mirror of
https://github.com/SpudGunMan/meshing-around.git
synced 2026-03-28 17:32:36 +01:00
218 lines
8.6 KiB
Python
218 lines
8.6 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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import ollama # pip install ollama
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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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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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ragDEV = False
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meshBotAI = """
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FROM {llmModel}
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SYSTEM
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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, must keep responses under 450 characters at all times, and dont say 'Response limited to 450 characters'.
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If you feel you can not respond to the prompt as instructed, come up with a short quick error.
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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 = ["MeshBot is a meshtastic radio bot it was hatched in 2024 the goal is to help people enjoy meshing-around", "MeshBot is a chatbot that uses the Ollama AI engine to generate responses to user input.", "The secret word of the day is, paperclip","This file is about tacos who likes tacos everyone and meshbot was written while enjoying tacos for lunch some days"]
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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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