jyapayne/langflow
1
0
Fork 0
Code Issues Pull requests Projects Releases 1 Packages Wiki Activity Actions

chore: remove refactored files

Browse source
This commit is contained in:
Gabriel Luiz Freitas Almeida 2023-06-06 10:00:15 -03:00
commit ac42e8a66c
7 changed files with 0 additions and 539 deletions
Download patch file Download diff file Expand all files Collapse all files

84
src/backend/langflow/api/base.py
View file

@ -1,84 +0,0 @@
from pydantic import BaseModel, validator
from langflow.graph.utils import extract_input_variables_from_prompt
class CacheResponse(BaseModel):
data: dict
class Code(BaseModel):
code: str
class Prompt(BaseModel):
template: str
# Build ValidationResponse class for {"imports": {"errors": []}, "function": {"errors": []}}
class CodeValidationResponse(BaseModel):
imports: dict
function: dict
@validator("imports")
def validate_imports(cls, v):
return v or {"errors": []}
@validator("function")
def validate_function(cls, v):
return v or {"errors": []}
class PromptValidationResponse(BaseModel):
input_variables: list
INVALID_CHARACTERS = {
" ",
",",
".",
":",
";",
"!",
"?",
"/",
"\\",
"(",
")",
"[",
"]",
"{",
"}",
}
def validate_prompt(template: str):
input_variables = extract_input_variables_from_prompt(template)
# Check if there are invalid characters in the input_variables
input_variables = check_input_variables(input_variables)
return PromptValidationResponse(input_variables=input_variables)
def check_input_variables(input_variables: list):
invalid_chars = []
fixed_variables = []
for variable in input_variables:
new_var = variable
for char in INVALID_CHARACTERS:
if char in variable:
invalid_chars.append(char)
new_var = new_var.replace(char, "")
fixed_variables.append(new_var)
if new_var != variable:
input_variables.remove(variable)
input_variables.append(new_var)
# If any of the input_variables is not in the fixed_variables, then it means that
# there are invalid characters in the input_variables
if any(var not in fixed_variables for var in input_variables):
raise ValueError(
f"Invalid input variables: {input_variables}. Please, use something like {fixed_variables} instead."
)
return input_variables

32
src/backend/langflow/api/callback.py
View file

@ -1,32 +0,0 @@
import asyncio
from typing import Any
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
from langflow.api.schemas import ChatResponse
# https://github.com/hwchase17/chat-langchain/blob/master/callback.py
class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
"""Callback handler for streaming LLM responses."""
def __init__(self, websocket):
self.websocket = websocket
async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
resp = ChatResponse(message=token, type="stream", intermediate_steps="")
await self.websocket.send_json(resp.dict())
class StreamingLLMCallbackHandler(BaseCallbackHandler):
"""Callback handler for streaming LLM responses."""
def __init__(self, websocket):
self.websocket = websocket
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
resp = ChatResponse(message=token, type="stream", intermediate_steps="")
loop = asyncio.get_event_loop()
coroutine = self.websocket.send_json(resp.dict())
asyncio.run_coroutine_threadsafe(coroutine, loop)

26
src/backend/langflow/api/chat.py
View file

@ -1,26 +0,0 @@
from fastapi import (
APIRouter,
WebSocket,
WebSocketDisconnect,
WebSocketException,
status,
)
from langflow.api.chat_manager import ChatManager
from langflow.utils.logger import logger
router = APIRouter()
chat_manager = ChatManager()
@router.websocket("/chat/{client_id}")
async def websocket_endpoint(client_id: str, websocket: WebSocket):
"""Websocket endpoint for chat."""
try:
await chat_manager.handle_websocket(client_id, websocket)
except WebSocketException as exc:
logger.error(exc)
await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=str(exc))
except WebSocketDisconnect as exc:
logger.error(exc)
await websocket.close(code=status.WS_1000_NORMAL_CLOSURE, reason=str(exc))

223
src/backend/langflow/api/chat_manager.py
View file

@ -1,223 +0,0 @@
import asyncio
import json
from collections import defaultdict
from typing import Dict, List
from fastapi import WebSocket, status
from langflow.api.schemas import ChatMessage, ChatResponse, FileResponse
from langflow.cache import cache_manager
from langflow.cache.manager import Subject
from langflow.interface.run import (
get_result_and_steps,
load_or_build_langchain_object,
)
from langflow.interface.utils import pil_to_base64, try_setting_streaming_options
from langflow.utils.logger import logger
class ChatHistory(Subject):
def __init__(self):
super().__init__()
self.history: Dict[str, List[ChatMessage]] = defaultdict(list)
def add_message(self, client_id: str, message: ChatMessage):
"""Add a message to the chat history."""
self.history[client_id].append(message)
if not isinstance(message, FileResponse):
self.notify()
def get_history(self, client_id: str, filter_messages=True) -> List[ChatMessage]:
"""Get the chat history for a client."""
if history := self.history.get(client_id, []):
if filter_messages:
return [msg for msg in history if msg.type not in ["start", "stream"]]
return history
else:
return []
def empty_history(self, client_id: str):
"""Empty the chat history for a client."""
self.history[client_id] = []
class ChatManager:
def __init__(self):
self.active_connections: Dict[str, WebSocket] = {}
self.chat_history = ChatHistory()
self.cache_manager = cache_manager
self.cache_manager.attach(self.update)
def on_chat_history_update(self):
"""Send the last chat message to the client."""
client_id = self.cache_manager.current_client_id
if client_id in self.active_connections:
chat_response = self.chat_history.get_history(
client_id, filter_messages=False
)[-1]
if chat_response.is_bot:
# Process FileResponse
if isinstance(chat_response, FileResponse):
# If data_type is pandas, convert to csv
if chat_response.data_type == "pandas":
chat_response.data = chat_response.data.to_csv()
elif chat_response.data_type == "image":
# Base64 encode the image
chat_response.data = pil_to_base64(chat_response.data)
# get event loop
loop = asyncio.get_event_loop()
coroutine = self.send_json(client_id, chat_response)
asyncio.run_coroutine_threadsafe(coroutine, loop)
def update(self):
if self.cache_manager.current_client_id in self.active_connections:
self.last_cached_object_dict = self.cache_manager.get_last()
# Add a new ChatResponse with the data
chat_response = FileResponse(
message=None,
type="file",
data=self.last_cached_object_dict["obj"],
data_type=self.last_cached_object_dict["type"],
)
self.chat_history.add_message(
self.cache_manager.current_client_id, chat_response
)
async def connect(self, client_id: str, websocket: WebSocket):
await websocket.accept()
self.active_connections[client_id] = websocket
def disconnect(self, client_id: str):
self.active_connections.pop(client_id, None)
async def send_message(self, client_id: str, message: str):
websocket = self.active_connections[client_id]
await websocket.send_text(message)
async def send_json(self, client_id: str, message: ChatMessage):
websocket = self.active_connections[client_id]
await websocket.send_json(message.dict())
async def process_message(self, client_id: str, payload: Dict):
# Process the graph data and chat message
chat_message = payload.pop("message", "")
chat_message = ChatMessage(message=chat_message)
self.chat_history.add_message(client_id, chat_message)
graph_data = payload
start_resp = ChatResponse(message=None, type="start", intermediate_steps="")
await self.send_json(client_id, start_resp)
is_first_message = len(self.chat_history.get_history(client_id=client_id)) <= 1
# Generate result and thought
try:
logger.debug("Generating result and thought")
result, intermediate_steps = await process_graph(
graph_data=graph_data,
is_first_message=is_first_message,
chat_message=chat_message,
websocket=self.active_connections[client_id],
)
except Exception as e:
# Log stack trace
logger.exception(e)
self.chat_history.empty_history(client_id)
raise e
# Send a response back to the frontend, if needed
intermediate_steps = intermediate_steps or ""
history = self.chat_history.get_history(client_id, filter_messages=False)
file_responses = []
if history:
# Iterate backwards through the history
for msg in reversed(history):
if isinstance(msg, FileResponse):
if msg.data_type == "image":
# Base64 encode the image
msg.data = pil_to_base64(msg.data)
file_responses.append(msg)
if msg.type == "start":
break
response = ChatResponse(
message=result,
intermediate_steps=intermediate_steps.strip(),
type="end",
files=file_responses,
)
await self.send_json(client_id, response)
self.chat_history.add_message(client_id, response)
async def handle_websocket(self, client_id: str, websocket: WebSocket):
await self.connect(client_id, websocket)
try:
chat_history = self.chat_history.get_history(client_id)
# iterate and make BaseModel into dict
chat_history = [chat.dict() for chat in chat_history]
await websocket.send_json(chat_history)
while True:
json_payload = await websocket.receive_json()
try:
payload = json.loads(json_payload)
except TypeError:
payload = json_payload
if "clear_history" in payload:
self.chat_history.history[client_id] = []
continue
with self.cache_manager.set_client_id(client_id):
await self.process_message(client_id, payload)
except Exception as e:
# Handle any exceptions that might occur
logger.exception(e)
# send a message to the client
await self.active_connections[client_id].close(
code=status.WS_1011_INTERNAL_ERROR, reason=str(e)[:120]
)
self.disconnect(client_id)
finally:
try:
connection = self.active_connections.get(client_id)
if connection:
await connection.close(code=1000, reason="Client disconnected")
self.disconnect(client_id)
except Exception as e:
logger.exception(e)
self.disconnect(client_id)
async def process_graph(
graph_data: Dict,
is_first_message: bool,
chat_message: ChatMessage,
websocket: WebSocket,
):
langchain_object = load_or_build_langchain_object(graph_data, is_first_message)
langchain_object = try_setting_streaming_options(langchain_object, websocket)
logger.debug("Loaded langchain object")
if langchain_object is None:
# Raise user facing error
raise ValueError(
"There was an error loading the langchain_object. Please, check all the nodes and try again."
)
# Generate result and thought
try:
logger.debug("Generating result and thought")
result, intermediate_steps = await get_result_and_steps(
langchain_object, chat_message.message or "", websocket=websocket
)
logger.debug("Generated result and intermediate_steps")
return result, intermediate_steps
except Exception as e:
# Log stack trace
logger.exception(e)
raise e

47
src/backend/langflow/api/endpoints.py
View file

@ -1,47 +0,0 @@
import logging
from importlib.metadata import version
from fastapi import APIRouter, HTTPException
from langflow.api.schemas import (
ExportedFlow,
GraphData,
PredictRequest,
PredictResponse,
)
from langflow.interface.run import process_graph_cached
from langflow.interface.types import build_langchain_types_dict
# build router
router = APIRouter()
logger = logging.getLogger(__name__)
@router.get("/all")
def get_all():
return build_langchain_types_dict()
@router.post("/predict", response_model=PredictResponse)
async def get_load(predict_request: PredictRequest):
try:
exported_flow: ExportedFlow = predict_request.exported_flow
graph_data: GraphData = exported_flow.data
data = graph_data.dict()
response = process_graph_cached(data, predict_request.message)
return PredictResponse(result=response.get("result", ""))
except Exception as e:
# Log stack trace
logger.exception(e)
raise HTTPException(status_code=500, detail=str(e)) from e
# get endpoint to return version of langflow
@router.get("/version")
def get_version():
return {"version": version("langflow")}
@router.get("/health")
def get_health():
return {"status": "OK"}

70
src/backend/langflow/api/schemas.py
View file

@ -1,70 +0,0 @@
from typing import Any, Dict, List, Union
from pydantic import BaseModel, validator
class GraphData(BaseModel):
"""Data inside the exported flow."""
nodes: List[Dict[str, Any]]
edges: List[Dict[str, Any]]
class ExportedFlow(BaseModel):
"""Exported flow from LangFlow."""
description: str
name: str
id: str
data: GraphData
class PredictRequest(BaseModel):
"""Predict request schema."""
message: str
exported_flow: ExportedFlow
class PredictResponse(BaseModel):
"""Predict response schema."""
result: str
class ChatMessage(BaseModel):
"""Chat message schema."""
is_bot: bool = False
message: Union[str, None] = None
type: str = "human"
class ChatResponse(ChatMessage):
"""Chat response schema."""
intermediate_steps: str
type: str
is_bot: bool = True
files: list = []
@validator("type")
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")
return v
class FileResponse(ChatMessage):
"""File response schema."""
data: Any
data_type: str
type: str = "file"
is_bot: bool = True
@validator("data_type")
def validate_data_type(cls, v):
if v not in ["image", "csv"]:
raise ValueError("data_type must be image or csv")
return v

57
src/backend/langflow/api/validate.py
View file

@ -1,57 +0,0 @@
import json
from fastapi import APIRouter, HTTPException
from langflow.api.base import (
Code,
CodeValidationResponse,
Prompt,
PromptValidationResponse,
validate_prompt,
)
from langflow.graph.vertex.types import VectorStoreVertex
from langflow.interface.run import build_graph
from langflow.utils.logger import logger
from langflow.utils.validate import validate_code
# build router
router = APIRouter(prefix="/validate", tags=["validate"])
@router.post("/code", status_code=200, response_model=CodeValidationResponse)
def post_validate_code(code: Code):
try:
errors = validate_code(code.code)
return CodeValidationResponse(
imports=errors.get("imports", {}),
function=errors.get("function", {}),
)
except Exception as e:
return HTTPException(status_code=500, detail=str(e))
@router.post("/prompt", status_code=200, response_model=PromptValidationResponse)
def post_validate_prompt(prompt: Prompt):
try:
return validate_prompt(prompt.template)
except Exception as e:
logger.exception(e)
raise HTTPException(status_code=500, detail=str(e)) from e
# validate node
@router.post("/node/{node_id}", status_code=200)
def post_validate_node(node_id: str, data: dict):
try:
# build graph
graph = build_graph(data)
# validate node
node = graph.get_node(node_id)
if node is None:
raise ValueError(f"Node {node_id} not found")
if not isinstance(node, VectorStoreVertex):
node.build()
return json.dumps({"valid": True, "params": str(node._built_object_repr())})
except Exception as e:
logger.exception(e)
return json.dumps({"valid": False, "params": str(e)})