Tobias Weise
83ea3a7a64
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181 lines
3.6 KiB
Python
181 lines
3.6 KiB
Python
import os
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from elasticsearch_dsl import Document, InnerDoc, Nested, Date, Integer, Keyword, Float, Long, Text, connections, Object, Boolean
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class User(Document):
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creation_date = Date()
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email = Keyword()
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#password_hash = Text(index=False)
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password_hash = Keyword()
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#password_hash = Text(index=True)
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role = Keyword()
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#salt = Text(index=False)
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#profileImage = Text(index=False)
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#profileImage = Keyword()
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isEmailVerified = Boolean()
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#status = Text()
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#otpExpires = Date()
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#resetPasswordToken = Text(index=False)
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#mailToken = Text(index=False)
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class Index:
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name = 'user'
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settings = {
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"number_of_shards": 1,
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}
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def save(self, ** kwargs):
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return super(User, self).save(**kwargs)
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class Chatbot(Document):
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creation_date = Date()
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changed_date = Date()
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name = Text()
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creator_id = Keyword()
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description = Text()
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systemPrompt = Text(index=False)
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#slug = Keyword()
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files = Nested()
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text = Text()
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links = Nested()
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#chatbotImage = Text(index=False)
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sourceCharacters = Integer()
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visibility = Keyword() #public, private, group?
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#status = Keyword()
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temperature = Float()
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llm_model = Keyword()
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class Index:
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name = 'chatbot'
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settings = {
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"number_of_shards": 1,
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}
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def save(self, ** kwargs):
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return super(Chatbot, self).save(**kwargs)
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class Text(Document):
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creation_date = Date()
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creator_id = Keyword()
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text = Text()
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md5 = Keyword()
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class Index:
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name = 'text'
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settings = {
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"number_of_shards": 1,
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}
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def save(self, ** kwargs):
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return super(Text, self).save(**kwargs)
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class Question(Document):
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question = Text(index=False, required=True)
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md5 = Keyword()
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class Index:
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name = 'question'
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settings = {
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"number_of_shards": 1,
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}
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def save(self, ** kwargs):
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return super(Question, self).save(**kwargs)
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class Answer(Document):
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question_id = Keyword()
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answer = Text(index=False, required=True)
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md5 = Keyword()
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class Index:
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name = 'answer'
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settings = {
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"number_of_shards": 1,
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}
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def save(self, ** kwargs):
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return super(Answer, self).save(**kwargs)
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class LogEntry(Document):
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message = Text(index=False, required=True)
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level = Keyword() #Integer(required=True)
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creation_time = Date()
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name = Keyword()
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# 'args': ('GET /socket.io/?EIO=4&transport=websocket&sid=MtyTmZQs5IA6DnvhAAAA HTTP/1.1', '200', '-'),
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pathname = Keyword()
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# 'pathname': '/usr/local/lib/python3.12/dist-packages/werkzeug/_internal.py',
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filename = Keyword()
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# 'filename': '_internal.py',
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module = Keyword()
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# 'module': '_internal',
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lineno = Integer(required=True)
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# 'lineno': 97,
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funcName = Keyword()
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# 'funcName': '_log',
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# 'created': 1725709403.1972203,
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# 'msecs': 197.0,
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threadName = Keyword()
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# 'threadName': 'Thread-15 (process_request_thread)',
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processName = Keyword()
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# 'processName': 'MainProcess',
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class Index:
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name = 'logentry'
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settings = {
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"number_of_shards": 1,
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}
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def save(self, ** kwargs):
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return super(LogEntry, self).save(**kwargs)
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#======= Query Log ===========
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#class Sources(InnerDoc):
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#score = Float()
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#tags = Text()
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#filename = Keyword()
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#page = Integer()
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#----------------------------------------------
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def init_indicies():
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"""
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Create the mappings in elasticsearch
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"""
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for Index in [LogEntry, Question, Answer, Chatbot, User, Text]:
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Index.init()
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