Create app.py

app.py

@@ -0,0 +1,53 @@
+from langchain.document_loaders.csv_loader import CSVLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.llms import CTransformers
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+import sys
+
+DB_FAISS_PATH = "vectorstore/db_faiss"
+loader = CSVLoader(file_path="data/2019.csv", encoding="utf-8", csv_args={'delimiter': ','})
+data = loader.load()
+print(data)
+
+# Split the text into Chunks
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
+text_chunks = text_splitter.split_documents(data)
+
+print(len(text_chunks))
+
+# Download Sentence Transformers Embedding From Hugging Face
+embeddings = HuggingFaceEmbeddings(model_name = 'sentence-transformers/all-MiniLM-L6-v2')
+
+# COnverting the text Chunks into embeddings and saving the embeddings into FAISS Knowledge Base
+docsearch = FAISS.from_documents(text_chunks, embeddings)
+
+docsearch.save_local(DB_FAISS_PATH)
+
+
+#query = "What is the value of GDP per capita of Finland provided in the data?"
+
+#docs = docsearch.similarity_search(query, k=3)
+
+#print("Result", docs)
+
+llm = CTransformers(model="models/llama-2-7b-chat.ggmlv3.q4_0.bin",
+                    model_type="llama",
+                    max_new_tokens=512,
+                    temperature=0.1)
+
+qa = ConversationalRetrievalChain.from_llm(llm, retriever=docsearch.as_retriever())
+
+while True:
+    chat_history = []
+    #query = "What is the value of  GDP per capita of Finland provided in the data?"
+    query = input(f"Input Prompt: ")
+    if query == 'exit':
+        print('Exiting')
+        sys.exit()
+    if query == '':
+        continue
+    result = qa({"question":query, "chat_history":chat_history})
+    print("Response: ", result['answer'])