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Merge branch 'zustand/io/migration' of github.com:logspace-ai/langflow into zustand/io/migration

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cristhianzl 2024-03-30 08:27:50 -03:00
commit 1676d98d1d
30 changed files with 367 additions and 73 deletions
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26
poetry.lock generated
View file

@ -6040,13 +6040,13 @@ xmp = ["defusedxml"]
[[package]]
name = "pinecone-client"
version = "3.2.1"
version = "3.2.2"
description = "Pinecone client and SDK"
optional = false
python-versions = "<4.0,>=3.8"
files = [
{file = "pinecone_client-3.2.1-py3-none-any.whl", hash = "sha256:e3e7983762509235250b9bcd543ec6283b7dffaed2e899f1631327f2b77859e3"},
{file = "pinecone_client-3.2.1.tar.gz", hash = "sha256:8560ffafb13b9c45a92eb9eb77a2db32d5a1fa7903a1db17f7af58ee1058bb60"},
{file = "pinecone_client-3.2.2-py3-none-any.whl", hash = "sha256:7e492fdda23c73726bc0cb94c689bb950d06fb94e82b701a0c610c2e830db327"},
{file = "pinecone_client-3.2.2.tar.gz", hash = "sha256:887a12405f90ac11c396490f605fc479f31cf282361034d1ae0fccc02ac75bee"},
]
[package.dependencies]
@ -7100,6 +7100,24 @@ tomli = {version = ">=1", markers = "python_version < \"3.11\""}
[package.extras]
testing = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
[[package]]
name = "pytest-asyncio"
version = "0.23.6"
description = "Pytest support for asyncio"
optional = false
python-versions = ">=3.8"
files = [
{file = "pytest-asyncio-0.23.6.tar.gz", hash = "sha256:ffe523a89c1c222598c76856e76852b787504ddb72dd5d9b6617ffa8aa2cde5f"},
{file = "pytest_asyncio-0.23.6-py3-none-any.whl", hash = "sha256:68516fdd1018ac57b846c9846b954f0393b26f094764a28c955eabb0536a4e8a"},
]
[package.dependencies]
pytest = ">=7.0.0,<9"
[package.extras]
docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
[[package]]
name = "pytest-cov"
version = "4.1.0"
@ -10222,4 +10240,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<3.12"
content-hash = "c259c7ed43f5f6c5e211780da445a10cf1863979b87fb2c6991b1090fbb1fb4a"
content-hash = "b66acb0ed04e62c9f311828307ac1503bc7a19912753c217d4ea6237f474543a"

1
pyproject.toml
View file

@ -105,6 +105,7 @@ types-pywin32 = "^306.0.0.4"
types-google-cloud-ndb = "^2.2.0.0"
pytest-sugar = "^1.0.0"
pytest-instafail = "^0.5.0"
pytest-asyncio = "^0.23.0"
respx = "^0.20.2"

2
src/backend/base/langflow/base/prompts/utils.py
View file

@ -45,7 +45,7 @@ def record_to_string(record: Record) -> str:
Returns:
str: The record as a string.
"""
return record.text
return record.get_text()
def document_to_string(document: Document) -> str:

6
src/backend/base/langflow/components/embeddings/AmazonBedrockEmbeddings.py
View file

@ -7,12 +7,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class AmazonBedrockEmeddingsComponent(CustomComponent):
"""
A custom component for implementing an Embeddings Model using Amazon Bedrock.
"""
display_name: str = "Amazon Bedrock Embeddings"
description: str = "Embeddings model from Amazon Bedrock."
description: str = "Generate embeddings using Amazon Bedrock models."
documentation = "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock"
def build_config(self):

4
src/backend/base/langflow/components/embeddings/AzureOpenAIEmbeddings.py
View file

@ -5,8 +5,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class AzureOpenAIEmbeddingsComponent(CustomComponent):
display_name: str = "AzureOpenAIEmbeddings"
description: str = "Embeddings model from Azure OpenAI."
display_name: str = "Azure OpenAI Embeddings"
description: str = "Generate embeddings using Azure OpenAI models."
documentation: str = "https://python.langchain.com/docs/integrations/text_embedding/azureopenai"
beta = False
icon = "Azure"

4
src/backend/base/langflow/components/embeddings/CohereEmbeddings.py
View file

@ -6,8 +6,8 @@ from langflow.custom import CustomComponent
class CohereEmbeddingsComponent(CustomComponent):
display_name = "CohereEmbeddings"
description = "Cohere embedding models."
display_name = "Cohere Embeddings"
description = "Generate embeddings using Cohere models."
def build_config(self):
return {

4
src/backend/base/langflow/components/embeddings/HuggingFaceEmbeddings.py
View file

@ -6,8 +6,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class HuggingFaceEmbeddingsComponent(CustomComponent):
display_name = "HuggingFaceEmbeddings"
description = "HuggingFace sentence_transformers embedding models."
display_name = "Hugging Face Embeddings"
description = "Generate embeddings using HuggingFace models."
documentation = (
"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
)

4
src/backend/base/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py
View file

@ -7,8 +7,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):
display_name = "HuggingFaceInferenceAPIEmbeddings"
description = "HuggingFace sentence_transformers embedding models, API version."
display_name = "Hugging Face API Embeddings"
description = "Generate embeddings using Hugging Face Inference API models."
documentation = "https://github.com/huggingface/text-embeddings-inference"
icon = "HuggingFace"

6
src/backend/base/langflow/components/embeddings/OllamaEmbeddings.py
View file

@ -7,12 +7,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class OllamaEmbeddingsComponent(CustomComponent):
"""
A custom component for implementing an Embeddings Model using Ollama.
"""
display_name: str = "Ollama Embeddings"
description: str = "Embeddings model from Ollama."
description: str = "Generate embeddings using Ollama models."
documentation = "https://python.langchain.com/docs/integrations/text_embedding/ollama"
def build_config(self):

4
src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py
View file

@ -7,8 +7,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class OpenAIEmbeddingsComponent(CustomComponent):
display_name = "OpenAIEmbeddings"
description = "OpenAI embedding models"
display_name = "OpenAI Embeddings"
description = "Generate embeddings using OpenAI models."
def build_config(self):
return {

4
src/backend/base/langflow/components/embeddings/VertexAIEmbeddings.py
View file

@ -6,8 +6,8 @@ from langflow.interface.custom.custom_component import CustomComponent
class VertexAIEmbeddingsComponent(CustomComponent):
display_name = "VertexAIEmbeddings"
description = "Google Cloud VertexAI embedding models."
display_name = "VertexAI Embeddings"
description = "Generate embeddings using Google Cloud VertexAI models."
def build_config(self):
return {

4
src/backend/base/langflow/components/helpers/__init__.py
View file

@ -1,8 +1,8 @@
from .CreateRecord import CreateRecordComponent
from .CustomComponent import Component
from .DocumentToRecord import DocumentToRecordComponent
from .IDGenerator import UUIDGeneratorComponent
from .MessageHistory import MessageHistoryComponent
from .TextToRecord import TextToRecordComponent
from .UpdateRecord import UpdateRecordComponent
__all__ = [
@ -12,6 +12,6 @@ __all__ = [
"UUIDGeneratorComponent",
"PythonFunctionComponent",
"RecordsToTextComponent",
"TextToRecordComponent",
"CreateRecordComponent",
"MessageHistoryComponent",
]

2
src/backend/base/langflow/components/inputs/Prompt.py
View file

@ -6,7 +6,7 @@ from langflow.interface.custom.custom_component import CustomComponent
class PromptComponent(CustomComponent):
display_name: str = "Prompt"
description: str = "Create prompt templates with dynamic variables. Prompts can help guide the behavior of a Language Model."
description: str = "Create a prompt template with dynamic variables."
icon = "terminal-square"
def build_config(self):

11
src/backend/base/langflow/components/models/AnthropicModel.py
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@ -12,6 +12,17 @@ class AnthropicLLM(LCModelComponent):
description: str = "Generate text using Anthropic Chat&Completion LLMs."
icon = "Anthropic"
field_order = [
"model",
"anthropic_api_key",
"max_tokens",
"temperature",
"anthropic_api_url",
"input_value",
"system_message",
"stream",
]
def build_config(self):
return {
"model": {

13
src/backend/base/langflow/components/models/AzureOpenAIModel.py
View file

@ -14,6 +14,19 @@ class AzureChatOpenAIComponent(LCModelComponent):
beta = False
icon = "Azure"
field_order = [
"model",
"azure_endpoint",
"azure_deployment",
"api_version",
"api_key",
"temperature",
"max_tokens",
"input_value",
"system_message",
"stream",
]
AZURE_OPENAI_MODELS = [
"gpt-35-turbo",
"gpt-35-turbo-16k",

13
src/backend/base/langflow/components/models/BaiduQianfanChatModel.py
View file

@ -13,6 +13,19 @@ class QianfanChatEndpointComponent(LCModelComponent):
documentation: str = "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint."
icon = "BaiduQianfan"
field_order = [
"model",
"qianfan_ak",
"qianfan_sk",
"top_p",
"temperature",
"penalty_score",
"endpoint",
"input_value",
"system_message",
"stream",
]
def build_config(self):
return {
"model": {

10
src/backend/base/langflow/components/models/CohereModel.py
View file

@ -1,6 +1,5 @@
from typing import Optional
from langchain_community.chat_models.cohere import ChatCohere
from pydantic.v1 import SecretStr
@ -15,6 +14,15 @@ class CohereComponent(LCModelComponent):
icon = "Cohere"
field_order = [
"cohere_api_key",
"max_tokens",
"temperature",
"input_value",
"system_message",
"stream",
]
def build_config(self):
return {
"cohere_api_key": {

14
src/backend/base/langflow/components/models/GoogleGenerativeAIModel.py
View file

@ -11,7 +11,19 @@ class GoogleGenerativeAIComponent(LCModelComponent):
display_name: str = "Google Generative AI"
description: str = "Generate text using Google Generative AI."
icon = "GoogleGenerativeAI"
icon = "Google"
field_order = [
"google_api_key",
"model",
"max_output_tokens",
"temperature",
"top_k",
"top_p",
"n",
"input_value",
"system_message",
"stream",
]
def build_config(self):
return {

10
src/backend/base/langflow/components/models/HuggingFaceModel.py
View file

@ -12,6 +12,16 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
description: str = "Generate text using Hugging Face Inference APIs."
icon = "HuggingFace"
field_order = [
"endpoint_url",
"task",
"huggingfacehub_api_token",
"model_kwargs",
"input_value",
"system_message",
"stream",
]
def build_config(self):
return {
"endpoint_url": {"display_name": "Endpoint URL", "password": True},

31
src/backend/base/langflow/components/models/OllamaModel.py
View file

@ -17,6 +17,37 @@ class ChatOllamaComponent(LCModelComponent):
description = "Generate text using Ollama Local LLMs."
icon = "Ollama"
field_order = [
"base_url",
"model",
"temperature",
"cache",
"callback_manager",
"callbacks",
"format",
"metadata",
"mirostat",
"mirostat_eta",
"mirostat_tau",
"num_ctx",
"num_gpu",
"num_thread",
"repeat_last_n",
"repeat_penalty",
"tfs_z",
"timeout",
"top_k",
"top_p",
"verbose",
"tags",
"stop",
"system",
"template",
"input_value",
"system_message",
"stream",
]
def build_config(self) -> dict:
return {
"base_url": {

12
src/backend/base/langflow/components/models/OpenAIModel.py
View file

@ -11,6 +11,18 @@ class OpenAIModelComponent(LCModelComponent):
description = "Generates text using OpenAI LLMs."
icon = "OpenAI"
field_order = [
"max_tokens",
"model_kwargs",
"model_name",
"openai_api_base",
"openai_api_key",
"temperature",
"input_value",
"system_message",
"stream",
]
def build_config(self):
return {
"input_value": {"display_name": "Input"},

16
src/backend/base/langflow/components/models/VertexAiModel.py
View file

@ -11,6 +11,22 @@ class ChatVertexAIComponent(LCModelComponent):
description = "Generate text using Vertex AI LLMs."
icon = "VertexAI"
field_order = [
"credentials",
"project",
"examples",
"location",
"max_output_tokens",
"model_name",
"temperature",
"top_k",
"top_p",
"verbose",
"input_value",
"system_message",
"stream",
]
def build_config(self):
return {
"credentials": {

4
src/backend/base/langflow/components/vectorsearch/ChromaSearch.py
View file

@ -9,10 +9,6 @@ from langflow.schema import Record
class ChromaSearchComponent(LCVectorStoreComponent):
"""
A custom component for implementing a Vector Store using Chroma.
"""
display_name: str = "Chroma Search"
description: str = "Search a Chroma collection for similar documents."
icon = "Chroma"

4
src/backend/base/langflow/components/vectorsearch/pgvectorSearch.py
View file

@ -9,10 +9,6 @@ from langflow.schema import Record
class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
"""
A custom component for implementing a Vector Store using PostgreSQL.
"""
display_name: str = "PGVector Search"
description: str = "Search a PGVector Store for similar documents."
documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector"

2
src/backend/base/langflow/graph/vertex/types.py
View file

@ -125,7 +125,7 @@ class DocumentLoaderVertex(Vertex):
# show how many documents are in the list?
if not isinstance(self._built_object, UnbuiltObject):
avg_length = sum(len(record.text) for record in self._built_object if hasattr(record, "text")) / len(
avg_length = sum(len(record.get_text()) for record in self._built_object if hasattr(record, "text")) / len(
self._built_object
)
return f"""{self.display_name}({len(self._built_object)} records)

11
src/backend/base/langflow/interface/custom/code_parser/utils.py
View file

@ -14,11 +14,10 @@ def extract_inner_type(return_type: str) -> str:
def extract_inner_type_from_generic_alias(return_type: GenericAlias) -> Any:
"""
Extracts the inner type from a type hint that is a list.
Extracts the inner type from a type hint that is a list or a Optional.
"""
if return_type.__origin__ == list:
return list(return_type.__args__)
return return_type
@ -36,4 +35,12 @@ def extract_union_types_from_generic_alias(return_type: GenericAlias) -> list:
"""
Extracts the inner type from a type hint that is a Union.
"""
if isinstance(return_type, list):
return [
_inner_arg
for _type in return_type
for _inner_arg in _type.__args__
if _inner_arg not in set((Any, type(None), type(Any)))
]
return list(return_type.__args__)

4
src/backend/base/langflow/interface/custom/custom_component/component.py
View file

@ -34,10 +34,6 @@ class Component:
if key == "user_id":
setattr(self, "_user_id", value)
else:
if key == "code" and "from langflow import CustomComponent" in value:
value = value.replace(
"from langflow import CustomComponent", "from langflow.custom import CustomComponent"
)
setattr(self, key, value)
def __setattr__(self, key, value):

7
src/backend/base/langflow/interface/custom/custom_component/custom_component.py
View file

@ -249,7 +249,7 @@ class CustomComponent(Component):
return ""
markdown_string = "---\n"
for record in records:
markdown_string += f"- Text: {record.text}"
markdown_string += f"- Text: {record.get_text()}"
if include_data:
markdown_string += f" Data: {record.data}"
markdown_string += "\n"
@ -318,9 +318,10 @@ class CustomComponent(Component):
return_type = extract_inner_type_from_generic_alias(return_type)
# If the return type is not a Union, then we just return it as a list
if not hasattr(return_type, "__origin__") or return_type.__origin__ != Union:
inner_type = return_type[0] if isinstance(return_type, list) else return_type
if not hasattr(inner_type, "__origin__") or inner_type.__origin__ != Union:
return return_type if isinstance(return_type, list) else [return_type]
# If the return type is a Union, then we need to parse itx
# If the return type is a Union, then we need to parse it
return_type = extract_union_types_from_generic_alias(return_type)
return return_type

68
src/backend/base/langflow/schema/schema.py
View file

@ -1,4 +1,5 @@
import copy
from typing import Optional
from langchain_core.documents import Document
from pydantic import BaseModel, model_validator
@ -12,8 +13,9 @@ class Record(BaseModel):
data (dict, optional): Additional data associated with the record.
"""
text_key: Optional[str] = "text"
data: dict = {}
_default_value: str = ""
default_value: Optional[str] = ""
@model_validator(mode="before")
def validate_data(cls, values):
@ -21,10 +23,22 @@ class Record(BaseModel):
values["data"] = {}
# Any other keyword should be added to the data dictionary
for key in values:
if key not in values["data"] and key != "data":
if key not in values["data"] and key not in {"text_key", "data", "default_value"}:
values["data"][key] = values[key]
return values
def get_text(self):
"""
Retrieves the text value from the data dictionary.
If the text key is present in the data dictionary, the corresponding value is returned.
Otherwise, the default value is returned.
Returns:
The text value from the data dictionary or the default value.
"""
return self.data.get(self.text_key, self.default_value)
@classmethod
def from_document(cls, document: Document) -> "Record":
"""
@ -38,19 +52,27 @@ class Record(BaseModel):
"""
data = document.metadata
data["text"] = document.page_content
return cls(data=data)
return cls(data=data, text_key="text")
def __add__(self, other: "Record") -> "Record":
"""
Concatenates the text of two records and combines their data.
Args:
other (Record): The other record to concatenate with.
Returns:
Record: The concatenated record.
Combines the data of two records by attempting to add values for overlapping keys
for all types that support the addition operation. Falls back to the value from 'other'
record when addition is not supported.
"""
combined_data = {**self.data, **other.data}
combined_data = self.data.copy()
for key, value in other.data.items():
# If the key exists in both records and both values support the addition operation
if key in combined_data:
try:
combined_data[key] += value
except TypeError:
# Fallback: Use the value from 'other' record if addition is not supported
combined_data[key] = value
else:
# If the key is not in the first record, simply add it
combined_data[key] = value
return Record(data=combined_data)
def to_lc_document(self) -> Document:
@ -60,17 +82,20 @@ class Record(BaseModel):
Returns:
Document: The converted Document.
"""
return Document(page_content=self.text, metadata=self.data)
text = self.data.pop(self.text_key, self.default_value)
return Document(page_content=text, metadata=self.data)
def __getattr__(self, key):
"""
Allows attribute-like access to the data dictionary.
"""
try:
if key == "data" or key.startswith("_"):
if key.startswith("__"):
return self.__getattribute__(key)
if key in {"data", "text_key"} or key.startswith("_"):
return super().__getattr__(key)
return self.data.get(key, self._default_value)
return self.data.get(key, self.default_value)
except KeyError:
# Fallback to default behavior to raise AttributeError for undefined attributes
raise AttributeError(f"'{type(self).__name__}' object has no attribute '{key}'")
@ -80,7 +105,7 @@ class Record(BaseModel):
Allows attribute-like setting of values in the data dictionary,
while still allowing direct assignment to class attributes.
"""
if key == "data" or key.startswith("_"):
if key in {"data", "text_key"} or key.startswith("_"):
super().__setattr__(key, value)
else:
self.data[key] = value
@ -89,7 +114,7 @@ class Record(BaseModel):
"""
Allows attribute-like deletion from the data dictionary.
"""
if key == "data" or key.startswith("_"):
if key in {"data", "text_key"} or key.startswith("_"):
super().__delattr__(key)
else:
del self.data[key]
@ -98,12 +123,8 @@ class Record(BaseModel):
"""
Custom deepcopy implementation to handle copying of the Record object.
"""
cls = self.__class__
result = cls.__new__(cls)
memo[id(self)] = result
for k, v in self.__dict__.items():
setattr(result, k, copy.deepcopy(v, memo))
return result
# Create a new Record object with a deep copy of the data dictionary
return Record(data=copy.deepcopy(self.data, memo), text_key=self.text_key, default_value=self.default_value)
def __str__(self) -> str:
"""
@ -114,7 +135,8 @@ class Record(BaseModel):
# build the string considering all keys in the data dictionary
prefix = "Record("
suffix = ")"
text = ", ".join([f"{k}={v}" for k, v in self.data.items()])
text = f"text_key={self.text_key}, "
text += ", ".join([f"{k}={v}" for k, v in self.data.items()])
return prefix + text + suffix
# check which attributes the Record has by checking the keys in the data dictionary

139
tests/test_record.py Normal file
View file

@ -0,0 +1,139 @@
from langchain_core.documents import Document
from langflow.schema import Record
def test_record_initialization():
record = Record(text_key="msg", data={"msg": "Hello, World!", "extra": "value"})
assert record.msg == "Hello, World!"
assert record.extra == "value"
def test_validate_data_with_extra_keys():
record = Record(dummy_key="dummy", data={"key": "value"})
assert record.data["dummy_key"] == "dummy"
assert "dummy_key" in record.data
assert record.key == "value"
def test_conversion_to_document():
record = Record(data={"text": "Sample text", "meta": "data"})
document = record.to_lc_document()
assert document.page_content == "Sample text"
assert document.metadata == {"meta": "data"}
def test_conversion_from_document():
document = Document(page_content="Doc content", metadata={"meta": "info"})
record = Record.from_document(document)
assert record.text == "Doc content"
assert record.meta == "info"
def test_add_method_for_strings():
record1 = Record(data={"text": "Hello"})
record2 = Record(data={"text": " World"})
combined = record1 + record2
assert combined.text == "Hello World"
def test_add_method_for_integers():
record1 = Record(data={"number": 5})
record2 = Record(data={"number": 10})
combined = record1 + record2
assert combined.number == 15
def test_add_method_with_non_overlapping_keys():
record1 = Record(data={"text": "Hello"})
record2 = Record(data={"number": 10})
combined = record1 + record2
assert combined.text == "Hello"
assert combined.number == 10
def test_custom_attribute_get_set_del():
record = Record()
record.custom_attr = "custom_value"
assert record.custom_attr == "custom_value"
del record.custom_attr
assert record.custom_attr == record.default_value
def test_deep_copy():
import copy
record1 = Record(data={"text": "Hello", "number": 10})
record2 = copy.deepcopy(record1)
assert record2.text == "Hello"
assert record2.number == 10
record2.text = "World"
assert record1.text == "Hello" # Ensure original is unchanged
def test_custom_attribute_setting_and_getting():
record = Record()
record.dynamic_attribute = "Dynamic Value"
assert record.dynamic_attribute == "Dynamic Value"
def test_str_and_dir_methods():
record = Record(text_key="text", data={"text": "Test Text", "key": "value"})
assert "Test Text" in str(record)
assert "key" in dir(record)
assert "data" in dir(record)
def test_dir_includes_data_keys():
record = Record(data={"text": "Hello", "new_attr": "value"})
dir_output = dir(record)
# Check for standard attributes
assert "data" in dir_output
assert "text_key" in dir_output
assert "__add__" in dir_output # Checking for a method
# Check for dynamic attributes from data
assert "text" in dir_output
assert "new_attr" in dir_output
# Optionally, verify that dynamically added attributes are listed
record.dynamic_attr = "dynamic"
assert "dynamic_attr" in dir_output or "dynamic_attr" in dir(record) # To account for the change
def test_dir_reflects_attribute_deletion():
record = Record(data={"removable": "I can be removed"})
assert "removable" in dir(record)
# Delete the attribute and check again
del record.removable
assert "removable" not in dir(record)
def test_get_text_with_text_key():
data = {"text": "Hello, World!"}
schema = Record(data=data, text_key="text", default_value="default")
result = schema.get_text()
assert result == "Hello, World!"
def test_get_text_without_text_key():
data = {"other_key": "Hello, World!"}
schema = Record(data=data, text_key="text", default_value="default")
result = schema.get_text()
assert result == "default"
def test_get_text_with_empty_data():
data = {}
schema = Record(data=data, text_key="text", default_value="default")
result = schema.get_text()
assert result == "default"
def test_get_text_with_none_data():
data = None
schema = Record(data=data, text_key="text", default_value="default")
result = schema.get_text()
assert result == "default"