Upload app.py

app.py

@@ -0,0 +1,84 @@
+pip install -qU cassio datasets langchain openai tiktoken
+
+# LangChain components to use
+from langchain.vectorstores.cassandra import Cassandra
+from langchain.indexes.vectorstore import VectorStoreIndexWrapper
+from langchain.llms import OpenAI
+from langchain.embeddings import OpenAIEmbeddings
+
+# Support for dataset retrieval with Hugging Face
+from datasets import load_dataset
+
+# With CassIO, the engine powering the Astra DB integration in LangChain,
+# you will also initialize the DB connection:
+import cassio
+
+pip install PyPDF2
+
+from PyPDF2 import PdfReader
+
+ASTRA_DB_APPLICATION_TOKEN =   "AstraCS:OsOjMKLLxkWFoUpmNbWeJwIP:d8b4df7fd17c288edd265f9d167fa821e97e9d97098842c2e3ed4140d756d02d"
+ASTRA_DB_ID = "f97bbcce-b48b-4b42-8ad0-fdc38b2e165e" # enter your Database ID
+OPENAI_API_KEY = "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" # enter your OpenAI key
+
+# provide the path of  pdf file/files.
+pdfreader = PdfReader('Ethics.pdf')
+
+from typing_extensions import Concatenate
+# read text from pdf
+raw_text = ''
+for i, page in enumerate(pdfreader.pages):
+    content = page.extract_text()
+    if content:
+        raw_text += content
+
+
+cassio.init(token=ASTRA_DB_APPLICATION_TOKEN, database_id=ASTRA_DB_ID)
+
+llm = OpenAI(openai_api_key=OPENAI_API_KEY)
+embedding = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+
+astra_vector_store = Cassandra(
+    embedding=embedding,
+    table_name="qa_mini_demo",
+    session=None,
+    keyspace=None,
+)
+
+
+from langchain.text_splitter import CharacterTextSplitter
+# We need to split the text using Character Text Split such that it sshould not increse token size
+text_splitter = CharacterTextSplitter(
+    separator = "\n",
+    chunk_size = 800,
+    chunk_overlap  = 200,
+    length_function = len,
+)
+texts = text_splitter.split_text(raw_text)
+
+
+
+astra_vector_store.add_texts(texts[:])
+
+print("Inserted %i headlines." % len(texts[:]))
+
+astra_vector_index = VectorStoreIndexWrapper(vectorstore=astra_vector_store)
+
+first_question = True
+while True:
+    if first_question:
+        query_text = input("\nEnter your question (or type 'quit' to exit): ").strip()
+    else:
+        query_text = input("\nWhat's your next question (or type 'quit' to exit): ").strip()
+
+    if query_text.lower() == "quit":
+        break
+
+    if query_text == "":
+        continue
+
+    first_question = False
+
+    print("\nQUESTION: \"%s\"" % query_text)
+    answer = astra_vector_index.query(query_text, llm=llm).strip()
+    print("ANSWER: \"%s\"\n" % answer)