reformulate query if the query is duplicate

apps/slackbot/task_agent.py

@@ -1,20 +1,18 @@
 import json
+from os import environ
 from typing import List, Optional
 
-from pydantic import ValidationError
 import openai
-
 from langchain.chains.llm import LLMChain
 from langchain.chat_models.base import BaseChatModel
 from langchain.schema import AIMessage, BaseMessage, Document, HumanMessage
 from langchain.tools.base import BaseTool
 from langchain.tools.human.tool import HumanInputRun
 from langchain.vectorstores.base import VectorStoreRetriever
-from pydantic import ValidationError
-
 from output_parser import BaseTaskOutputParser, TaskOutputParser
 from post_processors import md_link_to_slack
 from prompt import SlackBotPrompt
+from pydantic import ValidationError
 
 
 class TaskAgent:
@@ -93,6 +91,7 @@ def run(self, task: str) -> str:
 
         # Interaction Loop
 
+        previous_action = ""
         while True:
             # Discontinue if continuous limit is reached
             loop_count = self.loop_count
@@ -117,7 +116,7 @@ def run(self, task: str) -> str:
                     user_input=user_input,
                 )
             except openai.error.APIError as e:
-                return f"OpenAI API returned an API Error: {e}"  
+                return f"OpenAI API returned an API Error: {e}"
             except openai.error.APIConnectionError as e:
                 return f"Failed to connect to OpenAI API: {e}"
             except openai.error.RateLimitError as e:
@@ -130,7 +129,6 @@ def run(self, task: str) -> str:
                 return f"OpenAI API Service unavailable: {e}"
             except openai.error.InvalidRequestError as e:
                 return f"OpenAI API invalid request error: {e}"
-
 
             assistant_reply = self.chain.run(
                 task=task,
@@ -177,6 +175,33 @@ def run(self, task: str) -> str:
             action = self.output_parser.parse(assistant_reply)
             print("action:", action)
             tools = {t.name: t for t in self.tools}
+            if action == previous_action:
+                if action.name == "Search" or action.name == "Context Search":
+                    print(
+                        "Action name: ", action.name, "\nStart reformulating the query"
+                    )
+                    instruction = (
+                        f"You want to search for useful information to answer the query: {task}."
+                        f"The original query is: {action.args['query']}"
+                        f"Reformulate the query so that it can be used to search for relevant information."
+                        f"Only return one query instead of multiple queries."
+                        f"Reformulated query:\n\n"
+                    )
+                    openai.api_key = environ.get("OPENAI_KEY")
+                    response = openai.Completion.create(
+                        engine="text-davinci-003",
+                        prompt=" ".join(str(i) for i in self.previous_message)
+                        + "\n"
+                        + instruction,
+                        temperature=0.7,
+                        max_tokens=1024,
+                        top_p=1,
+                        frequency_penalty=0,
+                        presence_penalty=0,
+                    )
+                    reformulated_query = response["choices"][0]["text"]
+                    action.args["query"] = reformulated_query
+
             if action.name == "finish":
                 self.loop_count = self.max_iterations
                 result = "Finished task. "
@@ -220,6 +245,7 @@ def run(self, task: str) -> str:
 
             # self.memory.add_documents([Document(page_content=memory_to_add)])
             self.previous_message.append(HumanMessage(content=memory_to_add))
+            previous_action = action
 
     def set_user_input(self, user_input: str):
         result = f"Command UserInput returned: {user_input}"