Create app.py

app.py

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+import faiss
+from langchain_together.embeddings import TogetherEmbeddings
+import numpy as np
+import pickle
+import os
+import streamlit as st
+
+os.environ["TOGETHER_API_KEY"] = st.secrets["together_api_key"]
+
+@st.cache_data
+def load_data():
+    with open("list_of_texts.pkl", "rb") as f:
+        list_of_texts = pickle.load(f)
+    index = faiss.read_index("faiss.index")
+    return list_of_texts, index
+
+
+def response(sentence, embeddings, list_of_texts, index, ):
+    vector = embeddings.embed_query(sentence)
+    vector = np.array([vector]).astype('float32')
+    k = 5
+    D, I = index.search(vector, k)
+    nearest_texts = [list_of_texts[i] for i in I[0]]
+    return nearest_texts[0]
+
+embeddings = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval")
+list_of_texts, index = load_data()
+st.title("Ship Document Retreiver")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+    
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+
+if prompt := st.chat_input("What is up?"):
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    get_response = response(prompt, embeddings, list_of_texts, index)
+    with st.chat_message("assistant"):
+        st.markdown(get_response)
+    st.session_state.messages.append({"role": "assistant", "content": response})