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Added response time + highlights + summary to search step #143
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WalkthroughThe changes made in the Changes
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📜 Recent review detailsConfiguration used: CodeRabbit UI ⛔ Files ignored due to path filters (1)
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🔇 Additional comments (3)pyproject.toml (3)
The change to use caret versioning (
The addition of e2b-code-interpreter suggests new code interpretation capabilities. Let's verify its intended usage. ✅ Verification successfulThe e2b-code-interpreter dependency is properly integrated and used The dependency is correctly used in
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# Description: Check for the usage of e2b-code-interpreter in the codebase
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rg "from e2b" || rg "import e2b"
# Check if there are any new files related to code interpretation
fd -e py | xargs rg "code.*interpret"
Length of output: 205 Script: #!/bin/bash
# Let's check the implementation details of code_execution_v2.py to understand how e2b-code-interpreter is used
cat athina/steps/code_execution_v2.py
Length of output: 12328
The version bump (1.6.19 -> 1.6.20) and significant update to langchain-openai (0.0.8 -> 0.1.1) warrant verification of compatibility. Also applies to: 21-21 ✅ Verification successfulLet's examine the usage of langchain_openai in the identified file to understand the potential impact. The langchain-openai update (0.0.8 -> 0.1.1) appears safe to proceed The code uses only stable ChatOpenAI and AzureChatOpenAI classes from langchain-openai, and the changelog shows no breaking changes in the core functionality of these classes between versions. The usage is limited to a single file (ragas_evaluator.py) where it's used for model initialization with basic parameters that have remained consistent across versions. 🏁 Scripts executedThe following scripts were executed for the analysis: Script: #!/bin/bash
# Description: Check for breaking changes in langchain-openai
# Check the changelog or release notes
curl -s https://api.github.com/repos/langchain-ai/langchain/releases | jq '.[].body' | grep -i "langchain-openai"
# Look for imports and usage patterns that might be affected
rg -l "from langchain_openai" || rg -l "import langchain_openai"
Length of output: 1335 Script: #!/bin/bash
# Check the specific usage of langchain_openai in the file
cat athina/evals/ragas/ragas_evaluator.py
# Also check for any test files that might verify this functionality
fd "test.*ragas.*" --type f
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (3)
athina/steps/search.py (3)
75-80
: Consider adding query validationWhile the structure is correct per API requirements, consider adding validation for the query parameter to ensure it meets any API constraints (length, format, etc.).
class Search(Step): @property + def is_valid_query(self) -> bool: + return bool(self.query and len(self.query.strip()) > 0) + def execute(self, input_data: Any) -> Union[Dict[str, Any], None]: + if not self.is_valid_query: + return { + "status": "error", + "data": "Invalid query: Query cannot be empty", + "metadata": { + "response_time": 0 + } + }
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131-140
: Consider exponential backoff for retriesThe current retry mechanism uses a fixed 2-second delay. Consider implementing exponential backoff for more robust retry handling.
except requests.Timeout: if attempt < retries - 1: - time.sleep(2) + time.sleep((2 ** attempt) * 1.5) # exponential backoff with jitter continue
93-96
: Consider adding rate limiting protectionThe API calls could benefit from rate limiting protection to prevent accidental API key exhaustion and potential costs.
Consider implementing a rate limiter decorator:
from functools import wraps import time def rate_limit(calls: int, period: float): """Limit to `calls` calls per `period` seconds.""" min_interval = period / calls last_reset = time.time() calls_made = 0 def decorator(func): @wraps(func) def wrapper(*args, **kwargs): nonlocal last_reset, calls_made now = time.time() # Reset counter if period has elapsed if now - last_reset > period: calls_made = 0 last_reset = now # Enforce rate limit if calls_made >= calls: sleep_time = period - (now - last_reset) if sleep_time > 0: time.sleep(sleep_time) calls_made = 0 last_reset = time.time() calls_made += 1 return func(*args, **kwargs) return wrapper return decorator
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athina/steps/search.py
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🔇 Additional comments (3)
athina/steps/search.py (3)
12-15
: LGTM! Clean implementation of prepare_input_data
The dict comprehension implementation is concise and correctly handles the conversion of nested data structures.
51-52
: LGTM! Well-structured response time tracking
The implementation consistently tracks and reports response time in milliseconds across all response paths.
Also applies to: 117-119
61-63
: Verify PreserveUndefined implementation
The Jinja2 environment configuration looks good, but let's verify the PreserveUndefined implementation.
✅ Verification successful
PreserveUndefined implementation is correct and properly integrated
The implementation is verified:
- PreserveUndefined correctly inherits from Jinja2's Undefined class
- It's properly imported from jinja_helper in search.py
- The implementation preserves undefined variables by wrapping them in curly braces
- No duplicate implementations exist in the codebase
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Script:
#!/bin/bash
# Check PreserveUndefined implementation
rg -A 5 "class PreserveUndefined" athina/helpers/jinja_helper.py
Length of output: 172
Script:
#!/bin/bash
# Check if PreserveUndefined is imported from jinja_helper
rg -A 2 "from.*jinja_helper.*PreserveUndefined" athina/steps/search.py
# Check if there are any other PreserveUndefined implementations
rg -g '!athina/helpers/jinja_helper.py' "class PreserveUndefined"
# Check if Undefined is properly imported in jinja_helper
rg "from.*jinja2.*Undefined" athina/helpers/jinja_helper.py
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Summary by CodeRabbit
New Features
metadata
field that includesresponse_time
for better performance insights.highlights
andsummary
for improved data handling.Bug Fixes
metadata
in both successful and error responses, enhancing feedback during failures.Chores
1.6.20
and modified dependencies for improved compatibility and functionality.