from langasync import batch_chain

# Your existing chain — no changes needed
chain = prompt | model | parser

# Wrap for batch processing
batch_wrapper = batch_chain(chain)

# Submit and retrieve results
job = await batch_wrapper.submit(inputs)
results = await job.get_results()

import asyncio
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langasync import batch_chain, BatchPoller, BatchStatus

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "Explain {topic} in one paragraph.")
])
model = ChatOpenAI(model="gpt-4o-mini")
chain = prompt | model

batch_wrapper = batch_chain(chain)

async def main():
    # Submit — returns immediately
    job = await batch_wrapper.submit([
        {"topic": "quantum computing"},
        {"topic": "machine learning"},
        {"topic": "blockchain"},
    ])
    print(f"Batch submitted: {job.job_id}")

    # Poll until complete — batch APIs typically take minutes to hours
    poller = BatchPoller()
    async for result in poller.wait_all():
        if result.status_info.status == BatchStatus.COMPLETED:
            for r in result.results:
                print(r.content)

asyncio.run(main())

chain = prompt | model | parser
batch_wrapper = batch_chain(chain)

from pydantic import BaseModel
from langchain_core.output_parsers import PydanticOutputParser

class Analysis(BaseModel):
    sentiment: str
    confidence: float

parser = PydanticOutputParser(pydantic_object=Analysis)
chain = prompt | model | parser

model = ChatOpenAI().bind_tools([my_tool])
chain = prompt | model

from langchain_core.messages import HumanMessage, SystemMessage

batch_wrapper = batch_chain(model, settings)

await batch_wrapper.submit([
    [
        SystemMessage(content="You are a helpful assistant."),
        HumanMessage(content=[
            {"type": "text", "text": "Describe this image."},
            {"type": "image", "url": "https://example.com/photo.jpg"},
        ]),
    ],
])

from langasync import BatchPoller

# Later, in a new process
poller = BatchPoller()

async for result in poller.wait_all():
    print(f"Job {result.job_id}: {result.status_info.status}")

result = await job.get_results()

for r in result.results:
    if r.success:
        print(r.content)
    else:
        print(f"Failed: {r.error}")

from langasync import LangasyncSettings, batch_chain

settings = LangasyncSettings(
    openai_api_key="sk-...",
    batch_poll_interval=30.0,
    base_storage_path="./my_jobs",
)
batch_wrapper = batch_chain(chain, settings)