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This commit is contained in:
Gabriel Luiz Freitas Almeida 2024-03-26 22:50:46 -03:00
commit 73a23ca096
12 changed files with 19 additions and 241 deletions
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2
src/backend/base/langflow/api/v1/chat.py
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@ -1,7 +1,7 @@
import time
import uuid
from functools import partial
from typing import TYPE_CHECKING, Annotated, Callable, Optional
from typing import TYPE_CHECKING, Annotated, Optional
from fastapi import APIRouter, BackgroundTasks, Body, Depends, HTTPException
from fastapi.responses import StreamingResponse

18
src/backend/base/langflow/base/agents/agent.py
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@ -1,10 +1,11 @@
from typing import List, Optional, Union
from typing import List, Union
from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
from langchain.agents.agent import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
from langchain_core.runnables import Runnable
from langflow.custom import CustomComponent
from langflow.field_typing import BaseMemory, Text, Tool
from langflow.interface.custom.custom_component import CustomComponent
class LCAgentComponent(CustomComponent):
@ -44,7 +45,7 @@ class LCAgentComponent(CustomComponent):
inputs: str,
input_variables: list[str],
tools: List[Tool],
memory: Optional[BaseMemory] = None,
memory: BaseMemory = None,
handle_parsing_errors: bool = True,
output_key: str = "output",
) -> Text:
@ -52,11 +53,7 @@ class LCAgentComponent(CustomComponent):
runnable = agent
else:
runnable = AgentExecutor.from_agent_and_tools(
agent=agent, # type: ignore
tools=tools,
verbose=True,
memory=memory,
handle_parsing_errors=handle_parsing_errors,
agent=agent, tools=tools, verbose=True, memory=memory, handle_parsing_errors=handle_parsing_errors
)
input_dict = {"input": inputs}
for var in input_variables:
@ -72,5 +69,4 @@ class LCAgentComponent(CustomComponent):
else:
raise ValueError("Output key not found in result. Tried 'output'.")
output: str = result.get("output")
return output
return result.get("output")

1
src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py
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@ -1,7 +1,6 @@
from typing import Any, Callable, Dict, List, Optional, Union
from langchain_openai.embeddings.base import OpenAIEmbeddings
from pydantic.v1.types import SecretStr
from langflow.field_typing import NestedDict
from langflow.interface.custom.custom_component import CustomComponent

7
src/backend/base/langflow/components/experimental/FlowTool.py
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@ -1,4 +1,4 @@
from typing import Any, List, Optional
from typing import Any, List, Optional, Text
from langchain_core.tools import StructuredTool
from loguru import logger
@ -8,7 +8,6 @@ from langflow.field_typing import Tool
from langflow.graph.graph.base import Graph
from langflow.helpers.flow import build_function_and_schema
from langflow.schema.dotdict import dotdict
from langflow.schema.schema import Record
class FlowToolComponent(CustomComponent):
@ -20,7 +19,7 @@ class FlowToolComponent(CustomComponent):
flow_records = self.list_flows()
return [flow_record.data["name"] for flow_record in flow_records]
def get_flow(self, flow_name: str) -> Optional[Record]:
def get_flow(self, flow_name: str) -> Optional[Text]:
"""
Retrieves a flow by its name.
@ -83,4 +82,4 @@ class FlowToolComponent(CustomComponent):
description_repr = repr(tool.description).strip("'")
args_str = "\n".join([f"- {arg_name}: {arg_data['description']}" for arg_name, arg_data in tool.args.items()])
self.status = f"{description_repr}\nArguments:\n{args_str}"
return tool # type: ignore
return tool

15
src/backend/base/langflow/components/experimental/SubFlow.py
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@ -1,10 +1,11 @@
from typing import Any, List, Optional, Tuple
from typing import Any, List, Optional
from loguru import logger
from langflow.custom import CustomComponent
from langflow.graph.graph.base import Graph
from langflow.graph.schema import ResultData, RunOutputs
from langflow.graph.vertex.base import Vertex
from langflow.schema import Record
from langflow.schema.dotdict import dotdict
from langflow.template.field.base import TemplateField
@ -50,7 +51,7 @@ class SubFlowComponent(CustomComponent):
return build_config
def get_flow_inputs(self, graph: Graph) -> List[Record]:
def get_flow_inputs(self, graph: Graph) -> List[Vertex]:
inputs = []
for vertex in graph.vertices:
if vertex.is_input:
@ -58,13 +59,13 @@ class SubFlowComponent(CustomComponent):
logger.debug(inputs)
return inputs
def add_inputs_to_build_config(self, inputs: List[Tuple], build_config: dotdict):
def add_inputs_to_build_config(self, inputs: List[Vertex], build_config: dotdict):
new_fields: list[TemplateField] = []
for input_id, input_display_name, input_description in inputs:
for vertex in inputs:
field = TemplateField(
display_name=input_display_name,
name=input_id,
info=input_description,
display_name=vertex.display_name,
name=vertex.id,
info=vertex.description,
field_type="str",
default=None,
)

2
src/backend/base/langflow/interface/tools/base.py
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@ -5,8 +5,6 @@ from langflow.interface.base import LangChainTypeCreator
from langflow.interface.tools.constants import ALL_TOOLS_NAMES, CUSTOM_TOOLS, FILE_TOOLS, OTHER_TOOLS
from langflow.interface.tools.util import get_tool_params
from langflow.legacy_custom import customs
from langflow.interface.tools.util import get_tool_params
from langflow.legacy_custom import customs
from langflow.services.deps import get_settings_service
from langflow.template.field.base import TemplateField
from langflow.template.template.base import Template

70
src/backend/langflow/base/agents/agent.py
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@ -1,70 +0,0 @@
from typing import List, Union
from langchain.agents import (AgentExecutor, BaseMultiActionAgent,
from langflow.custom import CustomComponent
from langflow.field_typing import BaseMemory, Text, Tool
class LCAgentComponent(CustomComponent):
def build_config(self):
return {
"lc": {
"display_name": "LangChain",
"info": "The LangChain to interact with.",
},
"handle_parsing_errors": {
"display_name": "Handle Parsing Errors",
"info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
"advanced": True,
},
"output_key": {
"display_name": "Output Key",
"info": "The key to use to get the output from the agent.",
"advanced": True,
},
"memory": {
"display_name": "Memory",
"info": "Memory to use for the agent.",
},
"tools": {
"display_name": "Tools",
"info": "Tools the agent can use.",
},
"input_value": {
"display_name": "Input",
"info": "Input text to pass to the agent.",
},
}
async def run_agent(
self,
agent: Union[BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor],
inputs: str,
input_variables: list[str],
tools: List[Tool],
memory: BaseMemory = None,
handle_parsing_errors: bool = True,
output_key: str = "output",
) -> Text:
if isinstance(agent, AgentExecutor):
runnable = agent
else:
runnable = AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, verbose=True, memory=memory, handle_parsing_errors=handle_parsing_errors
)
input_dict = {"input": inputs}
for var in input_variables:
if var not in ["agent_scratchpad", "input"]:
input_dict[var] = ""
result = await runnable.ainvoke(input_dict)
self.status = result
if output_key in result:
return result.get(output_key)
elif "output" not in result:
if output_key != "output":
raise ValueError(f"Output key not found in result. Tried '{output_key}' and 'output'.")
else:
raise ValueError("Output key not found in result. Tried 'output'.")
return result.get("output")

84
src/backend/langflow/components/experimental/FlowTool.py
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@ -1,84 +0,0 @@
from typing import Any, List, Optional, Text
from langchain_core.tools import StructuredTool
from langflow.custom import CustomComponent
from langflow.field_typing import Tool
from langflow.graph.graph.base import Graph
from langflow.helpers.flow import build_function_and_schema
from langflow.schema.dotdict import dotdict
from loguru import logger
class FlowToolComponent(CustomComponent):
display_name = "Flow as Tool"
description = "Construct a Tool from a function that runs the loaded Flow."
field_order = ["flow_name", "name", "description", "return_direct"]
def get_flow_names(self) -> List[str]:
flow_records = self.list_flows()
return [flow_record.data["name"] for flow_record in flow_records]
def get_flow(self, flow_name: str) -> Optional[Text]:
"""
Retrieves a flow by its name.
Args:
flow_name (str): The name of the flow to retrieve.
Returns:
Optional[Text]: The flow record if found, None otherwise.
"""
flow_records = self.list_flows()
for flow_record in flow_records:
if flow_record.data["name"] == flow_name:
return flow_record
return None
def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
logger.debug(f"Updating build config with field value {field_value} and field name {field_name}")
if field_name == "flow_name":
build_config["flow_name"]["options"] = self.get_flow_names()
return build_config
def build_config(self):
return {
"flow_name": {
"display_name": "Flow Name",
"info": "The name of the flow to run.",
"options": [],
"real_time_refresh": True,
"refresh_button": True,
},
"name": {
"display_name": "Name",
"description": "The name of the tool.",
},
"description": {
"display_name": "Description",
"description": "The description of the tool.",
},
"return_direct": {
"display_name": "Return Direct",
"description": "Return the result directly from the Tool.",
"advanced": True,
},
}
async def build(self, flow_name: str, name: str, description: str, return_direct: bool = False) -> Tool:
flow_record = self.get_flow(flow_name)
if not flow_record:
raise ValueError("Flow not found.")
graph = Graph.from_payload(flow_record.data["data"])
dynamic_flow_function, schema = build_function_and_schema(flow_record, graph)
tool = StructuredTool.from_function(
coroutine=dynamic_flow_function,
name=name,
description=description,
return_direct=return_direct,
args_schema=schema,
)
description_repr = repr(tool.description).strip("'")
args_str = "\n".join([f"- {arg_name}: {arg_data['description']}" for arg_name, arg_data in tool.args.items()])
self.status = f"{description_repr}\nArguments:\n{args_str}"
return tool

25
src/backend/langflow/components/experimental/Schema.py
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@ -1,25 +0,0 @@
from langflow.custom import CustomComponent
class SchemaComponent(CustomComponent):
display_name = "Schema"
description = "Construct a Schema from a list of fields."
def build_config(self):
return {
"fields": {
"display_name": "Fields",
"info": "The fields to include in the schema.",
},
"name": {
"display_name": "Name",
"info": "The name of the schema.",
},
}
def build(self, name: str, fields: list[dict]):
# The idea for this component is to use create_model from pydantic to create a schema
# from a list of fields. This will be useful for creating schemas for the flow tool.
pass
# field is a simple list of dictionaries with the field name and

36
src/backend/langflow/components/tools/SearchAPITool.py
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@ -1,36 +0,0 @@
from langchain_community.tools.searchapi import SearchAPIRun
from langchain_community.utilities.searchapi import SearchApiAPIWrapper
from langflow.custom import CustomComponent
from langflow.field_typing import Tool
class SearchApiToolComponent(CustomComponent):
display_name: str = "SearchApi Tool"
description: str = "Real-time search engine results API."
documentation: str = "https://www.searchapi.io/docs/google"
field_config = {
"engine": {
"display_name": "Engine",
"field_type": "str",
"info": "The search engine to use.",
},
"api_key": {
"display_name": "API Key",
"field_type": "str",
"required": True,
"password": True,
"info": "The API key to use SearchApi.",
},
}
def build(
self,
engine: str,
api_key: str,
) -> Tool:
search_api_wrapper = SearchApiAPIWrapper(engine=engine, searchapi_api_key=api_key)
tool = SearchAPIRun(api_wrapper=search_api_wrapper)
self.status = tool
return tool

0
src/backend/langflow/base/agents/__init__.py → src/backend/langflow/py.typed
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0
src/backend/base/langflow/version.py → src/backend/langflow/version.py
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