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refactor1

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SpudGunMan
2025-10-27 17:31:47 -07:00
parent 27789d7508
commit 8d82823ccc
1 changed files with 21 additions and 97 deletions
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modules/llm.py
+21 -97
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@@ -4,7 +4,7 @@
# K7MHI Kelly Keeton 2024
from modules.log import logger
from modules.settings import (llmModel, ollamaHostName, rawLLMQuery,
llmUseWikiContext, useOpenWebUI, openWebUIURL, openWebUIAPIKey)
llmUseWikiContext, useOpenWebUI, openWebUIURL, openWebUIAPIKey, cmdBang, urlTimeoutSeconds)
# Ollama Client
# https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-configure-ollama-server
@@ -12,10 +12,6 @@ import requests
import json
from datetime import datetime
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"
openWebUIChatAPI = openWebUIURL + "/api/chat/completions"
@@ -23,13 +19,9 @@ openWebUIOllamaProxy = openWebUIURL + "/ollama/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")
@@ -55,24 +47,6 @@ meshBotAI = """
"""
if llmContext_fromGoogle:
meshBotAI = meshBotAI + """
CONTEXT
The following is the location of the user
{location_name}
The following is for context around the prompt to help guide your response.
{context}
"""
else:
meshBotAI = meshBotAI + """
CONTEXT
The following is the location of the user
{location_name}
"""
if llmEnableHistory:
meshBotAI = meshBotAI + """
HISTORY
@@ -104,22 +78,6 @@ def llmTool_math_calculator(expression):
except Exception as e:
return f"Error in calculation: {e}"
def llmTool_get_google(query, num_results=3):
"""
Example tool function to perform a Google search and return results.
:param query: The search query string.
:param num_results: Number of search results to return.
:return: A list of search result titles and descriptions.
"""
results = []
try:
googleSearch = search(query, advanced=True, num_results=num_results)
for result in googleSearch:
results.append(f"{result.title}: {result.description}")
return results
except Exception as e:
return [f"Error in Google search: {e}"]
llmFunctions = [
{
@@ -144,42 +102,8 @@ llmFunctions = [
"required": ["expression"]
}
},
{
"name": "llmTool_get_google",
"description": "Perform a Google search and return results.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The search query string."
},
"num_results": {
"type": "integer",
"description": "Number of search results to return.",
"default": 3
}
},
"required": ["query"]
}
}
]
def get_google_context(input, num_results):
# Get context from Google search results
googleResults = []
try:
googleSearch = search(input, advanced=True, num_results=num_results)
if googleSearch:
for result in googleSearch:
googleResults.append(f"{result.title} {result.description}")
else:
googleResults = ['no other context provided']
except Exception as e:
logger.debug(f"System: LLM Query: context gathering failed, likely due to network issues")
googleResults = ['no other context provided']
return googleResults
def get_wiki_context(input):
"""
Get context from Wikipedia/Kiwix for RAG enhancement
@@ -297,7 +221,7 @@ def send_openwebui_query(prompt, model=None, max_tokens=450, context=''):
def send_ollama_query(llmQuery):
# Send the query to the Ollama API and return the response
try:
result = requests.post(ollamaAPI, data=json.dumps(llmQuery), timeout=5)
result = requests.post(ollamaAPI, data=json.dumps(llmQuery), timeout= urlTimeoutSeconds * 4)
if result.status_code == 200:
result_json = result.json()
result = result_json.get("response", "")
@@ -336,20 +260,24 @@ def send_ollama_tooling_query(prompt, functions, model=None, max_tokens=450):
else:
raise Exception(f"HTTP Error: {result.status_code} - {result.text}")
def llm_query(input, nodeID=0, location_name=None):
def llm_query(input, nodeID=0, location_name=None, init=False):
global antiFloodLLM, llmChat_history
googleResults = []
wikiContext = ''
# 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:
if init 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
else:
elif init:
input = input.strip()
# classic model for gemma2, deepseek-r1, etc
logger.debug(f"System: Using classic LLM model framework, ideally for gemma2, deepseek-r1, etc")
logger.debug(f"System: Using SYSTEM model framework, ideally for gemma2, deepseek-r1, etc")
# Remove command bang if present
if cmdBang:
input = input[1:].strip()
if not location_name:
location_name = "no location provided "
@@ -371,20 +299,20 @@ def llm_query(input, nodeID=0, location_name=None):
# Get Wikipedia/Kiwix context if enabled (RAG)
if llmUseWikiContext and input != meshbotAIinit:
wikiContext = get_wiki_context(input)
# get_wiki_context returns a string, but we want to count the items before joining
search_terms = extract_search_terms(input)
wiki_context_list = []
for term in search_terms[:2]:
summary = get_wiki_context(term)
if summary and "error" not in summary.lower():
wiki_context_list.append(f"Wikipedia context for '{term}': {summary}")
wikiContext = '\n'.join(wiki_context_list) if wiki_context_list else ''
if wikiContext:
logger.debug(f"System: Wiki-Enhanced LLM Query with context")
# Get Google context if enabled and not using raw query
if llmContext_fromGoogle and not rawLLMQuery:
googleResults = get_google_context(input, googleSearchResults)
logger.debug(f"System: using Wikipedia/Kiwix context for LLM query got {len(wiki_context_list)} results")
history = llmChat_history.get(nodeID, ["", ""])
if googleResults or wikiContext:
logger.debug(f"System: Context-Enhanced LLM Query: {input} From:{nodeID}")
else:
logger.debug(f"System: LLM Query: {input} From:{nodeID}")
logger.debug(f"System: LLM Query: {input} From:{nodeID}")
response = ""
result = ""
@@ -399,8 +327,6 @@ def llm_query(input, nodeID=0, location_name=None):
combined_context = []
if wikiContext:
combined_context.append(wikiContext)
if googleResults:
combined_context.append("Google search results: " + '\n'.join(googleResults))
context_str = '\n\n'.join(combined_context)
@@ -434,8 +360,6 @@ def llm_query(input, nodeID=0, location_name=None):
all_context = []
if wikiContext:
all_context.append(wikiContext)
if googleResults:
all_context.extend(googleResults)
context_text = '\n'.join(all_context) if all_context else 'no other context provided'
modelPrompt = meshBotAI.format(