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public/elements/ImageGallery.jsx

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+// components/GlobalImageModal.tsx
+'use client'
+
+import { useEffect, useState } from "react"
+import { Dialog, DialogContent, DialogHeader, DialogTitle, DialogDescription } from "@/components/ui/dialog"
+
+export default function GlobalImageModal() {
+  const [selectedImageSrc, setSelectedImageSrc] = useState<string | null>(null)
+
+  useEffect(() => {
+    const handleClick = (e: MouseEvent) => {
+      const target = e.target as HTMLElement
+      if (target.tagName === 'IMG') {
+        const img = target as HTMLImageElement
+        setSelectedImageSrc(img.src)
+      }
+    }
+
+    document.addEventListener('click', handleClick)
+    return () => document.removeEventListener('click', handleClick)
+  }, [])
+
+  return (
+    <Dialog open={!!selectedImageSrc} onOpenChange={() => setSelectedImageSrc(null)}>
+      <DialogContent className="w-auto max-w-fit max-h-[90vh] flex flex-col items-center justify-center" >
+          {selectedImageSrc && (
+          <img
+            src={selectedImageSrc}
+            className="max-w-full max-h-[80vh] rounded-lg m-3"
+          />
+        )}
+      </DialogContent>
+    </Dialog>
+  )
+}

public/global.css

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+img {
+  cursor: pointer !important;
+  transform: scale(1.05);
+  transition: transform 0.2s ease;
+}

vdb.py

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+import hashlib
+import json
+import re
+from pathlib import Path
+
+from dotenv import load_dotenv
+from llama_index.core import (QueryBundle)
+from llama_index.core.postprocessor import LLMRerank
+from nest_asyncio import apply
+from openai import OpenAI
+from tqdm import tqdm
+
+from llama_index.core import VectorStoreIndex
+from llama_index.embeddings.openai import OpenAIEmbedding
+
+from llama_index.core import Document
+
+# Load variables from .env
+load_dotenv()
+
+def build_documents(sections):
+    docs = []
+    for s in sections:
+        metadata = {"section_title": s["title"]}
+        docs.append(Document(text=s["content"], metadata=metadata))
+    return docs
+
+def create_vector_index(docs):
+    embed_model = OpenAIEmbedding()
+    index = VectorStoreIndex.from_documents(docs, embed_model=embed_model)
+    return index
+
+def split_markdown_by_section(md_path: str):
+    text = Path(md_path).read_text(encoding="utf-8")
+    sections = re.split(r"(?m)^# ", text)
+    chunks = []
+    for section in sections:
+        if not section.strip():
+            continue
+        title, *content = section.split("\n", 1)
+        body = content[0].strip() if content else ""
+        chunks.append({"title": title.strip(), "content": body})
+    return chunks
+
+
+
+client = OpenAI()
+
+apply()
+
+tqdm.pandas()
+
+
+def hash_data(data):
+    json_str = json.dumps(data, sort_keys=True)
+
+    json_bytes = json_str.encode('utf-8')
+
+    hash_hex = hashlib.sha256(json_bytes).hexdigest()
+
+    return hash_hex
+
+
+def get_retrieved_nodes(query, index, vector_top_k=10, reranker_top_n=3, with_reranker=True):
+    query_bundle = QueryBundle(query)
+    retriever = index.as_retriever(similarity_top_k=vector_top_k)
+    retrieved_nodes = retriever.retrieve(query_bundle)
+
+    if with_reranker:
+        reranker = LLMRerank(choice_batch_size=5, top_n=reranker_top_n)
+        retrieved_nodes = reranker.postprocess_nodes(retrieved_nodes, query_bundle)
+
+    return retrieved_nodes
+
+
+def get_all_text(nodes):
+    return ' '.join(f"\n- {node.get_text()}" for node in nodes)
+
+
+async def further_retrieve(query, index, messages):
+    try:
+        retrieved_nodes = get_retrieved_nodes(query, index, vector_top_k=10, reranker_top_n=3, with_reranker=False)
+        return completion(query, get_all_text(retrieved_nodes), messages)
+    except Exception as e:
+        print(e)
+        return None
+
+
+async def completion(query, docs, messages):
+    messages.extend([
+        {
+            "role": "system",
+            "content": f"""
+Given tone and voice guidelines and customer support help documents, act as a customer support bot. 
+Answer any further questions as if you are customer support bot.
+TONE AND VOICE:
+promote the society, be gentle, be kind always positive.
+
+DOCUMENT:
+{docs}
+
+
+
+INSTRUCTIONS:
+
+- Answer the users QUESTION using the DOCUMENT text above.
+- Format formula into latex format between $...$ or \[...\]
+- Keep your answer ground in the facts of the DOCUMENT or chat history.
+- If document has an image markdown ,use it in your answer
+- Respond in same language as user Question
+- Use Markdown Structure
+- DOCUMENT can have images with there descriptions
+- if a text is followed by an image dont skip  the image
+QUESTION:
+              """
+        },
+        {
+            "role": "system",
+            "content": query
+        }
+    ])
+    completion = client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=messages,
+        stream=True
+    )
+    for chunk in completion:
+        if chunk.choices[0].delta.content:
+            yield chunk.choices[0].delta.content