A Gradio API and an OpenAI-compliant API are supported. One can also use curl to some extent for basic API.
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_clientthen running client code:
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)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 etc. tests use many different API calls for docs etc.
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)Any element in gradio_runner.py with api_name defined can be accessed via the gradio client.
The below is an example client code, which handles persistence of state when doing multiple queries, or avoids persistence to avoid issues when server goes up and down for a fixed client. Choose HOST to be the h2oGPT server, and as gradio client use function calls answer_question_using_context and summarize that handle question-answer or summarization using LangChain backend. 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 a text-generation inference server, to allow for high tokens/sec.
HOST = "localhost:7860" # choose
import ast
import os
import traceback
from enum import Enum
from typing import Union
from gradio_client.client import Job
os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
from gradio_client import Client
class GradioClient(Client):
"""
Parent class of gradio client
To handle automatically refreshing client if detect gradio server changed
"""
def __init__(self, *args, **kwargs):
self.args = args
self.kwargs = kwargs
super().__init__(*args, **kwargs)
self.server_hash = self.get_server_hash()
def get_server_hash(self):
"""
Get server hash using super without any refresh action triggered
Returns: git hash of gradio server
"""
return super().submit(api_name='/system_hash').result()
def refresh_client_if_should(self):
# get current hash in order to update api_name -> fn_index map in case gradio server changed
# FIXME: Could add cli api as hash
server_hash = self.get_server_hash()
if self.server_hash != server_hash:
self.refresh_client()
self.server_hash = server_hash
else:
self.reset_session()
def refresh_client(self):
"""
Ensure every client call is independent
Also ensure map between api_name and fn_index is updated in case server changed (e.g. restarted with new code)
Returns:
"""
# need session hash to be new every time, to avoid "generator already executing"
self.reset_session()
client = Client(*self.args, **self.kwargs)
for k, v in client.__dict__.items():
setattr(self, k, v)
def submit(
self,
*args,
api_name=None,
fn_index=None,
result_callbacks=None,
) -> Job:
# Note predict calls submit
try:
self.refresh_client_if_should()
job = super().submit(*args, api_name=api_name, fn_index=fn_index)
except Exception as e:
print("Hit e=%s" % str(e), flush=True)
# force reconfig in case only that
self.refresh_client()
job = super().submit(*args, api_name=api_name, fn_index=fn_index)
# see if immediately failed
e = job.future._exception
if e is not None:
print("GR job failed: %s %s" % (str(e), ''.join(traceback.format_tb(e.__traceback__))), flush=True)
# force reconfig in case only that
self.refresh_client()
job = super().submit(*args, api_name=api_name, fn_index=fn_index)
e2 = job.future._exception
if e2 is not None:
print("GR job failed again: %s\n%s" % (str(e2), ''.join(traceback.format_tb(e2.__traceback__))),
flush=True)
return job
from .settings import settings
# TODO use settings.llm_api_key for auth
client = GradioClient(settings.llm_address)
def _call_h2o_gpt_api(prompt: str) -> str:
# don't specify prompt_type etc., use whatever endpoint setup
kwargs = dict(
stream_output=False,
max_time=360,
instruction_nochat=prompt,
)
return client.predict(str(kwargs), api_name='/submit_nochat_api')
prompt_template = '''
"""
{context}
"""
{question}
'''
def answer_question_using_context(question: str, context: str) -> str:
prompt = prompt_template.format(context=context, question=question)
answer = _call_h2o_gpt_api(prompt)
return ast.literal_eval(answer)['response']
class LangChainAction(Enum):
"""LangChain action"""
QUERY = "Query"
SUMMARIZE_MAP = "Summarize"
def query(instruction: str = None,
text: str = None,
file: str = None,
url: str = None,
top_k_docs: int = 4,
pre_prompt_query: str = None,
prompt_query: str = None,
asserts: bool = True) -> str:
"""
Query using h2oGPT
"""
return query_or_summarize(instruction=instruction,
text=text,
file=file,
url=url,
langchain_action=LangChainAction.QUERY.value,
top_k_docs=top_k_docs,
pre_prompt_query=pre_prompt_query,
prompt_query=prompt_query,
asserts=asserts)
def summarize(text: str = None,
file: str = None,
url: str = None,
top_k_docs: int = 4,
pre_prompt_summary: str = None,
prompt_summary: str = None,
asserts: bool = True) -> str:
"""
Summarize using h2oGPT
"""
return query_or_summarize(text=text,
file=file,
url=url,
langchain_action=LangChainAction.SUMMARIZE_MAP.value,
top_k_docs=top_k_docs,
pre_prompt_summary=pre_prompt_summary,
prompt_summary=prompt_summary,
asserts=asserts)
def query_or_summarize(instruction: str = '',
text: Union[list[str], str] = None,
file: Union[list[str], str] = None,
url: Union[list[str], str] = None,
langchain_action: str = None,
embed: str = True,
top_k_docs: int = 4,
pre_prompt_query: str = None,
prompt_query: str = None,
pre_prompt_summary: str = None,
prompt_summary: str = None,
asserts: bool = True) -> str:
"""
Query or Summarize using h2oGPT
Args:
instruction: Query
For query, prompt template is:
"{pre_prompt_query}\"\"\"
{content}
\"\"\"\n{prompt_query}{instruction}"
If added to summarization, prompt template is
"{pre_prompt_summary}:\"\"\"
{content}
\"\"\"\n, Focusing on {instruction}, {prompt_summary}"
text: textual content or list of such contents
file: a local file to upload or files to upload
url: a url to give or urls to use
embed: whether to embed content uploaded
langchain_action: Action to take, "Query" or "Summarize"
top_k_docs: number of document parts.
When doing query, number of chunks
When doing summarization, not related to vectorDB chunks that are not used
E.g. if PDF, then number of pages
pre_prompt_query: Prompt that comes before document part
prompt_query: Prompt that comes after document part
pre_prompt_summary: Prompt that comes before document part
None makes h2oGPT internally use its defaults
E.g. "In order to write a concise single-paragraph or bulleted list summary, pay attention to the following text"
prompt_summary: Prompt that comes after document part
None makes h2oGPT internally use its defaults
E.g. "Using only the text above, write a condensed and concise summary of key results (preferably as bullet points):\n"
i.e. for some internal document part fstring, the template looks like:
template = "%s:
\"\"\"
%s
\"\"\"\n%s" % (pre_prompt_summary, fstring, prompt_summary)
asserts: whether to do asserts to ensure handling is correct
Returns: summary: str
"""
assert text or file or url, "Need to pass either text, file, or url"
# get persistent client
client_persist = Client(*client.args, **client.kwargs, serialize=True)
# chunking not used here
chunk = True
chunk_size = 512
# MyData specifies scratch space, only persisted for this individual client call
langchain_mode = 'MyData'
loaders = tuple([None, None, None, None])
doc_options = tuple([langchain_mode, chunk, chunk_size, embed])
if text:
res = client_persist.predict(text,
*doc_options,
*loaders,
api_name='/add_text')
if asserts:
assert res[0] is None
assert res[1] == langchain_mode
assert 'user_paste' in res[2]
assert res[3] == ''
if file:
# upload file(s). Can be list or single file
# after below call, "file" replaced with remote location of file
_, file = client_persist.predict(file, api_name='/upload_api')
res = client_persist.predict(file,
*doc_options,
*loaders,
api_name='/add_file_api')
if asserts:
assert res[0] is None
assert res[1] == langchain_mode
assert os.path.basename(file) in res[2]
assert res[3] == ''
if url:
res = client_persist.predict(url,
*doc_options,
*loaders,
api_name='/add_url')
if asserts:
assert res[0] is None
assert res[1] == langchain_mode
assert url in res[2]
assert res[3] == ''
if langchain_action == LangChainAction.SUMMARIZE_MAP.value:
# ensure, so full asyncio mode used when gradio connected to TGI server
stream_output = False
else:
# FIXME: should stream
stream_output = False
# ask for summary, need to use same client if using MyData
api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing
kwargs = dict(instruction=instruction,
langchain_mode=langchain_mode,
langchain_action=langchain_action, # uses full document, not vectorDB chunks
top_k_docs=top_k_docs,
stream_output=stream_output,
document_subset='Relevant',
document_choice='All',
max_new_tokens=256,
max_time=360,
do_sample=False,
pre_prompt_query=pre_prompt_query,
prompt_query=prompt_query,
pre_prompt_summary=pre_prompt_summary,
prompt_summary=prompt_summary,
)
# get result
res = client_persist.predict(
str(dict(kwargs)),
api_name=api_name,
)
res = ast.literal_eval(res)
response = res['response']
sources = res['sources']
if api_name == '/submit_nochat_api':
scores_out = [x[0] for x in sources]
texts_out = [x[1] for x in sources]
if asserts and text and not file and not url:
assert text == texts_out
assert len(text) == len(scores_out)
else:
if asserts:
# only pass back file link etc. if not nochat
if text:
assert 'user_paste' in sources
if file:
assert file in sources
if url:
assert url in sources
return responseSee tests in https://github.com/h2oai/h2ogpt/blob/main/tests/test_client_calls.py#L678-L1036 that this code is based upon.
>>> 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}An OpenAI compliant client is available. Refer the README for more details.
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.