File size: 2,579 Bytes
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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()
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