Skip to content

Latest commit

 

History

History
151 lines (118 loc) · 8.45 KB

README_CLIENT.md

File metadata and controls

151 lines (118 loc) · 8.45 KB

Client APIs

A Gradio API and an OpenAI-compliant API are supported. One can also use curl to some extent for basic API.

OpenAI Compliant Python Client Library

An OpenAI compliant client is available. Refer the README for more details.

Gradio Client API

h2oGPT's generate.py by default runs a gradio server, which also gives access to client API using Gradio client. One can use it with h2oGPT, or independently of h2oGPT repository by installing an env:

conda create -n gradioclient -y
conda activate gradioclient
conda install python=3.10 -y
pip install gradio_client==0.6.1

# Download Gradio Wrapper code if GradioClient class used, not needed for native Gradio Client
# No wheel for now
wget https://raw.githubusercontent.com/h2oai/h2ogpt/main/gradio_utils/grclient.py
mkdir -p gradio_utils
mv grclient.py gradio_utils

Run client code with Gradio's native client:

from gradio_client import Client
import ast

HOST_URL = "http://localhost:7860"
client = Client(HOST_URL)

# string of dict for input
kwargs = dict(instruction_nochat='Who are you?')
res = client.predict(str(dict(kwargs)), api_name='/submit_nochat_api')

# string of dict for output
response = ast.literal_eval(res)['response']
print(response)

One can also stream the response. Here is a complete example code of streaming to console each updated text fragment so appears to stream in console:

from gradio_client import Client
import ast
import time

HOST = 'http://localhost:7860'
client = Client(HOST)
api_name = '/submit_nochat_api'
prompt = "Who are you?"
kwargs = dict(instruction_nochat=prompt, stream_output=True)

job = client.submit(str(dict(kwargs)), api_name=api_name)

text_old = ''
while not job.done():
    outputs_list = job.communicator.job.outputs
    if outputs_list:
        res = job.communicator.job.outputs[-1]
        res_dict = ast.literal_eval(res)
        text = res_dict['response']
        new_text = text[len(text_old):]
        if new_text:
            print(new_text, end='', flush=True)
            text_old = text
        time.sleep(0.01)
# handle case if never got streaming response and already done
res_final = job.outputs()
if len(res_final) > 0:
    res = res_final[-1]
    res_dict = ast.literal_eval(res)
    text = res_dict['response']
    new_text = text[len(text_old):]
    print(new_text)

h2oGPT Gradio Wrapper

One can run client code with h2oGPT wrapper class for Gradio's client, which adds extra exception handling and adds h2oGPT specific calls.

For talking to just LLM, Document Q/A, summarization, and extraction, one can do:

def test_readme_example(local_server):
    # self-contained example used for readme, to be copied to README_CLIENT.md if changed, setting local_server = True at first
    import os
    # The grclient.py file can be copied from h2ogpt repo and used with local gradio_client for example use
    from gradio_utils.grclient import GradioClient

    if local_server:
        client = GradioClient("http://0.0.0.0:7860")
    else:
        h2ogpt_key = os.getenv('H2OGPT_KEY') or os.getenv('H2OGPT_H2OGPT_KEY')
        if h2ogpt_key is None:
            return
        # if you have API key for public instance:
        client = GradioClient("https://gpt.h2o.ai", h2ogpt_key=h2ogpt_key)

    # LLM
    print(client.question("Who are you?"))

    url = "https://cdn.openai.com/papers/whisper.pdf"

    # Q/A
    print(client.query("What is whisper?", url=url))
    # summarization (map_reduce over all pages if top_k_docs=-1)
    print(client.summarize("What is whisper?", url=url, top_k_docs=3))
    # extraction (map per page)
    print(client.extract("Give bullet for all key points", url=url, top_k_docs=3))
test_readme_example(local_server=True)

Other API calls

For other ways to use gradio client, see example test code or other tests in our tests. E.g. test_client_chat_stream_langchain_steps3 in client tests uses many different API calls for docs etc.s

Any element in gradio_runner.py with api_name defined can be accessed via the gradio client.

Listing models

>>> from gradio_client import Client
>>> client = Client('http://localhost:7860')
Loaded as API: http://localhost:7860/ ✔
>>> import ast
>>> res = client.predict(api_name='/model_names')
>>> {x['base_model']: x['max_seq_len'] for x in ast.literal_eval(res)}
{'h2oai/h2ogpt-4096-llama2-70b-chat': 4046, 'lmsys/vicuna-13b-v1.5-16k': 16334, 'mistralai/Mistral-7B-Instruct-v0.1': 4046, 'gpt-3.5-turbo-0613': 4046, 'gpt-3.5-turbo-16k-0613': 16335, 'gpt-4-0613': 8142, 'gpt-4-32k-0613': 32718}

h2oGPT Server options for efficient Summarization and Extraction

One can choose h2oGPT server to have --async_output=True and --num_async=10 (or some optimal value) to enable full parallel summarization when the h2oGPT server uses --inference_server that points to Gradio Inference Server, vLLM, text-generation inference (TGI) server, or OpenAI servers to allow for high tokens/sec.

Curl Client API

As long as objects within the gradio_runner.py for a given api_name are for a function without gr.State() objects, then curl can work. Full curl capability is not supported in Gradio yet.

For example, for a server launched as:

python generate.py --base_model=TheBloke/Llama-2-7b-Chat-GPTQ --load_gptq="model" --use_safetensors=True --prompt_type=llama2 --save_dir=fooasdf --system_prompt='auto'

one can use the submit_nochat_plain_api that has no state objects to perform chat via curl by doing:

curl 127.0.0.1:7860/api/submit_nochat_plain_api -X POST -d '{"data": ["{\"instruction_nochat\": \"Who are you?\"}"]}' -H 'Content-Type: application/json'

and get back for a 7B LLaMA2-chat GPTQ model:

{"data":["{'response': \" Hello! I'm just an AI assistant designed to provide helpful and informative responses to your questions. My purpose is to assist and provide accurate information to the best of my abilities, while adhering to ethical and moral guidelines. I am not capable of providing personal opinions or engaging in discussions that promote harmful or offensive content. My goal is to be a positive and respectful presence in your interactions with me. Is there anything else I can help you with?\", 'sources': '', 'save_dict': {'prompt': \"<s>[INST] <<SYS>>\\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\\n\\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\\n<</SYS>>\\n\\nWho are you? [/INST]\", 'output': \" Hello! I'm just an AI assistant designed to provide helpful and informative responses to your questions. My purpose is to assist and provide accurate information to the best of my abilities, while adhering to ethical and moral guidelines. I am not capable of providing personal opinions or engaging in discussions that promote harmful or offensive content. My goal is to be a positive and respectful presence in your interactions with me. Is there anything else I can help you with?\", 'base_model': 'TheBloke/Llama-2-7b-Chat-GPTQ', 'save_dir': 'fooasdf', 'where_from': 'evaluate_False', 'extra_dict': {'num_beams': 1, 'do_sample': False, 'repetition_penalty': 1.07, 'num_return_sequences': 1, 'renormalize_logits': True, 'remove_invalid_values': True, 'use_cache': True, 'eos_token_id': 2, 'bos_token_id': 1, 'num_prompt_tokens': 5, 't_generate': 9.243812322616577, 'ntokens': 120, 'tokens_persecond': 12.981605669647344}, 'error': None, 'extra': None}}"],"is_generating":true,"duration":39.33809685707092,"average_duration":39.33809685707092}

This contains the full dictionary of data from curl operation as well is the data contents that are a string of a dictionary like when using the API submit_nochat_api for Gradio client. This inner string of a dictionary can be parsed as a literal python string to get keys response, source, save_dict, where save_dict contains meta data about the query such as generation hyperparameters, tokens generated, etc.