add app.py

app.py

@@ -0,0 +1,92 @@
+import os
+import gradio as gr
+from langchain_community.vectorstores import FAISS
+from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
+
+from langchain_core.runnables.passthrough import RunnableAssign, RunnablePassthrough
+from langchain.memory import ConversationBufferMemory
+from langchain_core.messages import get_buffer_string
+from langchain_nvidia_ai_endpoints import ChatNVIDIA, NVIDIAEmbeddings
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+
+
+embedder = NVIDIAEmbeddings(model="nvolveqa_40k", model_type=None)
+db = FAISS.load_local("phuket_faiss", embedder, allow_dangerous_deserialization=True)
+
+# docs = new_db.similarity_search(query)
+
+nvidia_api_key = os.environ.get("NVIDIA_API_KEY", "")
+
+
+from operator import itemgetter
+
+
+# available models names
+# mixtral_8x7b
+# llama2_13b
+llm = ChatNVIDIA(model="mixtral_8x7b") | StrOutputParser()
+
+initial_msg = (
+    "Hello! I am Roam Mate to help you with your travel!"
+    f"\nHow can I help you?"
+)
+
+prompt_template = ChatPromptTemplate.from_messages([("system", """
+### [INST] Instruction: Answer the question based on your knowledge about places in Thailand. You are Roam Mate which is a chat bot to help users with their travel and recommending places according to their reference. Here is context to help:
+Document Retrieval:\n{context}\n
+(Answer only from retrieval. Only cite sources that are used. Make your response conversational.)
+
+
+### QUESTION:
+{question} [/INST]
+ """), ('user', '{question}')])
+
+chain = (
+    {
+        'context': db.as_retriever(search_type="similarity", search_kwargs={"k": 10}),
+        'question': (lambda x:x)
+    }
+    | prompt_template
+    # | RPrint()
+    | llm
+    | StrOutputParser()
+)
+
+conv_chain = (
+    prompt_template
+    # | RPrint()
+    | llm
+    | StrOutputParser()
+)
+
+def chat_gen(message, history, return_buffer=True):
+    buffer = ""
+
+    doc_retriever = db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.2, "k": 10})
+    retrieved_docs = doc_retriever.invoke(message)
+    print(len(retrieved_docs))
+    print(retrieved_docs)
+
+    if len(retrieved_docs) > 0:
+        state = {
+                'question': message,
+                'context': retrieved_docs
+        }
+        for token in conv_chain.stream(state):
+            buffer += token
+            yield buffer
+        
+        buffer += "I use the following websites data to generate the above answer: \n"
+        for docs in retrieved_docs:
+            buffer += f'{docs['metadata']['source']}\n'
+    else:
+      passage = "I am sorry. I do not have relevant information to answer on that specific topic. Please try another question."
+      buffer += passage
+      yield buffer if return_buffer else passage
+
+
+chatbot = gr.Chatbot(value = [[None, initial_msg]])
+iface = gr.ChatInterface(chat_gen, chatbot=chatbot).queue()
+iface.launch()

requirements.txt

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+langchain
+langchain-nvidia-ai-endpoints
+gradio
+faiss-cpu