finalHEADmaster

1 files changed, 51 insertions, 9 deletions

diff --git a/lang_prompt_demo.py b/lang_prompt_demo.py
index 3d8f1cd..cde0a59 100644
--- a/lang_prompt_demo.py
+++ b/lang_prompt_demo.py
@@ -5,6 +5,7 @@ from dotenv import load_dotenv
 
 from tools.contacts import get_all_contacts
 from tools.vocode import call_phone_number
+from tools.summarize import summarize
 from tools.get_user_inputs import get_desired_inputs
 from tools.email_tool import email_tasks
 from langchain.memory import ConversationBufferMemory
@@ -15,30 +16,71 @@ from stdout_filterer import RedactPhoneNumbers
 load_dotenv()
 
 from langchain.chat_models import ChatOpenAI
-from langchain.chat_models import BedrockChat
+# from langchain.chat_models import BedrockChat
 from langchain.agents import initialize_agent
 from langchain.agents import AgentType
 
+from langchain.tools import WikipediaQueryRun
+
+import argparse
+
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+tools=load_tools(["human", "wikipedia"]) + [get_all_contacts, call_phone_number, email_tasks, summarize]
+
+tools_desc = ""
+for tool in tools:
+    tools_desc += tool.name + " : "  + tool.description + "\n"
+
+def rephrase_prompt(objective: str) -> str:
+    # llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")  # type: ignore
+    # pred = llm.predict(f"Based on these tools {tools_desc} with the {objective} should be done in the following manner (outputting a single sentence), allowing for failure: ")
+    # print(pred)
+    # return pred
+    return f"{objective}"
+
+with open("info.txt") as f:
+    my_info = f.read()
+    memory.chat_memory.add_user_message("User information to us " + my_info + " end of user information.")
+
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Command line argument parser example")
+
+    parser.add_argument("--objective", type=str, help="Objective for the program")
+    parser.add_argument("--verbose", type=bool, help="Verbosity of the program", default=False)
+
+    # Parse the arguments
+    args = parser.parse_args()
+
+    # Get the value of --objective
+    objective_value = args.objective
+
+    # Get the value of --verbose
+    verbose_value = args.verbose
+
     # Redirect stdout to our custom class
     sys.stdout = typing.cast(typing.TextIO, RedactPhoneNumbers(sys.stdout))
 
+    if objective_value is None:
+        objective_value = input("What is your objective? ")
+
     OBJECTIVE = (
-        input("Objective: ")
-        + "make sure you use the proper tool before calling final action to meet objective, feel free to say you need more information or cannot do something."
+        objective_value
         or "Find a random person in my contacts and tell them a joke"
     )
-    #llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")  # type: ignore
-    llm = BedrockChat(model_id="anthropic.claude-instant-v1", model_kwargs={"temperature":0})  # type: ignore
-    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")  # type: ignore
+    #llm = BedrockChat(model_id="anthropic.claude-instant-v1", model_kwargs={"temperature":0})  # type: ignore
+    #memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
     # Logging of LLMChains
     verbose = True
     agent = initialize_agent(
-        tools=[get_all_contacts, call_phone_number, email_tasks] + load_tools(["serpapi", "human"]),
+        tools=tools,
         llm=llm,
         agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
-        verbose=verbose,
+        verbose=verbose_value,
         memory=memory,
     )
 
-    agent.run(OBJECTIVE)
+    out = agent.run(OBJECTIVE)
+    print(out)
+
+