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Merge pull request #178 from SpudGunMan/lab

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Gemma3 LLM
This commit is contained in:
Kelly
2025-08-26 11:00:31 -07:00
committed by GitHub
parent 85a2d90cff 50c3249edc
commit c5ba56a656
8 changed files with 116 additions and 128 deletions
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README.md
+6 -2
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@@ -211,6 +211,7 @@ defaultChannel = 0
ignoreDefaultChannel = False # ignoreDefaultChannel, the bot will ignore the default channel set above
ignoreChannels = # ignoreChannels is a comma separated list of channels to ignore, e.g. 4,5
cmdBang = False # require ! to be the first character in a command
explicitCmd = True # require explicit command, the message will only be processed if it starts with a command word disable to get more activity
```
### Location Settings
@@ -347,12 +348,12 @@ repeater_channels = [2, 3]
```
### Ollama (LLM/AI) Settings
For Ollama to work, the command line `ollama run 'model'` needs to work properly. Ensure you have enough RAM and your GPU is working as expected. The default model for this project is set to `gemma2:2b`. Ollama can be remote [Ollama Server](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-configure-ollama-server) works on a pi58GB with 40 second or less response time.
For Ollama to work, the command line `ollama run 'model'` needs to work properly. Ensure you have enough RAM and your GPU is working as expected. The default model for this project is set to `gemma3:270m`. Ollama can be remote [Ollama Server](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-configure-ollama-server) works on a pi58GB with 40 second or less response time.
```ini
# Enable ollama LLM see more at https://ollama.com
ollama = True # Ollama model to use (defaults to gemma2:2b)
ollamaModel = gemma2 #ollamaModel = llama3.1
ollamaModel = gemma3:latest # Ollama model to use (defaults to gemma3:270m)
ollamaHostName = http://localhost:11434 # server instance to use (defaults to local machine install)
```
@@ -360,6 +361,9 @@ Also see `llm.py` for changing the defaults of:
```ini
# LLM System Variables
rawQuery = True # if True, the input is sent raw to the LLM if False, it is processed by the meshBotAI template
# Used in the meshBotAI template (legacy)
llmEnableHistory = True # enable history for the LLM model to use in responses adds to compute time
llmContext_fromGoogle = True # enable context from google search results helps with responses accuracy
googleSearchResults = 3 # number of google search results to include in the context more results = more compute time
config.template
+6 -2
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@@ -36,6 +36,8 @@ ignoreDefaultChannel = False
ignoreChannels =
# require ! to be the first character in a command
cmdBang = False
# require explicit command, the message will only be processed if it starts with a command word
explicitCmd = True
# motd is reset to this value on boot
motd = Thanks for using MeshBOT! Have a good day!
@@ -56,13 +58,15 @@ wikipedia = True
# Enable ollama LLM see more at https://ollama.com
ollama = False
# Ollama model to use (defaults to gemma2:2b)
# ollamaModel = llama3.1
# Ollama model to use (defaults to gemma3:270m)
# ollamaModel = gemma3:latest
# server instance to use (defaults to local machine install)
ollamaHostName = http://localhost:11434
# Produce LLM replies to messages that aren't commands?
# If False, the LLM only replies to the "ask:" and "askai" commands.
llmReplyToNonCommands = True
# if True, the input is sent raw to the LLM, if False uses legacy template query
rawLLMQuery = True
# StoreForward Enabled and Limits
StoreForward = True
install.sh
+5 -5
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@@ -250,7 +250,7 @@ if [[ $(echo "${embedded}" | grep -i "^n") ]]; then
printf "\nOptionally if you want to install the multi gig LLM Ollama compnents we will execute the following commands\n"
printf "\ncurl -fsSL https://ollama.com/install.sh | sh\n"
printf "ollama pull gemma2:2b\n"
printf "ollama pull gemma3:latest\n"
printf "Total download is multi GB, recomend pi5/8GB or better for this\n"
# ask if the user wants to install the LLM Ollama components
printf "\nDo you want to install the LLM Ollama components? (y/n)"
@@ -258,12 +258,12 @@ if [[ $(echo "${embedded}" | grep -i "^n") ]]; then
if [[ $(echo "${ollama}" | grep -i "^y") ]]; then
curl -fsSL https://ollama.com/install.sh | sh
# ask if want to install gemma2:2b
printf "\n Ollama install done now we can install the Gemma2:2b components\n"
echo "Do you want to install the Gemma2:2b components? (y/n)"
# ask if want to install gemma3:latest
printf "\n Ollama install done now we can install the gemma3:latest components\n"
echo "Do you want to install the gemma3:latest components? (y/n)"
read gemma
if [[ $(echo "${gemma}" | grep -i "^y") ]]; then
ollama pull gemma2:2b
ollama pull gemma3:latest
fi
fi
mesh_bot.py
+7 -3
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@@ -1162,6 +1162,8 @@ def onReceive(packet, interface):
if 'decoded' in packet and packet['decoded']['portnum'] == 'TEXT_MESSAGE_APP':
message_bytes = packet['decoded']['payload']
message_string = message_bytes.decode('utf-8')
via_mqtt = packet['decoded'].get('viaMqtt', False)
rx_time = packet['decoded'].get('rxTime', time.time())
# check if the packet is from us
if message_from_id in [myNodeNum1, myNodeNum2, myNodeNum3, myNodeNum4, myNodeNum5, myNodeNum6, myNodeNum7, myNodeNum8, myNodeNum9]:
@@ -1201,13 +1203,15 @@ def onReceive(packet, interface):
if enableHopLogs:
logger.debug(f"System: Packet HopDebugger: hop_away:{hop_away} hop_limit:{hop_limit} hop_start:{hop_start}")
if hop_away == 0 and hop_limit == 0 and hop_start == 0:
logger.debug(f"System: Packet HopDebugger: No hop count found in PACKET {packet} END PACKET")
if hop_away == 0 and hop_limit == 0 and hop_start == 0:
hop = "Last Hop"
hop_count = 0
if hop_start == hop_limit:
hop = "Direct"
hop_count = 0
elif hop_start == 0 and hop_limit > 0:
elif hop_start == 0 and hop_limit > 0 or via_mqtt:
hop = "MQTT"
hop_count = 0
else:
modules/llm.py
+60 -108
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@@ -8,32 +8,34 @@ from modules.log import *
# https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-configure-ollama-server
import requests
import json
from googlesearch import search # pip install googlesearch-python
# This is my attempt at a simple RAG implementation it will require some setup
# you will need to have the RAG data in a folder named rag in the data directory (../data/rag)
# This is lighter weight and can be used in a standalone environment, needs chromadb
# "chat with a file" is the use concept here, the file is the RAG data
# is anyone using this please let me know if you are Dec62024 -kelly
ragDEV = False
if ragDEV:
import os
import ollama # pip install ollama
import chromadb # pip install chromadb
from ollama import Client as OllamaClient
ollamaClient = OllamaClient(host=ollamaHostName)
if not rawLLMQuery:
# this may be removed in the future
from googlesearch import search # pip install googlesearch-python
# LLM System Variables
ollamaAPI = ollamaHostName + "/api/generate"
tokens = 450 # max charcters for the LLM response, this is the max length of the response also in prompts
requestTruncation = True # if True, the LLM "will" truncate the response
openaiAPI = "https://api.openai.com/v1/completions" # not used, if you do push a enhancement!
# Used in the meshBotAI template
llmEnableHistory = True # enable last message history for the LLM model
llmContext_fromGoogle = True # enable context from google search results adds to compute time but really helps with responses accuracy
googleSearchResults = 3 # number of google search results to include in the context more results = more compute time
antiFloodLLM = []
llmChat_history = {}
trap_list_llm = ("ask:", "askai")
meshbotAIinit = """
keep responses as short as possible. chatbot assistant no followuyp questions, no asking for clarification.
You must respond in plain text standard ASCII characters or emojis.
"""
truncatePrompt = f"truncate this as short as possible:\n"
meshBotAI = """
FROM {llmModel}
SYSTEM
@@ -74,76 +76,16 @@ if llmEnableHistory:
"""
def llm_readTextFiles():
# read .txt files in ../data/rag
try:
text = []
directory = "../data/rag"
for filename in os.listdir(directory):
if filename.endswith(".txt"):
filepath = os.path.join(directory, filename)
with open(filepath, 'r') as f:
text.append(f.read())
return text
except Exception as e:
logger.debug(f"System: LLM readTextFiles: {e}")
return False
def store_text_embedding(text):
try:
# store each document in a vector embedding database
for i, d in enumerate(text):
response = ollama.embeddings(model="mxbai-embed-large", prompt=d)
embedding = response["embedding"]
collection.add(
ids=[str(i)],
embeddings=[embedding],
documents=[d]
)
except Exception as e:
logger.debug(f"System: Embedding failed: {e}")
return False
## INITALIZATION of RAG
if ragDEV:
try:
chromaHostname = "localhost:8000"
# connect to the chromaDB
chromaHost = chromaHostname.split(":")[0]
chromaPort = chromaHostname.split(":")[1]
if chromaHost == "localhost" and chromaPort == "8000":
# create a client using local python Client
chromaClient = chromadb.Client()
else:
# create a client using the remote python Client
# this isnt tested yet please test and report back
chromaClient = chromadb.Client(host=chromaHost, port=chromaPort)
clearCollection = False
if "meshBotAI" in chromaClient.list_collections() and clearCollection:
logger.debug(f"System: LLM: Clearing RAG files from chromaDB")
chromaClient.delete_collection("meshBotAI")
# create a new collection
collection = chromaClient.create_collection("meshBotAI")
logger.debug(f"System: LLM: Cataloging RAG data")
store_text_embedding(llm_readTextFiles())
except Exception as e:
logger.debug(f"System: LLM: RAG Initalization failed: {e}")
def query_collection(prompt):
# generate an embedding for the prompt and retrieve the most relevant doc
response = ollama.embeddings(prompt=prompt, model="mxbai-embed-large")
results = collection.query(query_embeddings=[response["embedding"]], n_results=1)
data = results['documents'][0][0]
return data
def llm_query(input, nodeID=0, location_name=None):
global antiFloodLLM, llmChat_history
googleResults = []
# if this is the first initialization of the LLM the query of " " should bring meshbotAIinit OTA shouldnt reach this?
# This is for LLM like gemma and others now?
if input == " " and rawLLMQuery:
logger.warning("System: These LLM models lack a traditional system prompt, they can be verbose and not very helpful be advised.")
input = meshbotAIinit
if not location_name:
location_name = "no location provided "
@@ -162,7 +104,7 @@ def llm_query(input, nodeID=0, location_name=None):
else:
antiFloodLLM.append(nodeID)
if llmContext_fromGoogle:
if llmContext_fromGoogle and not rawLLMQuery:
# grab some context from the internet using google search hits (if available)
# localization details at https://pypi.org/project/googlesearch-python/
@@ -193,36 +135,29 @@ def llm_query(input, nodeID=0, location_name=None):
location_name += f" at the current time of {datetime.now().strftime('%Y-%m-%d %H:%M:%S %Z')}"
try:
# RAG context inclusion testing
ragContext = False
if ragDEV:
ragContext = query_collection(input)
if ragContext:
ragContextGooogle = ragContext + '\n'.join(googleResults)
# Build the query from the template
modelPrompt = meshBotAI.format(input=input, context=ragContext, location_name=location_name, llmModel=llmModel, history=history)
# Query the model with RAG context
result = ollamaClient.generate(model=llmModel, prompt=modelPrompt)
# Condense the result to just needed
if isinstance(result, dict):
result = result.get("response")
if rawLLMQuery:
# sanitize the input to remove tool call syntax
if '```' in input:
logger.warning("System: LLM Query: Code markdown detected, removing for raw query")
input = input.replace('```bash', '').replace('```python', '').replace('```', '')
modelPrompt = input
else:
# Build the query from the template
modelPrompt = meshBotAI.format(input=input, context='\n'.join(googleResults), location_name=location_name, llmModel=llmModel, history=history)
llmQuery = {"model": llmModel, "prompt": modelPrompt, "stream": False}
# Query the model via Ollama web API
result = requests.post(ollamaAPI, data=json.dumps(llmQuery))
# Condense the result to just needed
if result.status_code == 200:
result_json = result.json()
result = result_json.get("response", "")
llmQuery = {"model": llmModel, "prompt": modelPrompt, "stream": False, "max_tokens": tokens}
# Query the model via Ollama web API
result = requests.post(ollamaAPI, data=json.dumps(llmQuery))
# Condense the result to just needed
if result.status_code == 200:
result_json = result.json()
result = result_json.get("response", "")
# deepseek-r1 has added <think> </think> tags to the response
if "<think>" in result:
result = result.split("</think>")[1]
else:
raise Exception(f"HTTP Error: {result.status_code}")
# deepseek-r1 has added <think> </think> tags to the response
if "<think>" in result:
result = result.split("</think>")[1]
else:
raise Exception(f"HTTP Error: {result.status_code}")
#logger.debug(f"System: LLM Response: " + result.strip().replace('\n', ' '))
except Exception as e:
@@ -231,6 +166,23 @@ def llm_query(input, nodeID=0, location_name=None):
# cleanup for message output
response = result.strip().replace('\n', ' ')
if rawLLMQuery and requestTruncation and len(response) > 450:
#retryy loop to truncate the response
logger.warning(f"System: LLM Query: Response exceeded {tokens} characters, requesting truncation")
truncateQuery = {"model": llmModel, "prompt": truncatePrompt + response, "stream": False, "max_tokens": tokens}
truncateResult = requests.post(ollamaAPI, data=json.dumps(truncateQuery))
if truncateResult.status_code == 200:
truncate_json = truncateResult.json()
result = truncate_json.get("response", "")
else:
#use the original result if truncation fails
logger.warning("System: LLM Query: Truncation failed, using original response")
# cleanup for message output
response = result.strip().replace('\n', ' ')
# done with the query, remove the user from the anti flood list
antiFloodLLM.remove(nodeID)
modules/settings.py
+4 -2
View File
@@ -197,6 +197,7 @@ try:
ignoreChannels = config['general'].get('ignoreChannels', '').split(',') # ignore these channels
ignoreDefaultChannel = config['general'].getboolean('ignoreDefaultChannel', False)
cmdBang = config['general'].getboolean('cmdBang', False) # default off
explicitCmd = config['general'].getboolean('explicitCmd', True) # default on
zuluTime = config['general'].getboolean('zuluTime', False) # aka 24 hour time
log_messages_to_file = config['general'].getboolean('LogMessagesToFile', False) # default off
log_backup_count = config['general'].getint('LogBackupCount', 32) # default 32 days
@@ -219,8 +220,9 @@ try:
solar_conditions_enabled = config['general'].getboolean('spaceWeather', True)
wikipedia_enabled = config['general'].getboolean('wikipedia', False)
llm_enabled = config['general'].getboolean('ollama', False) # https://ollama.com
llmModel = config['general'].get('ollamaModel', 'gemma2:2b') # default gemma2:2b
ollamaHostName = config['general'].get('ollamaHostName', 'http://localhost:11434') # default localhost
llmModel = config['general'].get('ollamaModel', 'gemma3:270m') # default gemma3:270m
rawLLMQuery = config['general'].getboolean('rawLLMQuery', True) #default True
llmReplyToNonCommands = config['general'].getboolean('llmReplyToNonCommands', True)
dont_retry_disconnect = config['general'].getboolean('dont_retry_disconnect', False) # default False, retry on disconnect
# emergency response
@@ -361,7 +363,7 @@ try:
splitDelay = config['messagingSettings'].getfloat('splitDelay', 0) # default 0
MESSAGE_CHUNK_SIZE = config['messagingSettings'].getint('MESSAGE_CHUNK_SIZE', 160) # default 160
wantAck = config['messagingSettings'].getboolean('wantAck', False) # default False
maxBuffer = config['messagingSettings'].getint('maxBuffer', 220) # default 220
maxBuffer = config['messagingSettings'].getint('maxBuffer', 200) # default 200
enableHopLogs = config['messagingSettings'].getboolean('enableHopLogs', False) # default False
except KeyError as e:
modules/system.py
+19 -5
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@@ -268,6 +268,7 @@ if ble_count > 1:
logger.debug(f"System: Initializing Interfaces")
interface1 = interface2 = interface3 = interface4 = interface5 = interface6 = interface7 = interface8 = interface9 = None
retry_int1 = retry_int2 = retry_int3 = retry_int4 = retry_int5 = retry_int6 = retry_int7 = retry_int8 = retry_int9 = False
myNodeNum1 = myNodeNum2 = myNodeNum3 = myNodeNum4 = myNodeNum5 = myNodeNum6 = myNodeNum7 = myNodeNum8 = myNodeNum9 = 777
max_retry_count1 = max_retry_count2 = max_retry_count3 = max_retry_count4 = max_retry_count5 = max_retry_count6 = max_retry_count7 = max_retry_count8 = max_retry_count9 = interface_retry_count
for i in range(1, 10):
interface_type = globals().get(f'interface{i}_type')
@@ -686,11 +687,24 @@ def messageTrap(msg):
message_list=msg.split(" ")
for m in message_list:
for t in trap_list:
# if word in message is in the trap list, return True
if t.lower() == m.lower():
return True
if cmdBang and m.startswith("!"):
return True
if not explicitCmd:
# if word in message is in the trap list, return True
if t.lower() == m.lower():
if cmdBang:
if m.startswith('!'):
return True
else:
continue
return True
else:
# if the index 0 of the message is a word in the trap list, return True
if t.lower() == m.lower() and message_list.index(m) == 0:
if cmdBang:
if m.startswith('!'):
return True
else:
continue
return True
# if no trap words found, run a search for near misses like ping? or cmd?
for m in message_list:
for t in range(len(trap_list)):
pong_bot.py
+9 -1
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@@ -254,6 +254,7 @@ def onReceive(packet, interface):
if 'decoded' in packet and packet['decoded']['portnum'] == 'TEXT_MESSAGE_APP':
message_bytes = packet['decoded']['payload']
message_string = message_bytes.decode('utf-8')
via_mqtt = packet['decoded'].get('viaMqtt', False)
# check if the packet is from us
if message_from_id == myNodeNum1 or message_from_id == myNodeNum2:
@@ -283,10 +284,17 @@ def onReceive(packet, interface):
else:
hop_start = 0
if enableHopLogs:
logger.debug(f"System: Packet HopDebugger: hop_away:{hop_away} hop_limit:{hop_limit} hop_start:{hop_start}")
if hop_away == 0 and hop_limit == 0 and hop_start == 0:
hop = "Last Hop"
hop_count = 0
if hop_start == hop_limit:
hop = "Direct"
hop_count = 0
elif hop_start == 0 and hop_limit > 0:
elif hop_start == 0 and hop_limit > 0 or via_mqtt:
hop = "MQTT"
hop_count = 0
else: