Create app.py

app.py

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+# Import Python modules
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import CharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.chains import create_retrieval_chain
+from langchain.vectorstores import Chroma
+
+# Set your API key
+GOOGLE_API_KEY = "AIzaSyCHLS-wFvSYxSTJjkRQQ-FiC5064112Eq8"
+
+# Load the models with the API key
+llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=GOOGLE_API_KEY)
+embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GOOGLE_API_KEY)
+
+# Load the PDF and create chunks
+loader = PyPDFLoader("handbook.pdf")
+text_splitter = CharacterTextSplitter(
+    separator=".",
+    chunk_size=500,
+    chunk_overlap=50,
+    length_function=len,
+    is_separator_regex=False,
+)
+pages = loader.load_and_split(text_splitter)
+
+# Turn the chunks into embeddings and store them in Chroma
+vectordb = Chroma.from_documents(pages, embeddings)
+
+# Configure Chroma as a retriever with top_k=5
+retriever = vectordb.as_retriever(search_kwargs={"k": 5})
+
+# Create the retrieval chain
+template = """
+You are a helpful AI assistant.
+Answer based on the context provided. 
+context: {context}
+input: {input}
+answer:
+"""
+prompt = PromptTemplate.from_template(template)
+combine_docs_chain = create_stuff_documents_chain(llm, prompt)
+retrieval_chain = create_retrieval_chain(retriever, combine_docs_chain)
+
+# Invoke the retrieval chain
+response = retrieval_chain.invoke({"input": "How do I apply for personal leave?"})
+
+# Print the answer to the question
+print(response["answer"])