chatbot #4523
chatbot #4523
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📝 WalkthroughWalkthroughThe changes introduce a new chatbot feature in a Flask-based application. A new POST route ( Changes
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## staging #4523 +/- ##
===========================================
- Coverage 11.51% 10.88% -0.63%
===========================================
Files 155 155
Lines 18709 19905 +1196
Branches 549 549
===========================================
+ Hits 2154 2167 +13
- Misses 16551 17734 +1183
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Actionable comments posted: 0
🧹 Nitpick comments (6)
src/spatial/controllers/controllers.py (1)
84-87: Consider renaming the function for consistent Python style.
Function names in Python typically use snake_case. Something likechatbot_views()might be more conventional thanChatbot_Views().-def Chatbot_Views(): +def chatbot_views(): return ChatbotView.chat_endpoint()src/spatial/views/chatbot_views.py (2)
4-4: Remove unused import to maintain cleanliness.
numpyis imported but never used; removing it can prevent confusion.-import numpy as np🧰 Tools
🪛 Ruff (0.8.2)
4-4:
numpyimported but unusedRemove unused import:
numpy(F401)
10-82: Well-structured endpoint with thorough validations.
The error handling and logging are clear. Consider adding unit tests to ensure correct behavior for valid/invalid payloads and verifying integration withAirQualityChatbot.Would you like help writing a set of tests to validate status codes and payload responses?
src/spatial/models/chatbot_model.py (3)
6-6: Remove unused imports to streamline the module.
requestandjsonifyare never used in this file. Clearing them out helps maintain clarity.-from flask import Flask, request, jsonify +from flask import Flask🧰 Tools
🪛 Ruff (0.8.2)
6-6:
flask.requestimported but unusedRemove unused import
(F401)
6-6:
flask.jsonifyimported but unusedRemove unused import
(F401)
17-42: Good use of caching and timeout handling.
Thefetch_air_quality_data_amethod is concise, with error logging for diverse exceptions. Consider returning more descriptive data (or error codes) instead ofNonewhen a token is missing or the request fails, to aid troubleshooting.
43-121: Great approach mixing rule-based and LLM responses.
Implementation is thoughtfully structured. As a future optimization, consider externalizing model initialization to reduce overhead, especially when under heavy traffic.
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📒 Files selected for processing (3)
src/spatial/controllers/controllers.py(3 hunks)src/spatial/models/chatbot_model.py(1 hunks)src/spatial/views/chatbot_views.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
src/spatial/views/chatbot_views.py
4-4: numpy imported but unused
Remove unused import: numpy
(F401)
src/spatial/models/chatbot_model.py
6-6: flask.request imported but unused
Remove unused import
(F401)
6-6: flask.jsonify imported but unused
Remove unused import
(F401)
⏰ Context from checks skipped due to timeout of 90000ms (3)
- GitHub Check: build-push-deploy-spatial
- GitHub Check: Analyze (python)
- GitHub Check: Analyze (javascript)
🔇 Additional comments (4)
src/spatial/controllers/controllers.py (3)
3-3: All good with the new import.
No issues detected with referencingChatbotView. The module usage looks correct.
16-16: No concerns with the import statement.
The trailing space is inconsequential; no formatting warnings have been flagged.
82-83: Verify the custom prompt function usage.
Ensure thatgenerate_air_quality_report_with_customised_prompt_gemini()is fully tested and documented, given that it returns domain-specific results.src/spatial/models/chatbot_model.py (1)
58-58: Clarify usage of the threading lock or remove it if unneeded.
If no operations useself.lock, consider removing it to avoid confusion.
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Actionable comments posted: 1
🧹 Nitpick comments (6)
src/spatial/views/chatbot_views.py (6)
1-6: Remove the unused numpy import.The static analysis shows that numpy is imported but never used in this file. Keeping unused imports can lead to confusion, slightly increased import time, and potential dependency issues.
from flask import request, jsonify # Assuming these are in models/chatbot_model.py based on the import from models.chatbot_model import AirQualityChatbot, DataFetcher -import numpy as np import logging🧰 Tools
🪛 Ruff (0.8.2)
4-4:
numpyimported but unusedRemove unused import:
numpy(F401)
7-7: Consider removing or enabling the commented logging configuration.There's a commented-out logging configuration line. Either remove it if it's not needed, or enable it if it's intended for debugging. Commented code can create confusion about the intended configuration.
-#logging.basicConfig(filename="report_log.log", level=logging.INFO, filemode="w")
20-25: Consider more granular validation for individual fields.Currently, the validation checks if all required fields are present, but provides a single error message. For a better user experience, consider validating each field individually and providing specific error messages.
- if not payload or not all(key in payload for key in ["grid_id", "start_time", "end_time", "prompt"]): - logger.error("Invalid payload: missing required fields") - return jsonify({ - "error": "Missing required fields: grid_id, start_time, end_time, prompt", - "status": "failure" - }), 400 + required_fields = ["grid_id", "start_time", "end_time", "prompt"] + missing_fields = [field for field in required_fields if field not in payload] + + if missing_fields: + logger.error(f"Invalid payload: missing required fields: {', '.join(missing_fields)}") + return jsonify({ + "error": f"Missing required fields: {', '.join(missing_fields)}", + "status": "failure" + }), 400
44-44: Clarify the purpose of the_asuffix in the method name.The method name
fetch_air_quality_data_asuggests there might be multiple fetch methods (a, b, c, etc.). If there are multiple methods, consider using more descriptive names that reflect their purpose or differences. If this is the only method, consider removing the suffix.
10-80: Consider refactoring the long static method into smaller, more focused functions.The
chat_endpointmethod is quite long and handles multiple responsibilities (validation, data fetching, chatbot interaction, response formation). Consider refactoring it into smaller functions for better maintainability and testability.For example:
@staticmethod def validate_payload(payload): """Validate the request payload and return extracted parameters or errors""" # Validation logic here return grid_id, start_time, end_time, user_prompt @staticmethod def fetch_and_validate_data(grid_id, start_time, end_time): """Fetch and validate air quality data""" # Data fetching and validation logic return air_quality_data or error_response @staticmethod def generate_chatbot_response(air_quality_data, user_prompt): """Generate chatbot response based on air quality data and user prompt""" # Chatbot interaction logic return response or error_response @staticmethod def chat_endpoint(): """Main endpoint handler that orchestrates the validation, data fetching, and response generation""" # Simplified orchestration logic using the methods above
53-56: Consider adding timeout handling for chatbot response.When initializing the chatbot and getting a response, there's no explicit timeout handling. If the chatbot processing is resource-intensive or uses external services (like an LLM API), it could potentially take a long time. Consider adding a timeout mechanism to ensure the request doesn't hang indefinitely.
# Initialize chatbot and get response chatbot = AirQualityChatbot(air_quality_data) -response = chatbot.chat(user_prompt) +try: + # Set an appropriate timeout value based on expected response times + import threading + result = [None] + exception = [None] + + def get_response(): + try: + result[0] = chatbot.chat(user_prompt) + except Exception as e: + exception[0] = e + + thread = threading.Thread(target=get_response) + thread.start() + thread.join(timeout=10) # 10 seconds timeout, adjust as needed + + if thread.is_alive(): + logger.warning(f"Chatbot response timed out for prompt: {user_prompt}") + return jsonify({ + "error": "Response generation timed out", + "status": "failure" + }), 504 # Gateway Timeout + + if exception[0]: + raise exception[0] + + response = result[0]
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src/spatial/views/chatbot_views.py(1 hunks)
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src/spatial/views/chatbot_views.py
4-4: numpy imported but unused
Remove unused import: numpy
(F401)
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🔇 Additional comments (2)
src/spatial/views/chatbot_views.py (2)
75-80:❓ Verification inconclusive
Avoid exposing exception details in production.
The current exception handling logs the exception details but returns a generic error message to the client. This is good practice. However, during development, you might want to include more detailed error information in the response for easier debugging.
Consider adding a configuration flag to control whether detailed error information is included in the response based on the environment (development vs. production).
Additionally, based on the past review comment about "Information exposure through an exception", ensure that stack traces are never exposed to users in production.
🏁 Script executed:
#!/bin/bash # Check if there are similar patterns of exception handling in other views grep -r "except Exception as e:" --include="*.py" src/spatial/views/Length of output: 1032
Action: Introduce Environment-Based Error Reporting
The exception handling in
src/spatial/views/chatbot_views.pyappropriately logs errors and returns a generic error message in production. However, to further aid debugging during development, please consider adding a configuration flag (e.g., based on theDEBUGenvironment flag) that conditionally includes more detailed error information in the response. This will ensure that detailed error details and potential stack traces are only exposed in a development environment while keeping production secure.Additionally, since similar exception patterns were found in other views (e.g., in
site_category_view.py,satellite_predictions.py,report_view.py, etc.), please verify that the new configuration mechanism is applied consistently across all modules to mitigate information exposure risks.
3-3:❓ Verification inconclusive
Verify the import path for chatbot models.
The import statement suggests that
AirQualityChatbotandDataFetcherare inmodels.chatbot_model, but based on the AI summary, these classes might be insrc/spatial/models/chatbot_model.py. Please verify the correct import path to ensure this code works as expected when deployed.
🏁 Script executed:
#!/bin/bash # Check the actual location of the chatbot model files find . -name "chatbot_model.py" -type f # Look for imports in controller files to see the correct pattern grep -r "from models" --include="*.py" src/spatial/controllers/ grep -r "from spatial.models" --include="*.py" src/spatial/Length of output: 205
Import Path Verification: Confirm Chatbot Model Location
- Verified that
chatbot_model.pyis located atsrc/spatial/models/chatbot_model.py.- The current import in
src/spatial/views/chatbot_views.py—from models.chatbot_model import AirQualityChatbot, DataFetcher—may not correctly reflect the file’s location.- Depending on your project’s configuration (e.g., how the PYTHONPATH is set up and whether you prefer relative or absolute imports), consider updating the import to one of the following:
- Absolute import:
from spatial.models.chatbot_model import AirQualityChatbot, DataFetcher- Relative import (if operating within the package):
from ..models.chatbot_model import AirQualityChatbot, DataFetcher- Please verify which approach aligns with your project’s overall import strategy and ensure it works as expected when deployed.
| # Validate prompt | ||
| if not user_prompt or not isinstance(user_prompt, str): | ||
| logger.error(f"Invalid prompt received: {user_prompt}") | ||
| return jsonify({ | ||
| "error": "No valid prompt provided", | ||
| "status": "failure" | ||
| }), 400 |
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🛠️ Refactor suggestion
Add additional validation for time format and grid_id.
The current implementation only validates the presence of grid_id, start_time, and end_time, and does some basic validation on the prompt. Consider adding more thorough validation for date/time formats and grid_id format/existence.
# After extracting parameters, add:
+ # Validate time formats
+ try:
+ # Use appropriate datetime parsing based on your expected format
+ from datetime import datetime
+ datetime.strptime(start_time, "%Y-%m-%dT%H:%M:%SZ")
+ datetime.strptime(end_time, "%Y-%m-%dT%H:%M:%SZ")
+
+ # Ensure start_time is before end_time
+ if datetime.strptime(start_time, "%Y-%m-%dT%H:%M:%SZ") >= datetime.strptime(end_time, "%Y-%m-%dT%H:%M:%SZ"):
+ logger.error(f"Invalid time range: start_time must be before end_time")
+ return jsonify({
+ "error": "Invalid time range: start_time must be before end_time",
+ "status": "failure"
+ }), 400
+ except ValueError:
+ logger.error(f"Invalid time format for start_time or end_time")
+ return jsonify({
+ "error": "Invalid time format. Expected format: YYYY-MM-DDThh:mm:ssZ",
+ "status": "failure"
+ }), 400
+
+ # Validate grid_id format if needed
+ # For example, if grid_id should be a UUID or follow a specific pattern📝 Committable suggestion
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| # Validate prompt | |
| if not user_prompt or not isinstance(user_prompt, str): | |
| logger.error(f"Invalid prompt received: {user_prompt}") | |
| return jsonify({ | |
| "error": "No valid prompt provided", | |
| "status": "failure" | |
| }), 400 | |
| # Assume parameters such as start_time, end_time, and grid_id have already been extracted | |
| # Validate time formats | |
| try: | |
| # Use appropriate datetime parsing based on your expected format | |
| from datetime import datetime | |
| datetime.strptime(start_time, "%Y-%m-%dT%H:%M:%SZ") | |
| datetime.strptime(end_time, "%Y-%m-%dT%H:%M:%SZ") | |
| # Ensure start_time is before end_time | |
| if datetime.strptime(start_time, "%Y-%m-%dT%H:%M:%SZ") >= datetime.strptime(end_time, "%Y-%m-%dT%H:%M:%SZ"): | |
| logger.error(f"Invalid time range: start_time must be before end_time") | |
| return jsonify({ | |
| "error": "Invalid time range: start_time must be before end_time", | |
| "status": "failure" | |
| }), 400 | |
| except ValueError: | |
| logger.error(f"Invalid time format for start_time or end_time") | |
| return jsonify({ | |
| "error": "Invalid time format. Expected format: YYYY-MM-DDThh:mm:ssZ", | |
| "status": "failure" | |
| }), 400 | |
| # Validate grid_id format if needed | |
| # For example, if grid_id should be a UUID or follow a specific pattern | |
| # Validate prompt | |
| if not user_prompt or not isinstance(user_prompt, str): | |
| logger.error(f"Invalid prompt received: {user_prompt}") | |
| return jsonify({ | |
| "error": "No valid prompt provided", | |
| "status": "failure" | |
| }), 400 |
|
Spatial changes in this PR available for preview here |
|
Spatial changes in this PR available for preview here |
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Actionable comments posted: 10
🧹 Nitpick comments (6)
src/spatial/models/chatbot_model.py (6)
1-9: Consider organizing imports more systematicallyThe imports are functional but could be better organized for improved readability and maintainability. Consider grouping them by type (standard library, third-party, and local) with standard library imports first.
- import requests - from configure import Config - import google.generativeai as genai - import logging - import re - from flask import Flask, request, jsonify - from functools import lru_cache - import threading + # Standard library imports + import logging + import re + import threading + from functools import lru_cache + + # Third-party imports + import requests + import google.generativeai as genai + from flask import Flask + + # Local imports + from configure import Config🧰 Tools
🪛 Ruff (0.8.2)
6-6:
flask.requestimported but unusedRemove unused import
(F401)
6-6:
flask.jsonifyimported but unusedRemove unused import
(F401)
14-15: Flask app instantiation in model file is unusualCreating a Flask app instance in a model file goes against the separation of concerns principle. This app instance is never used in the file and should be removed or relocated to an appropriate application entry point.
- # Flask app - app = Flask(__name__)
19-20: Consider parameterizing the LRU cache sizeThe hardcoded cache size of 100 might not be appropriate for all deployment environments. Consider making this configurable via the Config object.
- @lru_cache(maxsize=100) # Cache for speed + @lru_cache(maxsize=Config.AIRQO_CACHE_SIZE or 100) # Cache for speed
29-29: Remove unnecessary empty lineThere's an extra line break that seems unnecessary and inconsistent with the rest of the formatting.
payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} - try:
31-31: Consider making request timeout configurableThe timeout is hardcoded to 5 seconds, which might not be appropriate for all network conditions or API response times. Consider making this configurable.
- response = requests.post(url, json=payload, timeout=5) + response = requests.post(url, json=payload, timeout=Config.API_TIMEOUT or 5)
71-91: Enhance rule-based pattern matchingThe regex patterns are simple and could match unintended queries. Consider using more specific patterns or a more robust approach like intent classification.
def _rule_based_response(self, user_prompt): prompt = user_prompt.lower() - if re.search(r"(today|now).*air.*quality", prompt): + if re.search(r"\b(today|now|current).*\b(air.*quality|pm2[._]5|pollution)\b", prompt): return f"Today's PM2.5 in {self.grid_name} is {self.today_pm2_5} µg/m³."Note: Similar improvements could be applied to the other patterns.
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src/spatial/models/chatbot_model.py(1 hunks)src/spatial/models/report_datafetcher.py(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- src/spatial/models/report_datafetcher.py
🧰 Additional context used
🪛 Ruff (0.8.2)
src/spatial/models/chatbot_model.py
6-6: flask.request imported but unused
Remove unused import
(F401)
6-6: flask.jsonify imported but unused
Remove unused import
(F401)
⏰ Context from checks skipped due to timeout of 90000ms (3)
- GitHub Check: build-push-deploy-spatial
- GitHub Check: Analyze (python)
- GitHub Check: Analyze (javascript)
src/spatial/models/chatbot_model.py
Outdated
| import google.generativeai as genai | ||
| import logging | ||
| import re | ||
| from flask import Flask, request, jsonify |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Remove unused Flask imports
The static analysis correctly identifies that request and jsonify are imported but never used in this file.
- from flask import Flask, request, jsonify
+ from flask import Flask📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| from flask import Flask, request, jsonify | |
| from flask import Flask |
🧰 Tools
🪛 Ruff (0.8.2)
6-6: flask.request imported but unused
Remove unused import
(F401)
6-6: flask.jsonify imported but unused
Remove unused import
(F401)
src/spatial/models/chatbot_model.py
Outdated
|
|
||
| self.annual_pm2_5 = self.annual_data.get("pm2_5_calibrated_value", 'N/A') | ||
| self.gemini_model = genai.GenerativeModel('gemini-2.0-flash') | ||
| self.lock = threading.Lock() |
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💡 Verification agent
🧩 Analysis chain
Consider documenting the purpose of the threading lock
A threading lock is initialized but never used in the code. If it's intended for future use or for use by callers, consider adding documentation explaining its purpose.
🏁 Script executed:
#!/bin/bash
# Search for uses of the lock in this file or other files
grep -r "lock" --include="*.py" src/spatial/Length of output: 117
Action Required: Document or Remove the Unused Threading Lock
The grep search confirms that the threading.Lock() instantiated at src/spatial/models/chatbot_model.py line 57 isn’t referenced anywhere else in the codebase. If this lock is reserved for future synchronization or intended for use by other parts of the application, please add a comment or update the documentation to clarify its purpose and usage. Otherwise, consider removing it to eliminate unnecessary code confusion.
src/spatial/models/chatbot_model.py
Outdated
| url = f"{analytics_url}?token={token}" | ||
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} |
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🛠️ Refactor suggestion
Consider URL encoding for token in query parameters
The token is appended directly to the URL, which could lead to issues if it contains special characters. Consider using proper URL parameter encoding.
- url = f"{analytics_url}?token={token}"
+ from urllib.parse import urlencode
+ query_params = {'token': token}
+ url = f"{analytics_url}?{urlencode(query_params)}"📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| url = f"{analytics_url}?token={token}" | |
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} | |
| from urllib.parse import urlencode | |
| query_params = {'token': token} | |
| url = f"{analytics_url}?{urlencode(query_params)}" | |
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} |
src/spatial/models/chatbot_model.py
Outdated
| def chat(self, user_prompt): | ||
| rule_response = self._rule_based_response(user_prompt) | ||
| if rule_response: | ||
| return rule_response | ||
| return self._llm_response(user_prompt) |
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🛠️ Refactor suggestion
Add input validation for user prompts
The chat method lacks input validation for the user_prompt parameter. Consider adding validation to handle empty, overly long, or potentially harmful inputs.
def chat(self, user_prompt):
+ # Input validation
+ if not user_prompt or not isinstance(user_prompt, str):
+ return "Please provide a valid question about air quality."
+ if len(user_prompt) > 500: # Reasonable limit
+ return "Your question is too long. Please keep it under 500 characters."
+
rule_response = self._rule_based_response(user_prompt)
if rule_response:
return rule_response
return self._llm_response(user_prompt)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def chat(self, user_prompt): | |
| rule_response = self._rule_based_response(user_prompt) | |
| if rule_response: | |
| return rule_response | |
| return self._llm_response(user_prompt) | |
| def chat(self, user_prompt): | |
| # Input validation | |
| if not user_prompt or not isinstance(user_prompt, str): | |
| return "Please provide a valid question about air quality." | |
| if len(user_prompt) > 500: # Reasonable limit | |
| return "Your question is too long. Please keep it under 500 characters." | |
| rule_response = self._rule_based_response(user_prompt) | |
| if rule_response: | |
| return rule_response | |
| return self._llm_response(user_prompt) |
src/spatial/models/chatbot_model.py
Outdated
| def _llm_response(self, user_prompt): | ||
| if re.search(r"(report|summary|detailed|analysis|conclusion)", user_prompt.lower()): | ||
| full_prompt = ( | ||
| f"Data: {self._prepare_data_context()}\n" | ||
| f"User: {user_prompt}\n" | ||
| "Generate a concise conclusion or report based on the data. Focus on key insights." | ||
| ) | ||
| else: | ||
| full_prompt = ( | ||
| f"Data: {self._prepare_data_context()}\n" | ||
| f"User: {user_prompt}\n" | ||
| "Respond concisely and accurately based on the data." | ||
| ) | ||
|
|
||
| try: | ||
| response = self.gemini_model.generate_content(full_prompt) | ||
| return response.text | ||
| except Exception as e: | ||
| logging.error(f"LLM error: {e}") | ||
| return "Sorry, I couldn’t process that right now." | ||
|
|
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🛠️ Refactor suggestion
Implement thread safety in LLM response method
The class initializes a threading lock but doesn't use it when making external API calls. Consider using the lock to ensure thread safety, especially if this method might be called concurrently.
def _llm_response(self, user_prompt):
- if re.search(r"(report|summary|detailed|analysis|conclusion)", user_prompt.lower()):
- full_prompt = (
- f"Data: {self._prepare_data_context()}\n"
- f"User: {user_prompt}\n"
- "Generate a concise conclusion or report based on the data. Focus on key insights."
- )
- else:
- full_prompt = (
- f"Data: {self._prepare_data_context()}\n"
- f"User: {user_prompt}\n"
- "Respond concisely and accurately based on the data."
- )
-
- try:
- response = self.gemini_model.generate_content(full_prompt)
- return response.text
- except Exception as e:
- logging.error(f"LLM error: {e}")
- return "Sorry, I couldn't process that right now."
+ with self.lock: # Use lock for thread safety
+ if re.search(r"(report|summary|detailed|analysis|conclusion)", user_prompt.lower()):
+ full_prompt = (
+ f"Data: {self._prepare_data_context()}\n"
+ f"User: {user_prompt}\n"
+ "Generate a concise conclusion or report based on the data. Focus on key insights."
+ )
+ else:
+ full_prompt = (
+ f"Data: {self._prepare_data_context()}\n"
+ f"User: {user_prompt}\n"
+ "Respond concisely and accurately based on the data."
+ )
+
+ if not self.gemini_model:
+ return "AI model not available. Please try again later."
+
+ try:
+ response = self.gemini_model.generate_content(full_prompt)
+ return response.text
+ except Exception as e:
+ logging.error(f"LLM error: {e}")
+ return "Sorry, I couldn't process that right now."📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def _llm_response(self, user_prompt): | |
| if re.search(r"(report|summary|detailed|analysis|conclusion)", user_prompt.lower()): | |
| full_prompt = ( | |
| f"Data: {self._prepare_data_context()}\n" | |
| f"User: {user_prompt}\n" | |
| "Generate a concise conclusion or report based on the data. Focus on key insights." | |
| ) | |
| else: | |
| full_prompt = ( | |
| f"Data: {self._prepare_data_context()}\n" | |
| f"User: {user_prompt}\n" | |
| "Respond concisely and accurately based on the data." | |
| ) | |
| try: | |
| response = self.gemini_model.generate_content(full_prompt) | |
| return response.text | |
| except Exception as e: | |
| logging.error(f"LLM error: {e}") | |
| return "Sorry, I couldn’t process that right now." | |
| def _llm_response(self, user_prompt): | |
| with self.lock: # Use lock for thread safety | |
| if re.search(r"(report|summary|detailed|analysis|conclusion)", user_prompt.lower()): | |
| full_prompt = ( | |
| f"Data: {self._prepare_data_context()}\n" | |
| f"User: {user_prompt}\n" | |
| "Generate a concise conclusion or report based on the data. Focus on key insights." | |
| ) | |
| else: | |
| full_prompt = ( | |
| f"Data: {self._prepare_data_context()}\n" | |
| f"User: {user_prompt}\n" | |
| "Respond concisely and accurately based on the data." | |
| ) | |
| if not self.gemini_model: | |
| return "AI model not available. Please try again later." | |
| try: | |
| response = self.gemini_model.generate_content(full_prompt) | |
| return response.text | |
| except Exception as e: | |
| logging.error(f"LLM error: {e}") | |
| return "Sorry, I couldn't process that right now." |
src/spatial/models/chatbot_model.py
Outdated
| def _prepare_data_context(self): | ||
| return ( | ||
| f"AirQo data for {self.grid_name} ({self.starttime}-{self.endtime}): " | ||
| f"Annual PM2.5={self.annual_pm2_5} µg/m³, Sites={self.num_sites}, " | ||
| f"Daily sample={self.daily_mean_data[:1]}, Diurnal sample={self.diurnal[:1]}, " | ||
| f"Monthly sample={self.monthly_data[:1]}, Site names={self.site_names}" | ||
| ) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Limit data exposure in context preparation
The context preparation method includes raw data samples which might contain sensitive information. Consider limiting the amount of data exposed to the LLM and formatting it more appropriately.
def _prepare_data_context(self):
return (
f"AirQo data for {self.grid_name} ({self.starttime}-{self.endtime}): "
f"Annual PM2.5={self.annual_pm2_5} µg/m³, Sites={self.num_sites}, "
- f"Daily sample={self.daily_mean_data[:1]}, Diurnal sample={self.diurnal[:1]}, "
- f"Monthly sample={self.monthly_data[:1]}, Site names={self.site_names}"
+ f"Today's PM2.5={self.today_pm2_5} µg/m³, "
+ f"Peak hour={(self.peak_diurnal.get('hour', 'N/A') + ':00') if self.peak_diurnal else 'N/A'}, "
+ f"Site names={', '.join(self.site_names[:5])}{' and more' if len(self.site_names) > 5 else ''}"
)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def _prepare_data_context(self): | |
| return ( | |
| f"AirQo data for {self.grid_name} ({self.starttime}-{self.endtime}): " | |
| f"Annual PM2.5={self.annual_pm2_5} µg/m³, Sites={self.num_sites}, " | |
| f"Daily sample={self.daily_mean_data[:1]}, Diurnal sample={self.diurnal[:1]}, " | |
| f"Monthly sample={self.monthly_data[:1]}, Site names={self.site_names}" | |
| ) | |
| def _prepare_data_context(self): | |
| return ( | |
| f"AirQo data for {self.grid_name} ({self.starttime}-{self.endtime}): " | |
| f"Annual PM2.5={self.annual_pm2_5} µg/m³, Sites={self.num_sites}, " | |
| f"Today's PM2.5={self.today_pm2_5} µg/m³, " | |
| f"Peak hour={(self.peak_diurnal.get('hour', 'N/A') + ':00') if self.peak_diurnal else 'N/A'}, " | |
| f"Site names={', '.join(self.site_names[:5])}{' and more' if len(self.site_names) > 5 else ''}" | |
| ) |
src/spatial/models/chatbot_model.py
Outdated
| self.gemini_model = genai.GenerativeModel('gemini-2.0-flash') | ||
| self.lock = threading.Lock() |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add fallback mechanism for Gemini model initialization
The Gemini model is initialized without any error handling if the API key is missing or invalid. Consider adding a fallback mechanism or error check.
self.annual_pm2_5 = self.annual_data.get("pm2_5_calibrated_value", 'N/A')
- self.gemini_model = genai.GenerativeModel('gemini-2.0-flash')
+ try:
+ self.gemini_model = genai.GenerativeModel('gemini-2.0-flash')
+ except Exception as e:
+ logging.error(f"Failed to initialize Gemini model: {e}")
+ self.gemini_model = None
self.lock = threading.Lock()Committable suggestion skipped: line range outside the PR's diff.
|
Spatial changes in this PR available for preview here |
|
Spatial changes in this PR available for preview here |
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Actionable comments posted: 4
🧹 Nitpick comments (9)
src/spatial/requirements.txt (1)
29-29: Pin the Redis package version for stability.The addition of Redis for response caching is appropriate, but it's recommended to specify a version to ensure consistent behavior across different environments.
-redis +redis==5.0.1 # or another stable versionsrc/spatial/views/chatbot_views.py (4)
4-4: Remove unused numpy import.The numpy package is imported but never used in this file.
-import numpy as np🧰 Tools
🪛 Ruff (0.8.2)
4-4:
numpyimported but unusedRemove unused import:
numpy(F401)
19-25: Consider more detailed error messages.The current validation is good, but providing more specific information about which fields are missing could help API consumers debug issues more easily.
- if not payload or not all(key in payload for key in ["grid_id", "start_time", "end_time", "prompt"]): + required_fields = ["grid_id", "start_time", "end_time", "prompt"] + missing_fields = [field for field in required_fields if field not in payload] if payload else required_fields + if missing_fields: logger.error("Invalid payload: missing required fields") return jsonify({ - "error": "Missing required fields: grid_id, start_time, end_time, prompt", + "error": f"Missing required fields: {', '.join(missing_fields)}", "status": "failure" }), 400
41-52: Implement more robust error handling for data fetching.The code checks for the presence of the 'airquality' key, but could benefit from more detailed error handling to provide more specific feedback to users.
try: # Fetch air quality data with logging logger.info(f"Fetching data for grid_id: {grid_id}, {start_time} to {end_time}") air_quality_data = DataFetcher.fetch_air_quality_data(grid_id, start_time, end_time) - if not air_quality_data or 'airquality' not in air_quality_data: - logger.error(f"No valid air quality data returned for grid_id: {grid_id}") + if not air_quality_data: + logger.error(f"Failed to fetch any data for grid_id: {grid_id}") + return jsonify({ + "error": "Failed to fetch air quality data from the source", + "status": "failure" + }), 500 + + if 'airquality' not in air_quality_data: + logger.error(f"Invalid data structure returned for grid_id: {grid_id}") return jsonify({ - "error": "Failed to fetch air quality data", + "error": "Air quality data structure is invalid", "status": "failure" }), 500
53-73: Consider richer response metadata.The success response includes basic metadata, but could benefit from additional information like air quality indices or timestamp of when the data was last updated to provide more context to the user.
return jsonify({ "response": response, "status": "success", "grid_id": grid_id, "period": { "start_time": start_time, "end_time": end_time - } + }, + "metadata": { + "response_timestamp": datetime.now().isoformat(), + "data_source": "AirQo API" + } }), 200src/spatial/models/chatbot_model.py (4)
8-8: Flask import is unnecessary.The Flask app is initialized but never used in this file. Consider removing it unless there are plans to use it in the future.
-from flask import Flask
16-17: Remove unused Flask initialization.The Flask app is initialized but never used in this module. This can lead to confusion and potential memory usage in a module that doesn't need it.
-# Initialize Flask app -app = Flask(__name__)
20-21: Remove redundant comment.There are two consecutive comments about Redis initialization that are redundant.
-# Initialize Redis -# Redis connection +# Initialize Redis connection
1-164: Consider implementing rate limiting for API calls.The current implementation caches responses to reduce API load, but doesn't include rate limiting for external API calls (both to the air quality API and the LLM API). This could lead to excessive costs or service disruptions if the endpoint receives high traffic.
Consider implementing a rate limiter using Redis to track and limit the number of API calls within certain time periods. This would protect both your service and the external APIs from excessive load.
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src/spatial/configure.py(1 hunks)src/spatial/models/chatbot_model.py(1 hunks)src/spatial/requirements.txt(1 hunks)src/spatial/views/chatbot_views.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
src/spatial/views/chatbot_views.py
4-4: numpy imported but unused
Remove unused import: numpy
(F401)
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🔇 Additional comments (7)
src/spatial/configure.py (1)
29-33: Well-structured Redis configuration with appropriate defaults.The Redis configuration is well-implemented with sensible defaults and clear comments. The type conversion for numeric values is properly handled.
src/spatial/views/chatbot_views.py (2)
33-39: Add additional validation for time format and grid_id.The current implementation only validates the presence of
grid_id,start_time, andend_time, and some basic validation on the prompt. Consider adding more thorough validation for date/time formats and grid_id format/existence.
75-80:❓ Verification inconclusive
Avoid exposing exception details in production.
In production environments, it's best not to expose raw exception details to clients, as they may contain sensitive information or implementation details. Consider logging the full exception but returning a generic message to the client.
🏁 Script executed:
#!/bin/bash # Check if there's any environment detection in the codebase for controlling logging behavior # Look for environment detection patterns in the project rg -A 3 "FLASK_ENV|environment" --include="*.py" src/spatial/Length of output: 505
Action Required: Review Exception Handling Strategy
The code in
src/spatial/views/chatbot_views.py(lines 75–80) currently logs the full exception and returns a generic "Internal server error" message to the client, which complies with production best practices by not exposing sensitive details. However, to better balance transparency during development with security in production, consider incorporating environment-specific error handling (for example, checking an environment variable likeFLASK_ENV). This would allow more detailed error information to be available in development while ensuring that production responses remain generic.
- Verify if environment detection or conditional error responses are implemented elsewhere in the project.
- If not, consider enhancing this behavior so that detailed error messages can be provided in non-production environments if needed.
src/spatial/models/chatbot_model.py (4)
94-94: Document the purpose of the threading lock.A threading lock is initialized but never used in the code. If it's intended for future use or for use by callers, consider adding documentation explaining its purpose.
103-109: Limit data exposure in context preparation.The context preparation method includes raw data samples which might contain sensitive information. Consider limiting the amount of data exposed to the LLM and formatting it more appropriately.
133-153: Implement thread safety in LLM response method.The class initializes a threading lock but doesn't use it when making external API calls. Consider using the lock to ensure thread safety, especially if this method might be called concurrently.
154-163: Well-structured chat method with appropriate validation.The main chat method has good input validation and follows a logical flow: validate input, try rule-based response first, then fall back to LLM. The length limitation for prompts is also a good safeguard.
src/spatial/models/chatbot_model.py
Outdated
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} | ||
| print("Fetching air quality data: ", payload) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Replace print statements with proper logging.
Using print statements for debugging is not recommended in production code. Replace with proper logging calls for consistency.
- print("Fetching air quality data: ", payload)
+ logging.info(f"Fetching air quality data: {payload}")📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} | |
| print("Fetching air quality data: ", payload) | |
| payload = {"grid_id": grid_id, "start_time": start_time, "end_time": end_time} | |
| logging.info(f"Fetching air quality data: {payload}") |
src/spatial/models/chatbot_model.py
Outdated
| print("Data fetched: ", data) | ||
| return data |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Replace print statement with proper logging.
Similar to the previous issue, use logging instead of print for consistency across the codebase.
- print("Data fetched: ", data)
+ logging.info(f"Data fetched successfully for grid_id: {grid_id}")📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| print("Data fetched: ", data) | |
| return data | |
| logging.info(f"Data fetched successfully for grid_id: {grid_id}") | |
| return data |
src/spatial/models/chatbot_model.py
Outdated
| class AirQualityChatbot: | ||
| def __init__(self, grid_id, start_time, end_time): | ||
| cache_key = f"chatbot_data:{grid_id}:{start_time}:{end_time}" | ||
| cached_data = redis_client.get(cache_key) |
There was a problem hiding this comment.
Constructor parameters don't match implementation.
The AirQualityChatbot class constructor accepts grid_id, start_time, and end_time parameters, but the class instance in chat_views.py is created with just one parameter (air_quality_data).
Apply this diff to fix the constructor signature:
- def __init__(self, grid_id, start_time, end_time):
- cache_key = f"chatbot_data:{grid_id}:{start_time}:{end_time}"
+ def __init__(self, air_quality_data):
+ # Extract grid_id, start_time, and end_time from data
+ grid_id = air_quality_data.get('airquality', {}).get('sites', {}).get('grid_id', 'unknown')
+ start_time = air_quality_data.get('airquality', {}).get('period', {}).get('startTime', '')
+ end_time = air_quality_data.get('airquality', {}).get('period', {}).get('endTime', '')
+ cache_key = f"chatbot_data:{grid_id}:{start_time}:{end_time}"📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| class AirQualityChatbot: | |
| def __init__(self, grid_id, start_time, end_time): | |
| cache_key = f"chatbot_data:{grid_id}:{start_time}:{end_time}" | |
| cached_data = redis_client.get(cache_key) | |
| class AirQualityChatbot: | |
| def __init__(self, air_quality_data): | |
| # Extract grid_id, start_time, and end_time from data | |
| grid_id = air_quality_data.get('airquality', {}).get('sites', {}).get('grid_id', 'unknown') | |
| start_time = air_quality_data.get('airquality', {}).get('period', {}).get('startTime', '') | |
| end_time = air_quality_data.get('airquality', {}).get('period', {}).get('endTime', '') | |
| cache_key = f"chatbot_data:{grid_id}:{start_time}:{end_time}" | |
| cached_data = redis_client.get(cache_key) |
src/spatial/models/chatbot_model.py
Outdated
|
|
||
| if cached_data: | ||
| data = json.loads(cached_data) # Load cached chatbot data | ||
| else: | ||
| data = DataFetcher.fetch_air_quality_data(grid_id, start_time, end_time) | ||
| if data: | ||
| redis_client.setex(cache_key, 3600, json.dumps(data)) # Cache for 1 hour |
There was a problem hiding this comment.
Data fetching logic is inconsistent with constructor parameters.
The constructor seems confused about whether it should use cached data or fetch new data when it already receives data as a parameter.
- if cached_data:
- data = json.loads(cached_data) # Load cached chatbot data
- else:
- data = DataFetcher.fetch_air_quality_data(grid_id, start_time, end_time)
- if data:
- redis_client.setex(cache_key, 3600, json.dumps(data)) # Cache for 1 hour
-
- self.data = data or {}
+ self.data = air_quality_data or {}
+
+ # Cache the data for future use if it's not empty
+ if self.data and self.data != {}:
+ redis_client.setex(cache_key, 3600, json.dumps(self.data)) # Cache for 1 hour📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| if cached_data: | |
| data = json.loads(cached_data) # Load cached chatbot data | |
| else: | |
| data = DataFetcher.fetch_air_quality_data(grid_id, start_time, end_time) | |
| if data: | |
| redis_client.setex(cache_key, 3600, json.dumps(data)) # Cache for 1 hour | |
| self.data = air_quality_data or {} | |
| # Cache the data for future use if it's not empty | |
| if self.data and self.data != {}: | |
| redis_client.setex(cache_key, 3600, json.dumps(self.data)) # Cache for 1 hour |
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Actionable comments posted: 2
🧹 Nitpick comments (2)
src/spatial/models/chatbot_model.py (2)
17-17: Remove unused Flask app initialization.Since this is a model file that doesn't define routes or serve requests directly, the Flask app initialization is unnecessary here.
-# Initialize Flask app -app = Flask(__name__)
152-152: Improve error message for LLM failure.The current error message is quite generic. Consider providing a more informative message that might guide users on what to try next.
- return "Sorry, I couldn't generate a response." + return "Sorry, I couldn't process your query right now. Please try asking a more specific question about air quality or try again later."
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src/spatial/models/chatbot_model.py(1 hunks)
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- GitHub Check: build-push-deploy-spatial
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🔇 Additional comments (9)
src/spatial/models/chatbot_model.py (9)
8-8: Unnecessary Flask initialization.The code initializes a Flask app on line 17 but doesn't use it anywhere else in this file. Consider removing this import and initialization unless there's a specific reason for it.
-from flask import FlaskAnd also remove the app initialization:
-# Initialize Flask app -app = Flask(__name__)
48-48: Replace print statement with proper logging.Using print statements for debugging is not recommended in production code. Replace with proper logging calls for consistency.
- print("Fetching air quality data: ", payload) + logging.info(f"Fetching air quality data: {payload}")
56-56: Replace print statement with proper logging.Similar to the previous issue, use logging instead of print for consistency across the codebase.
- print("Data fetched: ", data) + logging.info(f"Data fetched successfully for grid_id: {grid_id}")
67-79: Constructor parameters don't match implementation.The AirQualityChatbot class constructor accepts
grid_id,start_time, andend_timeparameters, but the class instance in chat_views.py is created with just one parameter (air_quality_data).Apply this diff to fix the constructor signature:
- def __init__(self, grid_id, start_time, end_time): - cache_key = f"chatbot_data:{grid_id}:{start_time}:{end_time}" + def __init__(self, air_quality_data): + # Extract grid_id, start_time, and end_time from data + grid_id = air_quality_data.get('airquality', {}).get('sites', {}).get('grid_id', 'unknown') + start_time = air_quality_data.get('airquality', {}).get('period', {}).get('startTime', '') + end_time = air_quality_data.get('airquality', {}).get('period', {}).get('endTime', '') + cache_key = f"chatbot_data:{grid_id}:{start_time}:{end_time}"
93-94: Add fallback mechanism for Gemini model initialization.The Gemini model is initialized without any error handling if the API key is missing or invalid. Consider adding a fallback mechanism or error check.
- self.gemini_model = genai.GenerativeModel('gemini-2.0-flash') + try: + self.gemini_model = genai.GenerativeModel('gemini-2.0-flash') + except Exception as e: + logging.error(f"Failed to initialize Gemini model: {e}") + self.gemini_model = None
94-94: Consider documenting the purpose of the threading lock.A threading lock is initialized but never used in the code. If it's intended for future use or for use by callers, consider adding documentation explaining its purpose.
103-109: Limit data exposure in context preparation.The context preparation method includes raw data samples which might contain sensitive information. Consider limiting the amount of data exposed to the LLM and formatting it more appropriately.
def _prepare_data_context(self): return ( f"AirQo data for {self.grid_name} ({self.starttime}-{self.endtime}): " f"Annual PM2.5={self.annual_pm2_5} µg/m³, Sites={self.num_sites}, " - f"Daily sample={self.daily_mean_data[:1]}, Diurnal sample={self.diurnal[:1]}, " - f"Monthly sample={self.monthly_data[:1]}, Site names={self.site_names}." + f"Today's PM2.5={self.today_pm2_5} µg/m³, " + f"Peak hour={(self.peak_diurnal.get('hour', 'N/A') + ':00') if self.peak_diurnal else 'N/A'}, " + f"Site names={', '.join(self.site_names[:5])}{' and more' if len(self.site_names) > 5 else ''}." )
133-152: Implement thread safety in LLM response method.The class initializes a threading lock but doesn't use it when making external API calls. Consider using the lock to ensure thread safety, especially if this method might be called concurrently.
def _llm_response(self, user_prompt): - if re.search(r"(report|summary|detailed|analysis|conclusion)", user_prompt.lower()): - full_prompt = ( - f"Data: {self._prepare_data_context()}\n" - f"User: {user_prompt}\n" - "Generate a concise conclusion or report based on the data. Focus on key insights." - ) - else: - full_prompt = ( - f"Data: {self._prepare_data_context()}\n" - f"User: {user_prompt}\n" - "Respond concisely and accurately based on the data." - ) - - try: - response = self.gemini_model.generate_content(full_prompt) - return response.text - except Exception as e: - logging.error(f"LLM error: {e}") - return "Sorry, I couldn't generate a response." + with self.lock: # Use lock for thread safety + if re.search(r"(report|summary|detailed|analysis|conclusion)", user_prompt.lower()): + full_prompt = ( + f"Data: {self._prepare_data_context()}\n" + f"User: {user_prompt}\n" + "Generate a concise conclusion or report based on the data. Focus on key insights." + ) + else: + full_prompt = ( + f"Data: {self._prepare_data_context()}\n" + f"User: {user_prompt}\n" + "Respond concisely and accurately based on the data." + ) + + if not self.gemini_model: + return "AI model not available. Please try again later." + + try: + response = self.gemini_model.generate_content(full_prompt) + return response.text + except Exception as e: + logging.error(f"LLM error: {e}") + return "Sorry, I couldn't generate a response."
154-163: Well-implemented input validation in chat method.This is a well-designed function with proper input validation for empty strings and excessively long inputs. The implementation of trying rule-based responses first with a fallback to LLM is also an efficient approach.
src/spatial/models/chatbot_model.py
Outdated
| self.peak_diurnal = max(self.diurnal, key=lambda x: x.get('pm2_5_calibrated_value', 0)) if self.diurnal else {} | ||
|
|
||
| # Cache processed chatbot data for faster retrieval | ||
| redis_client.setex(cache_key, 3600, json.dumps(self.__dict__)) |
There was a problem hiding this comment.
Fix potential serialization issue with self.dict.
Attempting to serialize the entire self.dict may lead to errors as it could contain non-serializable objects like the Gemini model.
- redis_client.setex(cache_key, 3600, json.dumps(self.__dict__))
+ # Create a serializable dict with only the data we need to cache
+ cache_data = {
+ 'grid_name': self.grid_name,
+ 'annual_pm2_5': self.annual_pm2_5,
+ 'today_pm2_5': self.today_pm2_5,
+ 'num_sites': self.num_sites,
+ 'starttime': self.starttime,
+ 'endtime': self.endtime,
+ 'site_names': self.site_names,
+ # Add other serializable attributes as needed
+ }
+ redis_client.setex(cache_key, 3600, json.dumps(cache_data))📝 Committable suggestion
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| redis_client.setex(cache_key, 3600, json.dumps(self.__dict__)) | |
| # Create a serializable dict with only the data we need to cache | |
| cache_data = { | |
| 'grid_name': self.grid_name, | |
| 'annual_pm2_5': self.annual_pm2_5, | |
| 'today_pm2_5': self.today_pm2_5, | |
| 'num_sites': self.num_sites, | |
| 'starttime': self.starttime, | |
| 'endtime': self.endtime, | |
| 'site_names': self.site_names, | |
| # Add other serializable attributes as needed | |
| } | |
| redis_client.setex(cache_key, 3600, json.dumps(cache_data)) |
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src/spatial/models/chatbot_model.py (2)
69-100: 🛠️ Refactor suggestionDocument the purpose of the threading lock and check for null data access.
The threading lock is initialized but never used in the code. Additionally, there are multiple places where the code accesses nested dictionaries without checking if parent keys exist, which could lead to KeyErrors.
For data access, consider using a safer approach:
- self.grid_name = self.data.get('airquality', {}).get('sites', {}).get('grid name', ['Unknown'])[0] + # Safely access nested data with proper default values + sites = self.data.get('airquality', {}).get('sites', {}) + grid_name_list = sites.get('grid name', ['Unknown']) + self.grid_name = grid_name_list[0] if grid_name_list else 'Unknown'Also, add a comment explaining the purpose of the lock:
- self.lock = threading.Lock() + # Lock to ensure thread-safety when making external API calls + self.lock = threading.Lock()
142-159: 🛠️ Refactor suggestionImplement thread safety in the LLM response method.
The class initializes a threading lock but doesn't use it when making external API calls. Consider using the lock to ensure thread safety in the
_llm_responsemethod.def _llm_response(self, user_prompt): """Generate a response using the Gemini model for complex queries.""" if not self.gemini_model: return "Language model is not available." - full_prompt = ( - f"Data: {self._prepare_data_context()}\n" - f"User: {user_prompt}\n" - "Respond concisely and accurately based on the data." - ) - - try: - response = self.gemini_model.generate_content(full_prompt) - return response.text - except Exception as e: - logging.error(f"LLM error: {e}") - return "Sorry, I couldn't generate a response." + with self.lock: # Use lock for thread safety + full_prompt = ( + f"Data: {self._prepare_data_context()}\n" + f"User: {user_prompt}\n" + "Respond concisely and accurately based on the data." + ) + + try: + response = self.gemini_model.generate_content(full_prompt) + return response.text + except Exception as e: + logging.error(f"LLM error: {e}") + return "Sorry, I couldn't generate a response."
🧹 Nitpick comments (1)
src/spatial/models/chatbot_model.py (1)
1-172: Consider implementing rate limiting for LLM API calls.The current implementation doesn't include rate limiting for LLM API calls, which could lead to excessive costs or API quota exhaustion if the chatbot is heavily used.
You could implement a token bucket algorithm or a simple counter-based rate limiter:
# Add to imports import time from collections import deque # Add to AirQualityChatbot class def __init__(self, air_quality_data): # ... existing code ... # Rate limiting for LLM API self.max_requests_per_minute = 60 # Adjust based on your API limits self.request_timestamps = deque(maxlen=self.max_requests_per_minute) # Update _llm_response method def _llm_response(self, user_prompt): """Generate a response using the Gemini model for complex queries.""" if not self.gemini_model: return "Language model is not available." # Implement rate limiting with self.lock: current_time = time.time() # Remove timestamps older than 1 minute while self.request_timestamps and current_time - self.request_timestamps[0] > 60: self.request_timestamps.popleft() if len(self.request_timestamps) >= self.max_requests_per_minute: return "Service is currently busy. Please try again in a moment." # Add current timestamp self.request_timestamps.append(current_time) # Continue with existing code...
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src/spatial/models/chatbot_model.py (4)
1-11: Import optimization required: Flask is imported but never used.The code imports Flask but doesn't utilize it in the implementation. The app initialization at line 20 appears to be unnecessary since no Flask routes or application features are defined in this model file.
- from flask import Flask
31-68: Great implementation of the DataFetcher with caching mechanism.The DataFetcher class correctly implements caching to avoid redundant API calls, includes proper error handling, and uses URL encoding for query parameters. The class is well-structured with static methods for data fetching.
101-109: Limit data exposure in context preparation.The method includes raw site names which might be excessive if there are many sites. Consider limiting the number of sites shown and summarizing the data more concisely.
- f"Site names={self.site_names}." + f"Site names={', '.join(self.site_names[:5])}{' and more' if len(self.site_names) > 5 else ''}."
160-172: Well-implemented chat method with input validation.The
chatmethod appropriately validates user input, handles missing data scenarios, and provides a fallback mechanism from rule-based to LLM responses. The code follows good practices by checking for edge cases like empty prompts, non-string inputs, and overly long queries.
src/spatial/models/chatbot_model.py
Outdated
| redis_client = redis.StrictRedis( | ||
| host=Config.REDIS_HOST or 'localhost', | ||
| port=Config.REDIS_PORT or 6379, | ||
| db=Config.REDIS_DB or 0, | ||
| password=Config.REDIS_PASSWORD or None, | ||
| decode_responses=True | ||
| ) |
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🛠️ Refactor suggestion
Add error handling for Redis connection failures.
The Redis client is initialized without any error handling. If Redis is unavailable or misconfigured, this could lead to runtime errors when the application tries to access it.
- # Initialize Redis client
- redis_client = redis.StrictRedis(
- host=Config.REDIS_HOST or 'localhost',
- port=Config.REDIS_PORT or 6379,
- db=Config.REDIS_DB or 0,
- password=Config.REDIS_PASSWORD or None,
- decode_responses=True
- )
+ # Initialize Redis client
+ try:
+ redis_client = redis.StrictRedis(
+ host=Config.REDIS_HOST or 'localhost',
+ port=Config.REDIS_PORT or 6379,
+ db=Config.REDIS_DB or 0,
+ password=Config.REDIS_PASSWORD or None,
+ decode_responses=True
+ )
+ # Test the connection
+ redis_client.ping()
+ logging.info("Redis connection established successfully")
+ except redis.ConnectionError as e:
+ logging.error(f"Failed to connect to Redis: {e}")
+ redis_client = None📝 Committable suggestion
‼️ IMPORTANT
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| redis_client = redis.StrictRedis( | |
| host=Config.REDIS_HOST or 'localhost', | |
| port=Config.REDIS_PORT or 6379, | |
| db=Config.REDIS_DB or 0, | |
| password=Config.REDIS_PASSWORD or None, | |
| decode_responses=True | |
| ) | |
| # Initialize Redis client | |
| try: | |
| redis_client = redis.StrictRedis( | |
| host=Config.REDIS_HOST or 'localhost', | |
| port=Config.REDIS_PORT or 6379, | |
| db=Config.REDIS_DB or 0, | |
| password=Config.REDIS_PASSWORD or None, | |
| decode_responses=True | |
| ) | |
| # Test the connection | |
| redis_client.ping() | |
| logging.info("Redis connection established successfully") | |
| except redis.ConnectionError as e: | |
| logging.error(f"Failed to connect to Redis: {e}") | |
| redis_client = None |
src/spatial/models/chatbot_model.py
Outdated
| # Initialize Flask app | ||
| app = Flask(__name__) | ||
|
|
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🛠️ Refactor suggestion
Remove unnecessary Flask app initialization.
This Flask application is initialized but never used in this module. The model layer should be independent of the web framework, and the app initialization should happen in the controller/view layer.
- # Initialize Flask app
- app = Flask(__name__)📝 Committable suggestion
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| # Initialize Flask app | |
| app = Flask(__name__) |
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src/spatial/models/chatbot_model.py (1)
77-105: Consider adding data validation in the constructorThe initialization logic assumes the data structure will always match expectations. While there are fallbacks for empty values, there's no validation that the incoming data has the expected structure before attempting to access nested properties.
Consider adding a validation method to check the data structure before processing:
def __init__(self, air_quality_data): + # Validate data structure + if not self._validate_data_structure(air_quality_data): + logging.warning("Received invalid or incomplete air quality data structure") + self.data = air_quality_data or {} # Rest of the initialization code... +def _validate_data_structure(self, data): + """Validate that the data contains the expected structure.""" + if not data or not isinstance(data, dict): + return False + + # Check for required top-level keys + if 'airquality' not in data: + return False + + airquality = data.get('airquality', {}) + + # Check for required nested structures + required_keys = ['sites', 'annual_pm', 'period'] + return all(key in airquality for key in required_keys)
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Remove unused import: flask.Flask
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src/spatial/models/chatbot_model.py (8)
8-8: Remove unused Flask importThe
Flaskclass is imported but never used in this file. This matches the static analysis hint and should be removed for cleaner code.-from flask import Flask🧰 Tools
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8-8:
flask.Flaskimported but unusedRemove unused import:
flask.Flask(F401)
21-35: Well-implemented Redis connection with proper error handlingThe Redis client initialization includes excellent error handling with:
- Fallback values for configuration parameters
- Connection testing with
ping()- Clear logging of success and failure states
- Graceful degradation by setting
redis_client = Noneon failureThis ensures the application can continue functioning even if Redis is unavailable.
37-74: Robust data fetching implementation with cachingThe
DataFetcherclass implements a well-designed pattern for API data retrieval with:
- Efficient Redis caching to reduce redundant API calls
- Proper URL parameter encoding through
urlencode- Comprehensive error handling for HTTP, request, and JSON parsing errors
- Appropriate logging at each stage
- Reasonable timeout settings for external requests
This implementation will help minimize API latency and reduce the load on external services.
105-105: Document the purpose of the threading lockThe threading lock is initialized but not used in the code. If it's intended for future synchronization or for use by other parts of the application, please add a comment to clarify its purpose.
107-115: Nicely organized data context preparationThe data context preparation method creates a well-structured, concise summary of the air quality data that's appropriate for prompting an LLM. It includes key metrics without exposing excessive raw data.
117-146: Well-implemented rule-based response systemThe rule-based response implementation uses appropriate regular expressions to identify common query patterns and provides direct answers without unnecessary LLM processing. This approach is efficient and ensures consistent responses for standard questions.
148-165: Consider using the threading lock for LLM response generationWhile the method properly checks if the model is available and includes error handling, it doesn't use the threading lock initialized in the constructor. LLM inference can be resource-intensive, and using the lock would ensure thread safety if this method is called concurrently.
166-178: Comprehensive input validation and response fallback strategyThe
chatmethod includes:
- Thorough validation of input parameters
- Reasonable length limits for user prompts
- Clear error messages for invalid inputs
- A smart fallback strategy that tries rule-based responses first and only uses the LLM when necessary
This approach optimizes for both performance and user experience.
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src/spatial/models/chatbot_model.py (4)
8-8: Remove the unusedFlaskimport.
This import is never used. Removing it will keep the file clean and consistent.- from flask import Flask🧰 Tools
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8-8:
flask.Flaskimported but unusedRemove unused import:
flask.Flask(F401)
73-73: Complete or remove the partial comment.
The comment forsetexappears unfinished. Clarifying its purpose or removing it entirely will improve readability.- # setex is + # setex sets a key-value pair in Redis with an expiration time (in seconds).
37-86: Add unit tests forDataFetcherto validate caching and error handling.
Ensure that the function correctly fetches data, caches it in Redis, and gracefully handles exceptions.Would you like help creating tests for this class, or opening a new issue to track this work?
87-129: Add unit test coverage for the newAirQualityChatbotclass.
Testing the constructor logic, data parsing, and context preparation will help ensure reliable performance across different data scenarios.Would you like assistance in writing comprehensive test cases or filing a new issue to address missing coverage?
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src/spatial/models/chatbot_model.py (1)
161-178: Reintroduce thread safety in_llm_responseusing the lock.
A prior review recommended locking this block for concurrent safety. This remains outstanding.def _llm_response(self, user_prompt): + with self.lock: if not self.gemini_model: return "Language model is not available." full_prompt = ( f"Data: {self._prepare_data_context()}\n" f"User: {user_prompt}\n" "Respond concisely and accurately based on the data." ) ...
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src/spatial/models/chatbot_model.py (2)
73-73: Complete the unfinished comment about Redis setex.There's an incomplete comment that should be completed to maintain code clarity.
- # Cache response in Redis for 1 hour - # setex is + # Cache response in Redis for 1 hour + # setex sets a key with expiration time in seconds
88-118: Consider refactoring the dense initialization logic.The initialization contains many nested
.get()calls with default values, which makes the code harder to maintain. Consider extracting helper methods for better readability.def __init__(self, air_quality_data): self.data = air_quality_data or {} + # Extract data from airquality object + airquality = self.data.get('airquality', {}) + sites = airquality.get('sites', {}) + period = airquality.get('period', {}) - self.grid_name = self.data.get('airquality', {}).get('sites', {}).get('grid name', ['Unknown'])[0] - self.annual_data = self.data.get('airquality', {}).get('annual_pm', [{}])[0] or {} - self.daily_mean_data = self.data.get('airquality', {}).get('daily_mean_pm', []) or [] - self.diurnal = self.data.get('airquality', {}).get('diurnal', []) or [] - self.monthly_data = self.data.get('airquality', {}).get('site_monthly_mean_pm', []) or [] - self.site_names = [item.get('site_name', 'Unknown') for item in self.data.get('airquality', {}).get('site_annual_mean_pm', [])] or ['Unknown'] - self.num_sites = self.data.get('airquality', {}).get('sites', {}).get('number_of_sites', 'Unknown') - self.starttime = self.data.get('airquality', {}).get('period', {}).get('startTime', '')[:10] or 'N/A' - self.endtime = self.data.get('airquality', {}).get('period', {}).get('endTime', '')[:10] or 'N/A' + self.grid_name = sites.get('grid name', ['Unknown'])[0] + self.annual_data = airquality.get('annual_pm', [{}])[0] or {} + self.daily_mean_data = airquality.get('daily_mean_pm', []) or [] + self.diurnal = airquality.get('diurnal', []) or [] + self.monthly_data = airquality.get('site_monthly_mean_pm', []) or [] + self.site_names = [item.get('site_name', 'Unknown') for item in airquality.get('site_annual_mean_pm', [])] or ['Unknown'] + self.num_sites = sites.get('number_of_sites', 'Unknown') + self.starttime = period.get('startTime', '')[:10] or 'N/A' + self.endtime = period.get('endTime', '')[:10] or 'N/A'
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src/spatial/models/chatbot_model.py (9)
8-8: Remove unused Flask import.The Flask import is not used anywhere in this file. Removing unused imports improves code cleanliness and reduces potential confusion about dependencies.
- from flask import Flask🧰 Tools
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8-8:
flask.Flaskimported but unusedRemove unused import:
flask.Flask(F401)
19-35: Good Redis initialization with error handling and connection testing.The Redis client initialization includes proper error handling, default values, and connection testing. The explanatory comment for the threading lock is particularly helpful.
37-86: Well-structured data fetching with caching mechanism.The DataFetcher class has a robust implementation with caching, error handling, and proper logging. The use of the thread lock during cache updates is a good practice to prevent race conditions.
118-118: Document or use the threading lock.A threading lock is initialized but never used in this class. Either document its intended future use or implement it in the appropriate methods.
120-128: Consider limiting data exposure in the context preparation.The data context includes all site names, which could be excessive for large numbers of sites and potentially include sensitive location information.
def _prepare_data_context(self): """Prepare a concise data context for the LLM.""" return ( f"AirQo data for {self.grid_name} ({self.starttime}-{self.endtime}): " f"Annual PM2.5={self.annual_pm2_5} µg/m³, Sites={self.num_sites}, " f"Most recent daily PM2.5={self.today_pm2_5} µg/m³ on {self.today_date}, " f"Diurnal peak={self.peak_diurnal.get('pm2_5_calibrated_value', 'N/A')} µg/m³ at {self.peak_diurnal.get('hour', 'N/A')}:00, " - f"Site names={self.site_names}." + f"Site names={', '.join(self.site_names[:5])}{' and more' if len(self.site_names) > 5 else ''}." )
130-159: Well-implemented rule-based responses with appropriate fallbacks.The rule-based response mechanism is well-structured with clear regex patterns and appropriate fallback messages when data is not available.
161-178: Consider using the lock in the LLM response method.The
_llm_responsemethod makes external API calls but doesn't use the initialized lock to ensure thread safety for concurrent requests.def _llm_response(self, user_prompt): """Generate a response using the Gemini model for complex queries.""" if not self.gemini_model: return "Language model is not available." - full_prompt = ( - f"Data: {self._prepare_data_context()}\n" - f"User: {user_prompt}\n" - "Respond concisely and accurately based on the data." - ) - - try: - response = self.gemini_model.generate_content(full_prompt) - return response.text - except Exception as e: - logging.error(f"LLM error: {e}") - return "Sorry, I couldn't generate a response." + with self.lock: # Use lock for thread safety + full_prompt = ( + f"Data: {self._prepare_data_context()}\n" + f"User: {user_prompt}\n" + "Respond concisely and accurately based on the data." + ) + + try: + response = self.gemini_model.generate_content(full_prompt) + return response.text + except Exception as e: + logging.error(f"LLM error: {e}") + return "Sorry, I couldn't generate a response."
179-191: Good input validation in the chat method.The chat method includes proper validation for empty inputs, non-string inputs, and excessively long inputs, which is an important security practice.
1-191: Overall well-structured chatbot implementation with hybrid approach.The combination of rule-based responses and LLM fallback is a robust approach that balances performance, reliability, and flexibility. The code follows good practices for error handling, input validation, and caching.
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src/spatial/README.md (1)
144-144: Adjust Heading Level for ConsistencyThe markdown lint tool indicates that heading levels should increment by only one level at a time. Since the "Chatbot Tool" section is a level 3 heading (
###), the "Example Request Body" heading should be level 4 (####) instead of level 5.-##### Example Request Body +#### Example Request Body🧰 Tools
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144-144: Heading levels should only increment by one level at a time
Expected: h4; Actual: h5(MD001, heading-increment)
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Expected: h4; Actual: h5
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src/spatial/README.md (1)
136-153: New Chatbot Tool Section Documentation Looks GreatThe "Chatbot Tool" section neatly outlines the new POST endpoint along with an example request body. It clearly communicates the purpose and usage of the chatbot for air quality inquiries. Nice work integrating this new documentation with the existing content!
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144-144: Heading levels should only increment by one level at a time
Expected: h4; Actual: h5(MD001, heading-increment)
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src/spatial/models/chatbot_model.py (1)
161-178: 🛠️ Refactor suggestionMake _llm_response thread-safe using the lock
The class initializes a threading lock but doesn't use it when making external API calls. Use the lock to ensure thread safety, especially if this method might be called concurrently by multiple users.
def _llm_response(self, user_prompt): """Generate a response using the Gemini model for complex queries.""" if not self.gemini_model: return "Language model is not available." - full_prompt = ( - f"Data: {self._prepare_data_context()}\n" - f"User: {user_prompt}\n" - "Respond concisely and accurately based on the data." - ) - - try: - response = self.gemini_model.generate_content(full_prompt) - return response.text - except Exception as e: - logging.error(f"LLM error: {e}") - return "Sorry, I couldn't generate a response." + with self.lock: # Use lock for thread safety + full_prompt = ( + f"Data: {self._prepare_data_context()}\n" + f"User: {user_prompt}\n" + "Respond concisely and accurately based on the data." + ) + + try: + response = self.gemini_model.generate_content(full_prompt) + return response.text + except Exception as e: + logging.error(f"LLM error: {e}") + return "Sorry, I couldn't generate a response."
🧹 Nitpick comments (3)
src/spatial/models/chatbot_model.py (3)
8-8: Remove unused Flask importThe
Flaskimport is not used anywhere in the code. It's best practice to remove unused imports to keep the code clean and avoid potential confusion.-from flask import Flask🧰 Tools
🪛 Ruff (0.8.2)
8-8:
flask.Flaskimported but unusedRemove unused import:
flask.Flask(F401)
73-73: Complete or remove the incomplete commentThe comment "# setex is" appears to be incomplete and doesn't provide any useful information. Either complete the explanation about the Redis
setexcommand or remove the comment.- # Cache response in Redis for 1 hour - # setex is + # Cache response in Redis for 1 hour (setex sets key with expiration)
134-158: Consider adding more regex patterns for common air quality questionsThe rule-based response system is well-implemented, but you could enhance it with additional patterns to cover more common air quality questions. This would reduce reliance on the LLM, improving response time and consistency.
Potential additions could include:
- Health impact queries (e.g., "is air quality safe today")
- Trend questions (e.g., "is air quality improving")
- Comparison queries (e.g., "compare air quality with another location")
Here's an example for health impact queries:
if re.search(r"(where|which|list).*site|sites|locations", prompt): if self.site_names != ['Unknown']: return f"Monitoring sites in {self.grid_name}: {', '.join(self.site_names)}." return "Site information is not available." +if re.search(r"(safe|health|impact|dangerous).*air.*quality", prompt): + if self.today_pm2_5 != 'N/A': + if float(self.today_pm2_5) < 12: + return f"The current PM2.5 level of {self.today_pm2_5} µg/m³ in {self.grid_name} is considered good and has minimal health impact." + elif float(self.today_pm2_5) < 35: + return f"The current PM2.5 level of {self.today_pm2_5} µg/m³ in {self.grid_name} is moderate. Unusually sensitive individuals may experience respiratory symptoms." + elif float(self.today_pm2_5) < 55: + return f"The current PM2.5 level of {self.today_pm2_5} µg/m³ in {self.grid_name} is unhealthy for sensitive groups. People with respiratory or heart conditions should limit outdoor activity." + else: + return f"The current PM2.5 level of {self.today_pm2_5} µg/m³ in {self.grid_name} is unhealthy. Everyone may begin to experience health effects and should limit outdoor exertion." + return "Current air quality health impact cannot be determined without recent data."
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src/spatial/models/chatbot_model.py (4)
119-119: Use the initialized threading lock for concurrent operationsA threading lock is initialized but never used in any methods of this class. The
_llm_responsemethod should use this lock to ensure thread safety during API calls, especially if this method might be called concurrently from multiple users.Is the lock intended for future use, or should it be used in the
_llm_responsemethod? If it's not needed, consider removing it to avoid confusion.
123-128: Limit the number of site names in the context preparationThe context preparation includes all site names without any limit, which could be excessive and lead to token limit issues when sending to the LLM, especially if there are many monitoring sites. Consider limiting the number of site names included.
return ( f"AirQo data for {self.grid_name} ({self.starttime}-{self.endtime}): " f"Annual PM2.5={self.annual_pm2_5} µg/m³, Sites={self.num_sites}, " f"Most recent daily PM2.5={self.today_pm2_5} µg/m³ on {self.today_date}, " f"Diurnal peak={self.peak_diurnal.get('pm2_5_calibrated_value', 'N/A')} µg/m³ at {self.peak_diurnal.get('hour', 'N/A')}:00, " - f"Site names={self.site_names}." + f"Site names={', '.join(self.site_names[:5])}{' and more' if len(self.site_names) > 5 else ''}." )
179-191: Great input validation in the chat methodThe input validation in the chat method is well-implemented. It checks for:
- Availability of air quality data
- Validity of the user prompt (not empty and is a string)
- Length constraints to prevent excessively long prompts
This helps ensure robustness of the chatbot against various edge cases and potential misuse.
101-110: Well-structured data processing for daily and diurnal dataThe implementation for processing daily mean data and finding the peak diurnal value is well-structured and handles edge cases properly. Sorting daily data to get the most recent measurements and finding the peak diurnal hour using max() with a key function are elegant approaches.
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src/spatial/models/chatbot_model.py (2)
8-8: Remove unused importThe Flask import is not used in this file and should be removed for cleaner code.
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161-178: Consider making the model name configurableThe Gemini model name ('gemini-2.0-flash') is hard-coded. Consider making this configurable through the Config object to facilitate easier updates when new model versions are released.
- self.gemini_model = genai.GenerativeModel('gemini-2.0-flash') + self.gemini_model = genai.GenerativeModel(Config.GEMINI_MODEL_NAME or 'gemini-2.0-flash')Then add
GEMINI_MODEL_NAMEto your Config class.
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src/spatial/models/chatbot_model.py (7)
19-35: Well-implemented Redis connection with proper error handlingYour Redis connection implementation follows best practices with proper error handling, connection testing, and informative logging. The dedicated lock for thread safety shows good attention to concurrency concerns.
37-86: Excellent implementation of data fetching with cachingThe DataFetcher class is well-implemented with:
- Proper caching to avoid redundant API calls
- Thread safety using locks
- Comprehensive error handling
- Good logging practices
This approach will significantly improve performance for repeated queries to the same data.
112-119: Consider utilizing the threading lock for LLM responsesYou've initialized a lock but it's not being used when calling the LLM model. Since LLM calls can be resource-intensive, consider using the lock in the
_llm_responsemethod to prevent potential resource contention when multiple requests occur simultaneously.def _llm_response(self, user_prompt): """Generate a response using the Gemini model for complex queries.""" if not self.gemini_model: return "Language model is not available." + with self.lock: full_prompt = ( f"Data: {self._prepare_data_context()}\n" f"User: {user_prompt}\n" "Respond concisely and accurately based on the data." ) try: response = self.gemini_model.generate_content(full_prompt) return response.text except Exception as e: logging.error(f"LLM error: {e}") return "Sorry, I couldn't generate a response."
120-129: Consider limiting data exposure in the context preparationThe context preparation includes full site names, which might contain more information than necessary and could potentially expose sensitive location data to the LLM. Consider limiting the amount of data sent to the model.
def _prepare_data_context(self): """Prepare a concise data context for the LLM.""" return ( f"AirQo data for {self.grid_name} ({self.starttime}-{self.endtime}): " f"Annual PM2.5={self.annual_pm2_5} µg/m³, Sites={self.num_sites}, " f"Most recent daily PM2.5={self.today_pm2_5} µg/m³ on {self.today_date}, " f"Diurnal peak={self.peak_diurnal.get('pm2_5_calibrated_value', 'N/A')} µg/m³ at {self.peak_diurnal.get('hour', 'N/A')}:00, " - f"Site names={self.site_names}." + f"Site names={', '.join(self.site_names[:5])}{' and more' if len(self.site_names) > 5 else ''}." )
130-160: Well-designed rule-based response systemThe rule-based response system is well-designed with:
- Clear regex patterns for different types of queries
- Appropriate responses based on available data
- Graceful handling of missing data
- Logical organization of query patterns
This approach efficiently handles common queries without invoking the LLM, which will improve response time and reduce costs.
179-191: Well-implemented chat method with input validationThe chat method includes thorough input validation for:
- Empty or non-string inputs
- Excessively long prompts
- Missing data scenarios
The hybrid approach of trying rule-based responses first before falling back to the LLM is an excellent pattern that balances performance and flexibility.
1-191: Overall excellent implementation of the chatbot modelThe entire implementation demonstrates strong software engineering practices:
- Clear separation of concerns between data fetching and chatbot functionality
- Efficient caching to improve performance
- Comprehensive error handling
- Good logging practices
- Thread safety considerations
- Hybrid approach combining rule-based and LLM responses
This chatbot model should provide users with responsive and accurate air quality information.
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Description
New Features
Bug Fixes
Related Issues
Changes Made
Testing
Affected Services
Endpoints Ready for Testing
API Documentation Updated?
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Summary by CodeRabbit
New Features
Bug Fixes
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