Merge branch 'feature/store' of github.com:logspace-ai/langflow into feature/store
This commit is contained in:
cristhianzl
commit
9e653f2bdc
39 changed files with 638 additions and 478 deletions
108
poetry.lock
generated
108
poetry.lock
generated
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@ -411,17 +411,17 @@ files = [
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[[package]]
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name = "boto3"
|
||||
version = "1.29.5"
|
||||
version = "1.29.6"
|
||||
description = "The AWS SDK for Python"
|
||||
optional = false
|
||||
python-versions = ">= 3.7"
|
||||
files = [
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||||
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||||
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|
||||
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||||
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||||
[package.dependencies]
|
||||
botocore = ">=1.32.5,<1.33.0"
|
||||
botocore = ">=1.32.6,<1.33.0"
|
||||
jmespath = ">=0.7.1,<2.0.0"
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s3transfer = ">=0.7.0,<0.8.0"
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@ -430,13 +430,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
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[[package]]
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name = "botocore"
|
||||
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|
||||
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|
||||
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|
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||||
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[package.dependencies]
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@ -3488,13 +3488,13 @@ zookeeper = ["kazoo (>=2.8.0)"]
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[[package]]
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name = "langchain"
|
||||
version = "0.0.339"
|
||||
version = "0.0.340"
|
||||
description = "Building applications with LLMs through composability"
|
||||
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|
||||
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|
||||
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||||
[package.dependencies]
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@ -3559,13 +3559,13 @@ six = "*"
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|||
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[[package]]
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name = "langfuse"
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||||
version = "1.7.4"
|
||||
version = "1.7.5"
|
||||
description = "A client library for accessing langfuse"
|
||||
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|
||||
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|
||||
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|
||||
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@ -3936,13 +3936,13 @@ typing-extensions = "*"
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|||
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||||
[[package]]
|
||||
name = "metaphor-python"
|
||||
version = "0.1.20"
|
||||
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|
||||
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|
||||
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@ -4169,38 +4169,38 @@ dill = ">=0.3.7"
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[package.dependencies]
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@ -6143,6 +6143,24 @@ tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
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|||
[package.extras]
|
||||
testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
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||||
|
||||
[[package]]
|
||||
name = "pytest-asyncio"
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||||
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||||
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[package.dependencies]
|
||||
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||||
|
||||
[package.extras]
|
||||
docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
|
||||
testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "pytest-cov"
|
||||
version = "4.1.0"
|
||||
|
|
@ -9078,4 +9096,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.9,<3.11"
|
||||
content-hash = "8335fd767d4ff476b3caf2a9e05fcce1f353d4b3a2e181585080a618099669fa"
|
||||
content-hash = "62e47482eefda134f0801744360624549d7024861380f51f280f60450d768615"
|
||||
|
|
|
|||
|
|
@ -104,6 +104,7 @@ qianfan = "0.0.5"
|
|||
pgvector = "^0.2.3"
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pytest-asyncio = "^0.21.1"
|
||||
types-redis = "^4.6.0.5"
|
||||
ipykernel = "^6.21.2"
|
||||
mypy = "^1.1.1"
|
||||
|
|
|
|||
|
|
@ -8,21 +8,20 @@ from fastapi import (
|
|||
status,
|
||||
)
|
||||
from fastapi.responses import StreamingResponse
|
||||
from loguru import logger
|
||||
from sqlmodel import Session
|
||||
|
||||
from langflow.api.utils import build_input_keys_response
|
||||
from langflow.api.v1.schemas import BuildStatus, BuiltResponse, InitResponse, StreamData
|
||||
|
||||
from langflow.graph.graph.base import Graph
|
||||
from langflow.services.auth.utils import (
|
||||
get_current_active_user,
|
||||
get_current_user_by_jwt,
|
||||
)
|
||||
from langflow.services.cache.utils import update_build_status
|
||||
from loguru import logger
|
||||
from langflow.services.deps import get_chat_service, get_session, get_cache_service
|
||||
from sqlmodel import Session
|
||||
from langflow.services.chat.service import ChatService
|
||||
from langflow.services.cache.service import BaseCacheService
|
||||
|
||||
from langflow.services.cache.utils import update_build_status
|
||||
from langflow.services.chat.service import ChatService
|
||||
from langflow.services.deps import get_cache_service, get_chat_service, get_session
|
||||
|
||||
router = APIRouter(tags=["Chat"])
|
||||
|
||||
|
|
@ -164,9 +163,9 @@ async def stream_build(
|
|||
}
|
||||
yield str(StreamData(event="log", data=log_dict))
|
||||
if vertex.is_task:
|
||||
vertex = try_running_celery_task(vertex, user_id)
|
||||
vertex = await try_running_celery_task(vertex, user_id)
|
||||
else:
|
||||
vertex.build(user_id=user_id)
|
||||
await vertex.build(user_id=user_id)
|
||||
params = vertex._built_object_repr()
|
||||
valid = True
|
||||
logger.debug(f"Building node {str(vertex.vertex_type)}")
|
||||
|
|
@ -193,7 +192,7 @@ async def stream_build(
|
|||
|
||||
yield str(StreamData(event="message", data=response))
|
||||
|
||||
langchain_object = graph.build()
|
||||
langchain_object = await graph.build()
|
||||
# Now we need to check the input_keys to send them to the client
|
||||
if hasattr(langchain_object, "input_keys"):
|
||||
input_keys_response = build_input_keys_response(langchain_object, artifacts)
|
||||
|
|
@ -224,7 +223,7 @@ async def stream_build(
|
|||
raise HTTPException(status_code=500, detail=str(exc))
|
||||
|
||||
|
||||
def try_running_celery_task(vertex, user_id):
|
||||
async def try_running_celery_task(vertex, user_id):
|
||||
# Try running the task in celery
|
||||
# and set the task_id to the local vertex
|
||||
# if it fails, run the task locally
|
||||
|
|
@ -236,5 +235,5 @@ def try_running_celery_task(vertex, user_id):
|
|||
except Exception as exc:
|
||||
logger.debug(f"Error running task in celery: {exc}")
|
||||
vertex.task_id = None
|
||||
vertex.build(user_id=user_id)
|
||||
await vertex.build(user_id=user_id)
|
||||
return vertex
|
||||
|
|
|
|||
|
|
@ -1,33 +1,30 @@
|
|||
from http import HTTPStatus
|
||||
from typing import Annotated, Optional, Union
|
||||
from langflow.services.auth.utils import api_key_security, get_current_active_user
|
||||
|
||||
|
||||
from langflow.services.cache.utils import save_uploaded_file
|
||||
from langflow.services.database.models.flow import Flow
|
||||
from langflow.processing.process import process_graph_cached, process_tweaks
|
||||
from langflow.services.database.models.user.user import User
|
||||
from langflow.services.deps import (
|
||||
get_session_service,
|
||||
get_settings_service,
|
||||
get_task_service,
|
||||
)
|
||||
from loguru import logger
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile, Body, status
|
||||
import sqlalchemy as sa
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
||||
from fastapi import APIRouter, Body, Depends, HTTPException, UploadFile, status
|
||||
from loguru import logger
|
||||
|
||||
from langflow.api.v1.schemas import (
|
||||
CustomComponentCode,
|
||||
ProcessResponse,
|
||||
TaskResponse,
|
||||
TaskStatusResponse,
|
||||
UploadFileResponse,
|
||||
CustomComponentCode,
|
||||
)
|
||||
|
||||
|
||||
from langflow.services.deps import get_session
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.interface.custom.directory_reader import DirectoryReader
|
||||
from langflow.processing.process import process_graph_cached, process_tweaks
|
||||
from langflow.services.auth.utils import api_key_security, get_current_active_user
|
||||
from langflow.services.cache.utils import save_uploaded_file
|
||||
from langflow.services.database.models.flow import Flow
|
||||
from langflow.services.database.models.user.user import User
|
||||
from langflow.services.deps import (
|
||||
get_session,
|
||||
get_session_service,
|
||||
get_settings_service,
|
||||
get_task_service,
|
||||
)
|
||||
|
||||
try:
|
||||
from langflow.worker import process_graph_cached_task
|
||||
|
|
@ -39,7 +36,6 @@ except ImportError:
|
|||
|
||||
from sqlmodel import Session
|
||||
|
||||
|
||||
from langflow.services.task.service import TaskService
|
||||
|
||||
# build router
|
||||
|
|
@ -217,6 +213,25 @@ async def custom_component(
|
|||
)
|
||||
|
||||
extractor = CustomComponent(code=raw_code.code)
|
||||
extractor.is_check_valid()
|
||||
extractor.validate()
|
||||
|
||||
return build_langchain_template_custom_component(extractor, user_id=user.id)
|
||||
|
||||
|
||||
@router.post("/custom_component/reload", status_code=HTTPStatus.OK)
|
||||
async def reload_custom_component(path: str):
|
||||
from langflow.interface.types import (
|
||||
build_langchain_template_custom_component,
|
||||
)
|
||||
|
||||
try:
|
||||
reader = DirectoryReader("")
|
||||
valid, content = reader.process_file(path)
|
||||
if not valid:
|
||||
raise ValueError(content)
|
||||
|
||||
extractor = CustomComponent(code=content)
|
||||
extractor.validate()
|
||||
return build_langchain_template_custom_component(extractor, user_id=user.id)
|
||||
except Exception as exc:
|
||||
raise HTTPException(status_code=400, detail=str(exc))
|
||||
|
|
|
|||
37
src/backend/langflow/components/agents/AgentInitializer.py
Normal file
37
src/backend/langflow/components/agents/AgentInitializer.py
Normal file
|
|
@ -0,0 +1,37 @@
|
|||
from typing import Callable, List, Union
|
||||
|
||||
from langchain.agents import AgentExecutor, AgentType, initialize_agent, types
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool
|
||||
|
||||
|
||||
class AgentInitializerComponent(CustomComponent):
|
||||
display_name: str = "Agent Initializer"
|
||||
description: str = "Initialize a Langchain Agent."
|
||||
documentation: str = "https://python.langchain.com/docs/modules/agents/agent_types/"
|
||||
|
||||
def build_config(self):
|
||||
agents = list(types.AGENT_TO_CLASS.keys())
|
||||
# field_type and required are optional
|
||||
return {
|
||||
"agent": {"options": agents, "value": agents[0], "display_name": "Agent Type"},
|
||||
"max_iterations": {"display_name": "Max Iterations", "value": 10},
|
||||
"memory": {"display_name": "Memory"},
|
||||
"tools": {"display_name": "Tools"},
|
||||
"llm": {"display_name": "Language Model"},
|
||||
}
|
||||
|
||||
def build(
|
||||
self, agent: str, llm: BaseLanguageModel, memory: BaseChatMemory, tools: List[Tool], max_iterations: int
|
||||
) -> Union[AgentExecutor, Callable]:
|
||||
agent = AgentType(agent)
|
||||
return initialize_agent(
|
||||
tools=tools,
|
||||
llm=llm,
|
||||
agent=agent,
|
||||
memory=memory,
|
||||
return_intermediate_steps=True,
|
||||
handle_parsing_errors=True,
|
||||
max_iterations=max_iterations,
|
||||
)
|
||||
|
|
@ -1,17 +1,15 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import Optional
|
||||
from langchain.prompts import SystemMessagePromptTemplate
|
||||
from langchain.tools import Tool
|
||||
from langchain.schema.memory import BaseMemory
|
||||
from langchain.chat_models import ChatOpenAI
|
||||
from typing import List, Optional
|
||||
|
||||
from langchain.agents.agent import AgentExecutor
|
||||
from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import _get_default_system_message
|
||||
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
|
||||
from langchain.chat_models import ChatOpenAI
|
||||
from langchain.memory.token_buffer import ConversationTokenBufferMemory
|
||||
from langchain.prompts import SystemMessagePromptTemplate
|
||||
from langchain.prompts.chat import MessagesPlaceholder
|
||||
from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import (
|
||||
_get_default_system_message,
|
||||
)
|
||||
from langchain.schema.memory import BaseMemory
|
||||
from langchain.tools import Tool
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class ConversationalAgent(CustomComponent):
|
||||
|
|
@ -27,7 +25,7 @@ class ConversationalAgent(CustomComponent):
|
|||
"gpt-4-32k",
|
||||
]
|
||||
return {
|
||||
"tools": {"is_list": True, "display_name": "Tools"},
|
||||
"tools": {"display_name": "Tools"},
|
||||
"memory": {"display_name": "Memory"},
|
||||
"system_message": {"display_name": "System Message"},
|
||||
"max_token_limit": {"display_name": "Max Token Limit"},
|
||||
|
|
@ -43,7 +41,7 @@ class ConversationalAgent(CustomComponent):
|
|||
self,
|
||||
model_name: str,
|
||||
openai_api_key: str,
|
||||
tools: Tool,
|
||||
tools: List[Tool],
|
||||
openai_api_base: Optional[str] = None,
|
||||
memory: Optional[BaseMemory] = None,
|
||||
system_message: Optional[SystemMessagePromptTemplate] = None,
|
||||
|
|
@ -51,8 +49,8 @@ class ConversationalAgent(CustomComponent):
|
|||
) -> AgentExecutor:
|
||||
llm = ChatOpenAI(
|
||||
model=model_name,
|
||||
openai_api_key=openai_api_key,
|
||||
openai_api_base=openai_api_base,
|
||||
api_key=openai_api_key,
|
||||
base_url=openai_api_base,
|
||||
)
|
||||
if not memory:
|
||||
memory_key = "chat_history"
|
||||
|
|
|
|||
|
|
@ -1,119 +1,7 @@
|
|||
from langflow import CustomComponent
|
||||
from langchain.schema import Document
|
||||
from typing import Any, Dict, List
|
||||
|
||||
loaders_info: List[Dict[str, Any]] = [
|
||||
{
|
||||
"loader": "AirbyteJSONLoader",
|
||||
"name": "Airbyte JSON (.jsonl)",
|
||||
"import": "langchain.document_loaders.AirbyteJSONLoader",
|
||||
"defaultFor": ["jsonl"],
|
||||
"allowdTypes": ["jsonl"],
|
||||
},
|
||||
{
|
||||
"loader": "JSONLoader",
|
||||
"name": "JSON (.json)",
|
||||
"import": "langchain.document_loaders.JSONLoader",
|
||||
"defaultFor": ["json"],
|
||||
"allowdTypes": ["json"],
|
||||
},
|
||||
{
|
||||
"loader": "BSHTMLLoader",
|
||||
"name": "BeautifulSoup4 HTML (.html, .htm)",
|
||||
"import": "langchain.document_loaders.BSHTMLLoader",
|
||||
"allowdTypes": ["html", "htm"],
|
||||
},
|
||||
{
|
||||
"loader": "CSVLoader",
|
||||
"name": "CSV (.csv)",
|
||||
"import": "langchain.document_loaders.CSVLoader",
|
||||
"defaultFor": ["csv"],
|
||||
"allowdTypes": ["csv"],
|
||||
},
|
||||
{
|
||||
"loader": "CoNLLULoader",
|
||||
"name": "CoNLL-U (.conllu)",
|
||||
"import": "langchain.document_loaders.CoNLLULoader",
|
||||
"defaultFor": ["conllu"],
|
||||
"allowdTypes": ["conllu"],
|
||||
},
|
||||
{
|
||||
"loader": "EverNoteLoader",
|
||||
"name": "EverNote (.enex)",
|
||||
"import": "langchain.document_loaders.EverNoteLoader",
|
||||
"defaultFor": ["enex"],
|
||||
"allowdTypes": ["enex"],
|
||||
},
|
||||
{
|
||||
"loader": "FacebookChatLoader",
|
||||
"name": "Facebook Chat (.json)",
|
||||
"import": "langchain.document_loaders.FacebookChatLoader",
|
||||
"allowdTypes": ["json"],
|
||||
},
|
||||
{
|
||||
"loader": "OutlookMessageLoader",
|
||||
"name": "Outlook Message (.msg)",
|
||||
"import": "langchain.document_loaders.OutlookMessageLoader",
|
||||
"defaultFor": ["msg"],
|
||||
"allowdTypes": ["msg"],
|
||||
},
|
||||
{
|
||||
"loader": "PyPDFLoader",
|
||||
"name": "PyPDF (.pdf)",
|
||||
"import": "langchain.document_loaders.PyPDFLoader",
|
||||
"defaultFor": ["pdf"],
|
||||
"allowdTypes": ["pdf"],
|
||||
},
|
||||
{
|
||||
"loader": "STRLoader",
|
||||
"name": "Subtitle (.str)",
|
||||
"import": "langchain.document_loaders.STRLoader",
|
||||
"defaultFor": ["str"],
|
||||
"allowdTypes": ["str"],
|
||||
},
|
||||
{
|
||||
"loader": "TextLoader",
|
||||
"name": "Text (.txt)",
|
||||
"import": "langchain.document_loaders.TextLoader",
|
||||
"defaultFor": ["txt"],
|
||||
"allowdTypes": ["txt"],
|
||||
},
|
||||
{
|
||||
"loader": "UnstructuredEmailLoader",
|
||||
"name": "Unstructured Email (.eml)",
|
||||
"import": "langchain.document_loaders.UnstructuredEmailLoader",
|
||||
"defaultFor": ["eml"],
|
||||
"allowdTypes": ["eml"],
|
||||
},
|
||||
{
|
||||
"loader": "UnstructuredHTMLLoader",
|
||||
"name": "Unstructured HTML (.html, .htm)",
|
||||
"import": "langchain.document_loaders.UnstructuredHTMLLoader",
|
||||
"defaultFor": ["html", "htm"],
|
||||
"allowdTypes": ["html", "htm"],
|
||||
},
|
||||
{
|
||||
"loader": "UnstructuredMarkdownLoader",
|
||||
"name": "Unstructured Markdown (.md)",
|
||||
"import": "langchain.document_loaders.UnstructuredMarkdownLoader",
|
||||
"defaultFor": ["md"],
|
||||
"allowdTypes": ["md"],
|
||||
},
|
||||
{
|
||||
"loader": "UnstructuredPowerPointLoader",
|
||||
"name": "Unstructured PowerPoint (.pptx)",
|
||||
"import": "langchain.document_loaders.UnstructuredPowerPointLoader",
|
||||
"defaultFor": ["pptx"],
|
||||
"allowdTypes": ["pptx"],
|
||||
},
|
||||
{
|
||||
"loader": "UnstructuredWordLoader",
|
||||
"name": "Unstructured Word (.docx)",
|
||||
"import": "langchain.document_loaders.UnstructuredWordLoader",
|
||||
"defaultFor": ["docx"],
|
||||
"allowdTypes": ["docx"],
|
||||
},
|
||||
]
|
||||
from langflow import CustomComponent
|
||||
from langflow.utils.constants import LOADERS_INFO
|
||||
|
||||
|
||||
class FileLoaderComponent(CustomComponent):
|
||||
|
|
@ -122,12 +10,12 @@ class FileLoaderComponent(CustomComponent):
|
|||
beta = True
|
||||
|
||||
def build_config(self):
|
||||
loader_options = ["Automatic"] + [loader_info["name"] for loader_info in loaders_info]
|
||||
loader_options = ["Automatic"] + [loader_info["name"] for loader_info in LOADERS_INFO]
|
||||
|
||||
file_types = []
|
||||
suffixes = []
|
||||
|
||||
for loader_info in loaders_info:
|
||||
for loader_info in LOADERS_INFO:
|
||||
if "allowedTypes" in loader_info:
|
||||
file_types.extend(loader_info["allowedTypes"])
|
||||
suffixes.extend([f".{ext}" for ext in loader_info["allowedTypes"]])
|
||||
|
|
@ -189,7 +77,7 @@ class FileLoaderComponent(CustomComponent):
|
|||
|
||||
# Mapeie o nome do loader selecionado para suas informações
|
||||
selected_loader_info = None
|
||||
for loader_info in loaders_info:
|
||||
for loader_info in LOADERS_INFO:
|
||||
if loader_info["name"] == loader:
|
||||
selected_loader_info = loader_info
|
||||
break
|
||||
|
|
@ -200,7 +88,7 @@ class FileLoaderComponent(CustomComponent):
|
|||
if loader == "Automatic":
|
||||
# Determine o loader automaticamente com base na extensão do arquivo
|
||||
default_loader_info = None
|
||||
for info in loaders_info:
|
||||
for info in LOADERS_INFO:
|
||||
if "defaultFor" in info and file_type in info["defaultFor"]:
|
||||
default_loader_info = info
|
||||
break
|
||||
|
|
|
|||
|
|
@ -5,8 +5,6 @@ agents:
|
|||
documentation: "https://python.langchain.com/docs/modules/agents/toolkits/openapi"
|
||||
CSVAgent:
|
||||
documentation: "https://python.langchain.com/docs/modules/agents/toolkits/csv"
|
||||
AgentInitializer:
|
||||
documentation: "https://python.langchain.com/docs/modules/agents/agent_types/"
|
||||
VectorStoreAgent:
|
||||
documentation: ""
|
||||
VectorStoreRouterAgent:
|
||||
|
|
|
|||
|
|
@ -3,11 +3,12 @@ from typing import Callable, Dict, Union
|
|||
from langchain.agents.agent import AgentExecutor
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.document_loaders.base import BaseLoader
|
||||
from langchain.llms.base import BaseLanguageModel, BaseLLM
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain.memory.chat_memory import BaseChatMemory
|
||||
from langchain.prompts import BasePromptTemplate, ChatPromptTemplate, PromptTemplate
|
||||
from langchain.schema import BaseOutputParser, BaseRetriever, Document
|
||||
from langchain.schema.embeddings import Embeddings
|
||||
from langchain.schema.language_model import BaseLanguageModel
|
||||
from langchain.schema.memory import BaseMemory
|
||||
from langchain.text_splitter import TextSplitter
|
||||
from langchain.tools import Tool
|
||||
|
|
|
|||
|
|
@ -1,6 +1,8 @@
|
|||
from typing import Dict, Generator, List, Type, Union
|
||||
|
||||
from langchain.chains.base import Chain
|
||||
from loguru import logger
|
||||
|
||||
from langflow.graph.edge.base import Edge
|
||||
from langflow.graph.graph.constants import lazy_load_vertex_dict
|
||||
from langflow.graph.graph.utils import process_flow
|
||||
|
|
@ -8,7 +10,6 @@ from langflow.graph.vertex.base import Vertex
|
|||
from langflow.graph.vertex.types import FileToolVertex, LLMVertex, ToolkitVertex
|
||||
from langflow.interface.tools.constants import FILE_TOOLS
|
||||
from langflow.utils import payload
|
||||
from loguru import logger
|
||||
|
||||
|
||||
class Graph:
|
||||
|
|
@ -116,13 +117,13 @@ class Graph:
|
|||
connected_nodes: List[Vertex] = [edge.source for edge in self.edges if edge.target == node]
|
||||
return connected_nodes
|
||||
|
||||
def build(self) -> Chain:
|
||||
async def build(self) -> Chain:
|
||||
"""Builds the graph."""
|
||||
# Get root node
|
||||
root_node = payload.get_root_node(self)
|
||||
if root_node is None:
|
||||
raise ValueError("No root node found")
|
||||
return root_node.build()
|
||||
return await root_node.build()
|
||||
|
||||
def topological_sort(self) -> List[Vertex]:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -1,20 +1,18 @@
|
|||
import ast
|
||||
import inspect
|
||||
import pickle
|
||||
import types
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from langflow.graph.utils import UnbuiltObject
|
||||
from langflow.graph.vertex.utils import is_basic_type
|
||||
from langflow.interface.initialize import loading
|
||||
from langflow.interface.listing import lazy_load_dict
|
||||
from langflow.utils.constants import DIRECT_TYPES
|
||||
from loguru import logger
|
||||
from langflow.utils.util import sync_to_async
|
||||
|
||||
|
||||
import inspect
|
||||
import types
|
||||
from typing import Any, Dict, List, Optional
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.graph.edge.base import Edge
|
||||
|
||||
|
|
@ -216,18 +214,18 @@ class Vertex:
|
|||
self._raw_params = params
|
||||
self.params = params
|
||||
|
||||
def _build(self, user_id=None):
|
||||
async def _build(self, user_id=None):
|
||||
"""
|
||||
Initiate the build process.
|
||||
"""
|
||||
logger.debug(f"Building {self.vertex_type}")
|
||||
self._build_each_node_in_params_dict(user_id)
|
||||
self._get_and_instantiate_class(user_id)
|
||||
await self._build_each_node_in_params_dict(user_id)
|
||||
await self._get_and_instantiate_class(user_id)
|
||||
self._validate_built_object()
|
||||
|
||||
self._built = True
|
||||
|
||||
def _build_each_node_in_params_dict(self, user_id=None):
|
||||
async def _build_each_node_in_params_dict(self, user_id=None):
|
||||
"""
|
||||
Iterates over each node in the params dictionary and builds it.
|
||||
"""
|
||||
|
|
@ -236,9 +234,9 @@ class Vertex:
|
|||
if value == self:
|
||||
del self.params[key]
|
||||
continue
|
||||
self._build_node_and_update_params(key, value, user_id)
|
||||
await self._build_node_and_update_params(key, value, user_id)
|
||||
elif isinstance(value, list) and self._is_list_of_nodes(value):
|
||||
self._build_list_of_nodes_and_update_params(key, value, user_id)
|
||||
await self._build_list_of_nodes_and_update_params(key, value, user_id)
|
||||
|
||||
def _is_node(self, value):
|
||||
"""
|
||||
|
|
@ -252,7 +250,7 @@ class Vertex:
|
|||
"""
|
||||
return all(self._is_node(node) for node in value)
|
||||
|
||||
def get_result(self, user_id=None, timeout=None) -> Any:
|
||||
async def get_result(self, user_id=None, timeout=None) -> Any:
|
||||
# Check if the Vertex was built already
|
||||
if self._built:
|
||||
return self._built_object
|
||||
|
|
@ -268,27 +266,27 @@ class Vertex:
|
|||
pass
|
||||
|
||||
# If there's no task_id, build the vertex locally
|
||||
self.build(user_id)
|
||||
await self.build(user_id)
|
||||
return self._built_object
|
||||
|
||||
def _build_node_and_update_params(self, key, node, user_id=None):
|
||||
async def _build_node_and_update_params(self, key, node, user_id=None):
|
||||
"""
|
||||
Builds a given node and updates the params dictionary accordingly.
|
||||
"""
|
||||
|
||||
result = node.get_result(user_id)
|
||||
result = await node.get_result(user_id)
|
||||
self._handle_func(key, result)
|
||||
if isinstance(result, list):
|
||||
self._extend_params_list_with_result(key, result)
|
||||
self.params[key] = result
|
||||
|
||||
def _build_list_of_nodes_and_update_params(self, key, nodes: List["Vertex"], user_id=None):
|
||||
async def _build_list_of_nodes_and_update_params(self, key, nodes: List["Vertex"], user_id=None):
|
||||
"""
|
||||
Iterates over a list of nodes, builds each and updates the params dictionary.
|
||||
"""
|
||||
self.params[key] = []
|
||||
for node in nodes:
|
||||
built = node.get_result(user_id)
|
||||
built = await node.get_result(user_id)
|
||||
if isinstance(built, list):
|
||||
if key not in self.params:
|
||||
self.params[key] = []
|
||||
|
|
@ -318,14 +316,14 @@ class Vertex:
|
|||
if isinstance(self.params[key], list):
|
||||
self.params[key].extend(result)
|
||||
|
||||
def _get_and_instantiate_class(self, user_id=None):
|
||||
async def _get_and_instantiate_class(self, user_id=None):
|
||||
"""
|
||||
Gets the class from a dictionary and instantiates it with the params.
|
||||
"""
|
||||
if self.base_type is None:
|
||||
raise ValueError(f"Base type for node {self.vertex_type} not found")
|
||||
try:
|
||||
result = loading.instantiate_class(
|
||||
result = await loading.instantiate_class(
|
||||
node_type=self.vertex_type,
|
||||
base_type=self.base_type,
|
||||
params=self.params,
|
||||
|
|
@ -358,9 +356,9 @@ class Vertex:
|
|||
|
||||
raise ValueError(message)
|
||||
|
||||
def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
|
||||
async def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
|
||||
if not self._built or force:
|
||||
self._build(user_id, *args, **kwargs)
|
||||
await self._build(user_id, *args, **kwargs)
|
||||
|
||||
return self._built_object
|
||||
|
||||
|
|
|
|||
|
|
@ -1,8 +1,8 @@
|
|||
import ast
|
||||
from typing import Any, Dict, List, Optional, Union
|
||||
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
from langflow.graph.utils import flatten_list
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
from langflow.interface.utils import extract_input_variables_from_prompt
|
||||
|
||||
|
||||
|
|
@ -34,18 +34,18 @@ class AgentVertex(Vertex):
|
|||
elif isinstance(source_node, ChainVertex):
|
||||
self.chains.append(source_node)
|
||||
|
||||
def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
|
||||
async def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
|
||||
if not self._built or force:
|
||||
self._set_tools_and_chains()
|
||||
# First, build the tools
|
||||
for tool_node in self.tools:
|
||||
tool_node.build(user_id=user_id)
|
||||
await tool_node.build(user_id=user_id)
|
||||
|
||||
# Next, build the chains and the rest
|
||||
for chain_node in self.chains:
|
||||
chain_node.build(tools=self.tools, user_id=user_id)
|
||||
await chain_node.build(tools=self.tools, user_id=user_id)
|
||||
|
||||
self._build(user_id=user_id)
|
||||
await self._build(user_id=user_id)
|
||||
|
||||
return self._built_object
|
||||
|
||||
|
|
@ -62,13 +62,13 @@ class LLMVertex(Vertex):
|
|||
def __init__(self, data: Dict, params: Optional[Dict] = None):
|
||||
super().__init__(data, base_type="llms", params=params)
|
||||
|
||||
def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
|
||||
async def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
|
||||
# LLM is different because some models might take up too much memory
|
||||
# or time to load. So we only load them when we need them.ß
|
||||
if self.vertex_type == self.built_node_type:
|
||||
return self.class_built_object
|
||||
if not self._built or force:
|
||||
self._build(user_id=user_id)
|
||||
await self._build(user_id=user_id)
|
||||
self.built_node_type = self.vertex_type
|
||||
self.class_built_object = self._built_object
|
||||
# Avoid deepcopying the LLM
|
||||
|
|
@ -90,11 +90,11 @@ class WrapperVertex(Vertex):
|
|||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="wrappers")
|
||||
|
||||
def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
|
||||
async def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
|
||||
if not self._built or force:
|
||||
if "headers" in self.params:
|
||||
self.params["headers"] = ast.literal_eval(self.params["headers"])
|
||||
self._build(user_id=user_id)
|
||||
await self._build(user_id=user_id)
|
||||
return self._built_object
|
||||
|
||||
|
||||
|
|
@ -193,7 +193,7 @@ class ChainVertex(Vertex):
|
|||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="chains")
|
||||
|
||||
def build(
|
||||
async def build(
|
||||
self,
|
||||
force: bool = False,
|
||||
user_id=None,
|
||||
|
|
@ -212,9 +212,9 @@ class ChainVertex(Vertex):
|
|||
if isinstance(value, PromptVertex):
|
||||
# Build the PromptVertex, passing the tools if available
|
||||
tools = kwargs.get("tools", None)
|
||||
self.params[key] = value.build(tools=tools, force=force)
|
||||
self.params[key] = await value.build(tools=tools, force=force)
|
||||
|
||||
self._build(user_id=user_id)
|
||||
await self._build(user_id=user_id)
|
||||
|
||||
return self._built_object
|
||||
|
||||
|
|
@ -223,7 +223,7 @@ class PromptVertex(Vertex):
|
|||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="prompts")
|
||||
|
||||
def build(
|
||||
async def build(
|
||||
self,
|
||||
force: bool = False,
|
||||
user_id=None,
|
||||
|
|
@ -236,7 +236,7 @@ class PromptVertex(Vertex):
|
|||
self.params["input_variables"] = []
|
||||
# Check if it is a ZeroShotPrompt and needs a tool
|
||||
if "ShotPrompt" in self.vertex_type:
|
||||
tools = [tool_node.build(user_id=user_id) for tool_node in tools] if tools is not None else []
|
||||
tools = [await tool_node.build(user_id=user_id) for tool_node in tools] if tools is not None else []
|
||||
# flatten the list of tools if it is a list of lists
|
||||
# first check if it is a list
|
||||
if tools and isinstance(tools, list) and isinstance(tools[0], list):
|
||||
|
|
@ -257,7 +257,7 @@ class PromptVertex(Vertex):
|
|||
elif isinstance(self.params, dict):
|
||||
self.params.pop("input_variables", None)
|
||||
|
||||
self._build(user_id=user_id)
|
||||
await self._build(user_id=user_id)
|
||||
return self._built_object
|
||||
|
||||
def _built_object_repr(self):
|
||||
|
|
|
|||
|
|
@ -1,13 +1,6 @@
|
|||
from typing import Any, List, Optional
|
||||
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.agents import (
|
||||
AgentExecutor,
|
||||
Tool,
|
||||
ZeroShotAgent,
|
||||
initialize_agent,
|
||||
AgentType,
|
||||
)
|
||||
from langchain.agents import AgentExecutor, AgentType, Tool, ZeroShotAgent, initialize_agent
|
||||
from langchain.agents.agent_toolkits import (
|
||||
SQLDatabaseToolkit,
|
||||
VectorStoreInfo,
|
||||
|
|
@ -16,25 +9,18 @@ from langchain.agents.agent_toolkits import (
|
|||
)
|
||||
from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
|
||||
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
|
||||
from langchain_experimental.agents.agent_toolkits.pandas.prompt import (
|
||||
PREFIX as PANDAS_PREFIX,
|
||||
)
|
||||
from langchain_experimental.agents.agent_toolkits.pandas.prompt import (
|
||||
SUFFIX_WITH_DF as PANDAS_SUFFIX,
|
||||
)
|
||||
from langchain.agents.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX
|
||||
from langchain.agents.agent_toolkits.vectorstore.prompt import (
|
||||
PREFIX as VECTORSTORE_PREFIX,
|
||||
)
|
||||
from langchain.agents.agent_toolkits.vectorstore.prompt import (
|
||||
ROUTER_PREFIX as VECTORSTORE_ROUTER_PREFIX,
|
||||
)
|
||||
from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX as VECTORSTORE_PREFIX
|
||||
from langchain.agents.agent_toolkits.vectorstore.prompt import ROUTER_PREFIX as VECTORSTORE_ROUTER_PREFIX
|
||||
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
|
||||
from langchain.base_language import BaseLanguageModel
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.memory.chat_memory import BaseChatMemory
|
||||
from langchain.sql_database import SQLDatabase
|
||||
from langchain_experimental.tools.python.tool import PythonAstREPLTool
|
||||
from langchain.tools.sql_database.prompt import QUERY_CHECKER
|
||||
from langchain_experimental.agents.agent_toolkits.pandas.prompt import PREFIX as PANDAS_PREFIX
|
||||
from langchain_experimental.agents.agent_toolkits.pandas.prompt import SUFFIX_WITH_DF as PANDAS_SUFFIX
|
||||
from langchain_experimental.tools.python.tool import PythonAstREPLTool
|
||||
from langflow.interface.base import CustomAgentExecutor
|
||||
|
||||
|
||||
|
|
@ -55,7 +41,7 @@ class JsonAgent(CustomAgentExecutor):
|
|||
@classmethod
|
||||
def from_toolkit_and_llm(cls, toolkit: JsonToolkit, llm: BaseLanguageModel):
|
||||
tools = toolkit if isinstance(toolkit, list) else toolkit.get_tools()
|
||||
tool_names = {tool.name for tool in tools}
|
||||
tool_names = list({tool.name for tool in tools})
|
||||
prompt = ZeroShotAgent.create_prompt(
|
||||
tools,
|
||||
prefix=JSON_PREFIX,
|
||||
|
|
@ -112,7 +98,7 @@ class CSVAgent(CustomAgentExecutor):
|
|||
llm=llm,
|
||||
prompt=partial_prompt,
|
||||
)
|
||||
tool_names = {tool.name for tool in tools}
|
||||
tool_names = list({tool.name for tool in tools})
|
||||
agent = ZeroShotAgent(
|
||||
llm_chain=llm_chain,
|
||||
allowed_tools=tool_names,
|
||||
|
|
@ -151,7 +137,7 @@ class VectorStoreAgent(CustomAgentExecutor):
|
|||
llm=llm,
|
||||
prompt=prompt,
|
||||
)
|
||||
tool_names = {tool.name for tool in tools}
|
||||
tool_names = list({tool.name for tool in tools})
|
||||
agent = ZeroShotAgent(
|
||||
llm_chain=llm_chain,
|
||||
allowed_tools=tool_names,
|
||||
|
|
@ -217,7 +203,7 @@ class SQLAgent(CustomAgentExecutor):
|
|||
llm=llm,
|
||||
prompt=prompt,
|
||||
)
|
||||
tool_names = {tool.name for tool in tools} # type: ignore
|
||||
tool_names = list({tool.name for tool in tools}) # type: ignore
|
||||
agent = ZeroShotAgent(
|
||||
llm_chain=llm_chain,
|
||||
allowed_tools=tool_names,
|
||||
|
|
@ -266,7 +252,7 @@ class VectorStoreRouterAgent(CustomAgentExecutor):
|
|||
llm=llm,
|
||||
prompt=prompt,
|
||||
)
|
||||
tool_names = {tool.name for tool in tools}
|
||||
tool_names = list({tool.name for tool in tools})
|
||||
agent = ZeroShotAgent(
|
||||
llm_chain=llm_chain,
|
||||
allowed_tools=tool_names,
|
||||
|
|
|
|||
|
|
@ -1,9 +1,12 @@
|
|||
import ast
|
||||
import inspect
|
||||
import operator
|
||||
import traceback
|
||||
from typing import Any, Dict, List, Type, Union
|
||||
|
||||
from cachetools import TTLCache, cachedmethod, keys
|
||||
from fastapi import HTTPException
|
||||
|
||||
from langflow.interface.custom.schema import CallableCodeDetails, ClassCodeDetails
|
||||
|
||||
|
||||
|
|
@ -17,6 +20,13 @@ def get_data_type():
|
|||
return Data
|
||||
|
||||
|
||||
def imports_key(*args, **kwargs):
|
||||
imports = kwargs.pop("imports")
|
||||
key = keys.methodkey(*args, **kwargs)
|
||||
key += tuple(imports)
|
||||
return key
|
||||
|
||||
|
||||
class CodeParser:
|
||||
"""
|
||||
A parser for Python source code, extracting code details.
|
||||
|
|
@ -26,6 +36,7 @@ class CodeParser:
|
|||
"""
|
||||
Initializes the parser with the provided code.
|
||||
"""
|
||||
self.cache = TTLCache(maxsize=1024, ttl=60)
|
||||
if isinstance(code, type):
|
||||
if not inspect.isclass(code):
|
||||
raise ValueError("The provided code must be a class.")
|
||||
|
|
@ -101,13 +112,14 @@ class CodeParser:
|
|||
arg_dict["type"] = ast.unparse(arg.annotation)
|
||||
return arg_dict
|
||||
|
||||
def construct_eval_env(self, return_type_str: str) -> dict:
|
||||
@cachedmethod(operator.attrgetter("cache"))
|
||||
def construct_eval_env(self, return_type_str: str, imports) -> dict:
|
||||
"""
|
||||
Constructs an evaluation environment with the necessary imports for the return type,
|
||||
taking into account module aliases.
|
||||
"""
|
||||
eval_env = {}
|
||||
for import_entry in self.data["imports"]:
|
||||
eval_env: dict = {}
|
||||
for import_entry in imports:
|
||||
if isinstance(import_entry, tuple): # from module import name
|
||||
module, name = import_entry
|
||||
if name in return_type_str:
|
||||
|
|
@ -122,6 +134,7 @@ class CodeParser:
|
|||
exec(f"import {module} as {alias if alias else module}", eval_env)
|
||||
return eval_env
|
||||
|
||||
@cachedmethod(cache=operator.attrgetter("cache"))
|
||||
def parse_callable_details(self, node: ast.FunctionDef) -> Dict[str, Any]:
|
||||
"""
|
||||
Extracts details from a single function or method node.
|
||||
|
|
@ -129,7 +142,7 @@ class CodeParser:
|
|||
return_type = None
|
||||
if node.returns:
|
||||
return_type_str = ast.unparse(node.returns)
|
||||
eval_env = self.construct_eval_env(return_type_str)
|
||||
eval_env = self.construct_eval_env(return_type_str, tuple(self.data["imports"]))
|
||||
|
||||
try:
|
||||
return_type = eval(return_type_str, eval_env)
|
||||
|
|
@ -266,7 +279,7 @@ class CodeParser:
|
|||
elif isinstance(stmt, ast.AnnAssign):
|
||||
if attr := self.parse_ann_assign(stmt):
|
||||
class_details.attributes.append(attr)
|
||||
elif isinstance(stmt, ast.FunctionDef):
|
||||
elif isinstance(stmt, (ast.FunctionDef, ast.AsyncFunctionDef)):
|
||||
method, is_init = self.parse_function_def(stmt)
|
||||
if is_init:
|
||||
class_details.init = method
|
||||
|
|
|
|||
|
|
@ -1,8 +1,9 @@
|
|||
import ast
|
||||
import operator
|
||||
from typing import Any, ClassVar, Optional
|
||||
|
||||
from cachetools import TTLCache, cachedmethod
|
||||
from fastapi import HTTPException
|
||||
|
||||
from langflow.interface.custom.code_parser import CodeParser
|
||||
from langflow.utils import validate
|
||||
|
||||
|
|
@ -24,9 +25,11 @@ class Component:
|
|||
field_config: dict = {}
|
||||
|
||||
def __init__(self, **data):
|
||||
self.cache = TTLCache(maxsize=1024, ttl=60)
|
||||
for key, value in data.items():
|
||||
setattr(self, key, value)
|
||||
|
||||
@cachedmethod(cache=operator.attrgetter("cache"))
|
||||
def get_code_tree(self, code: str):
|
||||
parser = CodeParser(code)
|
||||
return parser.parse_code()
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
DEFAULT_CUSTOM_COMPONENT_CODE = """from langflow import CustomComponent
|
||||
|
||||
from typing import Optional, List, Dict, Union
|
||||
from langflow.field_typing import (
|
||||
AgentExecutor,
|
||||
BaseChatMemory,
|
||||
|
|
|
|||
|
|
@ -1,7 +1,9 @@
|
|||
import operator
|
||||
from typing import Any, Callable, ClassVar, List, Optional, Union
|
||||
from uuid import UUID
|
||||
|
||||
import yaml
|
||||
from cachetools import TTLCache, cachedmethod
|
||||
from fastapi import HTTPException
|
||||
|
||||
from langflow.field_typing.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
|
||||
|
|
@ -30,6 +32,7 @@ class CustomComponent(Component):
|
|||
status: Optional[str] = None
|
||||
|
||||
def __init__(self, **data):
|
||||
self.cache = TTLCache(maxsize=1024, ttl=60)
|
||||
super().__init__(**data)
|
||||
|
||||
@property
|
||||
|
|
@ -71,33 +74,19 @@ class CustomComponent(Component):
|
|||
"traceback": f"Type hint '{type_hint}' is used but not imported in the code.",
|
||||
}
|
||||
raise HTTPException(status_code=400, detail=error_detail)
|
||||
return True
|
||||
|
||||
def is_check_valid(self) -> bool:
|
||||
def validate(self) -> bool:
|
||||
return self._class_template_validation(self.code) if self.code else False
|
||||
|
||||
def get_code_tree(self, code: str):
|
||||
return super().get_code_tree(code)
|
||||
|
||||
@property
|
||||
def get_function_entrypoint_args(self) -> str:
|
||||
if not self.code:
|
||||
return ""
|
||||
tree = self.get_code_tree(self.code)
|
||||
|
||||
component_classes = [cls for cls in tree["classes"] if self.code_class_base_inheritance in cls["bases"]]
|
||||
if not component_classes:
|
||||
return ""
|
||||
|
||||
# Assume the first Component class is the one we're interested in
|
||||
component_class = component_classes[0]
|
||||
build_methods = [
|
||||
method for method in component_class["methods"] if method["name"] == self.function_entrypoint_name
|
||||
]
|
||||
|
||||
if not build_methods:
|
||||
return ""
|
||||
|
||||
build_method = build_methods[0]
|
||||
def get_function_entrypoint_args(self) -> list:
|
||||
build_method = self.get_build_method()
|
||||
if not build_method:
|
||||
return []
|
||||
|
||||
args = build_method["args"]
|
||||
for arg in args:
|
||||
|
|
@ -111,13 +100,13 @@ class CustomComponent(Component):
|
|||
),
|
||||
},
|
||||
)
|
||||
elif not arg.get("type"):
|
||||
elif not arg.get("type") and arg.get("name") != "self":
|
||||
# Set the type to Data
|
||||
arg["type"] = "Data"
|
||||
return args
|
||||
|
||||
@property
|
||||
def get_function_entrypoint_return_type(self) -> List[str]:
|
||||
@cachedmethod(operator.attrgetter("cache"))
|
||||
def get_build_method(self):
|
||||
if not self.code:
|
||||
return []
|
||||
tree = self.get_code_tree(self.code)
|
||||
|
|
@ -135,7 +124,13 @@ class CustomComponent(Component):
|
|||
if not build_methods:
|
||||
return []
|
||||
|
||||
build_method = build_methods[0]
|
||||
return build_methods[0]
|
||||
|
||||
@property
|
||||
def get_function_entrypoint_return_type(self) -> List[Any]:
|
||||
build_method = self.get_build_method()
|
||||
if not build_method:
|
||||
return build_method
|
||||
return_type = build_method["return_type"]
|
||||
if not return_type:
|
||||
return []
|
||||
|
|
@ -156,13 +151,15 @@ class CustomComponent(Component):
|
|||
|
||||
@property
|
||||
def get_main_class_name(self):
|
||||
if not self.code:
|
||||
return ""
|
||||
tree = self.get_code_tree(self.code)
|
||||
|
||||
base_name = self.code_class_base_inheritance
|
||||
method_name = self.function_entrypoint_name
|
||||
|
||||
classes = []
|
||||
for item in tree.get("classes"):
|
||||
for item in tree.get("classes", []):
|
||||
if base_name in item["bases"]:
|
||||
method_names = [method["name"] for method in item["methods"]]
|
||||
if method_name in method_names:
|
||||
|
|
@ -173,11 +170,13 @@ class CustomComponent(Component):
|
|||
|
||||
@property
|
||||
def build_template_config(self):
|
||||
if not self.code:
|
||||
return {}
|
||||
tree = self.get_code_tree(self.code)
|
||||
|
||||
attributes = [
|
||||
main_class["attributes"]
|
||||
for main_class in tree.get("classes")
|
||||
for main_class in tree.get("classes", [])
|
||||
if main_class["name"] == self.get_main_class_name
|
||||
]
|
||||
# Get just the first item
|
||||
|
|
@ -189,7 +188,7 @@ class CustomComponent(Component):
|
|||
def get_function(self):
|
||||
return validate.create_function(self.code, self.function_entrypoint_name)
|
||||
|
||||
def load_flow(self, flow_id: str, tweaks: Optional[dict] = None) -> Any:
|
||||
async def load_flow(self, flow_id: str, tweaks: Optional[dict] = None) -> Any:
|
||||
from langflow.processing.process import build_sorted_vertices, process_tweaks
|
||||
|
||||
db_service = get_db_service()
|
||||
|
|
@ -199,7 +198,7 @@ class CustomComponent(Component):
|
|||
raise ValueError(f"Flow {flow_id} not found")
|
||||
if tweaks:
|
||||
graph_data = process_tweaks(graph_data=graph_data, tweaks=tweaks)
|
||||
return build_sorted_vertices(graph_data, self.user_id)
|
||||
return await build_sorted_vertices(graph_data, self.user_id)
|
||||
|
||||
def list_flows(self, *, get_session: Optional[Callable] = None) -> List[Flow]:
|
||||
if not self.user_id:
|
||||
|
|
@ -213,7 +212,7 @@ class CustomComponent(Component):
|
|||
except Exception as e:
|
||||
raise ValueError("Session is invalid") from e
|
||||
|
||||
def get_flow(
|
||||
async def get_flow(
|
||||
self,
|
||||
*,
|
||||
flow_name: Optional[str] = None,
|
||||
|
|
@ -233,7 +232,7 @@ class CustomComponent(Component):
|
|||
|
||||
if not flow:
|
||||
raise ValueError(f"Flow {flow_name or flow_id} not found")
|
||||
return self.load_flow(flow.id, tweaks)
|
||||
return await self.load_flow(flow.id, tweaks)
|
||||
|
||||
def build(self, *args: Any, **kwargs: Any) -> Any:
|
||||
raise NotImplementedError
|
||||
|
|
|
|||
|
|
@ -22,11 +22,11 @@ def extract_inner_type_from_generic_alias(return_type: GenericAlias) -> Any:
|
|||
return return_type
|
||||
|
||||
|
||||
def extract_union_types_from_generic_alias(return_type: GenericAlias) -> tuple:
|
||||
def extract_union_types_from_generic_alias(return_type: GenericAlias) -> list:
|
||||
"""
|
||||
Extracts the inner type from a type hint that is a Union.
|
||||
"""
|
||||
return return_type.__args__
|
||||
return list(return_type.__args__)
|
||||
|
||||
|
||||
def extract_union_types(return_type: str) -> list[str]:
|
||||
|
|
|
|||
|
|
@ -3,15 +3,15 @@
|
|||
import importlib
|
||||
from typing import Any, Type
|
||||
|
||||
from langchain.prompts import PromptTemplate
|
||||
from langchain.agents import Agent
|
||||
from langchain.base_language import BaseLanguageModel
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.chat_models.base import BaseChatModel
|
||||
from langchain.prompts import PromptTemplate
|
||||
from langchain.tools import BaseTool
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.utils import validate
|
||||
from langflow.interface.wrappers.base import wrapper_creator
|
||||
from langflow.utils import validate
|
||||
|
||||
|
||||
def import_module(module_path: str) -> Any:
|
||||
|
|
@ -180,6 +180,7 @@ def get_function(code):
|
|||
return validate.create_function(code, function_name)
|
||||
|
||||
|
||||
def get_function_custom(code):
|
||||
def eval_custom_component_code(code: str) -> Type[CustomComponent]:
|
||||
"""Evaluate custom component code"""
|
||||
class_name = validate.extract_class_name(code)
|
||||
return validate.create_class(code, class_name)
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
import inspect
|
||||
import json
|
||||
from typing import TYPE_CHECKING, Any, Callable, Dict, Sequence, Type
|
||||
|
||||
|
|
@ -11,7 +12,7 @@ from langchain.document_loaders.base import BaseLoader
|
|||
from langchain.schema import Document
|
||||
from langchain.vectorstores.base import VectorStore
|
||||
from langflow.interface.custom_lists import CUSTOM_NODES
|
||||
from langflow.interface.importing.utils import get_function, get_function_custom, import_by_type
|
||||
from langflow.interface.importing.utils import eval_custom_component_code, get_function, import_by_type
|
||||
from langflow.interface.initialize.llm import initialize_vertexai
|
||||
from langflow.interface.initialize.utils import handle_format_kwargs, handle_node_type, handle_partial_variables
|
||||
from langflow.interface.initialize.vector_store import vecstore_initializer
|
||||
|
|
@ -35,7 +36,7 @@ def build_vertex_in_params(params: Dict) -> Dict:
|
|||
return {key: value.build() if isinstance(value, Vertex) else value for key, value in params.items()}
|
||||
|
||||
|
||||
def instantiate_class(node_type: str, base_type: str, params: Dict, user_id=None) -> Any:
|
||||
async def instantiate_class(node_type: str, base_type: str, params: Dict, user_id=None) -> Any:
|
||||
"""Instantiate class from module type and key, and params"""
|
||||
params = convert_params_to_sets(params)
|
||||
params = convert_kwargs(params)
|
||||
|
|
@ -47,7 +48,7 @@ def instantiate_class(node_type: str, base_type: str, params: Dict, user_id=None
|
|||
return custom_node(**params)
|
||||
logger.debug(f"Instantiating {node_type} of type {base_type}")
|
||||
class_object = import_by_type(_type=base_type, name=node_type)
|
||||
return instantiate_based_on_type(class_object, base_type, node_type, params, user_id=user_id)
|
||||
return await instantiate_based_on_type(class_object, base_type, node_type, params, user_id=user_id)
|
||||
|
||||
|
||||
def convert_params_to_sets(params):
|
||||
|
|
@ -74,7 +75,7 @@ def convert_kwargs(params):
|
|||
return params
|
||||
|
||||
|
||||
def instantiate_based_on_type(class_object, base_type, node_type, params, user_id):
|
||||
async def instantiate_based_on_type(class_object, base_type, node_type, params, user_id):
|
||||
if base_type == "agents":
|
||||
return instantiate_agent(node_type, class_object, params)
|
||||
elif base_type == "prompts":
|
||||
|
|
@ -108,20 +109,28 @@ def instantiate_based_on_type(class_object, base_type, node_type, params, user_i
|
|||
elif base_type == "memory":
|
||||
return instantiate_memory(node_type, class_object, params)
|
||||
elif base_type == "custom_components":
|
||||
return instantiate_custom_component(node_type, class_object, params, user_id)
|
||||
return await instantiate_custom_component(node_type, class_object, params, user_id)
|
||||
elif base_type == "wrappers":
|
||||
return instantiate_wrapper(node_type, class_object, params)
|
||||
else:
|
||||
return class_object(**params)
|
||||
|
||||
|
||||
def instantiate_custom_component(node_type, class_object, params, user_id):
|
||||
# we need to make a copy of the params because we will be
|
||||
# modifying it
|
||||
async def instantiate_custom_component(node_type, class_object, params, user_id):
|
||||
params_copy = params.copy()
|
||||
class_object: "CustomComponent" = get_function_custom(params_copy.pop("code"))
|
||||
class_object: "CustomComponent" = eval_custom_component_code(params_copy.pop("code"))
|
||||
custom_component = class_object(user_id=user_id)
|
||||
built_object = custom_component.build(**params_copy)
|
||||
|
||||
# Determine if the build method is asynchronous
|
||||
is_async = inspect.iscoroutinefunction(custom_component.build)
|
||||
|
||||
if is_async:
|
||||
# Await the build method directly if it's async
|
||||
built_object = await custom_component.build(**params_copy)
|
||||
else:
|
||||
# Call the build method directly if it's sync
|
||||
built_object = custom_component.build(**params_copy)
|
||||
|
||||
return built_object, {"repr": custom_component.custom_repr()}
|
||||
|
||||
|
||||
|
|
|
|||
Some files were not shown because too many files have changed in this diff Show more
Loading…
Reference in a new issue