Create pdf_bot.py

pdf_bot.py

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+import os
+from dotenv import load_dotenv
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain_community.llms import ChatGroq
+
+load_dotenv()
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+# Load PDF and prepare QA chain
+def create_qa_chain_from_pdf(pdf_path):
+    loader = PyPDFLoader(pdf_path)
+    documents = loader.load()
+    
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = splitter.split_documents(documents)
+    
+    embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-m3")
+    vectorstore = FAISS.from_documents(texts, embeddings)
+
+    llm = ChatGroq(
+        model="llama3-8b-8192",
+        temperature=0.3,
+        api_key=groq_api_key,
+    )
+
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=vectorstore.as_retriever(search_kwargs={"k": 1}),
+        return_source_documents=True
+    )
+    return qa_chain