Update app.py

app.py

@@ -43,8 +43,10 @@ gpu_llm = HuggingFacePipeline(
 
 )
 from langchain_core.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.schema import HumanMessage, SystemMessage, AIMessage
 
-alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+alpaca_prompt_simple = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
 
 ### Instruction:
 {question}
@@ -55,25 +57,87 @@ alpaca_prompt = """Below is an instruction that describes a task, paired with an
 ### Response:
 """
 
-prompt = PromptTemplate.from_template(alpaca_prompt)
+prompt = PromptTemplate.from_template(alpaca_prompt_simple)
+llm_chain_model = LLMChain(prompt=prompt, llm=gpu_llm.bind(skip_prompt=True))
+
+
+from langchain.prompts import  ChatPromptTemplate, FewShotChatMessagePromptTemplate
+
+
+
+examples = [
+ {
+        "query": "what is forex?",
+        "answer": "Forex is an abbreviation for foreign exchange. It involves trading currencies from different countries with one another at the current market price."
+    },
+]
+example_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("human", "{query}"),
+        ("ai", "{answer}"),
+    ]
+)
+
+
+few_shot_prompt = FewShotChatMessagePromptTemplate(
+    example_prompt=example_prompt,
+    examples=examples,
+)
+
+
+
+
+# with memory 
+from langchain_core.prompts import PromptTemplate
+from langchain.memory import ConversationBufferMemory
 
-gpu_chain = prompt | gpu_llm.bind(stop=["\n\n"])
+alpaca_prompt_memory = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+{chat_history}
+
+### Instruction:
+
+{question}
+
+
+
+### Input:
+
+### Response:
+"""
+
+prompt = PromptTemplate(
+    input_variables=["chat_history", "question"], template=alpaca_prompt_memory
+)
+memory = ConversationBufferMemory(memory_key="chat_history")
+
+llm_chain_memory = LLMChain(
+    llm=gpu_llm.bind(skip_prompt=True),
+    prompt=prompt,
+    verbose=True,
+    memory=memory,
+)
 
 # question = "give me suggestion about inevstment"
 
 def greet(question, model_type):
     print(f"question is {question}")
     if model_type == "With memory":
-        response_of_llm = gpu_chain.invoke({"question": question})
-        print("creating model created")
+        print("With memory")
+        response_of_llm = llm_chain_memory.predict(question=question)
     else:
-        template = """You are the Finiantial expert:
-        ### Instruction:
-        {question}
-        ### Input:
-        ### Response:
-        """
-        response_of_llm = gpu_chain.invoke({"question": question})
+        print("Without memory")
+        query = question
+        final_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", "You are a financial ai assitant "),
+                few_shot_prompt,
+                ("human", "{userInput}"),
+            ]
+        )
+        messages = final_prompt.format(userInput=query)
+        
+        ai_out = llm_chain_model.invoke(messages)
+        response_of_llm = ai_out['text']
 
     print(f"out is: {response_of_llm}")
     return response_of_llm