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

poetry.lock

@@ -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-3.2.2-py3-none-any.whl", hash = "sha256:7e492fdda23c73726bc0cb94c689bb950d06fb94e82b701a0c610c2e830db327"},
-    {file = "pinecone_client-3.2.1.tar.gz", hash = "sha256:8560ffafb13b9c45a92eb9eb77a2db32d5a1fa7903a1db17f7af58ee1058bb60"},
+    {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"

pyproject.toml

@@ -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"
 
 

src/backend/base/langflow/base/prompts/utils.py

@@ -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:

src/backend/base/langflow/components/embeddings/AmazonBedrockEmbeddings.py

@@ -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):

src/backend/base/langflow/components/embeddings/AzureOpenAIEmbeddings.py

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

src/backend/base/langflow/components/embeddings/CohereEmbeddings.py

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

src/backend/base/langflow/components/embeddings/HuggingFaceEmbeddings.py

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

src/backend/base/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py

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

src/backend/base/langflow/components/embeddings/OllamaEmbeddings.py

@@ -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):

src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py

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

src/backend/base/langflow/components/embeddings/VertexAIEmbeddings.py

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

src/backend/base/langflow/components/helpers/__init__.py

@@ -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",
 ]

src/backend/base/langflow/components/inputs/Prompt.py

@@ -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):

src/backend/base/langflow/components/models/AnthropicModel.py

@@ -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": {

src/backend/base/langflow/components/models/AzureOpenAIModel.py

@@ -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",

src/backend/base/langflow/components/models/BaiduQianfanChatModel.py

@@ -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": {

src/backend/base/langflow/components/models/CohereModel.py

@@ -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": {

src/backend/base/langflow/components/models/GoogleGenerativeAIModel.py

@@ -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 {

src/backend/base/langflow/components/models/HuggingFaceModel.py

@@ -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},

src/backend/base/langflow/components/models/OllamaModel.py

@@ -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": {

src/backend/base/langflow/components/models/OpenAIModel.py

@@ -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"},

src/backend/base/langflow/components/models/VertexAiModel.py

@@ -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": {

src/backend/base/langflow/components/vectorsearch/ChromaSearch.py

@@ -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"

src/backend/base/langflow/components/vectorsearch/pgvectorSearch.py

@@ -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"

src/backend/base/langflow/graph/vertex/types.py

@@ -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)

src/backend/base/langflow/interface/custom/code_parser/utils.py

@@ -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__)

src/backend/base/langflow/interface/custom/custom_component/component.py

@@ -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):

src/backend/base/langflow/interface/custom/custom_component/custom_component.py

@@ -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
 

src/backend/base/langflow/schema/schema.py

@@ -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.
+        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'
-        Args:
+        record when addition is not supported.
-            other (Record): The other record to concatenate with.
-
-        Returns:
-            Record: The concatenated record.
         """
-        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__
+        # Create a new Record object with a deep copy of the data dictionary
-        result = cls.__new__(cls)
+        return Record(data=copy.deepcopy(self.data, memo), text_key=self.text_key, default_value=self.default_value)
-        memo[id(self)] = result
-        for k, v in self.__dict__.items():
-            setattr(result, k, copy.deepcopy(v, memo))
-        return result
 
     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

tests/test_record.py

@@ -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"