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This commit is contained in:
parent
955b71459c
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2d227d438c
@ -13,12 +13,6 @@ docker-compose build
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docker-compose up -d
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```
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After deploy:
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### WebUI for Ollama:
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* http://localhost:8888
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* use to install model llama3 (or more https://ollama.com/library)
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----
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## Usage
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@ -43,7 +37,6 @@ After deploy:
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### Backend
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* FastAPI
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* RabbitMQ/Kafka?
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* OpenSearch
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@ -13,10 +13,16 @@ WORKDIR /code
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COPY requirements.txt /code/requirements.txt
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#RUN pip3 install --no-cache-dir --upgrade -r requirements.txt
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RUN pip3 install --no-cache-dir -r requirements.txt
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RUN pip3 freeze > current_requirements.txt
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COPY . .
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ENTRYPOINT ["python3", "/code/app.py"]
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#ENTRYPOINT ["fastapi", "run", "main.py", "--port", "8000"]
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#gunicorn -w 4 -b 0.0.0.0 'hello:create_app()'
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#ENTRYPOINT ["gunicorn", "-w", "1", "-b", "0.0.0.0", "app:create_app()"]
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#ENTRYPOINT ["gunicorn", "-w", "1", "-b", "0.0.0.0:5000", "app:create_app()"]
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#gunicorn app:app --worker-class eventlet -w 1 --bind 0.0.0.0:5000 --reload
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358
backend/app.py
358
backend/app.py
@ -1,10 +1,7 @@
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"""
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OpenAPI access via http://localhost:5000/openapi/ on local docker-compose deployment
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"""
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#import warnings
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#warnings.filterwarnings("ignore")
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#------std lib modules:-------
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import os, sys, json, time
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@ -17,50 +14,26 @@ from functools import wraps
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import base64
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#-------ext libs--------------
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#llm
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from langchain.callbacks.manager import CallbackManager
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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#import tiktoken
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.chains import RetrievalQA
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from langchain.callbacks.base import BaseCallbackHandler, BaseCallbackManager
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from langchain.prompts import PromptTemplate
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from langchain_community.llms import Ollama
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from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader
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from langchain_community.embeddings import OllamaEmbeddings
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#from langchain_community.vectorstores.elasticsearch import ElasticsearchStore #deprecated
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from langchain_elasticsearch import ElasticsearchStore
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from uuid import uuid4
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from elasticsearch import NotFoundError, Elasticsearch # for normal read/write without vectors
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from elasticsearch_dsl import Search, A, Document, Date, Integer, Keyword, Float, Long, Text, connections
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from elasticsearch.exceptions import ConnectionError
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from pydantic import BaseModel, Field
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import logging_loki
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import jwt as pyjwt
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#flask, openapi
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from flask import Flask, send_from_directory, send_file, Response, request, jsonify
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from flask_cors import CORS, cross_origin
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from werkzeug.utils import secure_filename
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from flask_openapi3 import Info, Tag, OpenAPI, Server, FileStorage
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from flask_openapi3 import Info, Tag, OpenAPI, Server #FileStorage
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from flask_socketio import SocketIO, join_room, leave_room, rooms, send
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#from werkzeug.utils import secure_filename
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from cryptography.fernet import Fernet
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from cryptography.hazmat.primitives import hashes
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from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
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import asyncio
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#----------home grown--------------
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from lib.funcs import group_by
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from lib.elastictools import get_by_id, update_by_id, delete_by_id, wait_for_elasticsearch
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from lib.models import init_indicies, QueryLog, Chatbot, User, Text
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from lib.chatbot import ask_bot, train_text, download_llm
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from lib.elastictools import get_by_id, wait_for_elasticsearch
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from lib.models import init_indicies, Chatbot, User, Text
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from lib.chatbot import ask_bot, ask_bot2, train_text, download_llm
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from lib.speech import text_to_speech
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from lib.mail import send_mail
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from lib.user import hash_password, create_user, create_default_users
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@ -69,6 +42,15 @@ from lib.user import hash_password, create_user, create_default_users
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BOT_ROOT_PATH = os.getenv("BOT_ROOT_PATH")
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assert BOT_ROOT_PATH
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ollama_url = os.getenv("OLLAMA_URI")
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assert ollama_url
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elastic_url = os.getenv("ELASTIC_URI")
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assert elastic_url
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jwt_secret = os.getenv("SECRET")
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assert jwt_secret
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# JWT Bearer Sample
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jwt = {
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@ -80,11 +62,24 @@ security_schemes = {"jwt": jwt}
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security = [{"jwt": []}]
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def get_module_versions():
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with open("requirements.txt", "r") as f:
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modules = {line.split("#")[0] for line in f.read().split("\n") if line.split("#")[0] != ""}
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with open("current_requirements.txt", "r") as f:
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d = {k: v for (k, v) in [line.split("#")[0].split("==") for line in f.read().split("\n") if line.split("#")[0] != ""]}
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return {k: v for k, v in d.items() if k in modules}
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import multiprocessing
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cpus = multiprocessing.cpu_count()
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info = Info(
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title="Chatbot-API",
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title="CreativeBots-API",
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version="1.0.0",
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summary="The REST-API",
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description="Default model: ..."
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summary="The REST-API to manage bots, users and more!",
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description="CPUs: " + str(cpus) + "<br>" + json.dumps(get_module_versions(), indent=4).replace("\n", "<br>")
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)
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servers = [
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Server(url=BOT_ROOT_PATH )
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@ -108,6 +103,9 @@ def uses_jwt(required=True):
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Wraps routes in a jwt-required logic and passes decoded jwt and user from elasticsearch to the route as keyword
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"""
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jwt_secret = os.getenv("SECRET")
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assert jwt_secret
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def non_param_deco(f):
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@wraps(f)
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def decorated_route(*args, **kwargs):
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@ -115,7 +113,6 @@ def uses_jwt(required=True):
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if "Authorization" in request.headers:
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token = request.headers["Authorization"].split(" ")[1]
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if not token:
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if required:
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return jsonify({
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@ -130,7 +127,7 @@ def uses_jwt(required=True):
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try:
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data = pyjwt.decode(token, app.config["jwt_secret"], algorithms=["HS256"])
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data = pyjwt.decode(token, jwt_secret, algorithms=["HS256"])
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except Exception as e:
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return jsonify({
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'status': 'error',
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@ -160,24 +157,6 @@ def uses_jwt(required=True):
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env_to_conf = {
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"ELASTIC_URI": "elastic_uri",
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"SECRET": "jwt_secret"
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}
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#import values from env into flask config and do existence check
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for env_key, conf_key in env_to_conf.items():
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x = os.getenv(env_key)
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if not x:
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msg = "Environment variable '%s' not set!" % env_key
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app.logger.fatal(msg)
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sys.exit(1)
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else:
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app.config[conf_key] = x
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socket = SocketIO(app, cors_allowed_origins="*")
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@socket.on('connect')
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@ -192,24 +171,60 @@ def sockcon(data):
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socket.emit('backend response', {'msg': f'Connected to room {room} !', "room": room}) # looks like iOS needs an answer
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#TODO: pydantic message type validation
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class SocketMessage(BaseModel):
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room: str = Field(None, description="Status Code")
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question: str = Field(None, description="Status Code")
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system_prompt: str = Field(None, description="Status Code")
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bot_id: str = Field(None, description="Status Code")
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#TODO: pydantic message type validation
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@socket.on('client message')
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def handle_message(message):
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SocketMessage.model_validate(message)
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#try:
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room = message["room"]
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question = message["question"]
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system_prompt = message["system_prompt"]
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bot_id = message["bot_id"]
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#except:
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# return
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start = datetime.now().timestamp()
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d = ask_bot2(system_prompt + " " + question, bot_id)
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for chunk in ask_bot(system_prompt + " " + question, bot_id):
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def get_scores(*args):
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score_docs = d["get_score_docs"]()
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return score_docs
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def do_streaming(*args):
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start_stream = datetime.now().timestamp()
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for chunk in d["answer_generator"]():
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socket.emit('backend token', {'data': chunk, "done": False}, to=room)
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socket.emit('backend token', {'done': True}, to=room)
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stream_duration = round(datetime.now().timestamp() - start_stream, 2)
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print("Stream duration: ", stream_duration, flush=True)
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async def f():
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ls = await asyncio.gather(
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asyncio.to_thread(get_scores, 1,2,3),
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asyncio.to_thread(do_streaming, 1,2,3)
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)
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return ls
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[score_docs, _] = asyncio.run(f())
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socket.emit('backend token', {
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'done': True,
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"score_docs": score_docs
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}, to=room)
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duration = round(datetime.now().timestamp() - start, 2)
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print("Total duration: ", duration, flush=True)
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#======================= TAGS =============================
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@ -240,8 +255,7 @@ def login(form: LoginRequest):
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'message': msg
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}), 400
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client = Elasticsearch(app.config['elastic_uri'])
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match get_by_id(client, index="user", id_field_name="email", id_value=form.email):
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match get_by_id(index="user", id_field_name="email", id_value=form.email):
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case []:
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msg = "User with email '%s' doesn't exist!" % form.email
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app.logger.error(msg)
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@ -252,7 +266,7 @@ def login(form: LoginRequest):
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case [user]:
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if user["password_hash"] == hash_password(form.password + form.email):
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token = pyjwt.encode({"email": form.email}, app.config['jwt_secret'], algorithm="HS256")
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token = pyjwt.encode({"email": form.email}, jwt_secret, algorithm="HS256")
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#app.logger.info(token)
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return jsonify({
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'status': 'success',
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@ -331,14 +345,50 @@ class GetSpeechRequest(BaseModel):
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@app.post('/text2speech', summary="", tags=[], security=security)
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def text2speech(form: GetSpeechRequest):
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file_name = text_to_speech(form.text)
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#return send_file(file_path, mimetype='audio/mpeg') #, attachment_filename= 'Audiofiles.zip', as_attachment = True)
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return jsonify({
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"status": "success",
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"file": "/" + file_name
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})
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#============ Bot CRUD ===============
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class CreateBotRequest(BaseModel):
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name: str = Field(None, description="The bot's name")
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visibility: str = Field('private', description="The bot's visibility to other users ('private', 'public')")
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description: str = Field('', description="The bot's description of purpose and being")
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system_prompt: str = Field('', description="The bot's defining system prompt")
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llm_model: str = Field("llama3", description="The bot's used LLM")
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#status = Keyword()
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#temperature = Float()
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@app.post('/bot', summary="", tags=[bot_tag], security=security)
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@uses_jwt()
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def create_bot(form: CreateBotRequest, decoded_jwt, user):
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"""
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Creates a chatbot for the JWT associated user.
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"""
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CreateBotRequest.model_validate(form)
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bot = Chatbot()
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bot.name = form.name
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bot.visibility = form.visibility
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bot.description = form.description
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bot.system_prompt = form.system_prompt
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bot.llm_model = form.llm_model
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#add meta data
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bot.creation_date = datetime.now()
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bot.creator_id = user.meta.id
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bot.save()
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return jsonify({
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"bot_id": bot.meta.id
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})
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class GetBotRequest(BaseModel):
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id: str = Field(None, description="The bot's id")
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@ -395,25 +445,23 @@ def get_bots(query: GetBotRequest, decoded_jwt, user):
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class CreateBotRequest(BaseModel):
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class UpdateBotRequest(BaseModel):
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id: str = Field(None, description="The bot's id")
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name: str = Field(None, description="The bot's name")
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visibility: str = Field('private', description="The bot's visibility to other users ('private', 'public')")
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description: str = Field('', description="The bot's description of purpose and being")
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system_prompt: str = Field('', description="The bot's defining system prompt")
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llm_model: str = Field("llama3", description="The bot's used LLM")
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#status = Keyword()
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#temperature = Float()
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visibility: str = Field(None, description="The bot's visibility to other users ('private', 'public')")
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description: str = Field(None, description="The bot's description of purpose and being")
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system_prompt: str = Field(None, description="The bot's defining system prompt")
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llm_model: str = Field(None, description="The bot's used LLM")
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@app.post('/bot', summary="", tags=[bot_tag], security=security)
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@app.put('/bot', summary="", tags=[bot_tag], security=security)
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@uses_jwt()
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def create_bot(form: CreateBotRequest, decoded_jwt, user):
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def update_bot(form: UpdateBotRequest, decoded_jwt, user):
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"""
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Creates a chatbot for the JWT associated user.
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Change a chatbot via it's id
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"""
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bot = Chatbot()
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bot = Chatbot.get(id=form.id)
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bot.name = form.name
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bot.visibility = form.visibility
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bot.description = form.description
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@ -421,16 +469,16 @@ def create_bot(form: CreateBotRequest, decoded_jwt, user):
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bot.llm_model = form.llm_model
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#add meta data
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bot.creation_date = datetime.now()
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bot.creator_id = user.meta.id
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bot.changed_date = datetime.now()
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bot.save()
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return jsonify({
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"bot_id": bot.meta.id
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"status": "success"
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})
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class DeleteBotRequest(BaseModel):
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id: str = Field(None, description="The bot's id")
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@ -442,26 +490,13 @@ def delete_bot(form: DeleteBotRequest, decoded_jwt, user):
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"""
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bot = Chatbot.get(id=form.id)
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bot.delete()
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return jsonify({
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"status": "success"
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})
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class UpdateBotRequest(BaseModel):
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id: str = Field(None, description="The bot's id")
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@app.put('/bot', summary="", tags=[bot_tag], security=security)
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@uses_jwt()
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def update_bot(form: UpdateBotRequest, decoded_jwt, user):
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"""
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Changes a chatbot
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"""
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return jsonify({
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"status": "success"
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})
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#============================================================================
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class AskBotRequest(BaseModel):
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@ -469,94 +504,53 @@ class AskBotRequest(BaseModel):
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question: str = Field(None, description="The question the bot should answer")
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from langchain.chains import create_retrieval_chain
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from langchain.chains.combine_documents import create_stuff_documents_chain
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from langchain_core.prompts import ChatPromptTemplate
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@app.get('/bot/ask', summary="", tags=[bot_tag], security=security)
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@uses_jwt()
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def query_bot(query: AskBotRequest, decoded_jwt, user):
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"""
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Asks a chatbot
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"""
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start = datetime.now().timestamp()
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bot_id = query.bot_id
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prompt = query.question
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question = query.question
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system_prompt = (
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"Antworte freundlich, mit einer ausführlichen Erklärung, sofern vorhanden auf Basis der folgenden Informationen. Please answer in the language of the question."
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"\n\n"
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"{context}"
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start = datetime.now().timestamp()
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d = ask_bot2(system_prompt + " " + question, bot_id)
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def get_scores(*args):
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score_docs = d["get_score_docs"]()
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return score_docs
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def do_streaming(*args):
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start_stream = datetime.now().timestamp()
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answer = ""
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for chunk in d["answer_generator"]():
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answer += chunk
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stream_duration = round(datetime.now().timestamp() - start_stream, 2)
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print("Stream duration: ", stream_duration, flush=True)
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return answer
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async def f():
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ls = await asyncio.gather(
|
||||
asyncio.to_thread(get_scores, 1,2,3),
|
||||
asyncio.to_thread(do_streaming, 1,2,3)
|
||||
)
|
||||
return ls
|
||||
|
||||
|
||||
ch_prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
("system", system_prompt),
|
||||
("human", "{input}"),
|
||||
]
|
||||
)
|
||||
|
||||
ollama_url = os.getenv("OLLAMA_URI")
|
||||
|
||||
|
||||
embeddings = OllamaEmbeddings(model="llama3", base_url=ollama_url)
|
||||
|
||||
vector_store = ElasticsearchStore(
|
||||
es_url=app.config['elastic_uri'],
|
||||
index_name= "chatbot_" + bot_id.lower(),
|
||||
distance_strategy="COSINE",
|
||||
embedding=embeddings
|
||||
)
|
||||
|
||||
|
||||
bot = Chatbot.get(id=bot_id)
|
||||
llm = Ollama(
|
||||
model=bot.llm_model,
|
||||
base_url=ollama_url
|
||||
)
|
||||
|
||||
k = 4
|
||||
scoredocs = vector_store.similarity_search_with_score(prompt, k=k)
|
||||
|
||||
retriever = vector_store.as_retriever()
|
||||
question_answer_chain = create_stuff_documents_chain(llm, ch_prompt)
|
||||
rag_chain = create_retrieval_chain(retriever, question_answer_chain)
|
||||
|
||||
|
||||
r = ""
|
||||
#for chunk in rag_chain.stream({"input": "What is Task Decomposition?"}):
|
||||
for chunk in rag_chain.stream({"input": prompt}):
|
||||
print(chunk, flush=True)
|
||||
if "answer" in chunk:
|
||||
r += chunk["answer"]
|
||||
|
||||
|
||||
|
||||
#for chunk in ask_bot(question=query.question, bot_id=query.bot_id):
|
||||
# r += chunk
|
||||
|
||||
|
||||
|
||||
xs = []
|
||||
for doc, score in scoredocs:
|
||||
#print(doc.__dict__, flush=True)
|
||||
#print(doc, flush=True)
|
||||
xs.append([dict(doc), score])
|
||||
|
||||
[score_docs, answer] = asyncio.run(f())
|
||||
|
||||
duration = round(datetime.now().timestamp() - start, 2)
|
||||
print("Total duration: ", duration, flush=True)
|
||||
|
||||
app.logger.info(duration)
|
||||
|
||||
return jsonify({
|
||||
"answer": r,
|
||||
"answer": answer,
|
||||
"duration": str(duration),
|
||||
#"docs": ls#,
|
||||
"score_docs": xs
|
||||
@ -608,12 +602,14 @@ def index():
|
||||
|
||||
@app.route('/<path:path>') #generische Route (auch Unterordner)
|
||||
def catchAll(path):
|
||||
#return send_from_directory('.', path)
|
||||
return send_from_directory('./public', path)
|
||||
|
||||
|
||||
#import logging_loki
|
||||
|
||||
def main():
|
||||
|
||||
|
||||
def create_app():
|
||||
LOG_LEVEL = os.getenv("LOG_LEVEL")
|
||||
if LOG_LEVEL:
|
||||
logging.basicConfig(level=eval("logging." + LOG_LEVEL))
|
||||
@ -621,32 +617,16 @@ def main():
|
||||
logging.basicConfig(level=logging.WARN)
|
||||
|
||||
#TODO: implement some kind of logging mechanism
|
||||
|
||||
"""
|
||||
USE_LOKI_LOGGER = os.getenv("USE_LOKI_LOGGER")
|
||||
if USE_LOKI_LOGGER:
|
||||
handler = logging_loki.LokiHandler(
|
||||
url="http://loki:3100/loki/api/v1/push",
|
||||
tags={"application": "CreativeBots"},
|
||||
#auth=("username", "password"),
|
||||
version="1",
|
||||
)
|
||||
app.logger.addHandler(handler)
|
||||
"""
|
||||
|
||||
#wait_for_elasticsearch()
|
||||
download_llm()
|
||||
connections.create_connection(hosts=app.config['elastic_uri'], request_timeout=60)
|
||||
download_llm("llama3")
|
||||
connections.create_connection(hosts=elastic_url, request_timeout=60)
|
||||
wait_for_elasticsearch()
|
||||
init_indicies()
|
||||
create_default_users()
|
||||
app.run(debug=False, threaded=True, host='0.0.0.0')
|
||||
|
||||
return app
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
|
||||
|
||||
app = create_app()
|
||||
app.run(debug=False, host='0.0.0.0')
|
||||
|
||||
|
||||
|
||||
@ -6,18 +6,9 @@ from uuid import uuid4
|
||||
from collections import namedtuple
|
||||
import os, hashlib, traceback, logging
|
||||
from datetime import datetime, date
|
||||
|
||||
#from elasticsearch_dsl import Document, Date, Integer, Keyword, Text, connections
|
||||
from elasticsearch_dsl import connections
|
||||
|
||||
#from langchain.callbacks.manager import CallbackManager
|
||||
#from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
|
||||
|
||||
#import tiktoken
|
||||
from langchain.text_splitter import RecursiveCharacterTextSplitter
|
||||
#from langchain.chains import RetrievalQA
|
||||
#from langchain.callbacks.base import BaseCallbackHandler, BaseCallbackManager
|
||||
#from langchain.prompts import PromptTemplate
|
||||
|
||||
from langchain_community.llms import Ollama
|
||||
from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader
|
||||
@ -29,14 +20,9 @@ from langchain.chains import create_retrieval_chain
|
||||
from langchain.chains.combine_documents import create_stuff_documents_chain
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
|
||||
|
||||
|
||||
from lib.models import Chatbot, Text, User
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
ollama_url = os.getenv("OLLAMA_URI")
|
||||
elastic_url = os.getenv("ELASTIC_URI")
|
||||
|
||||
@ -51,13 +37,19 @@ def train_text(bot_id, text):
|
||||
"""
|
||||
Caution: Long running request!
|
||||
"""
|
||||
txt_md5 = hashlib.md5(text.encode()).hexdigest()
|
||||
t = Text.get(id=txt_md5, ignore=404)
|
||||
if t is not None:
|
||||
return True
|
||||
|
||||
else:
|
||||
bot = Chatbot.get(id=bot_id)
|
||||
user = User.get(id=bot.creator_id)
|
||||
|
||||
t = Text(meta={'id': txt_md5})
|
||||
t = Text()
|
||||
t.text = text
|
||||
t.md5 = hashlib.md5(text.encode()).hexdigest()
|
||||
t.md5 = txt_md5
|
||||
|
||||
#add meta data
|
||||
t.creation_date = datetime.now()
|
||||
@ -110,13 +102,72 @@ def ask_bot(question, bot_id):
|
||||
|
||||
|
||||
#connections.get_connection()
|
||||
|
||||
#if es.indices.exists(index="index"):
|
||||
|
||||
|
||||
|
||||
|
||||
def ask_bot2(question, bot_id):
|
||||
"""
|
||||
Asks a chatbot
|
||||
"""
|
||||
|
||||
bot = Chatbot.get(id=bot_id)
|
||||
prompt = question
|
||||
system_prompt = bot.system_prompt + "\n\n{context}"
|
||||
|
||||
rag_index = "chatbot_" + bot_id.lower()
|
||||
|
||||
if connections.get_connection().indices.exists(index=rag_index):
|
||||
|
||||
vector_store = ElasticsearchStore(
|
||||
es_url=elastic_url,
|
||||
index_name=rag_index,
|
||||
distance_strategy="COSINE",
|
||||
embedding=OllamaEmbeddings(model=bot.llm_model, base_url=ollama_url)
|
||||
)
|
||||
|
||||
def gen_func():
|
||||
llm = Ollama(
|
||||
model=bot.llm_model,
|
||||
base_url=ollama_url
|
||||
)
|
||||
|
||||
ch_prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
("system", system_prompt),
|
||||
("human", "{input}"),
|
||||
]
|
||||
)
|
||||
|
||||
retriever = vector_store.as_retriever()
|
||||
question_answer_chain = create_stuff_documents_chain(llm, ch_prompt)
|
||||
rag_chain = create_retrieval_chain(retriever, question_answer_chain)
|
||||
for chunk in rag_chain.stream({"input": prompt}):
|
||||
print(chunk, flush=True)
|
||||
if "answer" in chunk:
|
||||
yield chunk["answer"]
|
||||
|
||||
|
||||
def get_score_docs():
|
||||
k = 4
|
||||
start_vec_search = datetime.now().timestamp()
|
||||
scoredocs = vector_store.similarity_search_with_score(prompt, k=k)
|
||||
vec_search_duration = round(datetime.now().timestamp() - start_vec_search, 2)
|
||||
print("Vec search duration: ", vec_search_duration, flush=True)
|
||||
xs = []
|
||||
for doc, score in scoredocs:
|
||||
#print(doc.__dict__, flush=True)
|
||||
#print(doc, flush=True)
|
||||
xs.append([score, dict(doc)])
|
||||
return xs
|
||||
|
||||
|
||||
return {
|
||||
"answer_generator": gen_func,
|
||||
"get_score_docs": get_score_docs
|
||||
}
|
||||
|
||||
else:
|
||||
|
||||
def gen_func():
|
||||
bot = Chatbot.get(id=bot_id)
|
||||
llm = Ollama(
|
||||
model=bot.llm_model,
|
||||
@ -126,93 +177,17 @@ def ask_bot2(question, bot_id):
|
||||
for chunk in llm.stream(query):
|
||||
yield chunk
|
||||
|
||||
return {
|
||||
"answer_generator": gen_func,
|
||||
"get_score_docs": lambda: []
|
||||
}
|
||||
|
||||
|
||||
#def query_bot(query: AskBotRequest, decoded_jwt, user):
|
||||
"""
|
||||
Asks a chatbot
|
||||
"""
|
||||
start = datetime.now().timestamp()
|
||||
|
||||
bot_id = query.bot_id
|
||||
prompt = query.question
|
||||
|
||||
|
||||
system_prompt = (
|
||||
"Antworte freundlich, mit einer ausführlichen Erklärung, sofern vorhanden auf Basis der folgenden Informationen. Please answer in the language of the question."
|
||||
"\n\n"
|
||||
"{context}"
|
||||
)
|
||||
|
||||
|
||||
ch_prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
("system", system_prompt),
|
||||
("human", "{input}"),
|
||||
]
|
||||
)
|
||||
|
||||
embeddings = OllamaEmbeddings(model="llama3", base_url=ollama_url)
|
||||
|
||||
vector_store = ElasticsearchStore(
|
||||
es_url=app.config['elastic_uri'],
|
||||
index_name= "chatbot_" + bot_id.lower(),
|
||||
distance_strategy="COSINE",
|
||||
embedding=embeddings
|
||||
)
|
||||
|
||||
|
||||
bot = Chatbot.get(id=bot_id)
|
||||
llm = Ollama(
|
||||
model=bot.llm_model,
|
||||
base_url=ollama_url
|
||||
)
|
||||
|
||||
k = 4
|
||||
scoredocs = vector_store.similarity_search_with_score(prompt, k=k)
|
||||
|
||||
retriever = vector_store.as_retriever()
|
||||
question_answer_chain = create_stuff_documents_chain(llm, ch_prompt)
|
||||
rag_chain = create_retrieval_chain(retriever, question_answer_chain)
|
||||
|
||||
|
||||
r = ""
|
||||
#for chunk in rag_chain.stream({"input": "What is Task Decomposition?"}):
|
||||
for chunk in rag_chain.stream({"input": prompt}):
|
||||
print(chunk, flush=True)
|
||||
if "answer" in chunk:
|
||||
r += chunk["answer"]
|
||||
|
||||
|
||||
|
||||
#for chunk in ask_bot(question=query.question, bot_id=query.bot_id):
|
||||
# r += chunk
|
||||
|
||||
|
||||
|
||||
xs = []
|
||||
for doc, score in scoredocs:
|
||||
#print(doc.__dict__, flush=True)
|
||||
#print(doc, flush=True)
|
||||
xs.append([dict(doc), score])
|
||||
|
||||
|
||||
duration = round(datetime.now().timestamp() - start, 2)
|
||||
|
||||
#app.logger.info(duration)
|
||||
|
||||
#return jsonify({
|
||||
# "answer": r,
|
||||
# "duration": str(duration),
|
||||
# #"docs": ls#,
|
||||
# "score_docs": xs
|
||||
#})
|
||||
|
||||
|
||||
|
||||
from ollama import Client as OllamaClient
|
||||
|
||||
def download_llm(model="llama3"):
|
||||
def download_llm(model):
|
||||
#print(ollama_url, flush=True)
|
||||
|
||||
#ollama_client = OllamaClient(host=ollama_url)
|
||||
@ -224,7 +199,3 @@ def download_llm(model="llama3"):
|
||||
s = """curl %s/api/pull -d '{ "name": "%s" }' """ % (ollama_url, model)
|
||||
print( os.system(s.strip()) ,flush=True)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -5,34 +5,22 @@ import time, json, os
|
||||
from typing import Any, Tuple, List, Dict, Any, Callable, Optional
|
||||
from elasticsearch import NotFoundError, Elasticsearch # for normal read/write without vectors
|
||||
from elasticsearch_dsl import Search, A, UpdateByQuery, Document, Date, Integer, Keyword, Float, Long, Text, connections
|
||||
from elasticsearch.exceptions import ConnectionError
|
||||
|
||||
|
||||
def get_by_id(client: Elasticsearch, index: str, id_field_name: str, id_value: str):
|
||||
def get_by_id(index: str, id_field_name: str, id_value: str):
|
||||
client = connections.get_connection()
|
||||
response = Search(using=client, index=index).filter("term", **{id_field_name: id_value})[0:10000].execute()
|
||||
return [hit.to_dict() for hit in response]
|
||||
|
||||
|
||||
def update_by_id(client: Elasticsearch, index: str, id_field_name: str, id_value: str, values_to_set: Dict[str, Any]) -> None:
|
||||
def update_by_id(index: str, id_field_name: str, id_value: str, values_to_set: Dict[str, Any]) -> None:
|
||||
client = connections.get_connection()
|
||||
#create painless insert script
|
||||
source = ""
|
||||
for k, v in values_to_set.items():
|
||||
source += f"ctx._source.{k} = {json.dumps(v)};"
|
||||
|
||||
"""
|
||||
body = {
|
||||
"query": {
|
||||
"term": {
|
||||
id_field_name: id_value
|
||||
}
|
||||
},
|
||||
"script": {
|
||||
"source": source,
|
||||
"lang": "painless"
|
||||
}
|
||||
}
|
||||
client.update_by_query(index=index, body=body)
|
||||
"""
|
||||
|
||||
ubq = UpdateByQuery(using=client, index=index) \
|
||||
.query("term", **{id_field_name: id_value}) \
|
||||
.script(source=source, lang="painless")
|
||||
@ -41,8 +29,8 @@ def update_by_id(client: Elasticsearch, index: str, id_field_name: str, id_value
|
||||
return response.success()
|
||||
|
||||
|
||||
|
||||
def delete_by_id(client: Elasticsearch, index: str, id_field_name: str, id_value: str):
|
||||
def delete_by_id(index: str, id_field_name: str, id_value: str):
|
||||
client = connections.get_connection()
|
||||
s = Search(using=client, index=index).filter("term", **{id_field_name: id_value})
|
||||
response = s.delete()
|
||||
#if not response.success():
|
||||
@ -68,7 +56,8 @@ def simplify_properties(d):
|
||||
return new_d
|
||||
|
||||
|
||||
def get_type_schema(client: Elasticsearch):
|
||||
def get_type_schema():
|
||||
client = connections.get_connection()
|
||||
d = client.indices.get(index="*").body
|
||||
new_d = {}
|
||||
for index, d2 in d.items():
|
||||
@ -80,22 +69,20 @@ def get_type_schema(client: Elasticsearch):
|
||||
def wait_for_elasticsearch():
|
||||
#TODO: find a clean way to wait without exceptions!
|
||||
#Wait for elasticsearch to start up!
|
||||
|
||||
elastic_url = os.getenv("ELASTIC_URI")
|
||||
assert elastic_url
|
||||
|
||||
#elastic_url = os.getenv("ELASTIC_URI")
|
||||
#assert elastic_url
|
||||
i = 1
|
||||
while True:
|
||||
try:
|
||||
client = Elasticsearch(hosts=elastic_url)
|
||||
#client = Elasticsearch(hosts=elastic_url)
|
||||
client = connections.get_connection()
|
||||
client.indices.get_alias(index="*")
|
||||
#connections.create_connection(hosts=app.config['elastic_uri'])
|
||||
#connections.get_connection().cluster.health(wait_for_status='yellow')
|
||||
#init_indicies()
|
||||
print("Elasticsearch found! Run Flask-app!", flush=True)
|
||||
break
|
||||
except:
|
||||
#except ConnectionError:
|
||||
return
|
||||
except ConnectionError:
|
||||
i *= 2 #1.5
|
||||
time.sleep(i)
|
||||
print("Elasticsearch not found! Wait %s seconds!" % i, flush=True)
|
||||
|
||||
@ -1,50 +1,8 @@
|
||||
|
||||
|
||||
from smtplib import *
|
||||
from email.mime.text import MIMEText
|
||||
|
||||
|
||||
"""
|
||||
import threading
|
||||
import smtpd
|
||||
import asyncore
|
||||
import smtplib
|
||||
|
||||
server = smtpd.SMTPServer(('localhost', 1025), None)
|
||||
|
||||
loop_thread = threading.Thread(target=asyncore.loop, name="Asyncore Loop")
|
||||
# If you want to make the thread a daemon
|
||||
# loop_thread.daemon = True
|
||||
loop_thread.start()
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from aiosmtpd.controller import Controller
|
||||
|
||||
class CustomHandler:
|
||||
|
||||
async def handle_DATA(self, server, session, envelope):
|
||||
peer = session.peer
|
||||
mail_from = envelope.mail_from
|
||||
rcpt_tos = envelope.rcpt_tos
|
||||
data = envelope.content # type: bytes
|
||||
# Process message data...
|
||||
|
||||
|
||||
#if error_occurred:
|
||||
# return '500 Could not process your message'
|
||||
|
||||
return '250 OK'
|
||||
|
||||
|
||||
|
||||
def send_mail2(target_mail, subject, sender_mail, msg):
|
||||
|
||||
#handler = CustomHandler()
|
||||
#controller = Controller(handler, hostname='127.0.0.1', port=1025)
|
||||
# Run the event loop in a separate thread.
|
||||
#controller.start()
|
||||
|
||||
def send_mail(target_mail, subject, sender_mail, msg):
|
||||
|
||||
msg = MIMEText(msg)
|
||||
msg['Subject'] = subject
|
||||
@ -55,93 +13,3 @@ def send_mail2(target_mail, subject, sender_mail, msg):
|
||||
smtp.sendmail("Creative Bots", [target_mail], msg.as_string())
|
||||
smtp.quit()
|
||||
|
||||
|
||||
#controller.stop()
|
||||
|
||||
|
||||
|
||||
|
||||
import smtplib, dns.resolver
|
||||
|
||||
|
||||
def send_mail3(target_mail, subject, sender_mail, msg):
|
||||
|
||||
msg = MIMEText(msg)
|
||||
msg['Subject'] = subject
|
||||
msg['From'] = sender_mail
|
||||
msg['To'] = target_mail
|
||||
|
||||
#smtp = SMTP('mailserver', port=10025)
|
||||
#smtp.sendmail("Creative Bots", [target_mail], msg.as_string())
|
||||
#smtp.quit()
|
||||
|
||||
|
||||
[nick, domain] = target_mail.split("@")
|
||||
|
||||
|
||||
#domain = 'example.com'
|
||||
records = dns.resolver.resolve(domain, 'MX')
|
||||
mx_record = records[0].exchange
|
||||
|
||||
server = smtplib.SMTP(mx_record, 25)
|
||||
|
||||
#server.sendmail('your_email@example.com', 'recipient_email@example.com', 'Hello, this is a test email.')
|
||||
server.sendmail(sender_mail, target_mail, msg.as_string())
|
||||
|
||||
|
||||
server.quit()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
import sys
|
||||
import chilkat
|
||||
|
||||
def send_mail(target_mail, subject, sender_mail, msg):
|
||||
|
||||
|
||||
msg = MIMEText(msg)
|
||||
msg['Subject'] = subject
|
||||
msg['From'] = sender_mail
|
||||
msg['To'] = target_mail
|
||||
|
||||
|
||||
|
||||
|
||||
# The mailman object is used for sending and receiving email.
|
||||
mailman = chilkat.CkMailMan()
|
||||
|
||||
recipient = target_mail
|
||||
|
||||
# Do a DNS MX lookup for the recipient's mail server.
|
||||
|
||||
smtpHostname = mailman.mxLookup(recipient)
|
||||
if (mailman.get_LastMethodSuccess() != True):
|
||||
print(mailman.lastErrorText(), flush=True)
|
||||
#sys.exit()
|
||||
return False
|
||||
|
||||
print(smtpHostname)
|
||||
|
||||
# Set the SMTP server.
|
||||
mailman.put_SmtpHost(smtpHostname)
|
||||
|
||||
# Create a new email object
|
||||
email = chilkat.CkEmail()
|
||||
|
||||
email.put_Subject(subject)
|
||||
email.put_Body(msg.as_string())
|
||||
email.put_From(sender_mail)
|
||||
email.AddTo("", recipient)
|
||||
|
||||
success = mailman.SendEmail(email)
|
||||
if (success != True):
|
||||
print(mailman.lastErrorText(), flush=True)
|
||||
return False
|
||||
else:
|
||||
print("Mail Sent!", flush=True)
|
||||
return True
|
||||
|
||||
|
||||
|
||||
|
||||
@ -32,6 +32,7 @@ class User(Document):
|
||||
|
||||
class Chatbot(Document):
|
||||
creation_date = Date()
|
||||
changed_date = Date()
|
||||
name = Text()
|
||||
creator_id = Keyword()
|
||||
description = Text()
|
||||
|
||||
@ -5,11 +5,10 @@ import os, json, hashlib, traceback, logging
|
||||
from elasticsearch import NotFoundError, Elasticsearch # for normal read/write without vectors
|
||||
|
||||
from lib.models import User
|
||||
from lib.elastictools import get_by_id, update_by_id, delete_by_id, wait_for_elasticsearch
|
||||
from lib.elastictools import get_by_id, update_by_id, wait_for_elasticsearch
|
||||
|
||||
|
||||
elastic_url = os.getenv("ELASTIC_URI")
|
||||
|
||||
assert elastic_url
|
||||
|
||||
|
||||
@ -27,11 +26,10 @@ def create_user(email, password, role="user", verified=False):
|
||||
|
||||
def create_default_users():
|
||||
#create default users
|
||||
client = Elasticsearch(elastic_url)
|
||||
default_users = os.getenv("DEFAULT_USERS")
|
||||
if default_users:
|
||||
for (email, pwd, role) in json.loads(default_users):
|
||||
if len(get_by_id(client, index="user", id_field_name="email", id_value=email)) == 0:
|
||||
if len(get_by_id(index="user", id_field_name="email", id_value=email)) == 0:
|
||||
create_user(email, pwd, role=role, verified=True)
|
||||
|
||||
|
||||
|
||||
@ -28,6 +28,28 @@
|
||||
<script async="async" src="jsnetworkx.js"></script>
|
||||
<script async="async" src="widget.js"></script>
|
||||
|
||||
<style>
|
||||
#fun222 {
|
||||
background-image: linear-gradient(
|
||||
45deg,
|
||||
red,
|
||||
blue
|
||||
);
|
||||
opacity: 0.75;
|
||||
}
|
||||
|
||||
|
||||
#fun22 {
|
||||
background-image: linear-gradient(
|
||||
135deg,
|
||||
red,
|
||||
blue
|
||||
);
|
||||
opacity: 0.75;
|
||||
}
|
||||
|
||||
</style>
|
||||
|
||||
</head>
|
||||
<body>
|
||||
|
||||
@ -68,7 +90,8 @@
|
||||
let ele = document.getElementById("register_password");
|
||||
if(ele.type === "password"){
|
||||
ele.type = "text";
|
||||
} else {
|
||||
}
|
||||
else {
|
||||
ele.type = "password";
|
||||
}
|
||||
}
|
||||
@ -121,7 +144,8 @@
|
||||
let ele = document.getElementById("pass");
|
||||
if(ele.type === "password"){
|
||||
ele.type = "text";
|
||||
} else {
|
||||
}
|
||||
else {
|
||||
ele.type = "password";
|
||||
}
|
||||
}
|
||||
@ -140,7 +164,7 @@
|
||||
|
||||
|
||||
|
||||
<div class="container-fluid p-3 bg-primary text-white text-center">
|
||||
<div id="fun" class="container-fluid p-3 bg-primary text-white text-center">
|
||||
<h1>Creative Bots</h1>
|
||||
<p>Create and talk to chatbots!</p>
|
||||
</div>
|
||||
@ -161,6 +185,7 @@
|
||||
<button id="login_btn" type="button" class="btn btn-primary" data-bs-toggle="modal" data-bs-target="#myModal">Login</button>
|
||||
<button id="logout_btn" type="button" class="btn btn-danger text-white">Logout</button>
|
||||
|
||||
<br>
|
||||
<br>
|
||||
|
||||
<label for="system_prompt">System prompt:</label>
|
||||
@ -168,6 +193,29 @@
|
||||
|
||||
<br>
|
||||
|
||||
<button onclick="add_geo_location()" type="button" class="btn btn-secondary text-white">Add Geo-Location</button>
|
||||
|
||||
<script>
|
||||
|
||||
function add_geo_location(){
|
||||
function callback(position) {
|
||||
let s = "My current position is: Latitude: " + position.coords.latitude + " Longitude: " + position.coords.longitude + " !";
|
||||
console.log(s);
|
||||
document.getElementById("system_prompt").innerHTML += " " + s;
|
||||
}
|
||||
if(navigator.geolocation){
|
||||
navigator.geolocation.getCurrentPosition(callback);
|
||||
}
|
||||
else {
|
||||
console.log("Geolocation is not supported by this browser.");
|
||||
}
|
||||
}
|
||||
|
||||
</script>
|
||||
|
||||
<br>
|
||||
<br>
|
||||
|
||||
<label for="views">Choose a view:</label>
|
||||
<select name="views" id="view_select" class="form-select">
|
||||
<option value="md">Markdown</option>
|
||||
@ -220,7 +268,7 @@
|
||||
<option value="Is a monad a burito?">
|
||||
<option value="Give the JSON of a graph linking Germanys 9 biggest cities">
|
||||
</datalist>
|
||||
<button id="submit_btn" class="btn btn-success" type="submit">Send</button>
|
||||
<button id="submit_btn" class="btn btn-success" type="button">Send</button>
|
||||
</div>
|
||||
|
||||
<!-- Button to open the offcanvas sidebar -->
|
||||
@ -269,18 +317,6 @@
|
||||
<label for="bot_system_prompt">System prompt:</label>
|
||||
<textarea id="bot_system_prompt" class="form-control" rows="8" name="text" placeholder="The prompt that defines the bot's main behaviour."></textarea>
|
||||
|
||||
|
||||
<br>
|
||||
|
||||
<label for="avatar">Text documents:</label>
|
||||
<input disabled class="form-control" type="file" id="avatar" name="avatar" multiple accept=".pdf,.xml,.txt,.md,.doc,.docx,.odt,application/msword,application/vnd.openxmlformats-officedocument.wordprocessingml.document" />
|
||||
|
||||
|
||||
<br>
|
||||
|
||||
<label for="url">Links:</label>
|
||||
<input disabled class="form-control" type="url" name="url" id="url" placeholder="https://example.com" pattern="https://.*" size="30" required />
|
||||
|
||||
<hr>
|
||||
|
||||
<div class="row">
|
||||
@ -299,19 +335,6 @@
|
||||
|
||||
<div id="alert_spawn"></div>
|
||||
|
||||
<!--
|
||||
<div id="alert_bot_created" style="display: none;" class="alert alert-success alert-dismissible fade show">
|
||||
<button type="button" class="btn-close" data-bs-dismiss="alert"></button>
|
||||
<strong>Success!</strong> Bot created!
|
||||
</div>
|
||||
|
||||
|
||||
<div id="alert_not_bot_created" style="display: none;" class="alert alert-danger alert-dismissible fade show">
|
||||
<button type="button" class="btn-close" data-bs-dismiss="alert"></button>
|
||||
<strong>Couldn't create bot!</strong> Something killed that bot!
|
||||
</div>
|
||||
-->
|
||||
|
||||
</form>
|
||||
|
||||
</div>
|
||||
@ -337,45 +360,60 @@
|
||||
<br>
|
||||
|
||||
<label for="bot_name" class="form-label">Name:</label>
|
||||
<input type="bot_name" class="form-control" id="bot_name" placeholder="The displayed name of the bot.">
|
||||
<input type="bot_name" class="form-control" id="change_bot_name" placeholder="The displayed name of the bot.">
|
||||
|
||||
<br>
|
||||
|
||||
<label for="bot_visibility">Visibility:</label>
|
||||
<select name="bot_visibility" id="bot_visibility_select" class="form-select">
|
||||
<select name="bot_visibility" id="change_bot_visibility_select" class="form-select">
|
||||
<option value="public">Public to All</option>
|
||||
<option value="private">Private to User</option>
|
||||
</select>
|
||||
|
||||
|
||||
<br>
|
||||
|
||||
<label for="bot_description">Description:</label>
|
||||
<textarea id="bot_description" class="form-control" rows="8" name="text" placeholder="A description of the bot and it's purpose."></textarea>
|
||||
<textarea id="change_bot_description" class="form-control" rows="8" name="text" placeholder="A description of the bot and it's purpose."></textarea>
|
||||
|
||||
<br>
|
||||
|
||||
<label for="bot_llm">Language model:</label>
|
||||
<select name="bot_llm" id="bot_llm_select" class="form-select">
|
||||
<select name="bot_llm" id="change_bot_llm_select" class="form-select">
|
||||
<option value="llama3">Llama3</option>
|
||||
</select>
|
||||
|
||||
<br>
|
||||
|
||||
<label for="bot_system_prompt">System prompt:</label>
|
||||
<textarea id="bot_system_prompt" class="form-control" rows="8" name="text" placeholder="The prompt that defines the bot's main behaviour."></textarea>
|
||||
<label for="bot_system_prompt">System prompt(behavior):</label>
|
||||
<textarea id="change_bot_system_prompt" class="form-control" rows="8" name="text" placeholder="The prompt that defines the bot's main behaviour."></textarea>
|
||||
|
||||
<br>
|
||||
|
||||
<h4>Knowledge resources:</h4>
|
||||
<br>
|
||||
|
||||
<label for="change_bot_rag_text_name">Text:</label>
|
||||
<textarea id="change_bot_rag_text" class="form-control" rows="16" name="change_bot_rag_text_name" placeholder="A text that contains information used for the bot's answers.'"></textarea>
|
||||
|
||||
<br>
|
||||
|
||||
<label for="avatar">Text documents:</label>
|
||||
<input disabled class="form-control" type="file" id="avatar" name="avatar" multiple accept=".pdf,.xml,.txt,.md,.doc,.docx,.odt,application/msword,application/vnd.openxmlformats-officedocument.wordprocessingml.document" />
|
||||
|
||||
|
||||
<br>
|
||||
|
||||
<label for="url">Links:</label>
|
||||
<input disabled class="form-control" type="url" name="url" id="url" placeholder="https://example.com" pattern="https://.*" size="30" required />
|
||||
<label for="rag_urls">Links:</label>
|
||||
<div class="input-group">
|
||||
<input disabled class="form-control" type="url" name="rag_urls" id="url" placeholder="https://example.com" pattern="https://.*" size="30" />
|
||||
<button disabled id="add_url_btn" class="btn btn-success" type="button">Add</button>
|
||||
</div>
|
||||
|
||||
|
||||
<!--
|
||||
<ol>
|
||||
<li>https://playwright.dev/docs/ci-intro</li>
|
||||
</ol>
|
||||
-->
|
||||
|
||||
<hr>
|
||||
|
||||
@ -412,18 +450,13 @@
|
||||
|
||||
</form>
|
||||
|
||||
|
||||
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
</div>
|
||||
|
||||
<footer>
|
||||
<div class="container-fluid p-3 bg-primary text-white mt-5">
|
||||
|
||||
<div id="fun2" class="container-fluid p-3 bg-primary text-white mt-5">
|
||||
<div class="row">
|
||||
<div class="col-sm-4">
|
||||
<h3>A simple UI</h3>
|
||||
@ -440,14 +473,11 @@
|
||||
<div class="col-sm-4">
|
||||
<h3>Who? Why?</h3>
|
||||
<p>The guy on this site: <a class="text-white" href="https://tobiasweise.dev">tobiasweise.dev</a></p>
|
||||
<p>For fun and learning...</p>
|
||||
<p>...and maybe getting a job that employs the used skills.</p>
|
||||
<p>For fun and learning.</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
</footer>
|
||||
|
||||
<script src="main.js"></script>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
@ -2,8 +2,6 @@
|
||||
"use strict";
|
||||
|
||||
|
||||
|
||||
|
||||
//idea: generate proxy opject via openapi.json api(url).login_now()
|
||||
|
||||
function API(jwt){
|
||||
@ -109,7 +107,8 @@ async function create_bot(jwt, name, visibility, description, llm, sys_prompt){
|
||||
return response.json();
|
||||
}
|
||||
|
||||
async function get_bots(jwt){
|
||||
async function get_bots(jwt, bot_id){
|
||||
if(!bot_id){
|
||||
if(jwt){
|
||||
const response = await fetch("/bot", {
|
||||
method: "GET",
|
||||
@ -129,10 +128,26 @@ async function get_bots(jwt){
|
||||
});
|
||||
return response.json();
|
||||
}
|
||||
}
|
||||
else{
|
||||
if(jwt){
|
||||
const response = await fetch("/bot?id=" + bot_id, {
|
||||
method: "GET",
|
||||
headers: {
|
||||
'accept': '*/*',
|
||||
'Authorization': 'Bearer ' + jwt
|
||||
}
|
||||
});
|
||||
return response.json();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async function change_bot(jwt, name, visibility, description, llm, sys_prompt){
|
||||
|
||||
|
||||
async function change_bot(jwt, id, name, visibility, description, llm, sys_prompt){
|
||||
const formData = new FormData();
|
||||
formData.append("id", id);
|
||||
formData.append("name", name);
|
||||
formData.append("visibility", visibility);
|
||||
formData.append("description", description);
|
||||
@ -165,6 +180,21 @@ async function delete_bot(jwt, bot_id){
|
||||
return response.json();
|
||||
}
|
||||
|
||||
async function bot_train_text(jwt, bot_id, text){
|
||||
const formData = new FormData();
|
||||
formData.append("bot_id", bot_id);
|
||||
formData.append("text", text);
|
||||
const response = await fetch("/bot/train/text", {
|
||||
method: "POST",
|
||||
headers: {
|
||||
'accept': '*/*',
|
||||
'Authorization': 'Bearer ' + jwt
|
||||
},
|
||||
body: formData
|
||||
});
|
||||
return response.json();
|
||||
}
|
||||
|
||||
|
||||
|
||||
async function* ask_question(bot_id, question, system_prompt=""){
|
||||
@ -207,7 +237,7 @@ async function* ask_question(bot_id, question, system_prompt=""){
|
||||
else{
|
||||
done = true;
|
||||
socket.off('backend token');
|
||||
dom_ele.dispatchEvent(new CustomEvent(evt_name, { detail: "" }));
|
||||
dom_ele.dispatchEvent(new CustomEvent(evt_name, { detail: obj }));
|
||||
}
|
||||
});
|
||||
socket.emit('client message', {question, system_prompt, bot_id, room});
|
||||
@ -343,6 +373,7 @@ window.onload = async ()=>{
|
||||
let change_bot_description = document.getElementById("change_bot_description");
|
||||
let change_bot_llm_select = document.getElementById("change_bot_llm_select");
|
||||
let change_bot_system_prompt = document.getElementById("change_bot_system_prompt");
|
||||
let change_bot_rag_text = document.getElementById("change_bot_rag_text");
|
||||
|
||||
let delete_bot_btn = document.getElementById("delete_bot_btn");
|
||||
|
||||
@ -367,14 +398,44 @@ window.onload = async ()=>{
|
||||
return bot_select.options[i].text;
|
||||
}
|
||||
|
||||
|
||||
|
||||
function clean_bot_create_form(){
|
||||
bot_name.value = "";
|
||||
bot_description.value = "";
|
||||
bot_system_prompt.value = "";
|
||||
}
|
||||
|
||||
async function fill_bot_change_form(bot_id){
|
||||
|
||||
let jwt = localStorage.getItem("jwt");
|
||||
if(jwt){
|
||||
let bot = await get_bots(jwt, bot_id);
|
||||
|
||||
change_bot_name.value = bot.name;
|
||||
change_bot_visibility_select.value = bot.visibility;
|
||||
change_bot_description.value = bot.description;
|
||||
change_bot_llm_select.value = bot.llm_model;
|
||||
change_bot_system_prompt.value = bot.system_prompt;
|
||||
|
||||
/*
|
||||
{
|
||||
"creation_date": "Fri, 26 Jul 2024 19:13:18 GMT",
|
||||
"creator_id": "cWG-75ABLLZSH2M7pxp8",
|
||||
"description": "basic bot",
|
||||
"id": "uqJ28JABAAhLOtEyOj2_",
|
||||
"llm_model": "llama3",
|
||||
"name": "Testbot",
|
||||
"system_prompt": "",
|
||||
"visibility": "public"
|
||||
}
|
||||
*/
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
function set_ui_loggedin(b){
|
||||
if(b){
|
||||
console.log("User logged in!");
|
||||
@ -429,6 +490,11 @@ window.onload = async ()=>{
|
||||
set_bot_list(bot_select, bots);
|
||||
set_bot_list(change_bot_select, bots);
|
||||
set_ui_loggedin(true);
|
||||
|
||||
|
||||
let bot_id = change_bot_select.value;
|
||||
await fill_bot_change_form(bot_id);
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
@ -491,10 +557,21 @@ window.onload = async ()=>{
|
||||
}
|
||||
};
|
||||
|
||||
change_bot_select.onchange = async ()=>{
|
||||
let jwt = localStorage.getItem("jwt");
|
||||
if(jwt){
|
||||
let bot_id = change_bot_select.value;
|
||||
await fill_bot_change_form(bot_id);
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
|
||||
change_bot_btn.onclick = async ()=>{
|
||||
let jwt = localStorage.getItem("jwt");
|
||||
if(jwt){
|
||||
let bot_id = change_bot_select.value
|
||||
|
||||
let name = change_bot_name.value;
|
||||
let visibility = change_bot_visibility_select.value;
|
||||
let description = change_bot_description.value;
|
||||
@ -512,7 +589,18 @@ window.onload = async ()=>{
|
||||
}
|
||||
|
||||
try{
|
||||
let {bot_id} = await change_bot(jwt, name, visibility, description, llm, sys_prompt);
|
||||
await change_bot(jwt, bot_id, name, visibility, description, llm, sys_prompt);
|
||||
|
||||
let text = change_bot_rag_text.value;
|
||||
if(text.trim() !== ""){
|
||||
|
||||
|
||||
await bot_train_text(jwt, bot_id, text);
|
||||
|
||||
//TODO: kill bot on partially failed creation?
|
||||
}
|
||||
|
||||
|
||||
alert_bot_change(true);
|
||||
await update_ui();
|
||||
}
|
||||
@ -724,8 +812,10 @@ window.onload = async ()=>{
|
||||
|
||||
let acc_text = "";
|
||||
for await (let token of ask_question(bot_select.value, input_string, system_prompt.value)){
|
||||
//console.log(token);
|
||||
acc_text += "" + token;
|
||||
console.log(token);
|
||||
|
||||
if(typeof token === "string"){
|
||||
acc_text += token;
|
||||
switch(view_select.value){
|
||||
case "md":
|
||||
table_cell.innerHTML = "";
|
||||
@ -739,6 +829,7 @@ window.onload = async ()=>{
|
||||
}
|
||||
scroll_down();
|
||||
}
|
||||
}
|
||||
|
||||
/*
|
||||
function play() {
|
||||
@ -783,17 +874,3 @@ window.onload = async ()=>{
|
||||
|
||||
};
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -7,19 +7,20 @@ bs4
|
||||
elasticsearch
|
||||
elasticsearch-dsl
|
||||
|
||||
ollama
|
||||
langchain
|
||||
langchain-community
|
||||
tiktoken
|
||||
langchain_ollama
|
||||
langchain-elasticsearch
|
||||
|
||||
pydantic
|
||||
|
||||
|
||||
fastapi
|
||||
|
||||
gunicorn
|
||||
Werkzeug
|
||||
flask
|
||||
Flask-Cors
|
||||
Flask-SocketIO
|
||||
flask-openapi3
|
||||
|
||||
@ -34,8 +35,3 @@ neo4j
|
||||
pyttsx3
|
||||
|
||||
|
||||
aiosmtpd
|
||||
dnspython
|
||||
chilkat
|
||||
|
||||
|
||||
|
||||
@ -18,7 +18,6 @@ services:
|
||||
|
||||
minio:
|
||||
container_name: ${APP_PREFIX}_minio
|
||||
#image: docker.io/bitnami/minio #:2022
|
||||
image: minio/minio
|
||||
ports:
|
||||
- "29000:9000"
|
||||
@ -36,7 +35,6 @@ services:
|
||||
elasticsearch:
|
||||
container_name: ${APP_PREFIX}_elasticsearch
|
||||
image: docker.elastic.co/elasticsearch/elasticsearch:8.11.0
|
||||
#image: opensearchproject/opensearch
|
||||
restart: always
|
||||
mem_limit: 4024m
|
||||
ports:
|
||||
@ -48,9 +46,6 @@ services:
|
||||
- xpack.security.enabled=false
|
||||
- xpack.security.http.ssl.enabled=false
|
||||
|
||||
#- OPENSEARCH_INITIAL_ADMIN_PASSWORD=${OPENSEARCH_INITIAL_ADMIN_PASSWORD} # Sets the demo admin user password when using demo configuration, required for OpenSearch 2.12 and later
|
||||
#- "OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m" # Set min and max JVM heap sizes to at least 50% of system RAM
|
||||
#- ES_JAVA_OPTS="-Xms2g -Xmx2g"
|
||||
volumes:
|
||||
- esdata:/usr/share/elasticsearch/data
|
||||
ulimits:
|
||||
@ -76,22 +71,22 @@ services:
|
||||
- llm_network
|
||||
#command: "ollama pull llama2"
|
||||
|
||||
ollama-webui:
|
||||
container_name: ${APP_PREFIX}_ollama-webui
|
||||
image: ghcr.io/ollama-webui/ollama-webui:main
|
||||
volumes:
|
||||
- ../ollama/ollama-webui:/app/backend/data
|
||||
depends_on:
|
||||
- ollama
|
||||
ports:
|
||||
- 8888:8080
|
||||
environment:
|
||||
- "/ollama/api=http://ollama:11434/api"
|
||||
extra_hosts:
|
||||
- host.docker.internal:host-gateway
|
||||
restart: unless-stopped
|
||||
networks:
|
||||
- llm_network
|
||||
#ollama-webui:
|
||||
# container_name: ${APP_PREFIX}_ollama-webui
|
||||
# image: ghcr.io/ollama-webui/ollama-webui:main
|
||||
# volumes:
|
||||
# - ../ollama/ollama-webui:/app/backend/data
|
||||
# depends_on:
|
||||
# - ollama
|
||||
# ports:
|
||||
# - 8888:8080
|
||||
# environment:
|
||||
# - "/ollama/api=http://ollama:11434/api"
|
||||
# extra_hosts:
|
||||
# - host.docker.internal:host-gateway
|
||||
# restart: unless-stopped
|
||||
# networks:
|
||||
# - llm_network
|
||||
|
||||
#frontend:
|
||||
# container_name: ${APP_PREFIX}_frontend
|
||||
|
||||
18
ideas.md
18
ideas.md
@ -19,13 +19,17 @@ https://favtutor.com/articles/meta-llama-3-jailbreak/
|
||||
* https://medium.com/@lucgagan/understanding-chatgpt-functions-and-how-to-use-them-6643a7d3c01a
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Use drivers for GPU!
|
||||
|
||||
"ollama recommends running the https://www.amd.com/en/support/linux-drivers: amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
|
||||
"detected amdgpu versions []"
|
||||
"all detected amdgpus are skipped, falling back to CPU"
|
||||
"no GPU detected"
|
||||
|
||||
|
||||
## Use PyPy for more speed
|
||||
|
||||
...
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user