AI Agents with LangGraph
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In this demo, you’ll add memory, structured output, and human-in-the-loop interaction to the localizer project that you’ve been working on. Open the localizer.ipynb notebook in the Starter folder. You’ll find it in the same state as you left it at the end of the last lesson.
app_strings = read_yaml_file('save_puppy.yaml')
screenshot = encode_image('save_puppy.png')
class State(TypedDict):
messages: Annotated[Sequence[BaseMessage], operator.add]
contextualized: str
translation: str
advice: str
prompt = f"""You are an expert in mobile app UI/UX and also
cross-cultural communication.A translator has submitted a translation
for the strings of a UI layout. Check the translation for accuracy.
If the translation is good, reply with one word: "good". However, if
there is anything that is ambiguous or might be translated wrong,
reply with a paragraph expressing your concern.
{app_strings}
Here is the translation with contextual comments:
{translation}
"""
def translation_good(state):
advice = state["advice"]
print(f"translation: \n{state["translation"]}\n")
print(f"advice: {advice}")
if advice == "good":
return "good"
else:
return "problem"
def human_review(state):
return state
prompt = f"""The text below is a translation in YAML format.
The first step is to remove the comments.
The second step is to convert it to format the key-value pairs
in a .strings file for iOS.
Don't change the key names or the translated text at all.
Don't make any commentary. Just give the output.
Here is the text:
{translation}
"""
graph.add_node("human_review", human_review)
graph.add_edge("human_review", "formatter")
graph.add_conditional_edges(
"checker",
translation_good,
{
"good": "formatter",
"problem": "human_review"
}
)
from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
app = graph.compile(checkpointer=memory, interrupt_before=["human_review"])
thread = {"configurable": {"thread_id": "1"}}
response = app.invoke(state, thread)
The translation for the "positive_button" as "Guardar" may cause confusion.
"Guardar" typically means "to save" in the context of storing or keeping
something, like a file, rather than rescuing or saving a life. A more
appropriate translation that conveys the idea of rescuing the puppy would
be "Salvar." This would ensure the intended meaning is clear to users.
user_input = input("Update any line that needs changing: ")
positive_button: Salvar
import re
key = user_input.split(":")[0]
pattern = rf"^{key}:.*$"
translation = app.get_state(thread).values["translation"]
updated = re.sub(pattern, user_input, translation, flags=re.MULTILINE)
app.update_state(thread, {"translation": updated})
result = app.invoke(None, thread)
formatted = app.get_state(thread).values["messages"][-1].content
print(formatted)