python coder

langchain-streamlit-demo/app.py

@@ -430,7 +430,7 @@ if st.session_state.llm:
  callbacks=callbacks,
  tags=["Streamlit Chat"],
  verbose=True,
- return_intermediate_steps=True,
+ return_intermediate_steps=False,
  )
  if st.session_state.provider == "Anthropic":
  config["max_concurrency"] = 5

langchain-streamlit-demo/llm_resources.py

@@ -3,9 +3,6 @@ from tempfile import NamedTemporaryFile
 from typing import Tuple, List, Optional, Dict
 
 from langchain.agents import AgentExecutor
-from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
- AgentTokenBufferMemory,
-)
 from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.chains import LLMChain
@@ -57,10 +54,17 @@ def get_agent(
  )
  agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
 
- agent_memory = AgentTokenBufferMemory(
+ # agent_memory = AgentTokenBufferMemory(
+ # chat_memory=chat_history,
+ # memory_key=memory_key,
+ # llm=llm,
+ # )
+ from langchain.memory import ConversationBufferMemory
+
+ agent_memory = ConversationBufferMemory(
  chat_memory=chat_history,
+ return_messages=True,
  memory_key=memory_key,
- llm=llm,
  )
 
  agent_executor = AgentExecutor(
@@ -68,7 +72,7 @@ def get_agent(
  tools=tools,
  memory=agent_memory,
  verbose=True,
- return_intermediate_steps=True,
+ return_intermediate_steps=False,
  callbacks=callbacks,
  )
  return (

langchain-streamlit-demo/python_coder.py

@@ -1,16 +1,14 @@
 """langchain python coder-- requires black, ruff, and mypy."""
 
-import argparse
 import os
 import re
 import subprocess # nosec
 import tempfile
-from importlib.util import find_spec
 
 from langchain.agents import initialize_agent, AgentType
 from langchain.agents.tools import Tool
-from langchain.chat_models import ChatOpenAI
 from langchain.llms.base import BaseLLM
+from langchain.memory import ConversationBufferMemory
 from langchain.prompts import ChatPromptTemplate
 from langchain.pydantic_v1 import BaseModel, validator, Field, ValidationError
 
@@ -177,35 +175,17 @@ def get_agent(
  llm: BaseLLM,
  agent_type: AgentType = AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
 ):
+ agent_memory = ConversationBufferMemory(
+ return_messages=True,
+ memory_key="chat_history",
+ )
  return initialize_agent(
  tools,
  llm,
  agent=agent_type,
  verbose=True,
  handle_parsing_errors=True,
+ memory=agent_memory,
  prompt=prompt,
- # return_intermediate_steps=True,
+ return_intermediate_steps=False,
  ) | (lambda output: output["output"])
-
-
-if __name__ == "__main__":
- for lib in ("black", "ruff", "mypy"):
- if find_spec(lib) is None:
- raise ImportError(
- "You must install black, ruff, and mypy to use this tool. "
- "You can install them with `pip install black ruff mypy`.",
- )
-
- parser = argparse.ArgumentParser()
- parser.add_argument("--model", "-m", default="gpt-4-1106-preview")
- parser.add_argument("instruction")
- args = parser.parse_args()
-
- instruction = args.instruction
- model = args.model
-
- llm = ChatOpenAI(model_name=model, temperature=0.0)
- agent = get_agent(llm)
-
- output = agent.invoke({"input": instruction}, config=dict(verbose=True))
- print(output)