fix: refine data handling in LambdaFilterComponent to ensure proper p… #11128
Conversation
WalkthroughA single file in the lambda filter component was updated to normalize payload structures by wrapping "result" lists in a "_results" key, and JSON serialization was tightened with compact separators and non-ASCII character preservation. These changes alter the data structure passed to downstream processing steps. Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~12 minutes
Pre-merge checks and finishing touchesImportant Pre-merge checks failedPlease resolve all errors before merging. Addressing warnings is optional. ❌ Failed checks (1 error, 2 warnings, 1 inconclusive)
✅ Passed checks (3 passed)
✨ Finishing touches
🧪 Generate unit tests (beta)
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. Comment |
github-actions
bot
added
bug
![coderabbitai[bot]](https://cdn.statically.io/img/avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 1
🧹 Nitpick comments (1)
src/lfx/src/lfx/components/llm_operations/lambda_filter.py (1)
159-159: JSON serialization improvements look good.The changes to use compact separators and preserve non-ASCII characters are appropriate for LLM prompts, as they:
- Maximize token efficiency with compact formatting
- Improve readability of international text by avoiding Unicode escapes
Note that this changes the exact format of logged output and LLM inputs, which could affect string-based tests or debugging workflows that expect specific formatting.
Optional: Consider extracting separators as a constant
For maintainability, you could define the separators at module level:
# At the top of the file, after imports _COMPACT_JSON_SEPARATORS = (",", ":")Then use it:
- dump = json.dumps(data, separators=(",", ":"), ensure_ascii=False) + dump = json.dumps(data, separators=_COMPACT_JSON_SEPARATORS, ensure_ascii=False)This makes the intent clearer and ensures consistency if this pattern is used elsewhere.
📜 Review details
Configuration used: Path: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
src/lfx/src/lfx/components/llm_operations/lambda_filter.py
🧰 Additional context used
🧬 Code graph analysis (1)
src/lfx/src/lfx/components/llm_operations/lambda_filter.py (2)
src/lfx/src/lfx/graph/graph/base.py (2)
dump(227-251)dumps(218-225)src/lfx/src/lfx/schema/message.py (1)
json(327-329)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (3)
- GitHub Check: Update Component Index
- GitHub Check: Run Ruff Check and Format
- GitHub Check: Update Starter Projects
| # Keep only the actual payload (common for HTTP/API fetch components) | ||
| if isinstance(data, dict) and "result" in data and isinstance(data["result"], list): | ||
| data = {"_results": data["result"]} |
There was a problem hiding this comment.
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
# Check for usage patterns of "result" and "_results" keys in data handling
# Find references to "result" key access in data processing
echo "=== Checking for 'result' key access patterns ==="
rg -n --type=py -C3 '\["result"\]|\[.result.\]|\.get\(.result.\)' src/lfx/ 2>/dev/null | head -60
# Find references to "_results" key
echo -e "\n=== Checking for '_results' key usage ==="
rg -n --type=py -C3 '\["_results"\]|\[._results.\]|_results' src/lfx/ 2>/dev/null | head -60
# Check for similar data transformation patterns
echo -e "\n=== Checking for similar data wrapping patterns ==="
rg -n --type=py -C2 'data\s*=\s*\{.*:.*data\[' src/lfx/ 2>/dev/null | head -40Repository: langflow-ai/langflow
Length of output: 11103
🏁 Script executed:
#!/bin/bash
# Examine the lambda_filter.py file around lines 156-159 to understand context
echo "=== Context around lines 156-160 in lambda_filter.py ==="
sed -n '140,170p' src/lfx/src/lfx/components/llm_operations/lambda_filter.py | cat -n
echo -e "\n=== Context around line 229 to see output wrapping pattern ==="
sed -n '220,235p' src/lfx/src/lfx/components/llm_operations/lambda_filter.py | cat -n
echo -e "\n=== Full method signature and initial lines ==="
sed -n '100,125p' src/lfx/src/lfx/components/llm_operations/lambda_filter.py | cat -nRepository: langflow-ai/langflow
Length of output: 3772
🏁 Script executed:
#!/bin/bash
# Find where _execute_lambda is called and understand the context
echo "=== Searching for calls to _execute_lambda ==="
rg -n --type=py '_execute_lambda' src/lfx/ 2>/dev/null | head -20
echo -e "\n=== Looking for HTTP/API fetch component definitions ==="
rg -n --type=py 'http.*fetch|fetch.*api|http_request' src/lfx/ -i 2>/dev/null | head -30
echo -e "\n=== Checking test files for lambda_filter ==="
fd -e py 'test.*lambda.*filter|lambda.*filter.*test' src/lfx/ 2>/dev/nullRepository: langflow-ai/langflow
Length of output: 2681
🏁 Script executed:
#!/bin/bash
# Get more context around the data transformation and how it's used
echo "=== Full context of _execute_lambda method (lines 123-250) ==="
sed -n '123,250p' src/lfx/src/lfx/components/llm_operations/lambda_filter.py | cat -n
echo -e "\n=== Checking how data structure is used in prompt ==="
sed -n '170,220p' src/lfx/src/lfx/components/llm_operations/lambda_filter.py | cat -nRepository: langflow-ai/langflow
Length of output: 8244
🏁 Script executed:
#!/bin/bash
# Look for where lambda_filter component is used and what feeds data into it
echo "=== Searching for lambda_filter component usage ==="
rg -n --type=py 'lambda_filter|LambdaFilter' src/lfx/ 2>/dev/null | head -30
echo -e "\n=== Checking component definitions and inputs ==="
sed -n '1,120p' src/lfx/src/lfx/components/llm_operations/lambda_filter.py | cat -n
echo -e "\n=== Looking for HTTP component that returns 'result' key ==="
rg -n --type=py '"result".*:' src/lfx/src/lfx/components/ 2>/dev/null | grep -v test | head -20Repository: langflow-ai/langflow
Length of output: 6798
🏁 Script executed:
#!/bin/bash
# Check if there's any documentation or test about this transformation
echo "=== Searching for test files ==="
find src/lfx -name "*test*.py" -path "*/llm_operations/*" 2>/dev/null
echo -e "\n=== Check for any PR or issue comments about this change ==="
git log --oneline -n 50 -- src/lfx/src/lfx/components/llm_operations/lambda_filter.py 2>/dev/null | head -20
echo -e "\n=== Check the git diff for this file ==="
git diff HEAD~5..HEAD -- src/lfx/src/lfx/components/llm_operations/lambda_filter.py 2>/dev/null | head -80Repository: langflow-ai/langflow
Length of output: 295
Clarify the scope and impact of the "result" key transformation.
The conditional transformation (lines 156-158) that converts {"result": [...]} to {"_results": [...]} applies to all data sources, not just HTTP/API fetch components as the comment suggests. This creates two issues:
-
Semantic mismatch in LLM prompt: When the transformation occurs, the LLM sees
_resultsinstead ofresult. If user instructions reference the "result" key (e.g., "filter items in result"), the LLM will be working with misaligned field names, potentially producing incorrect lambda functions. -
Unintended data loss: Any dict with a "result" key containing a list loses all other keys in the dict (metadata, pagination info, status codes, etc.). While this may be intentional for HTTP/API responses, the component accepts data from any upstream source without restriction.
Consider either:
- Restricting this transformation to specific upstream component types (HTTP/API fetch) rather than all data sources
- Preserving additional keys alongside the wrapped results:
{"_results": [...], **other_keys} - Adding tests to document the expected behavior and acceptable data loss
No test files currently exist for this component to verify the transformation behavior.
This pull request makes a targeted improvement to the
_execute_lambdamethod inlambda_filter.pyto streamline the payload structure before serialization. The main change ensures that when the data contains aresultlist, only this relevant information is retained and renamed to_resultsbefore being processed further.Payload handling improvement:
_execute_lambdato extract theresultlist from the input data (if present), wrap it under the_resultskey, and use compact JSON formatting with UTF-8 support. This helps standardize the payload structure for downstream processing.…ayload structureSummary by CodeRabbit
✏️ Tip: You can customize this high-level summary in your review settings.