mirror of https://github.com/arc53/DocsGPT
Merge branch 'main' into main
Mohit Dhote
9 months ago
committed by
GitHub
commit
9523a929af
@ -0,0 +1,23 @@
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repo:
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- '*'
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github:
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- .github/**/*
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application:
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- application/**/*
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docs:
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- docs/**/*
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extensions:
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- extensions/**/*
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frontend:
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- frontend/**/*
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scripts:
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- scripts/**/*
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tests:
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- tests/**/*
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@ -0,0 +1,15 @@
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# https://github.com/actions/labeler
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name: Pull Request Labeler
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on:
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- pull_request_target
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jobs:
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triage:
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permissions:
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contents: read
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pull-requests: write
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runs-on: ubuntu-latest
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steps:
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- uses: actions/labeler@v4
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with:
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repo-token: "${{ secrets.GITHUB_TOKEN }}"
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sync-labels: true
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@ -1,68 +1,44 @@
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import platform
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import dotenv
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from application.celery import celery
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from flask import Flask, request, redirect
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from application.core.settings import settings
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from application.api.user.routes import user
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from application.api.answer.routes import answer
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from application.api.internal.routes import internal
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# Redirect PosixPath to WindowsPath on Windows
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if platform.system() == "Windows":
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import pathlib
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temp = pathlib.PosixPath
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pathlib.PosixPath = pathlib.WindowsPath
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# loading the .env file
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dotenv.load_dotenv()
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app = Flask(__name__)
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app.register_blueprint(user)
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app.register_blueprint(answer)
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app.register_blueprint(internal)
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app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER = "inputs"
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app.config["CELERY_BROKER_URL"] = settings.CELERY_BROKER_URL
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app.config["CELERY_RESULT_BACKEND"] = settings.CELERY_RESULT_BACKEND
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app.config["MONGO_URI"] = settings.MONGO_URI
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app.config.update(
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UPLOAD_FOLDER="inputs",
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CELERY_BROKER_URL=settings.CELERY_BROKER_URL,
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CELERY_RESULT_BACKEND=settings.CELERY_RESULT_BACKEND,
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MONGO_URI=settings.MONGO_URI
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)
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celery.config_from_object("application.celeryconfig")
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@app.route("/")
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def home():
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"""
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The frontend source code lives in the /frontend directory of the repository.
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"""
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if request.remote_addr in ('0.0.0.0', '127.0.0.1', 'localhost', '172.18.0.1'):
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# If users locally try to access DocsGPT running in Docker,
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# they will be redirected to the Frontend application.
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return redirect('http://localhost:5173')
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else:
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# Handle other cases or render the default page
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return 'Welcome to DocsGPT Backend!'
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# handling CORS
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@app.after_request
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def after_request(response):
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response.headers.add("Access-Control-Allow-Origin", "*")
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response.headers.add("Access-Control-Allow-Headers", "Content-Type,Authorization")
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response.headers.add("Access-Control-Allow-Methods", "GET,PUT,POST,DELETE,OPTIONS")
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# response.headers.add("Access-Control-Allow-Credentials", "true")
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return response
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if __name__ == "__main__":
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app.run(debug=True, port=7091)
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@ -1,27 +1,139 @@
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from application.llm.base import BaseLLM
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from application.core.settings import settings
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import requests
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import json
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import io
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class LineIterator:
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"""
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A helper class for parsing the byte stream input.
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The output of the model will be in the following format:
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```
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b'{"outputs": [" a"]}\n'
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b'{"outputs": [" challenging"]}\n'
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b'{"outputs": [" problem"]}\n'
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...
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```
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While usually each PayloadPart event from the event stream will contain a byte array
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with a full json, this is not guaranteed and some of the json objects may be split across
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PayloadPart events. For example:
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```
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{'PayloadPart': {'Bytes': b'{"outputs": '}}
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{'PayloadPart': {'Bytes': b'[" problem"]}\n'}}
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```
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This class accounts for this by concatenating bytes written via the 'write' function
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and then exposing a method which will return lines (ending with a '\n' character) within
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the buffer via the 'scan_lines' function. It maintains the position of the last read
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position to ensure that previous bytes are not exposed again.
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"""
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def __init__(self, stream):
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self.byte_iterator = iter(stream)
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self.buffer = io.BytesIO()
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self.read_pos = 0
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def __iter__(self):
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return self
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def __next__(self):
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while True:
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self.buffer.seek(self.read_pos)
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line = self.buffer.readline()
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if line and line[-1] == ord('\n'):
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self.read_pos += len(line)
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return line[:-1]
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try:
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chunk = next(self.byte_iterator)
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except StopIteration:
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if self.read_pos < self.buffer.getbuffer().nbytes:
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continue
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raise
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if 'PayloadPart' not in chunk:
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print('Unknown event type:' + chunk)
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continue
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self.buffer.seek(0, io.SEEK_END)
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self.buffer.write(chunk['PayloadPart']['Bytes'])
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class SagemakerAPILLM(BaseLLM):
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def __init__(self, *args, **kwargs):
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self.url = settings.SAGEMAKER_API_URL
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import boto3
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runtime = boto3.client(
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'runtime.sagemaker',
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aws_access_key_id='xxx',
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aws_secret_access_key='xxx',
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region_name='us-west-2'
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)
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self.endpoint = settings.SAGEMAKER_ENDPOINT
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self.runtime = runtime
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def gen(self, model, engine, messages, stream=False, **kwargs):
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context = messages[0]['content']
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user_question = messages[-1]['content']
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prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
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response = requests.post(
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url=self.url,
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headers={
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"Content-Type": "application/json; charset=utf-8",
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},
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data=json.dumps({"input": prompt})
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)
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# Construct payload for endpoint
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payload = {
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"inputs": prompt,
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"stream": False,
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"parameters": {
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"do_sample": True,
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"temperature": 0.1,
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"max_new_tokens": 30,
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"repetition_penalty": 1.03,
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"stop": ["</s>", "###"]
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}
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}
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body_bytes = json.dumps(payload).encode('utf-8')
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return response.json()['answer']
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# Invoke the endpoint
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response = self.runtime.invoke_endpoint(EndpointName=self.endpoint,
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ContentType='application/json',
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Body=body_bytes)
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result = json.loads(response['Body'].read().decode())
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import sys
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print(result[0]['generated_text'], file=sys.stderr)
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return result[0]['generated_text'][len(prompt):]
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def gen_stream(self, model, engine, messages, stream=True, **kwargs):
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raise NotImplementedError("Sagemaker does not support streaming")
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context = messages[0]['content']
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user_question = messages[-1]['content']
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prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
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# Construct payload for endpoint
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payload = {
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"inputs": prompt,
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"stream": True,
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"parameters": {
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"do_sample": True,
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"temperature": 0.1,
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"max_new_tokens": 512,
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"repetition_penalty": 1.03,
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"stop": ["</s>", "###"]
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}
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}
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body_bytes = json.dumps(payload).encode('utf-8')
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# Invoke the endpoint
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response = self.runtime.invoke_endpoint_with_response_stream(EndpointName=self.endpoint,
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ContentType='application/json',
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Body=body_bytes)
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#result = json.loads(response['Body'].read().decode())
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event_stream = response['Body']
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start_json = b'{'
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for line in LineIterator(event_stream):
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if line != b'' and start_json in line:
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#print(line)
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data = json.loads(line[line.find(start_json):].decode('utf-8'))
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if data['token']['text'] not in ["</s>", "###"]:
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print(data['token']['text'],end='')
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yield data['token']['text']
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@ -1,4 +1,4 @@
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## To customise a main prompt navigate to `/application/prompt/combine_prompt.txt`
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## To customize a main prompt navigate to `/application/prompt/combine_prompt.txt`
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You can try editing it to see how the model responses.
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@ -0,0 +1,96 @@
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# FILEPATH: /path/to/test_sagemaker.py
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import json
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import unittest
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from unittest.mock import MagicMock, patch
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from application.llm.sagemaker import SagemakerAPILLM, LineIterator
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class TestSagemakerAPILLM(unittest.TestCase):
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def setUp(self):
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self.sagemaker = SagemakerAPILLM()
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self.context = "This is the context"
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self.user_question = "What is the answer?"
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self.messages = [
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{"content": self.context},
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{"content": "Some other message"},
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{"content": self.user_question}
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]
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self.prompt = f"### Instruction \n {self.user_question} \n ### Context \n {self.context} \n ### Answer \n"
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self.payload = {
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"inputs": self.prompt,
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"stream": False,
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"parameters": {
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"do_sample": True,
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"temperature": 0.1,
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"max_new_tokens": 30,
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"repetition_penalty": 1.03,
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"stop": ["</s>", "###"]
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}
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}
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self.payload_stream = {
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"inputs": self.prompt,
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"stream": True,
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"parameters": {
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"do_sample": True,
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"temperature": 0.1,
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"max_new_tokens": 512,
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"repetition_penalty": 1.03,
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"stop": ["</s>", "###"]
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}
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}
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self.body_bytes = json.dumps(self.payload).encode('utf-8')
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self.body_bytes_stream = json.dumps(self.payload_stream).encode('utf-8')
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self.response = {
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"Body": MagicMock()
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}
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self.result = [
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{
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"generated_text": "This is the generated text"
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}
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]
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self.response['Body'].read.return_value.decode.return_value = json.dumps(self.result)
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def test_gen(self):
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with patch.object(self.sagemaker.runtime, 'invoke_endpoint',
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return_value=self.response) as mock_invoke_endpoint:
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output = self.sagemaker.gen(None, None, self.messages)
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mock_invoke_endpoint.assert_called_once_with(
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EndpointName=self.sagemaker.endpoint,
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ContentType='application/json',
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Body=self.body_bytes
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)
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self.assertEqual(output,
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self.result[0]['generated_text'][len(self.prompt):])
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def test_gen_stream(self):
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with patch.object(self.sagemaker.runtime, 'invoke_endpoint_with_response_stream',
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return_value=self.response) as mock_invoke_endpoint:
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output = list(self.sagemaker.gen_stream(None, None, self.messages))
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mock_invoke_endpoint.assert_called_once_with(
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EndpointName=self.sagemaker.endpoint,
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ContentType='application/json',
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Body=self.body_bytes_stream
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)
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self.assertEqual(output, [])
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class TestLineIterator(unittest.TestCase):
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def setUp(self):
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self.stream = [
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{'PayloadPart': {'Bytes': b'{"outputs": [" a"]}\n'}},
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{'PayloadPart': {'Bytes': b'{"outputs": [" challenging"]}\n'}},
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{'PayloadPart': {'Bytes': b'{"outputs": [" problem"]}\n'}}
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]
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self.line_iterator = LineIterator(self.stream)
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def test_iter(self):
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self.assertEqual(iter(self.line_iterator), self.line_iterator)
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def test_next(self):
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self.assertEqual(next(self.line_iterator), b'{"outputs": [" a"]}')
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self.assertEqual(next(self.line_iterator), b'{"outputs": [" challenging"]}')
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self.assertEqual(next(self.line_iterator), b'{"outputs": [" problem"]}')
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if __name__ == '__main__':
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unittest.main()
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