create app.py

app.py

@@ -0,0 +1,70 @@
+import os
+from langchain_groq import ChatGroq
+from langchain_openai import OpenAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from dotenv import load_dotenv
+
+load_dotenv()
+#download embedding model
+def download_hugging_face_embeddings():
+    embeddings= HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    return embeddings
+# Load the GROQ and OpenAI API keys
+#os.environ['OPENAI_API_KEY'] = os.getenv("OPENAI_API_KEY")
+groq_api_key = ('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')
+
+# Initialize the LLM
+llm = ChatGroq(groq_api_key=groq_api_key,
+              model_name="Llama3-8b-8192")
+
+from langchain_core.prompts import ChatPromptTemplate
+
+prompt_template = """Use the following pieces of information to answer the user's question.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+Context: {context}
+Question: {input} 
+Only return the helpful answer below and nothing else.
+Helpful answer:"""
+
+prompt = ChatPromptTemplate.from_template(prompt_template)
+def vector_embedding():
+    """Embeds the documents and stores them in a FAISS vector store."""
+
+    #embeddings = OpenAIEmbeddings()
+    embeddings= HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    loader = PyPDFDirectoryLoader("/kaggle/input/book-pdf-1")  # Data Ingestion
+    docs = loader.load()  # Document Loading
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)  # Chunk Creation
+    final_documents = text_splitter.split_documents(docs[:20])  # Splitting
+    vectors = FAISS.from_documents(final_documents, embeddings)  # Vector OpenAI embeddings
+    return vectors
+
+# Get user input
+prompt1 = input("Enter Your Question From Documents: ")
+
+# Embed the documents
+vectors = vector_embedding()
+print("Vector Store DB Is Ready")
+
+import time
+
+if prompt1:
+    document_chain = create_stuff_documents_chain(llm, prompt)
+    retriever = vectors.as_retriever()
+    retrieval_chain = create_retrieval_chain(retriever, document_chain)
+    start = time.process_time()
+    response = retrieval_chain.invoke({'input': prompt1})
+    print("Response time :", time.process_time() - start)
+    print(response['answer'])
+
+    # Print similar documents
+    print("\nDocument Similarity Search:")
+    for i, doc in enumerate(response["context"]):
+        print(doc.page_content)
+        print("--------------------------------")