Steve Nyemba
2 weeks ago
parent
3d81a192b1
commit
7b8ed0bda0
@ -1,2 +1,3 @@
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xlsxwriter
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openpyxl
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smart-logger@git+https://dev.the-phi.com/git/library/smart-top
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@ -0,0 +1,35 @@
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var ai = {}
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ai.toggle = function (){
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if($('.ai-button').html().match(/check/i)){
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ai.disable()
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}else{
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ai.enable()
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}
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sessionStorage.AI = _val
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$('.ai-button').html (_icon)
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$('.ai-status').html(_status).css({color:_color})
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}
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ai.enable = function (){
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_val = 1
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_status = 'Enabled'
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_color = '#4682B4'
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_icon = '<i class="fa-solid fa-check" style="color:green"></i>'
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$('.ai-workbench-status').html('AI Enabled').css({'color':'#4682b4'})
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sessionStorage.AI = _val
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$('.ai-button').html (_icon)
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$('.ai-status').html(_status).css({color:_color})
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}
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ai.disable = function (){
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_val = 0
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_status = 'Disabled'
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_color = 'maroon'
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_icon = '<i class="fa-solid fa-times" style="color:maroon"></i>'
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$('.ai-workbench-status').html('AI Disabled').css({'color':'maroon'})
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sessionStorage.AI = _val
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$('.ai-button').html (_icon)
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$('.ai-status').html(_status).css({color:_color})
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}
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@ -0,0 +1,213 @@
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"""
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This is code that will interact with an LLM (AzureOpenAI/Ollama) leveraging langchain
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"""
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# _CATALOG = {
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# 'sqlite':{'sql':'select tbl_name as table_name, json_group_array(y.name) as columns from sqlite_master x INNER JOIN PRAGMA_TABLE_INFO(tbl_name) y group by table_name'} ,
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# 'postgresql':{'sql':"SELECT table_name, to_json(array_agg(column_name)) as columns FROM INFORMATION_SCHEMA.COLUMNS WHERE table_schema = 'public' GROUP BY table_name"},
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# 'bigquery':{'sql':'SELECT table_name, TO_JSON(ARRAY_AGG(column_name)) as columns FROM :dataset.INFORMATION_SCHEMA.COLUMNS','args':['dataset']},
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# 'duckdb':{'sql' :'SELECT table_name, TO_JSON(ARRAY_AGG(column_name)) as columns FROM INFORMATION_SCHEMA.COLUMNS GROUP BY table_name'}
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# }
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# _CATALOG['sqlite3'] = _CATALOG['sqlite']
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import transport
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import json
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import os
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import cms
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import pandas as pd
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from langchain_openai import AzureOpenAI, AzureChatOpenAI
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from langchain_ollama import OllamaEmbeddings, OllamaLLM
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from langchain_core.messages import HumanMessage, AIMessage
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from langchain.chains import LLMChain
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from langchain_core.prompts import PromptTemplate
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from langchain_core.runnables import RunnablePassthrough
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from langchain_core.output_parsers import StrOutputParser
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class Agent :
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def __init__(self,**_args) :
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"""
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:backend OpenAI or Ollama
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:kwargs The initialization parameters associated with the backend
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The arguments will contain temperature and model name to be used
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"""
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_instance = AzureChatOpenAI if _args['backend'].lower() in ['azureopenai', 'openai'] else OllamaLLM
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self._llm = _instance(**_args['kwargs'])
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def isSQL(self,_question):
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_template = """Is the provided text a valid sql statmement. Yes or No? Your answer is a properly formatted JSON object with three attributes.
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class (1 for valid sql statement and 0 for not a valid sql statement), explanation place a short explanation for the answer and original containing with the original text.
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text:
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{input_text}
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"""
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_prompt = PromptTemplate(temperature=0.1,input_variables=['input_text'],template=_template)
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r = self.apply(_prompt,input_text=_question)
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#
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# @TODO: Make sure the response is properly formatted (output not to be trusted)
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return json.loads(r)
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def apply(self,_prompt,**_args):
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chain = (
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RunnablePassthrough.assign()
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| _prompt
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| self._llm
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| StrOutputParser())
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_out = chain.invoke(_args)
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return _out #son.loads(_out)
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def toSQL(self,_question,_catalog,_about):
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_template="""Your task is to convert a question to an SQL query. The query will run on schema that will be provided in csv format.
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Output:
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The expected output will be a JSON object with two attributes sql and tables:
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- "sql": the SQL query to be executed.
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- "tables": list of relevant tables used.
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Guidelines:
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- If the question can not be answered with the provided schema return empty string in the sql attribute
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- Parse the question word by word so as to be able to identify tables, fields and operations associated (Joins, filters ...)
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- Under no circumstances will you provide an explanation of tables or reasoning detail.
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question:
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{question}
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Database schema:
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{catalog}
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additional information:
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{context}
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"""
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_prompt = PromptTemplate(temperature=0.1,input_variables=['question','catalog','context'],template=_template)
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r = self.apply(_prompt,question=_question,catalog=_catalog,context=_about)
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# print (' ############### ------------- #####################')
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# print (r)
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# print (' #########################################')
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#
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# @TODO: Make sure the response is properly formatted (output not to be trusted)
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if '```json' in r :
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r = r.split('```json')[-1].replace('```','')
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print (r)
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return json.loads(r)
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#
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# We are building an interface to execute an sql query
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#
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def AIProxy (_label,_query,_path,_CATALOG) :
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_qreader = transport.get.reader(label=_label)
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_entry = transport.registry.get(_label)
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_provider =_entry['provider']
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_about = _entry.get('about','')
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_database = _entry['database'] if 'database' in _entry else ''
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if 'dataset' in _entry :
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_database = _entry['dataset']
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_about = f'{_about}, with dataset name {_database}'
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else:
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_about = f'{_about}, with the database name {_database}'
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_catalog = None
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_kwargs = None
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_data = pd.DataFrame()
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r = None
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try:
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#
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# we should run the command here, assuming a valid query
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#
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_data = _qreader.apply(_query)
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except Exception as e:
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#
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# here we are assuming we are running text to be translated as a query
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# we need to make sure that we have a JSON configurator
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#
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if _provider in _CATALOG and os.path.exists(_path):
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#
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# -- reading arguments from the LLM config file, {backend,kwargs}
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_kwargs = json.loads((open(_path)).read())
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_agent = Agent(**_kwargs)
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_qcat = _CATALOG[_provider]
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# if 'args' in _CATALOG[_provider] :
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# _entry = transport.registry.get(_label)
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# for _var in _CATALOG[_provider]['args'] :
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# if _var in _entry:
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# _value = _entry[_var]
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# _about = f'{_about}\n{_var} = {_value}'
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# _qcat['sql'] = _qcat['sql'].replace(f':{_var}',_value)
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_catalog = _qreader.read(**_qcat).to_csv(index=0)
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_about = f"The queries will run on {_provider} database.\n{_about}"
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r = _agent.toSQL(_query,_catalog, _about)
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_data = _qreader.apply(r['sql'])
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#
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# returning the data and the information needed
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#
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_data = _data.to_dict(orient='split')
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del _data['index']
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if r :
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return {'data':_data,'query':r['sql']}
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return _data
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# if not _path :
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# #
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# # exececute the query as is !
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# pass
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# else:
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# if _provider in _CATALOG and os.path.exists(_path) :
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# #
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# # As the agent if it is an SQL Query
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# f = open(_path)
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# _kwargs = json.loads( f.read() )
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# f.close()
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# try:
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# print ([f"Running Model {_kwargs['kwargs']['model']}"])
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# _agent = Agent(**_kwargs)
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# # r = _agent.isSQL(_query)
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# # print (f"****** {_query}\n{r['class']}")
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# # if r and int(r['class']) == 0 :
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# _catalog = _qreader.read(**_CATALOG[_provider]).to_csv(index=0)
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# # print (['****** TABLES FOUND ', _catalog])
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# _about = _about if _about else ''
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# r = _agent.toSQL(_query,_catalog,f"This is a {_provider} database, and queries generated should account for this. {_about}")
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# _query = r['sql']
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# # else:
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# # #
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# # # provided an sql query
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# # pass
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# except Exception as e:
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# #
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# # AI Service is unavailable ... need to report this somehow
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# print (e)
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# else:
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# #
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# # Not in catalog ...
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# pass
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# _data = _qreader.apply(_query)
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# if _data.shape[0] :
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# _data = _data.astype(str).to_dict(orient='split')
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# if 'index' in _data :
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# del _data['index']
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# return _data
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@cms.Plugin(mimetype="application/json",method="POST")
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def apply (**_args):
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_request = _args['request']
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_label = _request.json['label']
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_query = _request.json['query']
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_source = _args['config']['system']['source']
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_path = _source.get('llm',None)
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_CATALOGS = _source.get('catalogs',{})
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return AIProxy(_label,_query,_path,_CATALOGS)
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@cms.Plugin(mimetype="text/plain")
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def enabled(**_args):
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_config = _args['config']
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return str(int('llm' in _config['system']['source'] ))
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@ -0,0 +1,47 @@
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<script>
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$(document).ready(()=>{
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var _aiTech = []
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$('.labels').children().each((_index)=>{
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var _data = $('.labels').children()[_index]._data
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if(_data.AI){
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_aiTech.push(_data.provider)
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}
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})
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_aiTech = jx.utils.unique(_aiTech)
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$('.ai-tech').html( _aiTech.join(' , '))
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$('.ai-setup .ai-button').on('click',ai.toggle)
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if (sessionStorage.AI == "0"){
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ai.disable()
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}else{
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ai.enable()
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}
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})
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</script>
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<div class="studio-dialog ai-setup">
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<p style="line-height:1.5; font-weight: lighter;">
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Setup system wide AI capability to translate a prompt to an SQL query. This feature is supported for the following technologies :
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<ul>
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<div style="margin:4px;"><i class="fa-solid fa-minus"></i> <span class="ai-tech bold"></span></div>
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<div style="margin:4px;"><i class="fa-solid fa-minus"></i> <span> Ollama & AzureOpenAI</span></div>
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</ul>
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<ul>
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<br>
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<p>
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<div class="border" style="align-items:center; width:34%; margin-left:33%; padding:8px; display:grid; grid-template-columns: auto 64px; gap:8px; background-color: #f3f3f3;">
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<div>
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<i class="fa-solid fa-microchip"></i>
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<span class="bold">AI</span>
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<span class="ai-status bold">Enabled</span>
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</div>
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<div align="center" class="active ai-button" >
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<span class="fa-solid fa-check" style="color:green"></span>
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</div>
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</div>
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</p>
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</ul>
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</p>
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</div>
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Loading…
Reference in new issue