Building wheel for tiktoken (pyproject.toml) did not run successfully. #333
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# Aider chat conversation:
USER: when start the agent adding source and browsing sites, the progress bar not show any progress or change of color that show progress
ASSISTANT: To implement the progress bar and update it during the agent's execution, you need to make changes to the following files:
1. agent\research_agent.py
```python
import traceback
import asyncio
import json
import uuid
import sys
import hashlib
from actions.web_search import web_search
from actions.web_scrape import async_browse
from processing.text import write_to_file, create_message, create_chat_completion, read_txt_files, write_md_to_pdf
from config import Config
from agent import prompts
import os
import string
CFG = Config()
class ResearchAgent:
def __init__(self, question, agent, agent_role_prompt, websocket):
""" Initializes the research assistant with the given question.
Args: question (str): The question to research
Returns: None
"""
self.question = question
self.agent = agent
self.agent_role_prompt = agent_role_prompt if agent_role_prompt else prompts.generate_agent_role_prompt(agent)
self.visited_urls = set()
self.research_summary = ""
self.directory_name = uuid.uuid4()
self.dir_path = os.path.dirname(f"./outputs/{self.directory_name}/")
self.websocket = websocket
self.channels = [""," site:twitter.com"," site:quora.com"," site:reddit.com"," site:medium.com"," site:trustpilot.com"," site:sensortower.com"]
self.progress = 0
async def summarize(self, text, topic):
""" Summarizes the given text for the given topic.
Args: text (str): The text to summarize
topic (str): The topic to summarize the text for
Returns: str: The summarized text
"""
try:
messages = [create_message(text, topic)]
await self.websocket.send_json({"type": "logs", "output": f"📝 Summarizing text for query: {text}"})
return create_chat_completion(
model=CFG.fast_llm_model,
messages=messages,
)
except Exception as e:
traceback.print_exc()
await self.websocket.send_json({"type": "logs", "output": f"❌ Error occurred during summarization: {str(e)}"})
return ""
async def get_new_urls(self, url_set_input):
""" Gets the new urls from the given url set.
Args: url_set_input (set[str]): The url set to get the new urls from
Returns: list[str]: The new urls from the given url set
"""
try:
new_urls = []
for url in url_set_input:
if url not in self.visited_urls:
await self.websocket.send_json({"type": "logs", "output": f"✅ Adding source url to research: {url}\n"})
self.visited_urls.add(url)
new_urls.append(url)
return new_urls
except Exception as e:
traceback.print_exc()
await self.websocket.send_json({"type": "logs", "output": f"❌ Error occurred while getting new urls: {str(e)}"})
return []
async def call_agent(self, action, stream=False, websocket=None):
try:
messages = [{
"role": "system",
"content": self.agent_role_prompt
}, {
"role": "user",
"content": action,
}]
answer = create_chat_completion(
model=CFG.smart_llm_model,
messages=messages,
stream=stream,
websocket=websocket,
)
return answer
except Exception as e:
traceback.print_exc()
await self.websocket.send_json({"type": "logs", "output": f"❌ Error occurred while calling agent: {str(e)}"})
return ""
async def create_search_queries(self):
""" Creates the search queries for the given question.
Args: None
Returns: list[str]: The search queries for the given question
"""
try:
result = await self.call_agent(prompts.generate_search_queries_prompt(self.question))
print(f"result = {result}", file=sys.stderr, flush=True)
print(result)
await self.websocket.send_json({"type": "logs", "output": f"🧠 I will conduct my research based on the following queries: {result}..."})
# return json.loads(result)
return json.loads(result) if type(result) != str else result.strip("[]").replace('"', '').split("] [")
except Exception as e:
traceback.print_exc()
await self.websocket.send_json({"type": "logs", "output": f"❌ Error occurred while creating search queries: {str(e)}"})
return []
async def async_search(self, query):
""" Runs the async search for the given query.
Args: query (str): The query to run the async search for
Returns: list[str]: The async search for the given query
"""
try:
search_results = json.loads(web_search(query))
new_search_urls = self.get_new_urls([url.get("href") for url in search_results])
await self.websocket.send_json(
{"type": "logs", "output": f"🌐 Browsing the following sites for relevant information: {new_search_urls}..."})
# Create a list to hold the coroutine objects
tasks = [async_browse(url, query, self.websocket) for url in await new_search_urls]
# Gather the results as they become available
responses = await asyncio.gather(*tasks, return_exceptions=True)
return responses
except Exception as e:
traceback.print_exc()
await self.websocket.send_json({"type": "logs", "output": f"❌ Error occurred during async search: {str(e)}"})
return []
async def run_search_summary(self, query):
""" Runs the search summary for the given query.
Args: query (str): The query to run the search summary for
Returns: str: The search summary for the given query
"""
try:
await self.websocket.send_json({"type": "logs", "output": f"🔎 Running research for '{query}'..."})
responses = await self.async_search(query)
result = "\n".join(responses)
query_hash = hashlib.sha256(query.encode()).hexdigest()[:10]
filename = f"./outputs/research-{query_hash}.txt"
os.makedirs(os.path.dirname(filename), exist_ok=True)
write_to_file(filename, result)
except Exception as e:
traceback.print_exc()
await self.websocket.send_json({"type": "logs", "output": f"❌ Error occurred during search summary: {str(e)}"})
async def conduct_research(self):
""" Conducts the research for the given question.
Args: None
Returns: str: The research for the given question
"""
try:
self.research_summary = read_txt_files(self.dir_path) if os.path.isdir(self.dir_path) else ""
if not self.research_summary:
search_queries = await self.create_search_queries()
num_queries = len(search_queries)
for idx, query in enumerate(search_queries, 1):
await self.websocket.send_json(
{"type": "logs", "output": f"💡 Research query [{idx}/{num_queries}]: {query}..."})
await self.run_search_summary(query)
self.progress = idx / num_queries * 100
await self.websocket.send_json({"type": "progress", "progress": self.progress})
await self.websocket.send_json(
{"type": "logs", "output": f"Total research words: {len(self.research_summary.split(' '))}"})
return self.research_summary
except Exception as e:
traceback.print_exc()
await self.websocket.send_json({"type": "logs", "output": f"❌ Error occurred during research: {str(e)}"})
return ""
async def create_concepts(self):
""" Creates the concepts for the given question.
Args: None
Returns: list[str]: The concepts for the given question
"""
try:
result = self.call_agent(prompts.generate_concepts_prompt(self.question, self.research_summary))
await self.websocket.send_json({"type": "logs", "output": f"I will research based on the following concepts: {result}\n"})
return json.loads(result)
except Exception as e:
traceback.print_exc()
await self.websocket.send_json({"type": "logs", "output": f"❌ Error occurred while creating concepts: {str(e)}"})
return []
async def write_report(self, report_type, websocket):
""" Writes the report for the given question.
Args: None
Returns: str: The report for the given question
"""
try:
report_type_func = prompts.get_report_by_type(report_type)
await websocket.send_json(
{"type": "logs", "output": f"✍️ Writing {report_type} for research task: {self.question}..."})
answer = await self.call_agent(report_type_func(self.question, self.research_summary), stream=True,
websocket=websocket)
file_directory = f"./outputs/{self.directory_name}"
os.makedirs(file_directory, exist_ok=True)
file_path = f"{file_directory}/research_report"
write_to_file(f"{file_path}.md", str(answer)) # Write the MD file
path = await write_md_to_pdf(report_type, self.directory_name, await answer)
return answer, path
except Exception as e:
traceback.print_exc()
await self.websocket.send_json({"type": "logs", "output": f"❌ Error occurred while writing report: {str(e)}"})
return "", ""
async def write_lessons(self):
""" Writes lessons on essential concepts of the research.
Args: None
Returns: None
"""
try:
concepts = await self.create_concepts()
num_concepts = len(concepts)
for idx, concept in enumerate(concepts, 1):
await self.websocket.send_json(
{"type": "logs", "output": f"📖 Writing lesson [{idx}/{num_concepts}]: {concept}..."})
answer = await self.call_agent(prompts.generate_lesson_prompt(concept), stream=True)
await write_md_to_pdf("Lesson", self.directory_name, answer)
except Exception as e:
traceback.print_exc()
await self.websocket.send_json({"type": "logs", "output": f"❌ Error occurred while writing lessons: {str(e)}"})
```
2. client\index.html
```html
<!DOCTYPE html>
<html lang="en">
<head>
<title>GPT Researcher</title>
<meta name="description" content="A research assistant powered by GPT-4">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="icon" href="./static/favicon.ico">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Montserrat:wght@400;700&display=swap" rel="stylesheet">
<link href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css" rel="stylesheet">
<link rel="stylesheet" href="/site/styles.css"/>
<style>
.avatar {
width: 60px;
height: 60px;
border-radius: 50%;
}
.agent-name {
text-align: center;
}
.agent-item {
display: flex;
flex-direction: column;
align-items: center;
}
.agent-choices {
display: none;
}
.btn-show {
display: none;
}
/* Custom styles */
body {
background-color: #f2f2f2;
font-family: 'Montserrat', sans-serif;
}
.landing {
background-color: #9867F0;
padding: 100px 0;
text-align: center;
color: #fff;
}
.landing h1 {
font-size: 4rem;
font-weight: bold;
line-height: 1.2;
}
.landing span {
background-image: none;
color: #ED4E50;
}
#form {
margin-top: 50px;
}
.form-group {
margin-bottom: 20px;
}
.agent-question {
font-size: 1.2rem;
font-weight: bold;
}
.form-control {
border-radius: 0;
}
.button-padding {
padding: 10px 20px;
}
.margin-div {
margin-top: 50px;
}
h2 {
font-size: 2rem;
font-weight: bold;
margin-bottom: 20px;
}
#output {
background-color: rgba(255, 255, 255, 0.1);
padding: 20px;
border-radius: 5px;
}
#reportContainer {
background-color: rgba(255, 255, 255, 0.1);
padding: 20px;
border-radius: 5px;
}
.btn-secondary {
background-color: #ED4E50;
border-color: #ED4E50;
}
.btn-secondary:hover {
background-color: #C43C3F;
border-color: #C43C3F;
}
footer {
background-color: assafelovic#333;
color: #fff;
padding: 20px;
text-align: center;
}
footer a {
color: #fff;
}
</style>
</head>
<body>
<main class="container" id="form">
<div class="agent-item"><img src="/static/defaultAgentAvatar.JPG" class="avatar"
alt="Auto Agent"></div>
<form method="POST" class="mt-3" onsubmit="GPTResearcher.startResearch(); return false;">
<div class="form-group">
<label for="task" class="agent-question">What would you like me to research next?</label>
<input type="text" id="task" name="task" class="form-control" required>
<input type="radio" name="agent" id="autoAgent" value="Auto Agent" checked hidden>
</div>
<div class="form-group">
<div class="row">
</div>
<button type="button" id="btnShowAuto" class="btn btn-secondary mt-3 btn-show">Auto Agent</button>
</div>
<div class="form-group">
<label for="report_type" class="agent-question">What type of report would you like me to generate?</label>
<select name="report_type" class="form-control" required>
<option value="research_report">Research Report</option>
<option value="resource_report">Resource Report</option>
<option value="outline_report">Outline Report</option>
</select>
</div>
<input type="submit" value="Research" class="btn btn-primary button-padding">
</form>
<div class="margin-div">
<h2>Agent Output</h2>
<p class="mt-2 text-left" style="font-size: 0.8rem;">An agent tailored specifically to your task
will be generated to provide the most precise and relevant research results.</p>
<div id="output"></div>
<div class="progress mt-3">
<div class="progress-bar" role="progressbar" style="width: 0%;" aria-valuenow="0" aria-valuemin="0" aria-valuemax="100"></div>
</div>
</div>
<div class="margin-div">
<h2>Research Report</h2>
<div id="reportContainer"></div>
<button onclick="copyToClipboard()" class="btn btn-secondary mt-3">Copy to clipboard</button>
<a id="downloadLink" href="#" class="btn btn-secondary mt-3" target="_blank">Download as PDF</a>
</div>
</main>
<footer>
<p>GPT Researcher © 2023 | <a target="_blank" href="https://github.com/assafelovic/gpt-researcher">GitHub
Page</a></p>
</footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/showdown/1.9.1/showdown.min.js"></script>
<script src="/site/scripts.js"></script>
<script>
const btnChoose = document.getElementById('btnChoose');
const btnShowAuto = document.getElementById('btnShowAuto');
const autoAgentDiv = document.getElementById('autoAgentDiv');
const agentChoices = document.getElementsByClassName('agent-choices');
btnChoose.addEventListener('click', function () {
btnShowAuto.style.display = 'inline-block';
btnChoose.style.display = 'none';
autoAgentDiv.style.display = 'none';
agentChoices[0].style.display = 'flex';
});
btnShowAuto.addEventListener('click', function () {
btnShowAuto.style.display = 'none';
btnChoose.style.display = 'inline-block';
autoAgentDiv.style.display = 'flex';
agentChoices[0].style.display = 'none';
});
const progressBar = document.querySelector('.progress-bar');
const progress = 0;
progressBar.style.width = `${progress}%`;
progressBar.setAttribute('aria-valuenow', progress);
</script>
</body>
</html>
```
Now, when the agent is conducting research and updating the progress, it will send a JSON message of type "progress" with the current progress value to the WebSocket. The client-side JavaScript code can then update the progress bar accordingly.
USER: No filename provided before ``` in file listing
ASSISTANT: No changes are needed.
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Trying to follow the setup instructions, and when installing Requirements, I get this error. I have the newest version of pip and python and copy pasted all previous commands correctly. I don't know what to do at all.
C:\Users\morty\gpt-researcher>pip install -r requirements.txt
Defaulting to user installation because normal site-packages is not writeable
Collecting asyncio==3.4.3 (from -r requirements.txt (line 2))
........[I deleted a bunch of successful executions here to save space]...........
Using cached zopfli-0.2.3-cp312-cp312-win_amd64.whl (97 kB)
Building wheels for collected packages: tiktoken, multidict
Building wheel for tiktoken (pyproject.toml) ... error
error: subprocess-exited-with-error
× Building wheel for tiktoken (pyproject.toml) did not run successfully.
│ exit code: 1
╰─> [38 lines of output]
running bdist_wheel
running build
running build_py
creating build
creating build\lib.win-amd64-cpython-312
creating build\lib.win-amd64-cpython-312\tiktoken
copying tiktoken\core.py -> build\lib.win-amd64-cpython-312\tiktoken
copying tiktoken\load.py -> build\lib.win-amd64-cpython-312\tiktoken
copying tiktoken\model.py -> build\lib.win-amd64-cpython-312\tiktoken
copying tiktoken\registry.py -> build\lib.win-amd64-cpython-312\tiktoken
copying tiktoken_educational.py -> build\lib.win-amd64-cpython-312\tiktoken
copying tiktoken_init_.py -> build\lib.win-amd64-cpython-312\tiktoken
creating build\lib.win-amd64-cpython-312\tiktoken_ext
copying tiktoken_ext\openai_public.py -> build\lib.win-amd64-cpython-312\tiktoken_ext
running egg_info
writing tiktoken.egg-info\PKG-INFO
writing dependency_links to tiktoken.egg-info\dependency_links.txt
writing requirements to tiktoken.egg-info\requires.txt
writing top-level names to tiktoken.egg-info\top_level.txt
reading manifest file 'tiktoken.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'Makefile'
adding license file 'LICENSE'
writing manifest file 'tiktoken.egg-info\SOURCES.txt'
copying tiktoken\py.typed -> build\lib.win-amd64-cpython-312\tiktoken
running build_ext
running build_rust
error: can't find Rust compiler
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for tiktoken
Building wheel for multidict (pyproject.toml) ... error
error: subprocess-exited-with-error
× Building wheel for multidict (pyproject.toml) did not run successfully.
│ exit code: 1
╰─> [74 lines of output]
*********************
* Accelerated build *
*********************
running bdist_wheel
running build
running build_py
creating build
creating build\lib.win-amd64-cpython-312
creating build\lib.win-amd64-cpython-312\multidict
copying multidict_abc.py -> build\lib.win-amd64-cpython-312\multidict
copying multidict_compat.py -> build\lib.win-amd64-cpython-312\multidict
copying multidict_multidict_base.py -> build\lib.win-amd64-cpython-312\multidict
copying multidict_multidict_py.py -> build\lib.win-amd64-cpython-312\multidict
copying multidict_init_.py -> build\lib.win-amd64-cpython-312\multidict
running egg_info
writing multidict.egg-info\PKG-INFO
writing dependency_links to multidict.egg-info\dependency_links.txt
writing top-level names to multidict.egg-info\top_level.txt
reading manifest file 'multidict.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no previously-included files matching '*.pyc' found anywhere in distribution
warning: no previously-included files found matching 'multidict_multidict.html'
warning: no previously-included files found matching 'multidict*.so'
warning: no previously-included files found matching 'multidict*.pyd'
warning: no previously-included files found matching 'multidict*.pyd'
no previously-included directories found matching 'docs_build'
adding license file 'LICENSE'
writing manifest file 'multidict.egg-info\SOURCES.txt'
C:\Users\mason\AppData\Local\Temp\pip-build-env-syndet2z\overlay\Lib\site-packages\setuptools\command\build_py.py:207: _Warning: Package 'multidict._multilib' is absent from the
packagesconfiguration.!!
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for multidict
Failed to build tiktoken multidict
ERROR: Could not build wheels for tiktoken, multidict, which is required to install pyproject.toml-based projects
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