Create app.py

app.py

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+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("faiss_index", 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 a chatbot to help with any questions about Data First Company."
+    f"\nHow can I help you?"
+)
+
+context_prompt = ChatPromptTemplate.from_messages([
+    ('system',
+        "You are a chatbot, and you are helping customer with their inquries about Data First Company."
+        "Answer the question using only the context provided. Do not include based on the context or based on the documents provided in your answer."
+        "Please help them with their question about the company. Remember that your job is to represent Data First company that create data solutions."
+        "Do not hallucinate any details, and make sure the knowledge base is not redundant."
+        "Please say you do not know if you do not know or you cannot find the information needed."
+        "\n\nQuestion: {question}\n\nContext: {context}"),
+        ('user', "{question}"
+)])
+
+chain = (
+    {
+        'context': db.as_retriever(search_type="similarity"),
+        'question': (lambda x:x)
+    }
+    | context_prompt
+    # | RPrint()
+    | llm
+    | StrOutputParser()
+)
+
+conv_chain = (
+    context_prompt
+    # | 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})
+    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
+    else:
+      passage = "I am sorry. I do not have relevant information to answer the question. 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()