Upload files to "www/html/_plugins"

www/html/_plugins/medix.py

@@ -1,19 +1,26 @@
+
 from langchain_openai import AzureOpenAIEmbeddings, AzureOpenAI, AzureChatOpenAI
 from langchain_ollama import OllamaEmbeddings, OllamaLLM
-import io
+from langchain_core.messages import HumanMessage, AIMessage
 from langchain.chains import LLMChain
 from langchain_core.prompts import PromptTemplate
 import os
 import json
 import transport
 
+# from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+
 import cms
 import uuid
 from multiprocessing import Process 
 import pandas as pd
 import copy
 
-from gtts import gTTS
+# from gtts import gTTS
 import io
 import requests
 
@@ -28,21 +35,56 @@ class ILLM :
         self._args = json.loads( f.read() )
         f.close()
         self._prompt = _args['prompt'] if 'prompt' in _args else {}
+        self._token = _args['token']
     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) :
+        """
+        This function will answer a question against a LLM backend (Ollama or OpenAI)
+        """
+        
         _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
+        _llm =  _pointer(**_kwargs)
+        _memory = ConversationBufferMemory(memory_key=f'{self._token}', return_messages=True)
+        
+        # chain   = LLMChain(llm=_llm,prompt=_prompt,memory=_memory)
+        # _input = {'context':_context,'question':_question,'chat_history':''}
+        _memory = ConversationBufferMemory(memory_key=f'{self._token}',return_messages=True)
+        _schema = 'openai' if self.USE_OPENAI else 'ollama'
+        pgr = transport.get.reader(label='llm', schema=_schema)
+        _sql = f"select question, answer::JSON ->>'summary' as answer from llm_logs where token = '{self._token}' ORDER BY _date DESC LIMIT 10"
+        _ldf = pgr.read(sql=_sql)
+        _ldf.apply(lambda row: [_memory.chat_memory.add_user_message(row.question), _memory.chat_memory.add_ai_message(row.answer)] , axis=1)
+        chain = (
+                RunnablePassthrough.assign(
+                    context=lambda _x: _context,
+                    chat_history=lambda _x : _memory.chat_memory.messages if _memory.chat_memory.messages else []
+                )
+                | _prompt
+                | _llm
+                | StrOutputParser()
+            )
+        chain.invoke({'question':_question})
+
+        # _input = json.loads(json.dumps(_input))
+        resp = chain.invoke( {'question':_question})
+        
+        # #
+        # # add question and answers to the _memory object so we can submit them next time around
+        # # @TODO:
+        # _memory.chat_memory.add_user_message(_question)
+        # _memory.chat_memory.add_ai_message(resp)
+
+        return {'text':resp}
     def schema(self):
         return 'openai' if self.USE_OPENAI else 'public'
     def documents(self,_vector) :
@@ -61,7 +103,7 @@ class ILLM :
         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)
+        # 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")
@@ -73,8 +115,9 @@ def answer (**_args):
     _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 
-
+    _config['token'] = token
     _llmproc = ILLM(**_config)
+    
 
     #
     # Turn the question into a vector and send it to the LLM Server
@@ -84,7 +127,7 @@ def answer (**_args):
     _pages = _df.apply(lambda row: row.content,axis=1).tolist()
     
     #
-    # submit the request to the 
+    # We should also pull the previous questions/answers
 
 
     # return _df[['name','page','similarity']].to_dict(orient='records')
@@ -95,7 +138,6 @@ def answer (**_args):
     #
     # @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')}
     
     
@@ -110,27 +152,43 @@ def answer (**_args):
         # send the thread 
         pthread = Process(target=_logger)
         pthread.start()
+        _out['answer'] = json.loads(_out['answer'])
     except Exception as e:
         print (e)
-    # _out['stream'] = f'{_context}/api/medix/audio?token={token}&index={_index}'
+    _context = _args['config']['system']['context'].strip()
+    _out['stream'] = f'{_context}/api/medix/audio?token={token}&index={_index}'
+    # _out['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]
+    return 'openai' if 'openai' in _config['system']['source']['llm'] else 'ollama'
 @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)
+    _config['token'] = _token
 
     _llmproc = ILLM(**_config)
-    _text = _llmproc.lookup(_index,_token)
-    g = gTTS(_text,lang='en')
-    return g.stream()
+    _stream = _llmproc.lookup(_index,_token)
+    if _stream.strip().startswith('{') :
+        _text = json.loads(_stream)['summary']
+    else:
+        _text = _stream.strip()
+    _text = _text.replace('\n',' ').replace('\r',' ').replace('  ',' ').strip()
+    
+    r = requests.post(f"http://localhost:5002/api/tts",headers={"text":f"""{_text}""","Content-Type":"application/json"},stream=True)
+    # r = requests.get(f"""http://localhost:5000/api/tts?text={_text}""")
+    # f = open('/home/steve/tmp/out.wav','w')
+    # f.write(r.content)
+    # f.close()
+    return r.content
+   
+    # g = gTTS(_text,lang='en')
+    # return g.stream()
     # stream = io.BytesIO()
     # for line in g.stream() :
     #     stream.write(line)