Upload app.py

app.py

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+import os
+import gradio as gr
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_community.vectorstores import Chroma
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_ollama import OllamaEmbeddings, OllamaLLM
+
+# -------------------------
+# 1. LOAD MULTIPLE PDFs
+# -------------------------
+
+def load_documents(folder_path="documents"):
+    documents = []
+    for file in os.listdir(folder_path):
+        if file.endswith(".pdf"):
+            loader = PyPDFLoader(os.path.join(folder_path, file))
+            docs = loader.load()
+            for doc in docs:
+                doc.metadata["source"] = file
+            documents.extend(docs)
+    return documents
+
+# -------------------------
+# 2. BUILD VECTOR DATABASE
+# -------------------------
+
+documents = load_documents("documents")
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=1000,
+    chunk_overlap=200
+)
+
+docs = text_splitter.split_documents(documents)
+
+embeddings = OllamaEmbeddings(model="mistral")
+db = Chroma.from_documents(docs, embeddings)
+
+llm = OllamaLLM(model="mistral")
+
+# -------------------------
+# 3. CHAT MEMORY
+# -------------------------
+
+chat_history = []
+
+# -------------------------
+# 4. QUESTION FUNCTION
+# -------------------------
+
+def ask_pdf(question):
+
+    global chat_history
+
+    retrieved_docs = db.similarity_search(question, k=3)
+
+    context = "\n\n".join(
+        [doc.page_content for doc in retrieved_docs]
+    )
+
+    sources = list(set([doc.metadata["source"] for doc in retrieved_docs]))
+
+    # Guardrail: if no context found
+    if not context.strip():
+        return "I don't have enough information from the documents to answer this."
+
+    prompt = f"""
+You are a helpful assistant.
+Answer ONLY from the provided context.
+If the answer is not in the context, say:
+"I don't have enough information from the documents."
+
+Chat history:
+{chat_history}
+
+Context:
+{context}
+
+Question:
+{question}
+
+Answer:
+"""
+
+    answer = llm.invoke(prompt)
+
+    chat_history.append({"question": question, "answer": answer})
+
+    citation_text = "\n\nSources: " + ", ".join(sources)
+
+    return answer + citation_text
+
+
+# -------------------------
+# 5. GRADIO UI
+# -------------------------
+
+interface = gr.Interface(
+    fn=ask_pdf,
+    inputs="text",
+    outputs="text",
+    title="Advanced Ask My PDF Bot",
+    description="Chat with multiple PDFs. Shows sources. Has memory. Guardrails enabled."
+)
+
+interface.launch()