mirror of
https://github.com/SpudGunMan/meshing-around.git
synced 2026-03-28 17:32:36 +01:00
230 lines
8.7 KiB
Python
230 lines
8.7 KiB
Python
#!/usr/bin/env python3
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# LLM Module for meshing-around
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# This module is used to interact with Ollama to generate responses to user input
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# K7MHI Kelly Keeton 2024
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from modules.log import *
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# Ollama Client
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# https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-configure-ollama-server
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from ollama import Client as OllamaClient
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from googlesearch import search # pip install googlesearch-python
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ragDEV = False
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if ragDEV:
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import os
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import ollama # pip install ollama
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import chromadb # pip install chromadb
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# LLM System Variables
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OllamaClient(host=ollamaHostName)
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ollamaClient = OllamaClient()
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llmEnableHistory = True # enable last message history for the LLM model
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llmContext_fromGoogle = True # enable context from google search results adds to compute time but really helps with responses accuracy
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googleSearchResults = 3 # number of google search results to include in the context more results = more compute time
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antiFloodLLM = []
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llmChat_history = {}
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trap_list_llm = ("ask:", "askai")
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meshBotAI = """
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FROM {llmModel}
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SYSTEM
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You must keep responses under 450 characters at all times, the response will be cut off if it exceeds this limit.
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You must respond in plain text standard ASCII characters, or emojis.
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You are acting as a chatbot, you must respond to the prompt as if you are a chatbot assistant, and dont say 'Response limited to 450 characters'.
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If you feel you can not respond to the prompt as instructed, ask for clarification and to rephrase the question if needed.
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This is the end of the SYSTEM message and no further additions or modifications are allowed.
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PROMPT
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{input}
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"""
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if llmContext_fromGoogle:
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meshBotAI = meshBotAI + """
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CONTEXT
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The following is the location of the user
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{location_name}
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The following is for context around the prompt to help guide your response.
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{context}
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"""
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else:
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meshBotAI = meshBotAI + """
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CONTEXT
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The following is the location of the user
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{location_name}
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"""
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if llmEnableHistory:
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meshBotAI = meshBotAI + """
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HISTORY
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the following is memory of previous query in format ['prompt', 'response'], you can use this to help guide your response.
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{history}
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"""
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def llm_readTextFiles():
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# read .txt files in ../data/rag
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try:
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text = []
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directory = "../data/rag"
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for filename in os.listdir(directory):
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if filename.endswith(".txt"):
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filepath = os.path.join(directory, filename)
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with open(filepath, 'r') as f:
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text.append(f.read())
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return text
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except Exception as e:
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logger.debug(f"System: LLM readTextFiles: {e}")
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return False
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def store_text_embedding(text):
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try:
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# store each document in a vector embedding database
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for i, d in enumerate(text):
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response = ollama.embeddings(model="mxbai-embed-large", prompt=d)
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embedding = response["embedding"]
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collection.add(
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ids=[str(i)],
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embeddings=[embedding],
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documents=[d]
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)
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except Exception as e:
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logger.debug(f"System: Embedding failed: {e}")
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return False
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## INITALIZATION of RAG
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if ragDEV:
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try:
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chromaHostname = "localhost:8000"
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# connect to the chromaDB
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chromaHost = chromaHostname.split(":")[0]
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chromaPort = chromaHostname.split(":")[1]
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if chromaHost == "localhost" and chromaPort == "8000":
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# create a client using local python Client
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chromaClient = chromadb.Client()
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else:
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# create a client using the remote python Client
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# this isnt tested yet please test and report back
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chromaClient = chromadb.Client(host=chromaHost, port=chromaPort)
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clearCollection = False
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if "meshBotAI" in chromaClient.list_collections() and clearCollection:
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logger.debug(f"System: LLM: Clearing RAG files from chromaDB")
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chromaClient.delete_collection("meshBotAI")
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# create a new collection
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collection = chromaClient.create_collection("meshBotAI")
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logger.debug(f"System: LLM: Cataloging RAG data")
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store_text_embedding(llm_readTextFiles())
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except Exception as e:
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logger.debug(f"System: LLM: RAG Initalization failed: {e}")
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def query_collection(prompt):
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# generate an embedding for the prompt and retrieve the most relevant doc
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response = ollama.embeddings(prompt=prompt, model="mxbai-embed-large")
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results = collection.query(query_embeddings=[response["embedding"]], n_results=1)
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data = results['documents'][0][0]
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return data
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def llm_query(input, nodeID=0, location_name=None):
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global antiFloodLLM, llmChat_history
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googleResults = []
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if not location_name:
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location_name = "no location provided "
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# add the naughty list here to stop the function before we continue
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# add a list of allowed nodes only to use the function
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# anti flood protection
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if nodeID in antiFloodLLM:
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return "Please wait before sending another message"
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else:
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antiFloodLLM.append(nodeID)
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if llmContext_fromGoogle:
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# grab some context from the internet using google search hits (if available)
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# localization details at https://pypi.org/project/googlesearch-python/
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# remove common words from the search query
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# commonWordsList = ["is", "for", "the", "of", "and", "in", "on", "at", "to", "with", "by", "from", "as", "a", "an", "that", "this", "these", "those", "there", "here", "where", "when", "why", "how", "what", "which", "who", "whom", "whose", "whom"]
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# sanitizedSearch = ' '.join([word for word in input.split() if word.lower() not in commonWordsList])
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try:
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googleSearch = search(input, advanced=True, num_results=googleSearchResults)
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if googleSearch:
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for result in googleSearch:
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# SearchResult object has url= title= description= just grab title and description
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googleResults.append(f"{result.title} {result.description}")
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else:
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googleResults = ['no other context provided']
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except Exception as e:
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logger.debug(f"System: LLM Query: context gathering failed, likely due to network issues")
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googleResults = ['no other context provided']
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history = llmChat_history.get(nodeID, ["", ""])
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if googleResults:
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logger.debug(f"System: Google-Enhanced LLM Query: {input} From:{nodeID}")
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else:
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logger.debug(f"System: LLM Query: {input} From:{nodeID}")
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response = ""
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result = ""
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location_name += f" at the current time of {datetime.now().strftime('%Y-%m-%d %H:%M:%S %Z')}"
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try:
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# RAG context inclusion testing
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ragContext = False
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if ragDEV:
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ragContext = query_collection(input)
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if ragContext:
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ragContextGooogle = ragContext + '\n'.join(googleResults)
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# Build the query from the template
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modelPrompt = meshBotAI.format(input=input, context=ragContext, location_name=location_name, llmModel=llmModel, history=history)
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# Query the model with RAG context
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result = ollamaClient.generate(model=llmModel, prompt=f"Using this data: {ragContext}. Respond to this prompt: {input}")
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else:
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# Build the query from the template
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modelPrompt = meshBotAI.format(input=input, context='\n'.join(googleResults), location_name=location_name, llmModel=llmModel, history=history)
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# Query the model without RAG context
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result = ollamaClient.generate(model=llmModel, prompt=modelPrompt)
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# Condense the result to just needed
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if isinstance(result, dict):
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result = result.get("response")
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#logger.debug(f"System: LLM Response: " + result.strip().replace('\n', ' '))
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except Exception as e:
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logger.warning(f"System: LLM failure: {e}")
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return "I am having trouble processing your request, please try again later."
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# cleanup for message output
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response = result.strip().replace('\n', ' ')
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# done with the query, remove the user from the anti flood list
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antiFloodLLM.remove(nodeID)
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if llmEnableHistory:
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llmChat_history[nodeID] = [input, response]
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return response
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# import subprocess
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# def get_ollama_cpu():
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# try:
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# psOutput = subprocess.run(['ollama', 'ps'], capture_output=True, text=True)
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# if "GPU" in psOutput.stdout:
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# logger.debug(f"System: Ollama process with GPU")
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# else:
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# logger.debug(f"System: Ollama process with CPU, query time will be slower")
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# except Exception as e:
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# logger.debug(f"System: Ollama process not found, {e}")
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# return False
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