Initial commit

README.md

@@ -1,12 +1 @@
----
-title: Mc Poc Gen Ai
-emoji: ⚡
-colorFrom: red
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Mastercard Documentation Genius

main.py

@@ -0,0 +1,73 @@
+import os
+import sys
+import time
+
+import InstructorEmbedding
+import pandas as pd
+import qdrant_client
+import streamlit as st
+from langchain.chains import RetrievalQA
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.llms import HuggingFaceHub
+from langchain.text_splitter import (
+    CharacterTextSplitter,
+    RecursiveCharacterTextSplitter,
+)
+from langchain.vectorstores import Qdrant
+from langchain_community.document_loaders import TextLoader
+from langchain_community.vectorstores import FAISS, Chroma
+from langchain_openai import OpenAIEmbeddings
+from qdrant_client.http import models
+
+from settings import SETTINGS
+
+for setting, value in SETTINGS.items():
+    os.environ[setting] = value
+
+client = qdrant_client.QdrantClient(
+    os.getenv("qdrant_host"), api_key=os.getenv("qdrant_key")
+)
+
+
+def main():
+    # st.set_page_config(page_title="Chat with multiple PDFs",
+    #                   page_icon=":books:")
+    # st.write(css, unsafe_allow_html=True)
+
+    st.set_page_config(page_title="Ask Qdrant", page_icon=":books:")
+    st.header("Ask your remote database 💬")
+
+    embeddings = OpenAIEmbeddings()
+
+    db = FAISS.load_local("faiss_index", embeddings)
+
+    llm = HuggingFaceHub(
+        repo_id="bigscience/bloom",
+        model_kwargs={"temperature": 0.2, "max_length": 512, "max_new_tokens": 100},
+    )
+
+    qa = RetrievalQA.from_chain_type(
+        llm=llm, chain_type="stuff", retriever=db.as_retriever()
+    )
+
+    # show user input
+    user_question = st.text_input("Ask a question Mastercard's available APIs:")
+    if user_question:
+        answer = qa.invoke(user_question)
+        st.write(f"Question: {answer['query']}")
+        st.write(f"Answer: {answer['result']}")
+
+    col1, col2, col3 = st.columns([1, 6, 1])
+
+    with col1:
+        st.write("")
+
+    with col2:
+        st.write("")
+
+    with col3:
+        st.image("mc_symbol_opt_73_3x.png")
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+InstructorEmbedding==1.0.1
+langchain==0.1.0
+langchain_community==0.0.11
+langchain_openai==0.0.2
+pandas==2.1.4
+qdrant_client==1.7.0
+Requests==2.31.0
+streamlit==1.18.1