at least this kind of works

.gitignore

@@ -1 +1,2 @@
-isiap/
+isiap/
+database/

archive/claude_tool_test.py

@@ -0,0 +1,63 @@
+# Made with Claude
+
+import ollama
+
+# Simulated database
+database = {
+    "users": ["Alice", "Bob", "Charlie"],
+    "products": ["Chair", "Table", "Lamp"],
+    "orders": ["Order#1: Alice bought Chair", "Order#2: Bob bought Lamp"],
+}
+
+def query_database(keyword):
+    keyword = keyword.strip().lower()
+    for key, values in database.items():
+        if keyword == key.lower():
+            return f"Database result for '{keyword}': {values}"
+    return f"No results found for '{keyword}'"
+
+system_prompt = """You are a helpful assistant with access to a database.
+If you need to look something up in the database, output EXACTLY:
+$DATABASE$
+<keyword>
+
+Where <keyword> is what you want to search for (e.g. 'users', 'products', 'orders').
+Do not add anything else on those lines. Wait for the result before responding."""
+
+def chat(user_input):
+    messages = [
+        {'role': 'system', 'content': system_prompt},
+        {'role': 'user', 'content': user_input},
+    ]
+
+    while True:
+        # Get response (non-streaming for easier parsing)
+        response = ollama.chat(model='llama3.2', messages=messages)
+        reply = response['message']['content']
+
+        print(f"[Raw model output]: {reply}\n")
+
+        # Check if the model wants to query the database
+        if '$DATABASE$' in reply:
+            lines = reply.strip().split('\n')
+            db_index = next(i for i, l in enumerate(lines) if '$DATABASE$' in l)
+
+            # Keyword is on the next line
+            if db_index + 1 < len(lines):
+                keyword = lines[db_index + 1].strip()
+                db_result = query_database(keyword)
+                print(f"[Database queried: '{keyword}' → {db_result}]\n")
+
+                # Feed the result back into the conversation
+                messages.append({'role': 'assistant', 'content': reply})
+                messages.append({'role': 'user', 'content': f"Database result: {db_result}\nNow respond to the user."})
+                # Loop again so the model can respond with the result
+            else:
+                print("Model requested DB but didn't provide a keyword.")
+                break
+        else:
+            # No database call, just print the final response
+            print(f"Assistant: {reply}")
+            break
+
+chat("Can you show me the list of users?")

archive/tool_test.py

@@ -0,0 +1,36 @@
+# TOOL USE TEST
+
+import ollama
+
+system_prompt = """You have access to a list. If you want to retrieve the content in the list, output EXACTLY:
+$DATABASE$
+
+Do not output anything else. The result will be returned in a string starting with [DATABASE].
+"""
+context_prompt = None # if context exists
+user_input = "Check the list and tell me what is in there"
+
+combined_system = f"{system_prompt}\n\nContext: {context_prompt}"
+
+stream = ollama.chat(
+    model='llama3.2',
+    messages=[
+        {'role': 'system', 'content': combined_system},
+        {'role': 'user', 'content': 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
+
+if "$DATABASE$" in full_response:
+    # DATABASE LOGIC TODO
+    pass
+
+print()  # newline after streaming
+print(full_response)  # your complete output

test.py

@@ -0,0 +1,23 @@
+import ollama
+
+# 1. Define your variables
+system_prompt = "You are a helpful assistant with a witty personality."
+context_prompt = "The current topic of conversation is quantum computing."
+user_input = "Can you explain it like I'm five?"
+
+# 2. Combine the prompts
+# We merge the system and context for the 'system' role
+combined_system = f"{system_prompt}\n\nContext: {context_prompt}"
+
+# 3. Stream the response
+stream = ollama.chat(
+    model='llama3.2', # or your preferred model
+    messages=[
+        {'role': 'system', 'content': combined_system},
+        {'role': 'user', 'content': user_input},
+    ],
+    stream=True,
+)
+
+for chunk in stream:
+    print(chunk['message']['content'], end='', flush=True)

tool_test_func.py

@@ -0,0 +1,47 @@
+# TOOL USE TEST
+
+import ollama
+
+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=[
+            {'role': 'system', 'content': combined_system},
+            {'role': 'user', 'content': 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
+
+def get_file_content(filename):
+    with open(filename, "r") as files:
+        bruh = files.read()
+    return bruh
+
+
+
+system_prompts = """You have access to a list. If you want to retrieve the content in the list, output EXACTLY:
+$DATABASE$
+
+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"
+
+prompt1 = llm_converse(system_prompts, context_prompts, user_inputs)
+if "$DATABASE$" in prompt1:
+    # DATABASE LOGIC TODO
+    pass
+
+print()  # newline after streaming
+print(full_response)  # your complete output