v2

pyproject.toml

@@ -33,7 +33,7 @@ google-search-results = "^2.4.1"
 google-api-python-client = "^2.79.0"
 typer = "^0.9.0"
 gunicorn = "^21.1.0"
-langchain = "^0.0.256"
+langchain = "^0.0.268"
 openai = "^0.27.8"
 pandas = "^2.0.0"
 chromadb = "^0.3.21"
@@ -60,7 +60,8 @@ sentence-transformers = { version = "^2.2.2", optional = true }
 ctransformers = { version = "^0.2.10", optional = true }
 cohere = "^4.11.0"
 python-multipart = "^0.0.6"
-sqlmodel = "^0.0.8"
+# install sqlmodel using https://github.com/honglei/sqlmodel.git
+sqlmodel = { git = "https://github.com/honglei/sqlmodel.git", branch = "main" }
 faiss-cpu = "^1.7.4"
 anthropic = "^0.3.0"
 orjson = "3.9.3"
@@ -82,6 +83,7 @@ passlib = "^1.7.4"
 bcrypt = "^4.0.1"
 python-jose = "^3.3.0"
 metaphor-python = "^0.1.11"
+pydantic-settings = "^2.0.3"
 
 [tool.poetry.group.dev.dependencies]
 black = "^23.1.0"

src/backend/langflow/api/v1/base.py

@@ -1,5 +1,5 @@
 from langflow.template.frontend_node.base import FrontendNode
-from pydantic import BaseModel, validator
+from pydantic import field_validator, BaseModel
 
 from langflow.interface.utils import extract_input_variables_from_prompt
 from langchain.prompts import PromptTemplate
@@ -28,11 +28,13 @@ class CodeValidationResponse(BaseModel):
     imports: dict
     function: dict
 
-    @validator("imports")
+    @field_validator("imports")
+    @classmethod
     def validate_imports(cls, v):
         return v or {"errors": []}
 
-    @validator("function")
+    @field_validator("function")
+    @classmethod
     def validate_function(cls, v):
         return v or {"errors": []}
 

src/backend/langflow/api/v1/schemas.py

@@ -2,7 +2,7 @@ from enum import Enum
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 from langflow.services.database.models.flow import FlowCreate, FlowRead
-from pydantic import BaseModel, Field, validator
+from pydantic import BaseModel, Field
 import json
 
 
@@ -66,7 +66,8 @@ class ChatResponse(ChatMessage):
     is_bot: bool = True
     files: list = []
 
-    @validator("type")
+    @field_validator("type")
+    @classmethod
     def validate_message_type(cls, v):
         if v not in ["start", "stream", "end", "error", "info", "file"]:
             raise ValueError("type must be start, stream, end, error, info, or file")
@@ -76,12 +77,13 @@ class ChatResponse(ChatMessage):
 class FileResponse(ChatMessage):
     """File response schema."""
 
-    data: Any
+    data: Any = None
     data_type: str
     type: str = "file"
     is_bot: bool = True
 
-    @validator("data_type")
+    @field_validator("data_type")
+    @classmethod
     def validate_data_type(cls, v):
         if v not in ["image", "csv"]:
             raise ValueError("data_type must be image or csv")

src/backend/langflow/interface/agents/base.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional
+from typing import ClassVar, Dict, List, Optional
 
 from langchain.agents import types
 
@@ -15,7 +15,7 @@ from langflow.utils.util import build_template_from_class, build_template_from_m
 class AgentCreator(LangChainTypeCreator):
     type_name: str = "agents"
 
-    from_method_nodes = {"ZeroShotAgent": "from_llm_and_tools"}
+    from_method_nodes: ClassVar[Dict] = {"ZeroShotAgent": "from_llm_and_tools"}
 
     @property
     def frontend_node_class(self) -> type[AgentFrontendNode]:

src/backend/langflow/interface/chains/base.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional, Type
+from typing import Any, ClassVar, Dict, List, Optional, Type
 
 from langflow.custom.customs import get_custom_nodes
 from langflow.interface.base import LangChainTypeCreator
@@ -9,7 +9,6 @@ from langflow.template.frontend_node.chains import ChainFrontendNode
 from langflow.utils.logger import logger
 from langflow.utils.util import build_template_from_class, build_template_from_method
 from langchain import chains
-from langchain_experimental.sql import SQLDatabaseChain  # type: ignore
 
 # Assuming necessary imports for Field, Template, and FrontendNode classes
 
@@ -22,7 +21,7 @@ class ChainCreator(LangChainTypeCreator):
         return ChainFrontendNode
 
     #! We need to find a better solution for this
-    from_method_nodes = {
+    from_method_nodes: ClassVar[Dict] = {
         "ConversationalRetrievalChain": "from_llm",
         "LLMCheckerChain": "from_llm",
         "SQLDatabaseChain": "from_llm",
@@ -38,7 +37,7 @@ class ChainCreator(LangChainTypeCreator):
             }
             from langflow.interface.chains.custom import CUSTOM_CHAINS
 
-            self.type_dict["SQLDatabaseChain"] = SQLDatabaseChain
+            # self.type_dict["SQLDatabaseChain"] = SQLDatabaseChain
 
             self.type_dict.update(CUSTOM_CHAINS)
             # Filter according to settings.chains

src/backend/langflow/interface/chains/custom.py

@@ -4,7 +4,7 @@ from langchain.chains import ConversationChain
 from langchain.memory.buffer import ConversationBufferMemory
 from langchain.schema import BaseMemory
 from langflow.interface.base import CustomChain
-from pydantic import Field, root_validator
+from pydantic.v1 import Field, root_validator
 from langchain.chains.question_answering import load_qa_chain
 from langflow.interface.utils import extract_input_variables_from_prompt
 from langchain.base_language import BaseLanguageModel

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

@@ -1,5 +1,5 @@
 import ast
-from typing import Any, Optional
+from typing import Any, ClassVar, Dict, Optional
 from pydantic import BaseModel
 from fastapi import HTTPException
 
@@ -16,13 +16,13 @@ class ComponentFunctionEntrypointNameNullError(HTTPException):
 
 
 class Component(BaseModel):
-    ERROR_CODE_NULL = "Python code must be provided."
-    ERROR_FUNCTION_ENTRYPOINT_NAME_NULL = (
-        "The name of the entrypoint function must be provided."
-    )
+    ERROR_CODE_NULL: ClassVar[Dict] = "Python code must be provided."
+    ERROR_FUNCTION_ENTRYPOINT_NAME_NULL: ClassVar[
+        Dict
+    ] = "The name of the entrypoint function must be provided."
 
-    code: Optional[str]
-    function_entrypoint_name = "build"
+    code: Optional[str] = None
+    function_entrypoint_name: ClassVar[Dict] = "build"
     field_config: dict = {}
 
     def __init__(self, **data):

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

@@ -1,4 +1,4 @@
-from typing import Any, Callable, List, Optional
+from typing import Any, Callable, ClassVar, Dict, List, Optional
 from fastapi import HTTPException
 from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
 from langflow.interface.custom.component import Component
@@ -14,12 +14,14 @@ import yaml
 
 
 class CustomComponent(Component, extra=Extra.allow):
-    code: Optional[str]
+    code: Optional[str] = None
     field_config: dict = {}
-    code_class_base_inheritance = "CustomComponent"
-    function_entrypoint_name = "build"
+    code_class_base_inheritance: ClassVar[Dict] = "CustomComponent"
+    function_entrypoint_name: ClassVar[Dict] = "build"
     function: Optional[Callable] = None
-    return_type_valid_list = list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys())
+    return_type_valid_list: ClassVar[Dict] = list(
+        CUSTOM_COMPONENT_SUPPORTED_TYPES.keys()
+    )
     repr_value: Optional[str] = ""
 
     def __init__(self, **data):

src/backend/langflow/interface/custom/schema.py

@@ -10,7 +10,7 @@ class ClassCodeDetails(BaseModel):
     """
 
     name: str
-    doc: Optional[str]
+    doc: Optional[str] = None
     bases: list
     attributes: list
     methods: list
@@ -23,7 +23,7 @@ class CallableCodeDetails(BaseModel):
     """
 
     name: str
-    doc: Optional[str]
+    doc: Optional[str] = None
     args: list
     body: list
-    return_type: Optional[str]
+    return_type: Optional[str] = None

src/backend/langflow/interface/initialize/loading.py

@@ -274,7 +274,7 @@ def instantiate_embedding(node_type, class_object, params: Dict):
         params = {
             key: value
             for key, value in params.items()
-            if key in class_object.__fields__
+            if key in class_object.model_fields
         }
         return class_object(**params)
 

src/backend/langflow/interface/memories/base.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional, Type
+from typing import ClassVar, Dict, List, Optional, Type
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.custom_lists import memory_type_to_cls_dict
@@ -14,7 +14,7 @@ from langflow.custom.customs import get_custom_nodes
 class MemoryCreator(LangChainTypeCreator):
     type_name: str = "memories"
 
-    from_method_nodes = {
+    from_method_nodes: ClassVar[Dict] = {
         "ZepChatMessageHistory": "__init__",
         "SQLiteEntityStore": "__init__",
     }

src/backend/langflow/interface/output_parsers/base.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional, Type
+from typing import ClassVar, Dict, List, Optional, Type
 
 from langchain import output_parsers
 
@@ -13,7 +13,7 @@ from langflow.utils.util import build_template_from_class, build_template_from_m
 
 class OutputParserCreator(LangChainTypeCreator):
     type_name: str = "output_parsers"
-    from_method_nodes = {
+    from_method_nodes: ClassVar[Dict] = {
         "StructuredOutputParser": "from_response_schemas",
     }
 

src/backend/langflow/interface/prompts/custom.py

@@ -1,7 +1,7 @@
 from typing import Dict, List, Optional, Type
 
 from langchain.prompts import PromptTemplate
-from pydantic import root_validator
+from pydantic.v1 import root_validator
 
 from langflow.interface.utils import extract_input_variables_from_prompt
 

src/backend/langflow/interface/retrievers/base.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional, Type
+from typing import Any, ClassVar, Dict, List, Optional, Type
 
 from langchain import retrievers
 
@@ -14,7 +14,10 @@ from langflow.utils.util import build_template_from_method, build_template_from_
 class RetrieverCreator(LangChainTypeCreator):
     type_name: str = "retrievers"
 
-    from_method_nodes = {"MultiQueryRetriever": "from_llm", "ZepRetriever": "__init__"}
+    from_method_nodes: ClassVar[Dict] = {
+        "MultiQueryRetriever": "from_llm",
+        "ZepRetriever": "__init__",
+    }
 
     @property
     def frontend_node_class(self) -> Type[RetrieverFrontendNode]:

src/backend/langflow/interface/tools/base.py

@@ -21,6 +21,7 @@ from langflow.template.field.base import TemplateField
 from langflow.template.template.base import Template
 from langflow.utils import util
 from langflow.utils.util import build_template_from_class
+from langflow.utils.logger import logger
 
 TOOL_INPUTS = {
     "str": TemplateField(
@@ -72,7 +73,11 @@ class ToolCreator(LangChainTypeCreator):
             all_tools = {}
 
             for tool, tool_fcn in ALL_TOOLS_NAMES.items():
-                tool_params = get_tool_params(tool_fcn)
+                try:
+                    tool_params = get_tool_params(tool_fcn)
+                except Exception:
+                    logger.error(f"Error getting params for tool {tool}")
+                    continue
 
                 tool_name = tool_params.get("name") or tool
 

src/backend/langflow/interface/tools/custom.py

@@ -1,7 +1,7 @@
 from typing import Callable, Optional
 from langflow.interface.importing.utils import get_function
 
-from pydantic import BaseModel, validator
+from pydantic.v1 import BaseModel, validator
 
 from langflow.utils import validate
 from langchain.agents.tools import Tool

src/backend/langflow/interface/wrappers/base.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional
+from typing import ClassVar, Dict, List, Optional
 
 from langchain import requests, sql_database
 
@@ -10,7 +10,7 @@ from langflow.utils.util import build_template_from_class, build_template_from_m
 class WrapperCreator(LangChainTypeCreator):
     type_name: str = "wrappers"
 
-    from_method_nodes = {"SQLDatabase": "from_uri"}
+    from_method_nodes: ClassVar[Dict] = {"SQLDatabase": "from_uri"}
 
     @property
     def type_to_loader_dict(self) -> Dict:

src/backend/langflow/services/database/models/base.py

@@ -1,5 +1,6 @@
 from sqlmodel import SQLModel
 import orjson
+from pydantic import ConfigDict
 
 
 def orjson_dumps(v, *, default):
@@ -8,7 +9,8 @@ def orjson_dumps(v, *, default):
 
 
 class SQLModelSerializable(SQLModel):
-    class Config:
-        orm_mode = True
-        json_loads = orjson.loads
-        json_dumps = orjson_dumps
+    # TODO[pydantic]: The following keys were removed: `json_loads`, `json_dumps`.
+    # Check https://docs.pydantic.dev/dev-v2/migration/#changes-to-config for more information.
+    model_config = ConfigDict(
+        from_attributes=True, json_loads=orjson.loads, json_dumps=orjson_dumps
+    )

src/backend/langflow/services/database/models/flow.py

@@ -1,7 +1,6 @@
 # Path: src/backend/langflow/database/models/flow.py
 
 from langflow.services.database.models.base import SQLModelSerializable
-from pydantic import validator
 from sqlmodel import Field, JSON, Column
 from uuid import UUID, uuid4
 from typing import Dict, Optional
@@ -14,8 +13,9 @@ class FlowBase(SQLModelSerializable):
     description: Optional[str] = Field(index=True)
     data: Optional[Dict] = Field(default=None)
 
-    @validator("data")
-    def validate_json(v):
+    @field_validator("data")
+    @classmethod
+    def validate_json(cls, v):
         # dict_keys(['description', 'name', 'id', 'data'])
         if not v:
             return v

src/backend/langflow/services/settings/base.py

@@ -7,7 +7,8 @@ from typing import Optional, List
 from pathlib import Path
 
 import yaml
-from pydantic import BaseSettings, root_validator, validator
+from pydantic_settings import SettingsConfigDict, BaseSettings
+from pydantic import field_validator
 from langflow.utils.logger import logger
 
 # BASE_COMPONENTS_PATH = str(Path(__file__).parent / "components")
@@ -109,7 +110,7 @@ class Settings(BaseSettings):
 
         return value
 
-    @validator("COMPONENTS_PATH", pre=True)
+    @field_validator("COMPONENTS_PATH", mode="before")
     def set_components_path(cls, value):
         if os.getenv("LANGFLOW_COMPONENTS_PATH"):
             logger.debug("Adding LANGFLOW_COMPONENTS_PATH to components_path")
@@ -141,17 +142,17 @@ class Settings(BaseSettings):
         logger.debug(f"Components path: {value}")
         return value
 
-    class Config:
-        validate_assignment = True
-        extra = "ignore"
-        env_prefix = "LANGFLOW_"
+    model_config = SettingsConfigDict(
+        validate_assignment=True, extra="ignore", env_prefix="LANGFLOW_"
+    )
 
-    @root_validator(allow_reuse=True)
-    def validate_lists(cls, values):
-        for key, value in values.items():
-            if key != "dev" and not value:
-                values[key] = []
-        return values
+    # @model_validator()
+    # @classmethod
+    # def validate_lists(cls, values):
+    #     for key, value in values.items():
+    #         if key != "dev" and not value:
+    #             values[key] = []
+    #     return values
 
     def update_from_yaml(self, file_path: str, dev: bool = False):
         new_settings = load_settings_from_yaml(file_path)
@@ -225,7 +226,7 @@ def load_settings_from_yaml(file_path: str) -> Settings:
         settings_dict = {k.upper(): v for k, v in settings_dict.items()}
 
         for key in settings_dict:
-            if key not in Settings.__fields__.keys():
+            if key not in Settings.model_fields.keys():
                 raise KeyError(f"Key {key} not found in settings")
             logger.debug(f"Loading {len(settings_dict[key])} {key} from {file_path}")
 

src/backend/langflow/template/frontend_node/base.py

@@ -1,6 +1,6 @@
 from collections import defaultdict
 import re
-from typing import List, Optional
+from typing import ClassVar, DefaultDict, Dict, List, Optional
 
 from pydantic import BaseModel, Field
 
@@ -15,10 +15,10 @@ from langflow.utils import constants
 
 
 class FieldFormatters(BaseModel):
-    formatters = {
+    formatters: ClassVar[Dict] = {
         "openai_api_key": field_formatters.OpenAIAPIKeyFormatter(),
     }
-    base_formatters = {
+    base_formatters: ClassVar[Dict] = {
         "kwargs": field_formatters.KwargsFormatter(),
         "optional": field_formatters.RemoveOptionalFormatter(),
         "list": field_formatters.ListTypeFormatter(),
@@ -49,7 +49,7 @@ class FrontendNode(BaseModel):
     name: str = ""
     display_name: str = ""
     documentation: str = ""
-    custom_fields: defaultdict = defaultdict(list)
+    custom_fields: Optional[DefaultDict[str, List[str]]] = defaultdict(list)
     output_types: List[str] = []
     field_formatters: FieldFormatters = Field(default_factory=FieldFormatters)
     beta: bool = False

src/backend/langflow/template/frontend_node/documentloaders.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import ClassVar, Dict, Optional
 from langflow.template.field.base import TemplateField
 from langflow.template.frontend_node.base import FrontendNode
 
@@ -23,7 +23,7 @@ class DocumentLoaderFrontNode(FrontendNode):
         self.base_classes = ["Document"]
         self.output_types = ["Document"]
 
-    file_path_templates = {
+    file_path_templates: ClassVar[Dict] = {
         "AirbyteJSONLoader": build_file_field(suffixes=[".json"], fileTypes=["json"]),
         "CoNLLULoader": build_file_field(suffixes=[".csv"], fileTypes=["csv"]),
         "CSVLoader": build_file_field(suffixes=[".csv"], fileTypes=["csv"]),

src/backend/langflow/template/frontend_node/formatter/field_formatters.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import ClassVar, Dict, Optional
 from langflow.template.field.base import TemplateField
 from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS
 from langflow.template.frontend_node.formatter.base import FieldFormatter
@@ -21,7 +21,7 @@ class OpenAIAPIKeyFormatter(FieldFormatter):
 
 
 class ModelSpecificFieldFormatter(FieldFormatter):
-    MODEL_DICT = {
+    MODEL_DICT: ClassVar[Dict] = {
         "OpenAI": OPENAI_MODELS,
         "ChatOpenAI": CHAT_OPENAI_MODELS,
         "Anthropic": ANTHROPIC_MODELS,
@@ -86,7 +86,7 @@ class UnionTypeFormatter(FieldFormatter):
 
 
 class SpecialFieldFormatter(FieldFormatter):
-    SPECIAL_FIELD_HANDLERS = {
+    SPECIAL_FIELD_HANDLERS: ClassVar[Dict] = {
         "allowed_tools": lambda field: "Tool",
         "max_value_length": lambda field: "int",
     }

src/backend/langflow/utils/util.py

@@ -31,7 +31,7 @@ def build_template_from_function(
             docs = parse(_class.__doc__)
 
             variables = {"_type": _type}
-            for class_field_items, value in _class.__fields__.items():
+            for class_field_items, value in _class.model_fields.items():
                 if class_field_items in ["callback_manager"]:
                     continue
                 variables[class_field_items] = {}
@@ -84,8 +84,8 @@ def build_template_from_class(
 
             variables = {"_type": _type}
 
-            if "__fields__" in _class.__dict__:
-                for class_field_items, value in _class.__fields__.items():
+            if "model_fields" in _class.__dict__:
+                for class_field_items, value in _class.model_fields.items():
                     if class_field_items in ["callback_manager"]:
                         continue
                     variables[class_field_items] = {}