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fix(docs): Correcting On_query Example (#754)
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abhifetch authored Jul 8, 2024
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Expand Up @@ -23,179 +23,217 @@ This documentation outlines the steps to set up and enable communication between
This agent handle queries. Asking the jobs available for given job title using serpAPI.

```py copy filename="JobSearchAgent.py"
# Import Required libraries
# import required libraries
import json

import aiohttp
from uagents import Agent, Context, Model
from uagents.setup import fund_agent_if_low
from serpapi import GoogleSearch
# Define Request and Response Models


# Define Request and Response Data Models
class Request(Model):
query: str


class Response(Model):
response: str

# Define function to handle job serach queries
async def get_job_summary(query, api_key):
# Parameters for the API request


# Define asynchronous function to handle job search queries using a serpapi url
async def get_job_summary(query: str, location: str, api_key: str):

url = "https://serpapi.com/search.json"
params = {
"engine": "google_jobs",
"q": query,
"hl": "en",
"api_key": api_key
"q": query + " careers",
"location": location,
"engine": "google_jobs",
"api_key": api_key,
}

# Perform the search
search = GoogleSearch(params)
results = search.get_dict()
jobs_results = results.get("jobs_results", [])

# Initialize the result string
res_str = ""
# Get summary for the first job result
if jobs_results:
job = jobs_results[0] # Get the first job
res_str += (
"\n_______________________________________________"
+ f"\nJob Title: {job.get('title', 'N/A')}"
+ f"Company Name: {job.get('company_name', 'N/A')}"
+ f"Location: {job.get('location', 'N/A')}"
+ f"Posted: {job['detected_extensions'].get('posted_at', 'N/A')}"
+ f"Type: {job['detected_extensions'].get('schedule_type', 'N/A')}"
+ f"Description: {job.get('description', 'N/A')[:500]}"
+ "_______________________________________________"
)
else:
res_str = "No job results found."

return res_str
async with aiohttp.ClientSession() as session:
async with session.get(url, params=params) as response:
if response.status == 200:
data = await response.json()
jobs = data.get("jobs_results", [])
if jobs:
job = jobs[0] if jobs else None # Ensuring there is a job to report
if not job:
return "No job details available."
res_str = json.dumps(
{
"Title": job.get("title", "N/A"),
"Company": job.get("company_name", "N/A"),
"Location": job.get("location", "N/A"),
"Link": job.get("link", "N/A"),
"Posted": job.get("detected_extensions", {}).get(
"posted_at", "N/A"
),
"Type": job.get("detected_extensions", {}).get(
"schedule_type", "N/A"
),
"Description": job.get("description", "N/A"),
},
indent=2,
)
return res_str
else:
return "No job results found."
else:
return f"Failed to fetch data: {response.status}"


# Define Search Agent
SearchAgent = Agent(
name="SearchAgent",
port=8000,
seed="SearchAgent secret phrase",
seed="<YOUR_AGENT_SECRET_PHRASE>",
endpoint=["http://127.0.0.1:8000/submit"],
)
) # Update your agent's secret phrase

# Registering agent on Almananc and funding it.
# Registering agent on Almanac and funding it
fund_agent_if_low(SearchAgent.wallet.address())



# On agent startup printing address
@SearchAgent.on_event('startup')
@SearchAgent.on_event("startup")
async def agent_details(ctx: Context):
ctx.logger.info(f'Search Agent Address is {SearchAgent.address}')
ctx.logger.info(f"Search Agent Address is {SearchAgent.address}")


# On_query handler
# On_query decorator to handle jobs request
@SearchAgent.on_query(model=Request, replies={Response})
async def query_handler(ctx: Context, sender: str, msg: Request):
ctx.logger.info("Query received")
try:
ctx.logger.info(f'Fetching job details for query: {msg.query}')
response = await get_job_summary(msg.query, "<YOUR_JOB_SEARCH_SERP_API_KEY>")
# Ensure the response is a string
response_str = str(response)
ctx.logger.info(f'Response: {response_str}')
await ctx.send(sender, Response(response=response_str))
ctx.logger.info(f"Fetching job details for query: {msg.query}")
response = await get_job_summary(
msg.query, "United Kingdom", "<YOUR_SERPAPI_API_KEY_HERE>" # Update your serpapi API key
) # Replace your serpAPI key.
ctx.logger.info(f"Response: {response}")
await ctx.send(sender, Response(response=response))
except Exception as e:
error_message = f"Error fetching job details: {str(e)}"
ctx.logger.error(error_message)
# Ensure the error message is sent as a string
await ctx.send(sender, Response(response=str(error_message)))


if __name__ == "__main__":
SearchAgent.run()
```

Please update agent's secret phrase and serpapi API key. You can find serpapi API key on [SerpAPI API key ↗️](https://serpapi.com/)

### Flask App Script

Async Agent-Based Flask App for Keyword base Job Search Results Retrieval.

```py copy filename="jobsearchapp.py"
# Import required libraries
from flask import Flask, request, jsonify
from uagents.query import query
from uagents import Model
import json
import asyncio

# Initialise flask app
from flask import Flask, jsonify, request
from uagents import Model
from uagents.query import query

app = Flask(__name__)

AGENT_ADDRESS = 'Provide your job search agent address'
# Define the address of your agent to send requests to
AGENT_ADDRESS = "<YOUR_AGENT_ADDRESS_HERE>" # Update your agent address here

# Definining request model

# Define a data model for incoming requests
class Request(Model):
query: str

# Defining endpoint to handle search requests
@app.route('/search-jobs')
def search_jobs():
keyword = request.args.get('q') # Get the query parameter

# Define a route to handle job search queries
@app.route("/search-jobs")
async def search_jobs():
keyword = request.args.get("q")
if not keyword:
return jsonify({"error": "No query provided."}), 400

response = synchronous_agent_query(keyword)

# Assuming 'response' is the data you want to return
return jsonify({"results": response})

# Define an asynchronous function to query the agent with the specified request
async def synchronous_agent_query(keyword):
req = Request(query=keyword)
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
response = loop.run_until_complete(make_agent_call(req))
loop.close()
return f'Search results for {keyword}: {response}'

# Define an asynchronous function to query the agent with the specified request
async def agent_query(req):
response = await query(destination=AGENT_ADDRESS, message=req, timeout=15.0)
data = json.loads(response.decode_payload())
return data["response"]
return jsonify({"error": "No query provided.", "status": "error"}), 400
# Make a call to the agent with the request and handle the response
response = await make_agent_call(Request(query=keyword))
# Check if the response indicates an unsuccessful agent call
if isinstance(response, str) and response.startswith("Unsuccessful"):
return jsonify({"error": response, "status": "failed"}), 500

# Define an asynchronous function to handle the agent call, which will be used within the synchronous Flask route
async def make_agent_call(req: Request):
try:
response = await agent_query(req)
return f"{response}"
# Parse the response to a Python dictionary
job_data = json.loads(response)
# Return the job data in a structured format
return jsonify({"results": job_data, "status": "success"})
except json.JSONDecodeError:
# Handle JSON decoding error
return jsonify({"error": "Invalid response format", "status": "error"}), 500


# Function to send a query to the agent
async def agent_query(req):
try:
response = await query(destination=AGENT_ADDRESS, message=req, timeout=15.0)
# Decode the response and extract data
data = json.loads(response.decode_payload())
return data.get("response", "No data found")
except Exception as e:
return f"unsuccessful agent call - {str(e)}"
# Return a formatted error message if the agent call fails
return f"Unsuccessful agent call - {str(e)}"

# Run the Flask application if this script is executed directly

# Function to initiate an agent call
async def make_agent_call(req: Request):
response = await agent_query(req)
return response


# Main function to run the Flask application
if __name__ == "__main__":
app.run(debug=True)
```

## Steps to run the app
- Update `Agent Address` in jobsearchapp.py and [`serp_api_key`](https://serpapi.com/google-jobs-api) in JobSearchAgent.py.
- Run both the script `JobSearchAgent.py` and `jobsearchapp.py` on your terminal.
- Update `Agent Address` in jobsearchapp.py and [`serpapi_api_key`](https://serpapi.com/google-jobs-api) in JobSearchAgent.py.
- Run both the script `JobSearchAgent.py` and update the agents address in `jobsearchapp.py`.
- Rerun `JobSearchAgent.py` and `jobsearchapp.py` together
- Open new termina window and run `curl "http://127.0.0.1:5000/search-jobs?q=<your_job_serach_query>"`
- Replace `your_job_serach_query` with your query in above command.

## Expected Output

- jobsearchapp.py
- JobSearchAgent.py

```
INFO: [SearchAgent]: Almanac registration is up to date!
INFO: [SearchAgent]: Search Agent Address is agent1qdmgt4txxlvve4fmk8cw6jcp4gd2l709ctllx7jvz0kx4jzace5hu8n00hj
INFO: [SearchAgent]: Starting server on http://0.0.0.0:8000 (Press CTRL+C to quit)
INFO: [SearchAgent]: Query received
INFO: [SearchAgent]: Fetching job details for query: software developer
INFO: [SearchAgent]: Response: _______________________________________________
Job Title: Software DeveloperCompany Name: SAIC Career SiteLocation: Chantilly, VA Posted: 1 day agoType: Full-timeDescription: Description
INFO: [SearchAgent]: Fetching job details for query: data analyst
INFO: [SearchAgent]: Fetching job details for query: data analyst
INFO: [SearchAgent]: Response: {
"Title": "Data Analyst - eCommerce",
"Company": "Next Plc",
"Location": " Leicester, United Kingdom ",
"Link": "N/A",
"Posted": "3 days ago",
"Type": "Full-time",
"Description": "Let\u2019s talk numbers. When it comes to UK retail, it\u2019s hard to find a bigger name. We sell thousands of items an hour and are expanding our e-commerce business by the second. For anyone in Data, this is the place to learn. To grow. And to thrive.\n\nOur International eCommerce Third parties team help to connect NEXT with other large selling platforms such as Zalando and La Redoute to sell thousands of products. These platforms deliver strong growth for NEXT and to continue to support this growth we need ever improving data points and manipulation.\n\nThe role\nAre you an expert in interpreting data? Here, you\u2019ll be at the forefront of understanding and interpreting customer data to drive strategic decisions and enhance online customer experiences. So what does this mean day-to-day for you as a Data Analyst at NEXT, specifically?\n\u2022 Presenting complex analytical findings into clear, concise reports and communicating your analysis to the wider team.\n\u2022 Building reports and visualisations for the... wider team and business.\n\u2022 Exploring the latest technologies and selecting the right tools for the job.\n\u2022 Interrogating large volumes of data from a range of sources, including transactional and online.\n\u2022 Supporting the creation of partner dashboards converting key partner information.\n\u2022 Proactively identifying opportunities to support improvements to the customer experience.\n\u2022 Manage and own new partner data requirements.\n\u2022 Communicating and collaborating with cross functional teams within eCommerce and the wider business.\n\nYou\u2019ll be doing all this from our Leicestershire Head Office. Our offices are inspiring, yes. But we understand that life happens. So, we\u2019re big on making sure your work, works for you which is why we offer flexible working. Bring your energy. Play to your strengths. Make things bigger and better than before. Let\u2019s Take It On.\n\nSo if you\u2019re an expert in this field, have strong SQL/Azure/Databricks skills and considerable data analysis knowledge, have a commercial understanding and background, work with initiative and can build great working relationships within the business, this is the place for you \u2013 and your career.\n\nThe benefits\n\nWe\u2019ll discuss more of what you\u2019ll get when you work for NEXT at interview, but here\u2019s an overview of what you can expect:\n\u2022 Profit\u2013related bonus\n\u2022 Contributions into a pension scheme\n\u2022 Life assurance\n\u2022 Sharesave scheme, which allows you to invest in NEXT and claim your share of our success\n\u2022 A newly refurbished subsidised restaurant and bar\n\u2022 On-site nursery (salary sacrifice scheme) in Leicester\n\u2022 Staff shop\n\u2022 Juice bar and coffee shop\n\u2022 Free parking on-site, including car sharing\n\u2022 Free company bus service to and from Leicester city centre and other areas\n\u2022 25% staff discount on most NEXT products \u2013 either in our stores or delivered directly to your desk\n\u2022 National and local discounts on goods and services\n\u2022 A digital GP healthcare service"
}
INTRODUCTION: The Sponsor supports a diverse set of corporate goals across the organization by conducting technical risk assessments and providing technical risk mitigation guidance on the use of various enabling technologies. The Sponsor requires subject matter expertise in technical risk analysis of enterprise and mission systems, IT systems and networks, mobile and wireless... networks, cloud-based computing, network management platforms, communication protocols, scripting or pro_______________________________________________
```

- Curl Response

```
{
"results": "Search results for software developer: _______________________________________________\nJob Title: Software DeveloperCompany Name: SAIC Career SiteLocation: Chantilly, VA Posted: 1 day agoType: Full-timeDescription: Description\n\nINTRODUCTION: The Sponsor supports a diverse set of corporate goals across the organization by conducting technical risk assessments and providing technical risk mitigation guidance on the use of various enabling technologies. The Sponsor requires subject matter expertise in technical risk analysis of enterprise and mission systems, IT systems and networks, mobile and wireless... networks, cloud-based computing, network management platforms, communication protocols, scripting or pro_______________________________________________"
"results": {
"Company": "Next Plc",
"Description": "Let\u2019s talk numbers. When it comes to UK retail, it\u2019s hard to find a bigger name. We sell thousands of items an hour and are expanding our e-commerce business by the second. For anyone in Data, this is the place to learn. To grow. And to thrive.\n\nOur International eCommerce Third parties team help to connect NEXT with other large selling platforms such as Zalando and La Redoute to sell thousands of products. These platforms deliver strong growth for NEXT and to continue to support this growth we need ever improving data points and manipulation.\n\nThe role\nAre you an expert in interpreting data? Here, you\u2019ll be at the forefront of understanding and interpreting customer data to drive strategic decisions and enhance online customer experiences. So what does this mean day-to-day for you as a Data Analyst at NEXT, specifically?\n\u2022 Presenting complex analytical findings into clear, concise reports and communicating your analysis to the wider team.\n\u2022 Building reports and visualisations for the... wider team and business.\n\u2022 Exploring the latest technologies and selecting the right tools for the job.\n\u2022 Interrogating large volumes of data from a range of sources, including transactional and online.\n\u2022 Supporting the creation of partner dashboards converting key partner information.\n\u2022 Proactively identifying opportunities to support improvements to the customer experience.\n\u2022 Manage and own new partner data requirements.\n\u2022 Communicating and collaborating with cross functional teams within eCommerce and the wider business.\n\nYou\u2019ll be doing all this from our Leicestershire Head Office. Our offices are inspiring, yes. But we understand that life happens. So, we\u2019re big on making sure your work, works for you which is why we offer flexible working. Bring your energy. Play to your strengths. Make things bigger and better than before. Let\u2019s Take It On.\n\nSo if you\u2019re an expert in this field, have strong SQL/Azure/Databricks skills and considerable data analysis knowledge, have a commercial understanding and background, work with initiative and can build great working relationships within the business, this is the place for you \u2013 and your career.\n\nThe benefits\n\nWe\u2019ll discuss more of what you\u2019ll get when you work for NEXT at interview, but here\u2019s an overview of what you can expect:\n\u2022 Profit\u2013related bonus\n\u2022 Contributions into a pension scheme\n\u2022 Life assurance\n\u2022 Sharesave scheme, which allows you to invest in NEXT and claim your share of our success\n\u2022 A newly refurbished subsidised restaurant and bar\n\u2022 On-site nursery (salary sacrifice scheme) in Leicester\n\u2022 Staff shop\n\u2022 Juice bar and coffee shop\n\u2022 Free parking on-site, including car sharing\n\u2022 Free company bus service to and from Leicester city centre and other areas\n\u2022 25% staff discount on most NEXT products \u2013 either in our stores or delivered directly to your desk\n\u2022 National and local discounts on goods and services\n\u2022 A digital GP healthcare service",
"Link": "N/A",
"Location": " Leicester, United Kingdom ",
"Posted": "3 days ago",
"Title": "Data Analyst - eCommerce",
"Type": "Full-time"
},
"status": "success"
}
```

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