From adf114b82e7192e044395612220472116e96647b Mon Sep 17 00:00:00 2001
From: "Steve L. Nyemba" <steve.l.nyemba@vumc.org>
Date: Wed, 19 Mar 2025 20:38:40 +0000
Subject: [PATCH] Upload files to "www/html"
---
www/html/demo.py | 13 +++++
www/html/medix.py | 138 ++++++++++++++++++++++++++++++++++++++++++++++
2 files changed, 151 insertions(+)
create mode 100644 www/html/demo.py
create mode 100644 www/html/medix.py
diff --git a/www/html/demo.py b/www/html/demo.py
new file mode 100644
index 0000000..e3e045a
--- /dev/null
+++ b/www/html/demo.py
@@ -0,0 +1,13 @@
+
+ import cms
+ #
+ # register this in config.plugins: {"demo":["info"]}
+
+
+ @cms.plugins(mimetype='application/json') :
+ def info (**_args):
+ _request= _args['request']
+ _config = _args['config']
+ return {"version":_config['system']['version'],'title':_config['layout']['header']['title']}
+ pass
+
\ No newline at end of file
diff --git a/www/html/medix.py b/www/html/medix.py
new file mode 100644
index 0000000..2f19882
--- /dev/null
+++ b/www/html/medix.py
@@ -0,0 +1,138 @@
+from langchain_openai import AzureOpenAIEmbeddings, AzureOpenAI, AzureChatOpenAI
+from langchain_ollama import OllamaEmbeddings, OllamaLLM
+import io
+from langchain.chains import LLMChain
+from langchain_core.prompts import PromptTemplate
+import os
+import json
+import transport
+
+import cms
+import uuid
+from multiprocessing import Process
+import pandas as pd
+import copy
+
+from gtts import gTTS
+import io
+import requests
+
+# USE_OPENAI = os.environ.get('USE_OPENAI',0)
+
+class ILLM :
+ def __init__(self,**_args) :
+ self.USE_OPENAI = 'openai' in _args
+ # self._args = _args['ollama'] if not self.USE_OPENAI else _args['openai']
+ _path = _args['ollama'] if not self.USE_OPENAI else _args['openai']
+ f = open(_path)
+ self._args = json.loads( f.read() )
+ f.close()
+ self._prompt = _args['prompt'] if 'prompt' in _args else {}
+ def embed(self,_question):
+ _pointer = AzureOpenAIEmbeddings if self.USE_OPENAI else OllamaEmbeddings
+ _kwargs = self._args if 'embedding' not in self._args else self._args['embedding']
+ _handler = _pointer(**_kwargs)
+ return _handler.embed_query(_question)
+ def answer(self,_question,_context) :
+ _pointer = AzureChatOpenAI if self.USE_OPENAI else OllamaLLM
+ _kwargs = self._args if 'completion' not in self._args else self._args['completion']
+ _llm = _pointer(**_kwargs)
+ _prompt = PromptTemplate(**self._prompt)
+ chain = LLMChain(llm=_llm,prompt=_prompt)
+ _input = {'context':_context,'question':_question}
+ _input = json.loads(json.dumps(_input))
+ resp = chain.invoke(_input)
+ return resp
+ def schema(self):
+ return 'openai' if self.USE_OPENAI else 'public'
+ def documents(self,_vector) :
+ _schema = 'openai' if self.USE_OPENAI else 'ollama'
+ pgr = transport.get.reader(label='llm', schema=_schema)
+ sql = f"""SELECT file, name, page, content, embeddings <-> '{json.dumps(_vector)}' similarity FROM {_schema}.documents
+ ORDER BY similarity ASC
+ LIMIT 5
+ """
+ _df = pgr.read(sql=sql)
+ pgr.close()
+
+ return _df
+ def lookup (self,index:int,token) :
+ _schema = 'openai' if self.USE_OPENAI else 'ollama'
+ pgr = transport.get.reader(label='llm', schema=_schema)
+ index = int(index) + 1
+ _sql = f"SELECT * FROM (select row_number() over(partition by token) as row_index, answer from llm_logs where token='{token}') as _x where row_index = {index}"
+ print (_sql)
+ _df = pgr.read(sql= _sql)
+ return _df.answer[0] if _df.shape[0] > 0 else None
+@cms.Plugin(mimetype="application/json",method="POST")
+def answer (**_args):
+
+ _request = _args['request']
+ _config = _args['config']['system']['source']['llm']
+
+ _question = _request.json['question']
+ token = str(uuid.uuid4()) if 'token' not in _request.json else _request.json['token']
+ _index = _request.json['index'] if 'index' in _request.json else 0
+
+ _llmproc = ILLM(**_config)
+
+ #
+ # Turn the question into a vector and send it to the LLM Server
+ #
+ _vector = _llmproc.embed(_question)
+ _df = _llmproc.documents(_vector)
+ _pages = _df.apply(lambda row: row.content,axis=1).tolist()
+
+ #
+ # submit the request to the
+
+
+ # return _df[['name','page','similarity']].to_dict(orient='records')
+ #
+ # Let us submit the question to the llm-server
+ #
+ resp = _llmproc.answer(_question, _pages)
+ #
+ # @TODO :
+ # - Log questions, answers and sources to see what things are like
+ _context = _args['config']['system']['context'].strip()
+ _out = {"token":token,"openai":_llmproc.USE_OPENAI,"answer":resp["text"],"documents": _df[["name","page"]].to_dict(orient='records')}
+
+
+ try:
+ def _logger():
+ _log = pd.DataFrame([dict(_out,**{'question':_question})])
+ _log.documents = _df[["name","page"]].to_json(orient='records')
+ pgw = transport.get.writer (label='llm',table='llm_logs')
+ pgw.write(_log)
+ pgw.close()
+ #
+ # send the thread
+ pthread = Process(target=_logger)
+ pthread.start()
+ except Exception as e:
+ print (e)
+ # _out['stream'] = f'{_context}/api/medix/audio?token={token}&index={_index}'
+ return _out
+
+@cms.Plugin(mimetype="text/plain",method="POST")
+def info (**_args):
+ _config = _args['config']
+ return list(_config['system']['source']['llm'].keys())[0]
+@cms.Plugin(mimetype="audio/mpeg",method="GET")
+def audio (**_args):
+ _request = _args['request']
+ _config = _args['config']['system']['source']['llm']
+ _index = _request.args['index']
+ _token = _request.args['token']
+ _llmproc = ILLM(**_config)
+
+ _llmproc = ILLM(**_config)
+ _text = _llmproc.lookup(_index,_token)
+ g = gTTS(_text,lang='en')
+ return g.stream()
+ # stream = io.BytesIO()
+ # for line in g.stream() :
+ # stream.write(line)
+ # stream.seek(0)
+ # return stream #g.stream()
\ No newline at end of file