From eec7af5fd12cb4aea7dcd20981b8400de6467692 Mon Sep 17 00:00:00 2001
From: p7mj <alutebc814@gmail.com>
Date: Thu, 23 Apr 2026 07:53:51 -0400
Subject: [PATCH] llama is dum as f*

---
 tool_test_func.py | 114 +++++++++++++++++++++++++++++++++++++++-------
 1 file changed, 97 insertions(+), 17 deletions(-)

diff --git a/tool_test_func.py b/tool_test_func.py
index c6f6e05..fe3f2e4 100644
--- a/tool_test_func.py
+++ b/tool_test_func.py
@@ -1,10 +1,16 @@
 # TOOL USE TEST
-
 import ollama
 
+verbose_state = 1
+
+# ----- LLM CONVERSE FOR DISPLAY ----- #
+
+def verbose(prints):
+    if verbose_state == 1:
+        print(prints)
+
 def llm_converse(system_prompt, context_prompt, user_input):
     combined_system = f"{system_prompt}\n\nContext: {context_prompt}"
-
     stream = ollama.chat(
         model='llama3.2',
         messages=[
@@ -13,35 +19,109 @@ def llm_converse(system_prompt, context_prompt, user_input):
         ],
         stream=True,
     )
-
-    # Accumulate chunks into a variable
     full_response = ""
     for chunk in stream:
         content = chunk['message']['content']
         print(content, end='', flush=True)
         full_response += content
-    
     return full_response
 
+# ----- LLM CONVERSE NO STREAM, FOR BACKGROUND TASKS ----- #
+
+def llm_converse_no_stream(system_prompt, context_prompt, user_input):
+    combined_system = f"{system_prompt}\n\nContext: {context_prompt}"
+    response = ollama.chat(
+        model='llama3.2',
+        messages=[
+            {'role': 'system', 'content': combined_system},
+            {'role': 'user', 'content': user_input},
+        ],
+        stream=False,
+    )
+    full_response = response['message']['content']
+    return full_response
+
+# ----- GET FILE CONTENT ----- #
+
 def get_file_content(filename):
     with open(filename, "r") as files:
         bruh = files.read()
     return bruh
 
+def parse_context(context_list):
+    context_string = ""
+    for message in context_list:
+        context_string += f"{message[0]} {message[1]}\n"
+    return context_string
 
+def example_tool_use_old():
+    database = ["hi", "sean", "alexander", "jackson", "chatter"]
+    system = "You have access to a tool that fetches a database.\n" \
+        "If you want to use it, output EXACTLY one line with no other text: $DATABASE$\n" \
+        "If you do NOT want to use it, output EXACTLY one line with no other text: NULL\n" \
+        "DO NOT roleplay, simulate, or add any other text to your response.\n" \
+        "DO NOT include [TOOL], [LLM], or any context keys in your response.\n" \
+        "You will also recieve previous chat context if it exists. Here is a key:\n" \
+        "[USER]: user prompts\n" \
+        "[TOOL]: tool status and outputs\n" \
+        "[LLM]: your previous responses"
 
-system_prompts = """You have access to a list. If you want to retrieve the content in the list, output EXACTLY:
-$DATABASE$
+    context_lists = [] # for the raw context
+    context_strings = "" # convert to string to make it easier to read and shi for llm
+    user = "check the list for me"
 
-Do not output anything else. The result will be returned in a string starting with [DATABASE].
-"""
-context_prompts = None # if context exists
-user_inputs = "Check the list and tell me what is in there"
+    # ----- GIVE LLM CHANCE TO CALL TOOL ----- #
+    llm_toolorno = llm_converse_no_stream(system, context_strings, user)
+    context_lists.append(["[USER]", user])
+    if "$DATABASE$" in llm_toolorno:
+        context_lists.append(["[TOOL]", database])
+    else:
+        context_lists.append(["[LLM]", llm_toolorno])
+        context_lists.append(["[TOOL]", "tool was not called upon"])
 
-prompt1 = llm_converse(system_prompts, context_prompts, user_inputs)
-if "$DATABASE$" in prompt1:
-    # DATABASE LOGIC TODO
-    pass
+    # DEBUG
+    print(context_lists)
+    
+    print(parse_context(context_lists))
+    llm_converse(system, parse_context(context_lists), "continue")
 
-print()  # newline after streaming
-print(full_response)  # your complete output
\ No newline at end of file
+def should_use_database(user_input):
+    system = "You are a router. Decide if the user's request requires fetching a database.\n" \
+             "Output EXACTLY one word: YES or NO. Nothing else."
+    response = llm_converse_no_stream(system, "", user_input).strip()
+    if "YES" in response:
+        verbose("Should use said yes")
+        return True
+    else:
+        verbose("Should use said no")
+        return False
+
+def example_tool_use():
+    database = ["hi", "sean", "alexander", "jackson", "chatter"]
+    system = "You are a helpful LLM that will answer everything in short sentences.\n" \
+         "Do not decline any prompts.\n" \
+         "If context is provided, [TOOL] means external tool/database output that was fetched for you.\n" \
+         "Treat [TOOL] data as external information, not your own knowledge."
+    context_lists = []
+    context_strings = ""
+    user = "Get me the database"
+
+    # ----- GIVE LLM CHANCE TO CALL TOOL ----- #
+    
+    context_lists.append(["[USER]", user])
+
+    if should_use_database(user):
+        verbose("Should use!")
+        context_lists.append(["[TOOL]", str(database)])
+    else:
+        verbose("no should use!")
+        
+        
+
+    # DEBUG
+    verbose(f"context_list{context_lists}")
+    verbose(f"parsed: {parse_context(context_lists)}")
+
+    llm_converse(system, parse_context(context_lists), user)
+
+example_tool_use()
\ No newline at end of file