className="h-8"></div> */}
+
+              {/* Suggestions Grid */}
+              <div className="grid grid-cols-1 md:grid-cols-2 gap-3 w-full">
+                {SUGGESTIONS.map((s, i) => (
+                  <button
+                    key={i}
+                    onClick={() => {
+                      // Just send immediately without prefilling input state visually
+                      if (!isStreaming) send(s.text);
+                    }}
+                    className="group flex items-start gap-4 p-4 rounded-2xl bg-white/5 hover:bg-white/10 border border-white/5 hover:border-white/10 text-left transition-all hover:-translate-y-0.5 hover:shadow-lg hover:shadow-indigo-500/10"
+                  >
+                    <span className="text-xl grayscale group-hover:grayscale-0 transition-all">
+                      {s.icon}
+                    </span>
+                    <div>
+                      <div className="text-sm font-medium text-zinc-200 group-hover:text-indigo-300 transition-colors">
+                        {s.title}
                       </div>
-                    </div>
-
-                    {/* actions row – only for assistant & only when not streaming */}
-                    {isAssistant && !isStreaming && (
-                      <div className="mt-1 pl-1 flex justify-start">
-                        <MsgActions
-                          content={m.content}
-                          onEdit={() => {
-                            // Prefill composer with the *last user* prompt (ChatGPT-style “Edit”)
-                            const lastUser = getLastUserMessage(messages);
-                            setInput(lastUser ? lastUser.content : m.content);
-                            requestAnimationFrame(() =>
-                              inputRef.current?.focus()
-                            );
-                          }}
-                          onRegenerate={() => {
-                            // Resend last user prompt
-                            const lastUser = getLastUserMessage(messages);
-                            if (!lastUser) return;
-                            sendPrefixed(REGEN_PREFIX, lastUser.content);
-                          }}
-                        />
+                      <div className="text-xs text-zinc-500 mt-1 line-clamp-1 group-hover:text-zinc-400 transition-colors">
+                        {s.text}
                       </div>
-                    )}
-                  </div>
-                );
-              })}
-              {/* Thinking indicator (only BEFORE first token) */}
+                    </div>
+                  </button>
+                ))}
+              </div>
+            </div>
+          ) : (
+            <div className="max-w-3xl mx-auto space-y-6">
+              {messages.map((m, idx) => (
+                <Bubble
+                  key={m.id || idx}
+                  message={m}
+                  isStreaming={isStreaming && idx === messages.length - 1}
+                />
+              ))}
+              {/* Streaming Indicator */}
               {isStreaming && !hasFirstToken && (
-                <div className="pointer-events-none relative top-3 left-0 bottom-0 translate-y-2 z-20">
-                  <TypingDots />
+                <div className="flex gap-1 ml-14">
+                  <span className="w-2 h-2 rounded-full bg-zinc-600 animate-bounce" />
+                  <span className="w-2 h-2 rounded-full bg-zinc-600 animate-bounce [animation-delay:0.1s]" />
+                  <span className="w-2 h-2 rounded-full bg-zinc-600 animate-bounce [animation-delay:0.2s]" />
                 </div>
               )}
-              <div ref={bottomRef} />
+              <div ref={bottomRef} className="h-4" />
             </div>
           )}
         </div>
-        {/* Warm bottom glow (simple bar + blur)
-        <div
-          aria-hidden
-          className="fixed inset-x-0 bottom-0 z-30 pointer-events-none"
-        >
-          <div className="mx-auto max-w-3xl relative h-0">
-            <div className="absolute left-6 right-6 bottom-0 h-[6px] rounded-full bg-amber-300/40 blur-3xl" />
-          </div>
-        </div> */}
-
-        {/* Composer */}
-        <div className="shrink-0 border-t border-zinc-800/60 bg-zinc-950/80">
-          <div className="mx-auto max-w-3xl p-3 md:p-4">
-            <div className="flex gap-2 items-end">
-              <div className="relative flex-1">
-                <textarea
-                  ref={inputRef}
-                  className="w-full flex-1 resize-none rounded-2xl bg-zinc-900 text-zinc-100 placeholder-zinc-500 p-3 pr-8 outline-none focus:ring-2 focus:ring-emerald-500/60 min-h-[56px] max-h-48 border border-zinc-800/60"
-                  placeholder="Type a message…"
-                  value={input}
-                  onChange={(e) => setInput(e.target.value)}
-                  onKeyDown={onKeyDown}
-                  disabled={isStreaming}
-                />
-                {input && (
+
+        {/* --- Input Area --- */}
+        <div className="p-4 md:p-6 pb-8">
+          <div className="max-w-3xl mx-auto relative">
+            <div className="relative flex flex-col gap-2 p-2 rounded-3xl bg-zinc-900/50 backdrop-blur-xl border border-white/10 shadow-2xl focus-within:ring-1 focus-within:ring-indigo-500/50 transition-all">
+              <textarea
+                ref={inputRef}
+                value={input}
+                onChange={(e) => setInput(e.target.value)}
+                onKeyDown={handleKeyDown}
+                placeholder="Type a message..."
+                className="w-full bg-transparent text-zinc-100 placeholder-zinc-500 px-4 py-3 min-h-[50px] max-h-[200px] resize-none outline-none text-[15px] leading-relaxed scrollbar-hide"
+                rows={1}
+              />
+
+              {/* Input Actions Bar */}
+              <div className="flex items-center justify-between px-2 pb-1">
+                <div className="flex items-center gap-1">
                   <button
-                    onClick={() => {
-                      setInput("");
-                      requestAnimationFrame(() => inputRef.current?.focus());
-                    }}
-                    className="absolute right-2 top-2.5 h-6 w-6 rounded-md border border-zinc-800 bg-zinc-950/70 hover:bg-zinc-800/70 text-zinc-400"
-                    title="Clear"
-                    aria-label="Clear input"
+                    className="cursor-not-allowed opacity-50 p-2 rounded-xl text-zinc-400 hover:text-zinc-200 hover:bg-white/5 transition-colors"
+                    title="Attach file (coming soon)"
                   >
-                    ×
+                    <PaperClipIcon className="w-5 h-5" />
                   </button>
-                )}
+                  <button
+                    onClick={toggleListening}
+                    className={`p-2 rounded-xl transition-all duration-300 ${
+                      isListening
+                        ? "text-red-400 bg-red-500/10 animate-pulse ring-1 ring-red-500/20"
+                        : "text-zinc-400 hover:text-zinc-200 hover:bg-white/5"
+                    }`}
+                    title={isListening ? "Stop Listening" : "Voice Input"}
+                  >
+                    <MicrophoneIcon className="w-5 h-5" />
+                  </button>
+                </div>
+
+                <button
+                  onClick={sendMessage}
+                  disabled={!input.trim() || isStreaming}
+                  className={`p-2 rounded-xl flex items-center gap-2 transition-all ${
+                    input.trim() && !isStreaming
+                      ? "bg-indigo-600 text-white shadow-lg shadow-indigo-500/20 hover:bg-indigo-500"
+                      : "bg-zinc-800 text-zinc-500 cursor-not-allowed"
+                  }`}
+                >
+                  {isStreaming ? (
+                    <span className="w-5 h-5 border-2 border-white/20 border-t-white rounded-full animate-spin" />
+                  ) : (
+                    <PaperAirplaneIcon className="w-5 h-5 -ml-0.5 transform -rotate-45 translate-x-0.5" />
+                  )}
+                </button>
               </div>
-              <button
-                className="rounded-2xl px-4 py-3 bg-emerald-600 text-white hover:bg-emerald-500 disabled:opacity-50 disabled:cursor-not-allowed"
-                onClick={sendMessage}
-                disabled={!input.trim() || isStreaming}
-              >
-                Send
-              </button>
+            </div>
+            <div className="text-center mt-3">
+              <p className="text-[10px] text-zinc-600">
+                AI can make mistakes. Please verify important information.
+              </p>
             </div>
           </div>
         </div>
@@ -560,121 +457,118 @@ export default function App() {
   );
 }
 
-function MsgActions({
-  content,
-  onEdit,
-  onRegenerate,
+// --- Message Bubble Component ---
+function Bubble({
+  message,
+  isStreaming,
 }: {
-  content: string;
-  onEdit: () => void;
-  onRegenerate: () => void;
+  message: any;
+  isStreaming: boolean;
 }) {
-  return (
-    <div className="mt-2 flex items-center gap-1 opacity-0 group-hover:opacity-100 transition-opacity">
-      <button
-        onClick={() => copyToClipboard(content)}
-        className="text-xs px-2 py-1 rounded border border-zinc-800 bg-zinc-900 hover:bg-zinc-800"
-        title="Copy"
-      >
-        Copy
-      </button>
-      <button
-        onClick={onEdit}
-        className="text-xs px-2 py-1 rounded border border-zinc-800 bg-zinc-900 hover:bg-zinc-800"
-        title="Edit this as new prompt"
-      >
-        Edit
-      </button>
-      <button
-        onClick={onRegenerate}
-        className="text-xs px-2 py-1 rounded border border-zinc-800 bg-zinc-900 hover:bg-zinc-800"
-        title="Regenerate"
-      >
-        Regenerate
-      </button>
-    </div>
-  );
-}
+  const isUser = message.role === "user";
 
-function EmptyState({
-  onSelect,
-  onSend,
-}: {
-  onSelect: (text: string) => void;
-  onSend: (text: string) => void;
-}) {
   return (
-    <div className="h-full flex items-center justify-center">
-      <div className="mx-auto max-w-3xl w-full px-3 md:px-6">
-        <div className="text-center text-zinc-400 mb-6">
-          <h2 className="text-xl text-zinc-200 mb-2">Ask me anything</h2>
-          <p className="text-sm">
-            The agent can call tools and remember the conversation (per chatbot
-            id).
-          </p>
+    <div
+      className={`flex gap-4 ${isUser ? "justify-end" : "justify-start group"}`}
+    >
+      {!isUser && (
+        <div className="shrink-0 flex flex-col gap-2">
+          <img
+            src={BOT_AVATAR}
+            alt="AI"
+            className="w-8 h-8 rounded-full ring-1 ring-white/10"
+          />
         </div>
-
-        {/* Starter prompts */}
-        <div className="grid grid-cols-1 sm:grid-cols-2 gap-3">
-          {SUGGESTIONS.map((s) => (
-            <div
-              role="button"
-              tabIndex={0}
-              onClick={() => onSelect(s.text)}
-              className="group text-left rounded-2xl border border-zinc-800/60 bg-zinc-900/60 hover:bg-zinc-900/90 transition-colors p-4 shadow-sm focus:outline-none focus:ring-2 focus:ring-emerald-500/60"
-              title="Click to prefill. Use the arrow to send."
-            >
-              <div className="flex items-start gap-3">
-                <div className="shrink-0 h-8 w-8 rounded-xl bg-zinc-800/80 flex items-center justify-center">
-                  <span className="text-sm text-zinc-300">💡</span>
-                </div>
-                <div className="flex-1">
-                  <div className="text-sm font-medium text-zinc-200">
-                    {s.title}
-                  </div>
-                  <div className="text-xs text-zinc-400 mt-1 line-clamp-2">
-                    {s.text}
-                  </div>
-                </div>
-                <button
-                  type="button"
-                  aria-label="Send this suggestion"
-                  onClick={(e) => {
-                    e.stopPropagation();
-                    onSend(s.text);
-                  }}
-                  className="shrink-0 rounded-xl border border-zinc-700/60 bg-zinc-950/60 px-2 py-2 hover:bg-zinc-900/80"
-                  title="Send now"
-                >
-                  {/* Arrow icon */}
-                  <svg
-                    viewBox="0 0 24 24"
-                    className="h-4 w-4 text-zinc-300 group-hover:text-emerald-400"
-                    fill="none"
-                    stroke="currentColor"
-                    strokeWidth={2}
-                    strokeLinecap="round"
-                    strokeLinejoin="round"
+      )}
+
+      <div
+        className={`relative max-w-[85%] md:max-w-[75%] rounded-2xl px-5 py-3.5 shadow-sm text-[15px] leading-7 ${
+          isUser
+            ? "bg-gradient-to-br from-indigo-600 to-violet-600 text-white rounded-br-sm shadow-md shadow-indigo-500/10"
+            : "bg-white/5 border border-white/5 text-zinc-100 rounded-bl-sm backdrop-blur-md"
+        }`}
+      >
+        <ReactMarkdown
+          remarkPlugins={[remarkGfm]}
+          components={{
+            a: ({ ...props }) => (
+              <a
+                {...props}
+                className="text-blue-400 hover:underline"
+                target="_blank"
+              />
+            ),
+            code: ({ inline, className, children, ...props }: any) => {
+              if (inline)
+                return (
+                  <code
+                    className="bg-white/10 px-1 py-0.5 rounded font-mono text-sm"
+                    {...props}
                   >
-                    <path d="M7 17L17 7M7 7h10v10" />
-                  </svg>
-                </button>
-              </div>
-            </div>
-          ))}
-        </div>
+                    {children}
+                  </code>
+                );
+              return (
+                <pre className="bg-zinc-950/50 p-3 rounded-xl border border-white/5 overflow-x-auto my-2 text-sm">
+                  <code className={className} {...props}>
+                    {children}
+                  </code>
+                </pre>
+              );
+            },
+            ul: (props) => (
+              <ul
+                className="list-disc list-inside ml-2 space-y-1 my-2"
+                {...props}
+              />
+            ),
+            ol: (props) => (
+              <ol
+                className="list-decimal list-inside ml-2 space-y-1 my-2"
+                {...props}
+              />
+            ),
+            p: (props) => <p className="mb-2 last:mb-0" {...props} />,
+          }}
+        >
+          {message.content}
+        </ReactMarkdown>
+
+        {/* Actions for Assistant */}
+        {!isUser && !isStreaming && (
+          <div className="absolute -bottom-6 left-0 opacity-0 group-hover:opacity-100 transition-opacity flex items-center gap-2">
+            <ActionButton
+              icon={<DocumentDuplicateIcon className="w-3.5 h-3.5" />}
+              label="Copy"
+              onClick={() => navigator.clipboard.writeText(message.content)}
+            />
+            <ActionButton
+              icon={<ArrowPathIcon className="w-3.5 h-3.5" />}
+              label="Regenerate"
+              onClick={() => {}}
+            />
+          </div>
+        )}
       </div>
     </div>
   );
 }
 
-function TypingDots() {
+function ActionButton({
+  icon,
+  label,
+  onClick,
+}: {
+  icon: any;
+  label: string;
+  onClick: () => void;
+}) {
   return (
-    <span className="inline-flex items-center gap-1 align-middle">
-      <span className="sr-only">Assistant is typing…</span>
-      <span className="h-1.5 w-1.5 rounded-full bg-zinc-300 animate-bounce [animation-delay:-0.2s]" />
-      <span className="h-1.5 w-1.5 rounded-full bg-zinc-300 animate-bounce" />
-      <span className="h-1.5 w-1.5 rounded-full bg-zinc-300 animate-bounce [animation-delay:0.2s]" />
-    </span>
+    <button
+      onClick={onClick}
+      className="flex items-center gap-1 text-[10px] text-zinc-500 hover:text-zinc-300 bg-white/5 px-2 py-1 rounded-md transition-colors"
+    >
+      {icon} {label}
+    </button>
   );
 }

ui/src/index.css

@@ -1 +1,19 @@
 @import "tailwindcss";
+
+@layer utilities {
+  /* Custom Scrollbar for Dark Mode */
+  ::-webkit-scrollbar {
+    width: 6px;
+    height: 6px;
+  }
+  ::-webkit-scrollbar-track {
+    background: transparent;
+  }
+  ::-webkit-scrollbar-thumb {
+    background: #3f3f46; /* zinc-700 */
+    border-radius: 10px;
+  }
+  ::-webkit-scrollbar-thumb:hover {
+    background: #52525b; /* zinc-600 */
+  }
+}

ui/src/main.tsx

@@ -2,7 +2,6 @@ import { StrictMode } from "react";
 import { createRoot } from "react-dom/client";
 import "./index.css";
 import App from "./App.tsx";
-import "./tailwind.css";
 
 createRoot(document.getElementById("root")!).render(
   <StrictMode>

ui/vite.config.ts

@@ -1,8 +1,10 @@
 import { defineConfig } from "vite";
 import react from "@vitejs/plugin-react";
+import tailwindcss from "@tailwindcss/vite";
+
 export default defineConfig({
-  plugins: [react()],
+  plugins: [react(), tailwindcss()],
   server: {
-    proxy: { "/chat": { target: "http://localhost:8000", changeOrigin: true } },
+    proxy: { "/chat": { target: "http://127.0.0.1:8000", changeOrigin: true } },
   },
 });