Add datetime to log

#2
by riversky - opened
.github/workflows/sync-to-hf.yaml CHANGED
@@ -14,8 +14,7 @@ jobs:
14
  with:
15
  fetch-depth: 0
16
  lfs: true
17
- ref: hf-active
18
  - name: Push to hub
19
  env:
20
  HF_TOKEN: ${{ secrets.HF_TOKEN }}
21
- run: git push https://omkarenator:$HF_TOKEN@huggingface.co/spaces/AutoLLM/AutoAgents hf-active:main
 
14
  with:
15
  fetch-depth: 0
16
  lfs: true
 
17
  - name: Push to hub
18
  env:
19
  HF_TOKEN: ${{ secrets.HF_TOKEN }}
20
+ run: git push https://omkarenator:$HF_TOKEN@huggingface.co/spaces/AutoLLM/AutoAgents main
LICENSE DELETED
@@ -1,21 +0,0 @@
1
- MIT License
2
-
3
- Copyright (c) 2023 AutoLLM
4
-
5
- Permission is hereby granted, free of charge, to any person obtaining a copy
6
- of this software and associated documentation files (the "Software"), to deal
7
- in the Software without restriction, including without limitation the rights
8
- to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9
- copies of the Software, and to permit persons to whom the Software is
10
- furnished to do so, subject to the following conditions:
11
-
12
- The above copyright notice and this permission notice shall be included in all
13
- copies or substantial portions of the Software.
14
-
15
- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16
- IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17
- FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18
- AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19
- LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20
- OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21
- SOFTWARE.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
README-main.md CHANGED
@@ -1,103 +1 @@
1
- # AutoAgents
2
-
3
- <p align="center"><img src="https://raw.githubusercontent.com/AutoLLM/AutoAgents/assets/images/logo.png?raw=true" width=400/></p>
4
-
5
- Unlock complex question answering in LLMs with enhanced chain-of-thought reasoning and information-seeking capabilities.
6
-
7
- ## 👉 Overview
8
-
9
- The purpose of this project is to extend LLMs ability to answer more complex questions through chain-of-thought reasoning and information-seeking actions.
10
-
11
- We are excited to release the initial version of AutoAgents, a proof-of-concept on what can be achieved with only well-written prompts. This is the initial step towards our first big milestone, releasing and open-sourcing the AutoAgents 7B model!
12
-
13
- Come try out our [Huggingface Space](https://huggingface.co/spaces/AutoLLM/AutoAgents)!
14
-
15
-
16
-
17
- ## 🤖 The AutoAgents Project
18
-
19
- This project demonstrates LLMs capability to execute a complex user goal: understand a user's goal, generate a plan, use proper tools, and deliver a final result.
20
-
21
- For simplicity, our first attempt starts with a Web Search Agent.
22
-
23
-
24
-
25
- ## 💫 How it works:
26
-
27
- <p align="left"><img src="https://raw.githubusercontent.com/AutoLLM/AutoAgents/assets/images/agent.png" width=830/></p>
28
-
29
-
30
-
31
- ## 📔 Examples
32
-
33
- Ask your AutoAgent to do what a real person would do using the internet:
34
-
35
- For example:
36
-
37
- *1. Recommend a kid friendly movie that is playing at a theater near Sunnyvale. Give me the showtimes and a link to purchase the tickets*
38
-
39
- *2. What is the average age of the past three president when they took office*
40
-
41
- *3. What is the mortgage rate right now and how does that compare to the past two years*
42
-
43
-
44
-
45
- ## 💁 Roadmap
46
-
47
- * ~~HuggingFace Space demo using OpenAI models~~ [LINK](https://huggingface.co/spaces/AutoLLM/AutoAgents)
48
- * AutoAgents [7B] Model
49
- * Initial Release:
50
- * Finetune and release a 7B parameter fine-tuned search model
51
- * AutoAgents Dataset
52
- * A high-quality dataset for a diverse set of search scenarios (why quality and diversity?<sup>[1](https://arxiv.org/abs/2305.11206)</sup>)
53
- * Reduce Model Inference Overhead
54
- * Affordance Modeling <sup>[2](https://en.wikipedia.org/wiki/Affordance)</sup>
55
- * Extend Support to Additional Tools
56
- * Customizable Document Search set (e.g. personal documents)
57
- * Support Multi-turn Dialogue
58
- * Advanced Flow Control in Plan Execution
59
-
60
- We are actively developing a few interesting things, check back here or follow us on [Twitter](https://twitter.com/AutoLLM) for any new development.
61
-
62
- If you are interested in any other problems, feel free to shoot us an issue.
63
-
64
-
65
-
66
- ## 🧭 How to use this repo?
67
-
68
- This repo contains the entire code to run the search agent from your local browser. All you need is an OpenAI API key to begin.
69
-
70
- To run the search agent locally:
71
-
72
- 1. Clone the repo and change the directory
73
-
74
- ```bash
75
- git clone https://github.com/AutoLLM/AutoAgents.git
76
- cd AutoAgents
77
- ```
78
-
79
- 2. Install the dependencies
80
-
81
- ```bash
82
- pip install -r requirements.txt
83
- ```
84
-
85
- 3. Install the `autoagents` package
86
-
87
- ```bash
88
- pip install -e .
89
- ```
90
-
91
- 4. Make sure you have your OpenAI API key set as an environment variable. Alternatively, you can also feed it through the input text-box on the sidebar.
92
-
93
- ```bash
94
- export OPENAI_API_KEY=sk-xxxxxx
95
- ```
96
-
97
- 5. Run the Streamlit app
98
-
99
- ```bash
100
- streamlit run autoagents/spaces/app.py
101
- ```
102
-
103
- This should open a browser window where you can type your search query.
 
1
+ # AutoAgents!
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
README.md CHANGED
@@ -1,5 +1,5 @@
1
  ---
2
- title: AutoAgents
3
  emoji: 🐢
4
  colorFrom: green
5
  colorTo: purple
@@ -7,7 +7,7 @@ sdk: streamlit
7
  sdk_version: 1.21.0
8
  python_version: 3.10.11
9
  app_file: autoagents/spaces/app.py
10
- pinned: true
11
  ---
12
 
13
  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
1
  ---
2
+ title: Search Llm
3
  emoji: 🐢
4
  colorFrom: green
5
  colorTo: purple
 
7
  sdk_version: 1.21.0
8
  python_version: 3.10.11
9
  app_file: autoagents/spaces/app.py
10
+ pinned: false
11
  ---
12
 
13
  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
autoagents/agents/search.py CHANGED
@@ -4,7 +4,6 @@ import re
4
  from datetime import date
5
  import asyncio
6
  from collections import defaultdict
7
- import os
8
 
9
  from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
10
  from langchain.prompts import StringPromptTemplate
@@ -14,7 +13,7 @@ from langchain.schema import AgentAction, AgentFinish
14
  from langchain.callbacks import get_openai_callback
15
  from langchain.callbacks.base import AsyncCallbackHandler
16
  from langchain.callbacks.manager import AsyncCallbackManager
17
- from langchain.base_language import BaseLanguageModel
18
 
19
  from autoagents.tools.tools import search_tool, note_tool, rewrite_search_query
20
  from autoagents.utils.logger import InteractionsLogger
@@ -78,7 +77,7 @@ class CustomPromptTemplate(StringPromptTemplate):
78
  outputs += f"{action.log}\n"
79
  if len(intermediate_steps) > 0:
80
  action, observation = intermediate_steps[-1]
81
- # self.ialogger.add_system({"action": action, "observation": observation})
82
  if action.tool not in ("Search", "Notepad"):
83
  raise Exception("Invalid tool requested by the model.")
84
  if action.tool == "Notepad":
@@ -115,7 +114,9 @@ class CustomPromptTemplate(StringPromptTemplate):
115
  kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
116
  kwargs["today"] = date.today()
117
  final_prompt = self.template.format(**kwargs)
118
- self.ialogger.add_system({"value": final_prompt})
 
 
119
  return final_prompt
120
 
121
 
@@ -123,8 +124,9 @@ class CustomOutputParser(AgentOutputParser):
123
  class Config:
124
  arbitrary_types_allowed = True
125
  ialogger: InteractionsLogger
126
- llm: BaseLanguageModel
127
  new_action_input: Optional[str]
 
128
  action_history = defaultdict(set)
129
 
130
  def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
@@ -152,8 +154,7 @@ class CustomOutputParser(AgentOutputParser):
152
  if action_input in self.action_history[action]:
153
  new_action_input = rewrite_search_query(action_input,
154
  self.action_history[action],
155
- self.llm)
156
- self.ialogger.add_message({"query_rewrite": True})
157
  self.new_action_input = new_action_input
158
  self.action_history[action].add(new_action_input)
159
  return AgentAction(tool=action, tool_input=new_action_input, log=llm_output)
@@ -164,10 +165,7 @@ class CustomOutputParser(AgentOutputParser):
164
 
165
 
166
  class ActionRunner:
167
- def __init__(self,
168
- outputq,
169
- llm: BaseLanguageModel,
170
- persist_logs: bool = False):
171
  self.ialogger = InteractionsLogger(name=f"{uuid.uuid4().hex[:6]}", persist=persist_logs)
172
  tools = [search_tool, note_tool]
173
  prompt = CustomPromptTemplate(
@@ -176,7 +174,7 @@ class ActionRunner:
176
  input_variables=["input", "intermediate_steps"],
177
  ialogger=self.ialogger)
178
 
179
- output_parser = CustomOutputParser(ialogger=self.ialogger, llm=llm)
180
 
181
  class MyCustomHandler(AsyncCallbackHandler):
182
  def __init__(self):
@@ -222,6 +220,7 @@ class ActionRunner:
222
 
223
  handler = MyCustomHandler()
224
 
 
225
  llm_chain = LLMChain(llm=llm, prompt=prompt, callbacks=[handler])
226
  tool_names = [tool.name for tool in tools]
227
  for tool in tools:
 
4
  from datetime import date
5
  import asyncio
6
  from collections import defaultdict
 
7
 
8
  from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
9
  from langchain.prompts import StringPromptTemplate
 
13
  from langchain.callbacks import get_openai_callback
14
  from langchain.callbacks.base import AsyncCallbackHandler
15
  from langchain.callbacks.manager import AsyncCallbackManager
16
+
17
 
18
  from autoagents.tools.tools import search_tool, note_tool, rewrite_search_query
19
  from autoagents.utils.logger import InteractionsLogger
 
77
  outputs += f"{action.log}\n"
78
  if len(intermediate_steps) > 0:
79
  action, observation = intermediate_steps[-1]
80
+ self.ialogger.add_system({"action": action, "observation": observation})
81
  if action.tool not in ("Search", "Notepad"):
82
  raise Exception("Invalid tool requested by the model.")
83
  if action.tool == "Notepad":
 
114
  kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
115
  kwargs["today"] = date.today()
116
  final_prompt = self.template.format(**kwargs)
117
+ if not intermediate_steps:
118
+ # first iteration
119
+ self.ialogger.add_system({"prompt": final_prompt})
120
  return final_prompt
121
 
122
 
 
124
  class Config:
125
  arbitrary_types_allowed = True
126
  ialogger: InteractionsLogger
127
+ api_key: str
128
  new_action_input: Optional[str]
129
+
130
  action_history = defaultdict(set)
131
 
132
  def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
 
154
  if action_input in self.action_history[action]:
155
  new_action_input = rewrite_search_query(action_input,
156
  self.action_history[action],
157
+ self.api_key)
 
158
  self.new_action_input = new_action_input
159
  self.action_history[action].add(new_action_input)
160
  return AgentAction(tool=action, tool_input=new_action_input, log=llm_output)
 
165
 
166
 
167
  class ActionRunner:
168
+ def __init__(self, outputq, api_key: str, model_name: str, persist_logs=False):
 
 
 
169
  self.ialogger = InteractionsLogger(name=f"{uuid.uuid4().hex[:6]}", persist=persist_logs)
170
  tools = [search_tool, note_tool]
171
  prompt = CustomPromptTemplate(
 
174
  input_variables=["input", "intermediate_steps"],
175
  ialogger=self.ialogger)
176
 
177
+ output_parser = CustomOutputParser(ialogger=self.ialogger, api_key=api_key)
178
 
179
  class MyCustomHandler(AsyncCallbackHandler):
180
  def __init__(self):
 
220
 
221
  handler = MyCustomHandler()
222
 
223
+ llm = ChatOpenAI(openai_api_key=api_key, temperature=0, model_name=model_name)
224
  llm_chain = LLMChain(llm=llm, prompt=prompt, callbacks=[handler])
225
  tool_names = [tool.name for tool in tools]
226
  for tool in tools:
autoagents/models/custom.py DELETED
@@ -1,33 +0,0 @@
1
- import requests
2
-
3
- from langchain.llms.base import LLM
4
-
5
-
6
- class CustomLLM(LLM):
7
- @property
8
- def _llm_type(self) -> str:
9
- return "custom"
10
-
11
- def _call(self, prompt: str, stop=None) -> str:
12
- r = requests.post(
13
- "http://localhost:8000/v1/chat/completions",
14
- json={
15
- "model": "283-vicuna-7b",
16
- "messages": [{"role": "user", "content": prompt}],
17
- "stop": stop
18
- },
19
- )
20
- result = r.json()
21
- return result["choices"][0]["message"]["content"]
22
-
23
- async def _acall(self, prompt: str, stop=None) -> str:
24
- r = requests.post(
25
- "http://localhost:8000/v1/chat/completions",
26
- json={
27
- "model": "283-vicuna-7b",
28
- "messages": [{"role": "user", "content": prompt}],
29
- "stop": stop
30
- },
31
- )
32
- result = r.json()
33
- return result["choices"][0]["message"]["content"]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
autoagents/spaces/app.py CHANGED
@@ -1,7 +1,6 @@
1
  import os
2
  import asyncio
3
  import random
4
- from datetime import date, datetime, timezone, timedelta
5
  from ast import literal_eval
6
 
7
  import streamlit as st
@@ -10,8 +9,6 @@ import openai
10
  from autoagents.utils.constants import MAIN_HEADER, MAIN_CAPTION, SAMPLE_QUESTIONS
11
  from autoagents.agents.search import ActionRunner
12
 
13
- from langchain.chat_models import ChatOpenAI
14
-
15
 
16
  async def run():
17
  output_acc = ""
@@ -45,14 +42,13 @@ async def run():
45
  )
46
 
47
  # Ask the user to enter their OpenAI API key
48
- if (api_key := st.sidebar.text_input("OpenAI api-key", type="password")):
49
- api_org = None
50
- else:
51
- api_key, api_org = os.getenv("OPENAI_API_KEY"), os.getenv("OPENAI_API_ORG")
52
  with st.sidebar:
53
  model_dict = {
54
  "gpt-3.5-turbo": "GPT-3.5-turbo",
55
- "gpt-4": "GPT-4 (Better but slower)",
56
  }
57
  st.radio(
58
  "OpenAI model",
@@ -61,29 +57,22 @@ async def run():
61
  format_func=lambda x: model_dict[x],
62
  )
63
 
64
- time_zone = str(datetime.now(timezone(timedelta(0))).astimezone().tzinfo)
65
- st.markdown(f"**The system time zone is {time_zone} and the date is {date.today()}**")
66
-
67
  st.markdown("**Example Queries:**")
68
  for q in SAMPLE_QUESTIONS:
69
  st.markdown(f"*{q}*")
70
 
71
- if not api_key:
72
  st.warning(
73
  "API key required to try this app. The API key is not stored in any form. [This](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) might help."
74
  )
75
- elif api_org and st.session_state.model_name == "gpt-4":
76
- st.warning(
77
- "The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key."
78
- )
79
  else:
80
  outputq = asyncio.Queue()
81
- runner = ActionRunner(outputq,
82
- ChatOpenAI(openai_api_key=api_key,
83
- openai_organization=api_org,
84
- temperature=0,
85
- model_name=st.session_state.model_name),
86
- persist_logs=True) # log to HF-dataset
87
 
88
  async def cleanup(e):
89
  st.error(e)
@@ -111,9 +100,6 @@ async def run():
111
  if isinstance(output, Exception):
112
  if isinstance(output, openai.error.AuthenticationError):
113
  await cleanup(f"AuthenticationError: Invalid OpenAI API key.")
114
- elif isinstance(output, openai.error.InvalidRequestError) \
115
- and output._message == "The model: `gpt-4` does not exist":
116
- await cleanup(f"The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key.")
117
  elif isinstance(output, openai.error.OpenAIError):
118
  await cleanup(output)
119
  elif isinstance(output, RuntimeWarning):
 
1
  import os
2
  import asyncio
3
  import random
 
4
  from ast import literal_eval
5
 
6
  import streamlit as st
 
9
  from autoagents.utils.constants import MAIN_HEADER, MAIN_CAPTION, SAMPLE_QUESTIONS
10
  from autoagents.agents.search import ActionRunner
11
 
 
 
12
 
13
  async def run():
14
  output_acc = ""
 
42
  )
43
 
44
  # Ask the user to enter their OpenAI API key
45
+ API_O = st.sidebar.text_input("OpenAI api-key", type="password") or os.getenv(
46
+ "OPENAI_API_KEY"
47
+ )
 
48
  with st.sidebar:
49
  model_dict = {
50
  "gpt-3.5-turbo": "GPT-3.5-turbo",
51
+ "gpt-4": "GPT-4 (Recommneded for better quality results)",
52
  }
53
  st.radio(
54
  "OpenAI model",
 
57
  format_func=lambda x: model_dict[x],
58
  )
59
 
 
 
 
60
  st.markdown("**Example Queries:**")
61
  for q in SAMPLE_QUESTIONS:
62
  st.markdown(f"*{q}*")
63
 
64
+ if not API_O:
65
  st.warning(
66
  "API key required to try this app. The API key is not stored in any form. [This](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) might help."
67
  )
 
 
 
 
68
  else:
69
  outputq = asyncio.Queue()
70
+ runner = ActionRunner(
71
+ outputq,
72
+ api_key=API_O,
73
+ model_name=st.session_state.model_name,
74
+ persist_logs=True,
75
+ ) # log to HF-dataset
76
 
77
  async def cleanup(e):
78
  st.error(e)
 
100
  if isinstance(output, Exception):
101
  if isinstance(output, openai.error.AuthenticationError):
102
  await cleanup(f"AuthenticationError: Invalid OpenAI API key.")
 
 
 
103
  elif isinstance(output, openai.error.OpenAIError):
104
  await cleanup(output)
105
  elif isinstance(output, RuntimeWarning):
autoagents/tools/tools.py CHANGED
@@ -1,9 +1,7 @@
1
- import os
2
-
3
- from duckpy import Client
4
  from langchain import PromptTemplate, OpenAI, LLMChain
5
  from langchain.agents import Tool
6
- from langchain.base_language import BaseLanguageModel
 
7
 
8
 
9
  MAX_SEARCH_RESULTS = 20 # Number of search results to observe at a time
@@ -53,12 +51,13 @@ note_tool = Tool(name="Notepad",
53
  description=notepad_description)
54
 
55
 
56
- def rewrite_search_query(q: str, search_history, llm: BaseLanguageModel) -> str:
57
  history_string = '\n'.join(search_history)
58
  template ="""We are using the Search tool.
59
  # Previous queries:
60
  {history_string}. \n\n Rewrite query {action_input} to be
61
  different from the previous ones."""
 
62
  prompt = PromptTemplate(template=template,
63
  input_variables=["action_input", "history_string"])
64
  llm_chain = LLMChain(prompt=prompt, llm=llm)
 
 
 
 
1
  from langchain import PromptTemplate, OpenAI, LLMChain
2
  from langchain.agents import Tool
3
+ from duckpy import Client
4
+ from langchain.chat_models import ChatOpenAI
5
 
6
 
7
  MAX_SEARCH_RESULTS = 20 # Number of search results to observe at a time
 
51
  description=notepad_description)
52
 
53
 
54
+ def rewrite_search_query(q: str, search_history, api_key: str) -> str:
55
  history_string = '\n'.join(search_history)
56
  template ="""We are using the Search tool.
57
  # Previous queries:
58
  {history_string}. \n\n Rewrite query {action_input} to be
59
  different from the previous ones."""
60
+ llm = ChatOpenAI(temperature=0, openai_api_key=api_key)
61
  prompt = PromptTemplate(template=template,
62
  input_variables=["action_input", "history_string"])
63
  llm_chain = LLMChain(prompt=prompt, llm=llm)
autoagents/utils/constants.py CHANGED
@@ -1,21 +1,13 @@
1
  MAIN_HEADER = "Web Search Agent"
2
 
3
- MAIN_CAPTION = """This is a proof-of-concept search agent that reasons, plans,
4
- and executes web searches to collect information on your behalf. It aims to
5
- resolve your question by breaking it down into step-by-step subtasks. All the
6
- intermediate results will be presented.
7
-
8
- *DISCLAIMER*: We are collecting search queries, so please refrain from
9
- providing any personal information. If you wish to avoid this, you can run the
10
- app locally by following the instructions on our
11
- [Github](https://github.com/AutoLLM/AutoAgents)."""
12
 
13
  SAMPLE_QUESTIONS = [
14
- "Recommend me a movie in theater now to watch with kids.",
15
- "Who is the most recent NBA MVP? Which team does he play for? What are his career stats?",
16
- "Who is the head coach of AC Milan now? How long has he been coaching the team?",
17
- "What is the mortgage rate right now and how does that compare to the past two years?",
18
- "What is the weather like in San Francisco today? What about tomorrow?",
19
- "When and where is the upcoming concert for Taylor Swift? Share a link to purchase tickets.",
20
- "Find me recent studies focusing on hallucination in large language models. Provide the link to each study found.",
21
  ]
 
1
  MAIN_HEADER = "Web Search Agent"
2
 
3
+ MAIN_CAPTION = """ This is a proof-of-concept search engine built on ReAct-style
4
+ prompting which acts as a search agent that plans and executes web searches on
5
+ your behalf. Given a high-level search query the agent tries to come up with a
6
+ concluding answer based off multiple rounds of searches. You can observe all
7
+ the intermediate interactions with the search engine below."""
 
 
 
 
8
 
9
  SAMPLE_QUESTIONS = [
10
+ "What has David Sacks written about SAAS? Can you provide some links?",
11
+ "Where is the all-in summit 2023 being held and how much are the tickets?",
12
+ "Did Stan Druckenmiller buy Nvidia recently?",
 
 
 
 
13
  ]
autoagents/utils/logger.py CHANGED
@@ -57,4 +57,4 @@ class InteractionsLogger:
57
  commit_url = self.repo.push_to_hub()
58
 
59
  def add_cost(self, cost):
60
- self.messages.append({"metrics": cost})
 
57
  commit_url = self.repo.push_to_hub()
58
 
59
  def add_cost(self, cost):
60
+ self.messages.append({"metrics": cost})
requirements.txt CHANGED
@@ -1,5 +1,5 @@
1
  openai>=0.27.7
2
- langchain>=0.0.193
3
  duckpy
4
  huggingface_hub
5
- pytz
 
1
  openai>=0.27.7
2
+ langchain
3
  duckpy
4
  huggingface_hub
5
+ pytz
setup.py DELETED
@@ -1,7 +0,0 @@
1
- from setuptools import setup, find_packages
2
-
3
- setup(
4
- name='autoagents',
5
- version='0.1.0',
6
- packages=find_packages(include=['autoagents', 'autoagents.*'])
7
- )
 
 
 
 
 
 
 
 
test.py CHANGED
@@ -1,22 +1,16 @@
1
  import os
2
  import asyncio
3
-
 
4
  from pprint import pprint
 
5
  from ast import literal_eval
6
- from multiprocessing import Pool, TimeoutError
7
-
8
- from autoagents.agents.search import ActionRunner
9
- from langchain.callbacks import get_openai_callback
10
- from langchain.chat_models import ChatOpenAI
11
 
12
-
13
- async def work(user_input):
14
  outputq = asyncio.Queue()
15
- llm = ChatOpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"),
16
- openai_organization=os.getenv("OPENAI_API_ORG"),
17
- temperature=0,
18
- model_name="gpt-3.5-turbo")
19
- runner = ActionRunner(outputq, llm=llm)
20
  task = asyncio.create_task(runner.run(user_input, outputq))
21
 
22
  while True:
@@ -33,11 +27,12 @@ async def work(user_input):
33
  break
34
  await task
35
 
 
 
 
 
 
36
  Q = [
37
- "list 5 cities and their current populations where Paramore is playing this year.",
38
- "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?",
39
- "How many watermelons can fit in a Tesla Model S?",
40
- "Recommend me some laptops suitable for UI designers under $2000. Please include brand and price."
41
  "Build me a vacation plan for Rome and Milan this summer for seven days. Include place to visit and hotels to stay. ",
42
  "What is the sum of ages of the wives of Barack Obama and Donald Trump?",
43
  "Who is the most recent NBA MVP? Which team does he play for? What is his season stats?",
@@ -49,9 +44,6 @@ Q = [
49
  "Who are some top researchers in the field of machine learning systems nowadays?"
50
  ]
51
 
52
- def main(q):
53
- asyncio.run(work(q))
54
-
55
- if __name__ == "__main__":
56
- with Pool(processes=10) as pool:
57
- print(pool.map(main, Q))
 
1
  import os
2
  import asyncio
3
+ from action import ActionRunner
4
+ from langchain.callbacks import get_openai_callback
5
  from pprint import pprint
6
+ import pdb
7
  from ast import literal_eval
 
 
 
 
 
8
 
9
+ async def main(user_input):
 
10
  outputq = asyncio.Queue()
11
+
12
+ API_O = os.getenv("OPENAI_API_KEY")
13
+ runner = ActionRunner(outputq, api_key=API_O, model_name="gpt-3.5-turbo")
 
 
14
  task = asyncio.create_task(runner.run(user_input, outputq))
15
 
16
  while True:
 
27
  break
28
  await task
29
 
30
+
31
+ # "list 5 cities and their current populations where Paramore is playing this year.",
32
+ # "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?",
33
+ # "How many watermelons can fit in a Tesla Model S?",
34
+ # "Recommend me some laptops suitable for UI designers under $2000. Please include brand and price."
35
  Q = [
 
 
 
 
36
  "Build me a vacation plan for Rome and Milan this summer for seven days. Include place to visit and hotels to stay. ",
37
  "What is the sum of ages of the wives of Barack Obama and Donald Trump?",
38
  "Who is the most recent NBA MVP? Which team does he play for? What is his season stats?",
 
44
  "Who are some top researchers in the field of machine learning systems nowadays?"
45
  ]
46
 
47
+ loop = asyncio.new_event_loop()
48
+ for i in range(len(Q)):
49
+ loop.run_until_complete(main(Q[i]))