7f4eea1e59
✨ feat(utils.py): add process_graph function to process graph data and generate result and thought
The ChatManager class manages active connections and chat history. The ChatHistory class manages the chat history for a client. The process_graph function processes graph data and generates a result and thought. This function is used in the ChatManager class to generate a response back to the frontend.
41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
from fastapi import WebSocket
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from langflow.api.v1.schemas import ChatMessage
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from langflow.processing.process import (
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load_or_build_langchain_object,
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)
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from langflow.processing.base import get_result_and_steps
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from langflow.interface.utils import try_setting_streaming_options
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from langflow.utils.logger import logger
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from typing import Dict
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async def process_graph(
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graph_data: Dict,
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is_first_message: bool,
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chat_message: ChatMessage,
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websocket: WebSocket,
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):
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langchain_object = load_or_build_langchain_object(graph_data, is_first_message)
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langchain_object = try_setting_streaming_options(langchain_object, websocket)
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logger.debug("Loaded langchain object")
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if langchain_object is None:
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# Raise user facing error
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raise ValueError(
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"There was an error loading the langchain_object. Please, check all the nodes and try again."
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)
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# Generate result and thought
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try:
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logger.debug("Generating result and thought")
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result, intermediate_steps = await get_result_and_steps(
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langchain_object, chat_message.message or "", websocket=websocket
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)
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logger.debug("Generated result and intermediate_steps")
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return result, intermediate_steps
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except Exception as e:
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# Log stack trace
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logger.exception(e)
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raise e
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