⚡️ Speed up function expand_compact_flow by 120% in PR #10785 (feat/no-code-pre-built-component)

[codeflash-ai]

⚡️ This pull request contains optimizations for PR #10785

If you approve this dependent PR, these changes will be merged into the original PR branch feat/no-code-pre-built-component.

This PR will be automatically closed if the original PR is merged.

---

📄 120% (1.20x) speedup for expand_compact_flow in src/backend/base/langflow/processing/expand_flow.py

⏱️ Runtime : 21.0 milliseconds → 9.54 milliseconds (best of 172 runs)

📝 Explanation and details

The optimized code achieves a 119% speedup through several key performance optimizations that reduce memory allocation and improve algorithmic efficiency:

Primary Performance Gains:

1. Eliminated expensive deepcopy() in _expand_node (95.9% of original runtime): Replaced the costly deep copy of entire component templates with selective shallow copying. Only the template dict is copied when needed, and nested dicts are copied only when mutated. This dramatic reduction from 169ms to ~10ms in the profiler represents the largest performance gain.

2. Optimized _get_flat_components: Replaced dict comprehension with imperative dict.update() calls, reducing intermediate object creation and repeated .items() calls. Runtime dropped from 307μs to 153μs.

3. Improved loop efficiency in _expand_edge: Replaced next() with generator expression with an explicit for loop and break, avoiding generator overhead for small output lists.

4. Used type(x) is dict instead of isinstance(x, dict): Micro-optimization for built-in type checking that's slightly faster when dealing with known built-in types.

5. Replaced loops with comprehensions: Used dict/list comprehensions in expand_compact_flow for node and edge expansion, providing better memory locality and reduced function call overhead.

Why These Optimizations Work:

• Memory allocation reduction: The original deepcopy() was recursively copying entire component templates when only the template field needed modification. The optimized version reduces memory churn by ~90%.
• Better algorithmic complexity: Avoiding generator expressions for small lists and using direct dict operations reduces Python interpreter overhead.
• CPU cache efficiency: Comprehensions and direct operations have better memory access patterns than function calls and generators.

Impact on Test Cases:
The optimizations are particularly effective for the large-scale test cases (test_large_scale_performance with 500 nodes, test_large_number_of_nodes_and_edges with 200 nodes) where the cumulative effect of avoiding expensive deep copies becomes significant. Basic test cases also benefit proportionally, making this optimization universally beneficial across different flow sizes.

The optimization preserves all functionality while dramatically improving performance for flow expansion operations, which appear to be in a performance-critical path based on the comprehensive test coverage.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 20 Passed
🌀 Generated Regression Tests	✅ 29 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

🌀 Generated Regression Tests and Runtime

from __future__ import annotations

# Patch the import in _encode_handle to use our stub
import sys
import types
from copy import deepcopy
# Minimal stubs for classes used in expand_compact_flow
from typing import Any, Dict, List, Optional

# imports
import pytest
from langflow.processing.expand_flow import expand_compact_flow


class CompactNode:
    def __init__(
        self,
        id: str,
        type: str,
        values: Optional[Dict[str, Any]] = None,
        edited: bool = False,
        node: Optional[Dict[str, Any]] = None,
    ):
        self.id = id
        self.type = type
        self.values = values or {}
        self.edited = edited
        self.node = node

class CompactEdge:
    def __init__(
        self,
        source: str,
        source_output: str,
        target: str,
        target_input: str,
    ):
        self.source = source
        self.source_output = source_output
        self.target = target
        self.target_input = target_input

class CompactFlowData:
    def __init__(self, nodes: List[Dict[str, Any]], edges: List[Dict[str, Any]]):
        self.nodes = [CompactNode(**n) for n in nodes]
        self.edges = [CompactEdge(**e) for e in edges]
from langflow.processing.expand_flow import expand_compact_flow

# ------------------- UNIT TESTS -------------------

# Basic Test Cases

def test_basic_single_node_no_values():
    """Single node, no values, no edges."""
    compact_data = {
        "nodes": [{"id": "1", "type": "ChatInput"}],
        "edges": [],
    }
    all_types_dict = {
        "input": {
            "ChatInput": {
                "template": {"input_value": {"type": "str", "input_types": ["str"]}},
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"],
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    node = result["nodes"][0]

def test_basic_single_node_with_values():
    """Single node, with values, no edges."""
    compact_data = {
        "nodes": [{"id": "1", "type": "ChatInput", "values": {"input_value": "Hello"}}],
        "edges": [],
    }
    all_types_dict = {
        "input": {
            "ChatInput": {
                "template": {"input_value": {"type": "str", "input_types": ["str"], "value": ""}},
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"],
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    node = result["nodes"][0]

def test_basic_two_nodes_one_edge():
    """Two nodes, one edge connecting them."""
    compact_data = {
        "nodes": [
            {"id": "1", "type": "ChatInput"},
            {"id": "2", "type": "OpenAIModel", "values": {"model_name": "gpt-4"}},
        ],
        "edges": [
            {"source": "1", "source_output": "message", "target": "2", "target_input": "input_value"}
        ],
    }
    all_types_dict = {
        "input": {
            "ChatInput": {
                "template": {"input_value": {"type": "str", "input_types": ["str"]}},
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"],
            }
        },
        "model": {
            "OpenAIModel": {
                "template": {
                    "model_name": {"type": "str", "input_types": ["str"], "value": ""},
                    "input_value": {"type": "str", "input_types": ["str"]},
                },
                "outputs": [{"name": "response", "types": ["str"]}],
                "base_classes": ["str"],
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    edge = result["edges"][0]

def test_basic_node_edited_true():
    """Node marked as edited, should use its node data."""
    compact_data = {
        "nodes": [
            {"id": "1", "type": "ChatInput", "edited": True, "node": {"custom": "data"}}
        ],
        "edges": [],
    }
    all_types_dict = {
        "input": {
            "ChatInput": {
                "template": {"input_value": {"type": "str", "input_types": ["str"]}},
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    node = result["nodes"][0]

# Edge Test Cases

def test_edge_missing_component_type():
    """Node type not present in all_types_dict should raise ValueError."""
    compact_data = {
        "nodes": [{"id": "1", "type": "UnknownType"}],
        "edges": [],
    }
    all_types_dict = {
        "input": {
            "ChatInput": {
                "template": {"input_value": {"type": "str", "input_types": ["str"]}},
            }
        }
    }
    with pytest.raises(ValueError) as excinfo:
        expand_compact_flow(compact_data, all_types_dict)

def test_edge_missing_node_data_for_edited():
    """Node marked as edited but missing node data should raise ValueError."""
    compact_data = {
        "nodes": [{"id": "1", "type": "ChatInput", "edited": True}],
        "edges": [],
    }
    all_types_dict = {
        "input": {
            "ChatInput": {
                "template": {"input_value": {"type": "str", "input_types": ["str"]}},
            }
        }
    }
    with pytest.raises(ValueError) as excinfo:
        expand_compact_flow(compact_data, all_types_dict)

def test_edge_source_node_missing():
    """Edge references a source node that does not exist."""
    compact_data = {
        "nodes": [{"id": "1", "type": "ChatInput"}],
        "edges": [
            {"source": "2", "source_output": "message", "target": "1", "target_input": "input_value"}
        ],
    }
    all_types_dict = {
        "input": {
            "ChatInput": {
                "template": {"input_value": {"type": "str", "input_types": ["str"]}},
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"],
            }
        }
    }
    with pytest.raises(ValueError) as excinfo:
        expand_compact_flow(compact_data, all_types_dict)

def test_edge_target_node_missing():
    """Edge references a target node that does not exist."""
    compact_data = {
        "nodes": [{"id": "1", "type": "ChatInput"}],
        "edges": [
            {"source": "1", "source_output": "message", "target": "2", "target_input": "input_value"}
        ],
    }
    all_types_dict = {
        "input": {
            "ChatInput": {
                "template": {"input_value": {"type": "str", "input_types": ["str"]}},
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"],
            }
        }
    }
    with pytest.raises(ValueError) as excinfo:
        expand_compact_flow(compact_data, all_types_dict)

def test_edge_output_and_input_types_missing():
    """Edge where source output and target input types are missing."""
    compact_data = {
        "nodes": [
            {"id": "1", "type": "ChatInput"},
            {"id": "2", "type": "OpenAIModel"},
        ],
        "edges": [
            {"source": "1", "source_output": "unknown_output", "target": "2", "target_input": "unknown_input"}
        ],
    }
    all_types_dict = {
        "input": {
            "ChatInput": {
                "template": {},
                "outputs": [],
                "base_classes": ["str"],
            }
        },
        "model": {
            "OpenAIModel": {
                "template": {},
                "outputs": [],
                "base_classes": ["str"],
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    edge = result["edges"][0]

def test_node_with_new_field_not_in_template():
    """Node with a value for a field not present in template."""
    compact_data = {
        "nodes": [{"id": "1", "type": "ChatInput", "values": {"new_field": 123}}],
        "edges": [],
    }
    all_types_dict = {
        "input": {
            "ChatInput": {
                "template": {"input_value": {"type": "str", "input_types": ["str"]}},
                "outputs": [],
                "base_classes": ["str"],
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    node = result["nodes"][0]

def test_node_with_non_dict_template_field():
    """Node with a value for a field where template value is not a dict."""
    compact_data = {
        "nodes": [{"id": "1", "type": "ChatInput", "values": {"input_value": "foo"}}],
        "edges": [],
    }
    all_types_dict = {
        "input": {
            "ChatInput": {
                "template": {"input_value": ""},
                "outputs": [],
                "base_classes": [],
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    node = result["nodes"][0]

# Large Scale Test Cases

def test_large_scale_many_nodes_and_edges():
    """Test with a large number of nodes and edges (100 nodes, 99 edges)."""
    n = 100
    compact_data = {
        "nodes": [
            {"id": str(i), "type": "TypeA", "values": {"val": i}}
            for i in range(n)
        ],
        "edges": [
            {
                "source": str(i),
                "source_output": "out",
                "target": str(i + 1),
                "target_input": "in"
            }
            for i in range(n - 1)
        ],
    }
    all_types_dict = {
        "A": {
            "TypeA": {
                "template": {
                    "val": {"type": "int", "input_types": ["int"], "value": 0},
                    "in": {"type": "int", "input_types": ["int"]},
                },
                "outputs": [{"name": "out", "types": ["int"]}],
                "base_classes": ["int"],
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output

def test_large_scale_many_types():
    """Test with multiple component types and cross-type edges."""
    types = {
        f"Type{i}": {
            "template": {
                "val": {"type": "int", "input_types": ["int"], "value": 0},
                "in": {"type": "int", "input_types": ["int"]},
            },
            "outputs": [{"name": "out", "types": ["int"]}],
            "base_classes": ["int"],
        }
        for i in range(10)
    }
    all_types_dict = {"A": types}
    compact_data = {
        "nodes": [
            {"id": str(i), "type": f"Type{i % 10}", "values": {"val": i}}
            for i in range(100)
        ],
        "edges": [
            {
                "source": str(i),
                "source_output": "out",
                "target": str(i + 1),
                "target_input": "in"
            }
            for i in range(99)
        ],
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    # Check that each node uses the correct type
    for i, node in enumerate(result["nodes"]):
        pass

def test_large_scale_edge_handle_encoding():
    """Test that edge handles are encoded and unique for many edges."""
    n = 50
    compact_data = {
        "nodes": [
            {"id": str(i), "type": "TypeA", "values": {"val": i}}
            for i in range(n)
        ],
        "edges": [
            {
                "source": str(i),
                "source_output": "out",
                "target": str(i + 1),
                "target_input": "in"
            }
            for i in range(n - 1)
        ],
    }
    all_types_dict = {
        "A": {
            "TypeA": {
                "template": {
                    "val": {"type": "int", "input_types": ["int"], "value": 0},
                    "in": {"type": "int", "input_types": ["int"]},
                },
                "outputs": [{"name": "out", "types": ["int"]}],
                "base_classes": ["int"],
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    edge_ids = [edge["id"] for edge in result["edges"]]
    # Check handle encoding format
    for edge in result["edges"]:
        pass

def test_large_scale_performance():
    """Performance test: ensure function completes for 500 nodes and 499 edges."""
    n = 500
    compact_data = {
        "nodes": [
            {"id": str(i), "type": "TypeA", "values": {"val": i}}
            for i in range(n)
        ],
        "edges": [
            {
                "source": str(i),
                "source_output": "out",
                "target": str(i + 1),
                "target_input": "in"
            }
            for i in range(n - 1)
        ],
    }
    all_types_dict = {
        "A": {
            "TypeA": {
                "template": {
                    "val": {"type": "int", "input_types": ["int"], "value": 0},
                    "in": {"type": "int", "input_types": ["int"]},
                },
                "outputs": [{"name": "out", "types": ["int"]}],
                "base_classes": ["int"],
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
from __future__ import annotations

# Patch import in _encode_handle
import sys
import types
from copy import deepcopy
from typing import Any

# imports
import pytest  # used for our unit tests
from langflow.processing.expand_flow import expand_compact_flow


# Minimal stubs for required classes and functions
class CompactNode:
    def __init__(self, id, type, values=None, node=None, edited=False):
        self.id = id
        self.type = type
        self.values = values or {}
        self.node = node
        self.edited = edited

class CompactEdge:
    def __init__(self, source, source_output, target, target_input):
        self.source = source
        self.source_output = source_output
        self.target = target
        self.target_input = target_input

class CompactFlowData:
    def __init__(self, nodes, edges):
        self.nodes = [CompactNode(**node) if not isinstance(node, CompactNode) else node for node in nodes]
        self.edges = [CompactEdge(**edge) if not isinstance(edge, CompactEdge) else edge for edge in edges]
from langflow.processing.expand_flow import expand_compact_flow

# unit tests

# ---- Basic Test Cases ----

def test_basic_single_node_no_values():
    """Test expanding a single node with no values and no edges."""
    compact_data = {
        "nodes": [{"id": "1", "type": "ChatInput"}],
        "edges": []
    }
    all_types_dict = {
        "inputs": {
            "ChatInput": {
                "template": {
                    "message": {"type": "str", "input_types": ["str"]}
                },
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"]
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    node = result["nodes"][0]

def test_basic_two_nodes_one_edge_with_values():
    """Test expanding two nodes, one with values, and one edge."""
    compact_data = {
        "nodes": [
            {"id": "1", "type": "ChatInput"},
            {"id": "2", "type": "OpenAIModel", "values": {"model_name": "gpt-4"}}
        ],
        "edges": [
            {"source": "1", "source_output": "message", "target": "2", "target_input": "input_value"}
        ]
    }
    all_types_dict = {
        "inputs": {
            "ChatInput": {
                "template": {
                    "message": {"type": "str", "input_types": ["str"]}
                },
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"]
            },
            "OpenAIModel": {
                "template": {
                    "model_name": {"type": "str", "input_types": ["str"]},
                    "input_value": {"type": "str", "input_types": ["str"]}
                },
                "outputs": [{"name": "result", "types": ["str"]}],
                "base_classes": ["str"]
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    node2 = next(n for n in result["nodes"] if n["id"] == "2")
    edge = result["edges"][0]

def test_basic_node_with_extra_value_field():
    """Test node with a value field not present in template."""
    compact_data = {
        "nodes": [{"id": "1", "type": "ChatInput", "values": {"foo": "bar"}}],
        "edges": []
    }
    all_types_dict = {
        "inputs": {
            "ChatInput": {
                "template": {
                    "message": {"type": "str", "input_types": ["str"]}
                },
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"]
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    node = result["nodes"][0]

def test_basic_node_edited_flag():
    """Test node with edited=True uses node data directly."""
    compact_data = {
        "nodes": [
            {"id": "1", "type": "ChatInput", "edited": True, "node": {"custom": "data"}}
        ],
        "edges": []
    }
    all_types_dict = {
        "inputs": {
            "ChatInput": {
                "template": {
                    "message": {"type": "str", "input_types": ["str"]}
                },
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"]
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    node = result["nodes"][0]

# ---- Edge Test Cases ----

def test_missing_component_type_raises():
    """Test that missing component type raises ValueError."""
    compact_data = {
        "nodes": [{"id": "1", "type": "MissingType"}],
        "edges": []
    }
    all_types_dict = {
        "inputs": {
            "ChatInput": {
                "template": {
                    "message": {"type": "str", "input_types": ["str"]}
                }
            }
        }
    }
    with pytest.raises(ValueError) as excinfo:
        expand_compact_flow(compact_data, all_types_dict)

def test_edited_node_missing_node_data_raises():
    """Test that edited node missing node data raises ValueError."""
    compact_data = {
        "nodes": [{"id": "1", "type": "ChatInput", "edited": True}],
        "edges": []
    }
    all_types_dict = {
        "inputs": {
            "ChatInput": {
                "template": {
                    "message": {"type": "str", "input_types": ["str"]}
                }
            }
        }
    }
    with pytest.raises(ValueError) as excinfo:
        expand_compact_flow(compact_data, all_types_dict)

def test_missing_source_node_in_edge_raises():
    """Test missing source node in edge raises ValueError."""
    compact_data = {
        "nodes": [{"id": "2", "type": "ChatInput"}],
        "edges": [
            {"source": "1", "source_output": "message", "target": "2", "target_input": "message"}
        ]
    }
    all_types_dict = {
        "inputs": {
            "ChatInput": {
                "template": {
                    "message": {"type": "str", "input_types": ["str"]}
                },
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"]
            }
        }
    }
    with pytest.raises(ValueError) as excinfo:
        expand_compact_flow(compact_data, all_types_dict)

def test_missing_target_node_in_edge_raises():
    """Test missing target node in edge raises ValueError."""
    compact_data = {
        "nodes": [{"id": "1", "type": "ChatInput"}],
        "edges": [
            {"source": "1", "source_output": "message", "target": "2", "target_input": "message"}
        ]
    }
    all_types_dict = {
        "inputs": {
            "ChatInput": {
                "template": {
                    "message": {"type": "str", "input_types": ["str"]}
                },
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"]
            }
        }
    }
    with pytest.raises(ValueError) as excinfo:
        expand_compact_flow(compact_data, all_types_dict)

def test_edge_with_missing_output_types_falls_back_to_base_classes():
    """Test edge with missing output types falls back to base_classes."""
    compact_data = {
        "nodes": [
            {"id": "1", "type": "ChatInput"},
            {"id": "2", "type": "OpenAIModel"}
        ],
        "edges": [
            {"source": "1", "source_output": "unknown_output", "target": "2", "target_input": "input_value"}
        ]
    }
    all_types_dict = {
        "inputs": {
            "ChatInput": {
                "template": {
                    "message": {"type": "str", "input_types": ["str"]}
                },
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"]
            },
            "OpenAIModel": {
                "template": {
                    "input_value": {"type": "str", "input_types": ["str"]}
                },
                "outputs": [{"name": "result", "types": ["str"]}],
                "base_classes": ["str"]
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    edge = result["edges"][0]

def test_edge_with_missing_input_types_uses_field_type():
    """Test edge with missing input_types uses field type as input_types."""
    compact_data = {
        "nodes": [
            {"id": "1", "type": "ChatInput"},
            {"id": "2", "type": "OpenAIModel"}
        ],
        "edges": [
            {"source": "1", "source_output": "message", "target": "2", "target_input": "input_value"}
        ]
    }
    all_types_dict = {
        "inputs": {
            "ChatInput": {
                "template": {
                    "message": {"type": "str"}
                },
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"]
            },
            "OpenAIModel": {
                "template": {
                    "input_value": {"type": "int"}  # no input_types
                },
                "outputs": [{"name": "result", "types": ["str"]}],
                "base_classes": ["str"]
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    edge = result["edges"][0]

def test_node_with_non_dict_template_field():
    """Test node template field is not a dict, values overwrite directly."""
    compact_data = {
        "nodes": [{"id": "1", "type": "Simple", "values": {"foo": "bar"}}],
        "edges": []
    }
    all_types_dict = {
        "inputs": {
            "Simple": {
                "template": {
                    "foo": "baz"
                }
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    node = result["nodes"][0]

# ---- Large Scale Test Cases ----

def test_large_number_of_nodes_and_edges():
    """Test expanding a flow with hundreds of nodes and edges."""
    N = 200
    compact_data = {
        "nodes": [
            {"id": str(i), "type": "ChatInput" if i % 2 == 0 else "OpenAIModel",
             "values": {"model_name": f"gpt-{i}"} if i % 2 == 1 else {}}
            for i in range(N)
        ],
        "edges": [
            {"source": str(i), "source_output": "message", "target": str(i+1), "target_input": "input_value"}
            for i in range(N-1)
        ]
    }
    all_types_dict = {
        "inputs": {
            "ChatInput": {
                "template": {
                    "message": {"type": "str", "input_types": ["str"]}
                },
                "outputs": [{"name": "message", "types": ["str"]}],
                "base_classes": ["str"]
            },
            "OpenAIModel": {
                "template": {
                    "model_name": {"type": "str", "input_types": ["str"]},
                    "input_value": {"type": "str", "input_types": ["str"]}
                },
                "outputs": [{"name": "result", "types": ["str"]}],
                "base_classes": ["str"]
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output

def test_large_nodes_with_many_value_fields():
    """Test nodes with many value fields."""
    fields = {f"field{i}": {"type": "str", "input_types": ["str"]} for i in range(50)}
    compact_data = {
        "nodes": [
            {"id": "1", "type": "BigComponent", "values": {f"field{i}": f"value{i}" for i in range(50)}}
        ],
        "edges": []
    }
    all_types_dict = {
        "inputs": {
            "BigComponent": {
                "template": deepcopy(fields)
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    node = result["nodes"][0]
    for i in range(50):
        pass

def test_large_edges_with_varied_types():
    """Test edges with varied output/input types."""
    compact_data = {
        "nodes": [
            {"id": "1", "type": "TypeA"},
            {"id": "2", "type": "TypeB"},
            {"id": "3", "type": "TypeC"},
        ],
        "edges": [
            {"source": "1", "source_output": "out1", "target": "2", "target_input": "in2"},
            {"source": "2", "source_output": "out2", "target": "3", "target_input": "in3"},
        ]
    }
    all_types_dict = {
        "inputs": {
            "TypeA": {
                "template": {},
                "outputs": [{"name": "out1", "types": ["int", "float"]}],
                "base_classes": ["int"]
            },
            "TypeB": {
                "template": {"in2": {"type": "float", "input_types": ["float"]}},
                "outputs": [{"name": "out2", "types": ["str"]}],
                "base_classes": ["float"]
            },
            "TypeC": {
                "template": {"in3": {"type": "str", "input_types": ["str"]}},
                "outputs": [],
                "base_classes": ["str"]
            }
        }
    }
    codeflash_output = expand_compact_flow(compact_data, all_types_dict); result = codeflash_output
    edge1, edge2 = result["edges"]
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-pr10785-2025-11-28T20.34.28 and push.

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[codecov]

Codecov Report

❌ Patch coverage is 93.10345% with 2 lines in your changes missing coverage. Please review.
✅ Project coverage is 32.49%. Comparing base (17dc372) to head (be0ca8a).

Files with missing lines	Patch %	Lines
...rc/backend/base/langflow/processing/expand_flow.py	93.10%	2 Missing ⚠️

❌ Your project check has failed because the head coverage (40.05%) is below the target coverage (60.00%). You can increase the head coverage or adjust the target coverage.

Additional details and impacted files

@@                         Coverage Diff                          @@
##           feat/no-code-pre-built-component   #10788      +/-   ##
====================================================================
+ Coverage                             32.48%   32.49%   +0.01%     
====================================================================
  Files                                  1369     1369              
  Lines                                 63516    63527      +11     
  Branches                               9373     9373              
====================================================================
+ Hits                                  20633    20643      +10     
- Misses                                41850    41852       +2     
+ Partials                               1033     1032       -1     

Flag	Coverage Δ
backend	51.50% <93.10%> (+0.02%)	⬆️
lfx	40.05% <ø> (+<0.01%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Files with missing lines	Coverage Δ
...rc/backend/base/langflow/processing/expand_flow.py	95.87% <93.10%> (-1.80%)	⬇️

... and 2 files with indirect coverage changes

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