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from langchain.llms import OpenAI
from langchain import OpenAI, LLMChain, PromptTemplate, SerpAPIWrapper
from langchain.agents import ZeroShotAgent, AgentExecutor, initialize_agent, Tool
import importlib
import json
import os
import requests
import yaml
from swarms.tools.agent.apitool import RequestTool
from swarms.tools.agent.executor import Executor, AgentExecutorWithTranslation
from swarms.tools import get_logger
from swarms.tools.agent.BabyagiTools import BabyAGI
# from swarms.tools
# .models.customllm import CustomLLM
logger = get_logger(__name__)
def import_all_apis(tool_json):
"""import all apis that is a tool"""
doc_url = tool_json["api"]["url"]
response = requests.get(doc_url)
logger.info("Doc string URL: {}".format(doc_url))
if doc_url.endswith("yaml") or doc_url.endswith("yml"):
plugin = yaml.safe_load(response.text)
else:
plugin = json.loads(response.text)
server_url = plugin["servers"][0]["url"]
if server_url.startswith("/"):
server_url = "http://127.0.0.1:8079" + server_url
logger.info("server_url {}".format(server_url))
all_apis = []
for key in plugin["paths"]:
value = plugin["paths"][key]
for method in value:
api = RequestTool(
root_url=server_url, func_url=key, method=method, request_info=value
)
all_apis.append(api)
return all_apis
def load_single_tools(tool_name, tool_url):
# tool_name, tool_url = "datasette", "https://datasette.io/"
# tool_name, tool_url = "klarna", "https://www.klarna.com/"
# tool_name, tool_url = 'chemical-prop', "http://127.0.0.1:8079/tools/chemical-prop/"
# tool_name, tool_url = 'douban-film', "http://127.0.0.1:8079/tools/douban-film/"
# tool_name, tool_url = 'weather', "http://127.0.0.1:8079/tools/weather/"
# tool_name, tool_url = 'wikipedia', "http://127.0.0.1:8079/tools/wikipedia/"
# tool_name, tool_url = 'wolframalpha', "http://127.0.0.1:8079/tools/wolframalpha/"
# tool_name, tool_url = 'klarna', "https://www.klarna.com/"
get_url = tool_url + ".well-known/ai-plugin.json"
response = requests.get(get_url)
if response.status_code == 200:
tool_config_json = response.json()
else:
raise RuntimeError("Your URL of the tool is invalid.")
return tool_name, tool_config_json
class STQuestionAnswerer:
def __init__(self, openai_api_key="", stream_output=False, llm="ChatGPT"):
if len(openai_api_key) < 3: # not valid key (TODO: more rigorous checking)
openai_api_key = os.environ.get("OPENAI_API_KEY")
self.openai_api_key = openai_api_key
self.llm_model = llm
self.set_openai_api_key(openai_api_key)
self.stream_output = stream_output
def set_openai_api_key(self, key):
logger.info("Using {}".format(self.llm_model))
if self.llm_model == "GPT-3.5":
self.llm = OpenAI(temperature=0.0, openai_api_key=key) # use text-darvinci
elif self.llm_model == "ChatGPT":
self.llm = OpenAI(
model_name="gpt-3.5-turbo", temperature=0.0, openai_api_key=key
) # use chatgpt
else:
raise RuntimeError("Your model is not available.")
def load_tools(
self,
name,
meta_info,
prompt_type="react-with-tool-description",
return_intermediate_steps=True,
):
self.all_tools_map = {}
self.all_tools_map[name] = import_all_apis(meta_info)
logger.info(
"Tool [{}] has the following apis: {}".format(
name, self.all_tools_map[name]
)
)
if prompt_type == "zero-shot-react-description":
subagent = initialize_agent(
self.all_tools_map[name],
self.llm,
agent="zero-shot-react-description",
verbose=True,
return_intermediate_steps=return_intermediate_steps,
)
elif prompt_type == "react-with-tool-description":
# customllm = CustomLLM()
description_for_model = (
meta_info["description_for_model"]
.replace("{", "{{")
.replace("}", "}}")
.strip()
)
prefix = f"""Answer the following questions as best you can. General instructions are: {description_for_model}. Specifically, you have access to the following APIs:"""
# suffix = """Begin! Remember: (1) Follow the format, i.e,\nThought:\nAction:\nAction Input:\nObservation:\nFinal Answer:\n (2) Provide as much as useful information in your Final Answer. (3) YOU MUST INCLUDE all relevant IMAGES in your Final Answer using format , and include relevant links. (3) Do not make up anything, and if your Observation has no link, DO NOT hallucihate one. (4) If you have enough information, please use \nThought: I have got enough information\nFinal Answer: \n\nQuestion: {input}\n{agent_scratchpad}"""
suffix = """Begin! Remember: (1) Follow the format, i.e,\nThought:\nAction:\nAction Input:\nObservation:\nFinal Answer:\n. The action you generate must be exact one of the given API names instead of a sentence or any other redundant text. The action input is one json format dict without any redundant text or bracket descriptions . (2) Provide as much as useful information (such as useful values/file paths in your observation) in your Final Answer. Do not describe the process you achieve the goal, but only provide the detailed answer or response to the task goal. (3) Do not make up anything. DO NOT generate observation content by yourself. (4) Read the observation carefully, and pay attention to the messages even if an error occurs. (5) Once you have enough information, please immediately use \nThought: I have got enough information\nFinal Answer: \n\nTask: {input}\n{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
self.all_tools_map[name],
prefix=prefix,
suffix=suffix,
input_variables=["input", "agent_scratchpad"],
)
llm_chain = LLMChain(llm=self.llm, prompt=prompt)
# llm_chain = LLMChain(llm=customllm, prompt=prompt)
logger.info("Full prompt template: {}".format(prompt.template))
tool_names = [tool.name for tool in self.all_tools_map[name]]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
if self.stream_output:
agent_executor = Executor.from_agent_and_tools(
agent=agent,
tools=self.all_tools_map[name],
verbose=True,
return_intermediate_steps=return_intermediate_steps,
)
else:
agent_executor = AgentExecutorWithTranslation.from_agent_and_tools(
agent=agent,
tools=self.all_tools_map[name],
verbose=True,
return_intermediate_steps=return_intermediate_steps,
)
return agent_executor
elif prompt_type == "babyagi":
# customllm = CustomLLM()
tool_str = "; ".join([t.name for t in self.all_tools_map[name]])
prefix = """You are an AI who performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}.\n You have access to the following APIs:"""
suffix = (
"""YOUR CONSTRAINTS: (1) YOU MUST follow this format:
\nThought:\nAction:\nAction Input: \n or \nThought:\nFinal Answer:\n (2) Do not make up anything, and if your Observation has no link, DO NOT hallucihate one. (3) The Action: MUST be one of the following: """
+ tool_str
+ """\nQuestion: {task}\n Agent scratchpad (history actions): {agent_scratchpad}."""
)
prompt = ZeroShotAgent.create_prompt(
self.all_tools_map[name],
prefix=prefix,
suffix=suffix,
input_variables=["objective", "task", "context", "agent_scratchpad"],
)
logger.info("Full prompt template: {}".format(prompt.template))
# specify the maximum number of iterations you want babyAGI to perform
max_iterations = 10
baby_agi = BabyAGI.from_llm(
llm=self.llm,
# llm=customllm,
prompt=prompt,
verbose=False,
tools=self.all_tools_map[name],
stream_output=self.stream_output,
return_intermediate_steps=return_intermediate_steps,
max_iterations=max_iterations,
)
return baby_agi
elif prompt_type == "autogpt":
from langchain.vectorstores import FAISS
from langchain.docstore import InMemoryDocstore
from langchain.embeddings import OpenAIEmbeddings
from langchain.tools.file_management.write import WriteFileTool
from langchain.tools.file_management.read import ReadFileTool
# Define your embedding model
embeddings_model = OpenAIEmbeddings()
# Initialize the vectorstore as empty
import faiss
embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(
embeddings_model.embed_query, index, InMemoryDocstore({}), {}
)
from .autogpt.agent import AutoGPT
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
AIMessage,
ChatGeneration,
ChatMessage,
ChatResult,
HumanMessage,
SystemMessage,
)
# customllm = CustomLLM()
# class MyChatOpenAI(ChatOpenAI):
# def _create_chat_result(self, response):
# generations = []
# for res in response["choices"]:
# message = self._convert_dict_to_message(res["message"])
# gen = ChatGeneration(message=message)
# generations.append(gen)
# llm_output = {"token_usage": response["usage"], "model_name": self.model_name}
# return ChatResult(generations=generations, llm_output=llm_output)
# def _generate(self, messages, stop):
# message_dicts, params = self._create_message_dicts(messages, stop)
# response = customllm(message_dicts)
# response = json.loads(response)
# # response = self.completion_with_retry(messages=message_dicts, **params)
# return self._create_chat_result(response)
# def _convert_dict_to_message(self, _dict: dict):
# role = _dict["role"]
# if role == "user":
# return HumanMessage(content=_dict["content"])
# elif role == "assistant":
# return AIMessage(content=_dict["content"])
# elif role == "system":
# return SystemMessage(content=_dict["content"])
# else:
# return ChatMessage(content=_dict["content"], role=role)
# should integrate WriteFile and ReadFile into tools, will fix later.
# for tool in [WriteFileTool(), ReadFileTool()]:
# self.all_tools_map[name].append(tool)
agent = AutoGPT.from_llm_and_tools(
ai_name="Tom",
ai_role="Assistant",
tools=self.all_tools_map[name],
llm=ChatOpenAI(temperature=0),
# llm=MyChatOpenAI(temperature=0),
memory=vectorstore.as_retriever(),
)
# Set verbose to be true
agent.chain.verbose = True
return agent
if __name__ == "__main__":
tools_name, tools_config = load_single_tools()
print(tools_name, tools_config)
qa = STQuestionAnswerer()
agent = qa.load_tools(tools_name, tools_config)
agent("Calc integral of sin(x)+2x^2+3x+1 from 0 to 1")
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