forked from Archives/langchain
e2d7677526
# Docs: compound ecosystem and integrations **Problem statement:** We have a big overlap between the References/Integrations and Ecosystem/LongChain Ecosystem pages. It confuses users. It creates a situation when new integration is added only on one of these pages, which creates even more confusion. - removed References/Integrations page (but move all its information into the individual integration pages - in the next PR). - renamed Ecosystem/LongChain Ecosystem into Integrations/Integrations. I like the Ecosystem term. It is more generic and semantically richer than the Integration term. But it mentally overloads users. The `integration` term is more concrete. UPDATE: after discussion, the Ecosystem is the term. Ecosystem/Integrations is the page (in place of Ecosystem/LongChain Ecosystem). As a result, a user gets a single place to start with the individual integration.
66 lines
1.6 KiB
Markdown
66 lines
1.6 KiB
Markdown
# Modal
|
|
|
|
This page covers how to use the Modal ecosystem within LangChain.
|
|
It is broken into two parts: installation and setup, and then references to specific Modal wrappers.
|
|
|
|
## Installation and Setup
|
|
- Install with `pip install modal-client`
|
|
- Run `modal token new`
|
|
|
|
## Define your Modal Functions and Webhooks
|
|
|
|
You must include a prompt. There is a rigid response structure.
|
|
|
|
```python
|
|
class Item(BaseModel):
|
|
prompt: str
|
|
|
|
@stub.webhook(method="POST")
|
|
def my_webhook(item: Item):
|
|
return {"prompt": my_function.call(item.prompt)}
|
|
```
|
|
|
|
An example with GPT2:
|
|
|
|
```python
|
|
from pydantic import BaseModel
|
|
|
|
import modal
|
|
|
|
stub = modal.Stub("example-get-started")
|
|
|
|
volume = modal.SharedVolume().persist("gpt2_model_vol")
|
|
CACHE_PATH = "/root/model_cache"
|
|
|
|
@stub.function(
|
|
gpu="any",
|
|
image=modal.Image.debian_slim().pip_install(
|
|
"tokenizers", "transformers", "torch", "accelerate"
|
|
),
|
|
shared_volumes={CACHE_PATH: volume},
|
|
retries=3,
|
|
)
|
|
def run_gpt2(text: str):
|
|
from transformers import GPT2Tokenizer, GPT2LMHeadModel
|
|
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
|
|
model = GPT2LMHeadModel.from_pretrained('gpt2')
|
|
encoded_input = tokenizer(text, return_tensors='pt').input_ids
|
|
output = model.generate(encoded_input, max_length=50, do_sample=True)
|
|
return tokenizer.decode(output[0], skip_special_tokens=True)
|
|
|
|
class Item(BaseModel):
|
|
prompt: str
|
|
|
|
@stub.webhook(method="POST")
|
|
def get_text(item: Item):
|
|
return {"prompt": run_gpt2.call(item.prompt)}
|
|
```
|
|
|
|
## Wrappers
|
|
|
|
### LLM
|
|
|
|
There exists an Modal LLM wrapper, which you can access with
|
|
```python
|
|
from langchain.llms import Modal
|
|
``` |