Dockerfile and pulumi stack for infra

agent/agent.py

@@ -1,3 +1,4 @@
+import json
 from langchain_core.tools import tool
 from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser
 from langchain_core.prompts import ChatPromptTemplate,MessagesPlaceholder, PromptTemplate
@@ -16,7 +17,15 @@ from langchain_core.agents import AgentAction, AgentFinish
 from langchain.agents.output_parsers import ReActSingleInputOutputParser
 from langchain.tools import Tool
 import os
-llm = ChatBedrock(
+def find_tool_by_name(tools: list[Tool],tool_name:str):
+    for tool in tools:
+        if tool.name==tool_name:
+            print(tool.name)
+            print("\n\n")
+            return tool
+    raise ValueError(f"Tool with name {tool_name} not found")
+def agent_call(event,context):
+    llm = ChatBedrock(
     model_id="arn:aws:bedrock:us-east-1:654654422992:application-inference-profile/d9blf0g3fzqz",
     region_name="us-east-1",
     aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
@@ -25,18 +34,12 @@ llm = ChatBedrock(
     model_kwargs={"temperature": 0.2, 'max_tokens': 1000,},
     provider='anthropic'
 )
-retriever = AmazonKnowledgeBasesRetriever(
+    retriever = AmazonKnowledgeBasesRetriever(
     knowledge_base_id="RBD9TI5HYU",
     region_name="us-east-1",
     retrieval_config={"vectorSearchConfiguration": {"numberOfResults": 4}},
     
 )
-def find_tool_by_name(tools: list[Tool],tool_name:str):
-    for tool in tools:
-        if tool.name==tool_name:
-            return tool
-    raise ValueError(f"Tool with name {tool_name} not found")
-if __name__=="__main__":
     print("Hello React Langhain")
     tools=[retriever.as_tool()]
     template="""Você é um assistente para alunos de diversos campus diferentes do instituto federal de são paulo, sua função é responder perguntas 
@@ -62,13 +65,12 @@ Question: {input}
 Chat history:{chat_history}
 Thought: {agent_scratchpad}
 """
-
     prompt=PromptTemplate.from_template(template=template).partial(tools=render_text_description(tools), tool_names=','.join([t.name for t in tools]))
     #llm=ChatOpenAI(model="gpt-4o-mini",temperature=0,stop_sequences=["\nObservation:"])
     intermediate_steps=[]
 
-    agent= {"input": lambda x:x["input"],"agent_scratchpad": lambda x:format_log_to_str(x["agent_scratchpad"]),"chat_history":lambda x:x["chat_history"]}|prompt | llm
-    agent_step: Union[AgentAction,AgentFinish]=agent.invoke({"input": "Quanto é o valor do auxilio moradia?","agent_scratchpad":intermediate_steps,"chat_history":{"role":"user","content":""}})
+    agent= {"input": lambda x:x["input"],"agent_scratchpad": lambda x:format_log_to_str(x["agent_scratchpad"]),"chat_history":lambda x:x["chat_history"]}|prompt | llm|ReActJsonSingleInputOutputParser()
+    agent_step: Union[AgentAction,AgentFinish]=agent.invoke({"input": "Que dia é hoje?","agent_scratchpad":intermediate_steps,"chat_history":{"role":"user","content":"sou do campus sao paulo"}})
     #print(agent_step)
     if isinstance(agent_step,AgentAction):
         tool_name=agent_step.tool
@@ -77,5 +79,10 @@ Thought: {agent_scratchpad}
         observation=tool_to_use.func(str(tool_input))
         print(f"{observation=}")
         intermediate_steps.append((agent_step,str(observation)))
-    agent_step: Union[AgentAction,AgentFinish]=agent.invoke({"input": "Quanto é o valor do auxiliomoradia?","agent_scratchpad":intermediate_steps,"chat_history":{"role":"user","content":""}})
-    print(agent_step)
+    agent_step: Union[AgentAction,AgentFinish]=agent.invoke({"input": "Quanto é o valor do auxilio moradia?","agent_scratchpad":intermediate_steps,"chat_history":{"role":"user","content":"sou do campus sao paulo"}})
+    return agent_step
+def hello(event,context):
+    return{
+        "statusCode":200,
+        "body":json.dumps("hello_world")
+    }