Create app.py

app.py

@@ -0,0 +1,123 @@
+### 010125-daysoff-assistant-api
+
+import os
+import time
+import json
+import torch
+
+from api_docs_mck import daysoff_api_docs
+
+import chainlit as cl
+
+from langchain import hub
+from langchain.chains import LLMChain, APIChain
+from langchain_core.prompts import PromptTemplate
+from langchain_community.llms import HuggingFaceHub
+from langchain.memory.buffer import ConversationBufferMemory
+
+
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+LANGCHAIN_API_KEY = os.environ.get("LANGCHAIN_API_KEY")
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+#os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:true"
+
+dtype = torch.float16
+device = torch.device("cuda")
+
+daysoff_assistant_booking_template = """
+You are a customer support assistant for Daysoff.no. Your expertise is
+retrieving booking information for a given booking ID (’bestillingsnummer’)"
+Chat History: {chat_history}
+Question: {question}
+Answer:
+"""
+daysoff_assistant_booking_prompt= PromptTemplate(
+    input_variables=["chat_history", "question"],
+    template=daysoff_assistant_booking_template
+)
+
+api_url_template = """
+Given the following API Documentation for Daysoff's official
+booking information API: {api_docs_mck}
+Your task is to construct the most efficient API URL to answer
+the user's question, ensuring the
+call is optimized to include only the necessary information.
+Question: {question}
+API URL:
+"""
+api_url_prompt = PromptTemplate(input_variables=['api_docs_mck', 'question'],
+                                template=api_url_template)
+
+api_response_template = """"
+With the API Documentation for Daysoff's official API: {api_docs_mck}
+and the specific user question: {question} in mind,
+and given this API URL: {api_url} for querying, here is the
+response from Daysoff's API: {api_response}.
+Please provide a summary that directly addresses the user's question,
+omitting technical details like response format, and
+focusing on delivering the answer with clarity and conciseness,
+as if a human customer service agent is providing this information.
+Adapt to user's language. By default, you speak Norwegian.
+Summary:
+"""
+api_response_prompt = PromptTemplate(input_variables=['api_docs_mck',
+                                                      'question',
+                                                      'api_url',
+                                                      'api_response'],
+                                     template=api_response_template)
+
+
+# --model, memory object, and llm_chain
+@cl.on_chat_start
+def setup_multiple_chains():
+    llm = HuggingFaceHub(repo_id="google/gemma-2-2b-it",
+                         temperature=0.7,
+                         huggingface_api_token=HUGGINGFACEHUB_API_TOKEN,
+                         device=device)
+
+    conversation_memory = ConversationBufferMemory(memory_key="chat_history",
+                                                   max_len=200,
+                                                   return_messages=True,
+                                                   )
+    llm_chain = LLMChain(llm=llm,
+                         prompt=daysoff_assistant_booking_prompt,
+                         memory=conversation_memory
+                        )
+
+    cl.user_session.set("llm_chain", llm_chain)
+
+    api_chain = APIChain.from_llm_and_api_docs_mck(
+        llm=llm,
+        api_docs_mck=daysoff_api_docs,
+        api_url_prompt=api_url_prompt,
+        api_response_prompt=api_response_prompt,
+        verbose=True,
+        limit_to_domains=None)
+
+    cl.user_session.set("api_chain", api_chain)
+
+
+# --wrapper function around the @cl.on_message decorator; chain trigger(s)
+@cl.on_message
+async def handle_message(message: cl.Message):
+    user_message = message.content.lower()
+    llm_chain = cl.user_session.get("llm_chain")
+    api_chain = cl.user_session.get("api_chain")
+
+    if any(keyword in user_message for keyword in ["booking_id", "full_name", "amount",  # + "bestillingsnummer", "checkin", "checkout" for api_docs
+                                                   "date", "address", "amount", "user_id"]):
+        # --if any keywords in user_message, use api_chain
+        response = await api_chain.acall(user_message,
+                                         callbacks=[cl.AsyncLangchainCallbackHandler()])
+    else:
+        # --defaults to llm_chain4general queries
+        response = await llm_chain.acall(user_message,
+                                         callbacks=[cl.AsyncLangchainCallbackHandler()])
+    response_key = "output" if "output" in response else "text"
+    await cl.Message(response.get(response_key, "")).send()
+
+
+
+if __name__ == '__main__':
+    cl.launch()