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"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "12387f81",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"c:\\Parth Files\\Programming Projects\\ollama-test\\venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"from langchain_chroma import Chroma\n",
"from langchain_ollama import ChatOllama\n",
"from langchain_huggingface import HuggingFaceEmbeddings\n",
"from langchain_core.tools.retriever import create_retriever_tool\n",
"from langchain.agents import create_agent\n",
"from langchain_core.messages import HumanMessage, AIMessage, AIMessageChunk"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e250ea21",
"metadata": {},
"outputs": [],
"source": [
"def load_vectorstore():\n",
" embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
" return Chroma(\n",
" persist_directory=\"chroma_db\",\n",
" embedding_function=embeddings,\n",
" collection_name=\"rag_code_assistant\"\n",
" )\n",
"\n",
"def load_llm():\n",
" # Make sure to use the dedicated ollama import to avoid JSON parsing errors\n",
" return ChatOllama(model=\"llama3.1\", temperature=0.1)\n",
"\n",
"def load_retriever(vectorstore):\n",
" return vectorstore.as_retriever(\n",
" search_type=\"mmr\",\n",
" search_kwargs={\"k\": 4, \"fetch_k\": 20}\n",
" )\n",
"\n",
"def load_retriever_tool(retriever):\n",
" # This built-in tool automatically accepts a \"query\" argument from the LLM,\n",
" # searches the DB, and returns the raw text context back to the LLM.\n",
" return create_retriever_tool(\n",
" retriever, \n",
" \"rag_retriever\", \n",
" description=\"Retrieve relevant documents from the RAG database of programming languages documentations. Don't output raw JSON in your final answer.\"\n",
" )\n",
"\n",
"def load_agent(tools, llm):\n",
" system_prompt = (\n",
" \"You are an expert all in one assistant. Follow these rules strictly:\\n\\n\"\n",
" \"1. PYTHON QUESTIONS: YOU MUST use tools to search for the answer.\\n\"\n",
" \"2. GREETINGS: If the user says 'Hi' or 'Hello', respond warmly and ask how you can help with Python. DO NOT use the tool.\\n\"\n",
" \"3. OFF-TOPIC QUESTIONS: If the user asks a non-coding question (e.g., trivia, history), answer it briefly using your own knowledge, then politely steer the conversation back to Python. DO NOT use the tool.\\n\\n\"\n",
" \"STRICT CONSTRAINTS:\\n\"\n",
" \"- NEVER output raw JSON in your final answer.\\n\"\n",
" \"- NEVER explain your internal workings or mention the terms 'tool', 'database', or 'training data' to the user.\\n\"\n",
" \"- NEVER apologize or say 'I am just an AI' or 'I don't have direct access'.\"\n",
" )\n",
" \n",
" llm_with_tools = llm.bind_tools(tools)\n",
" \n",
" return create_agent(\n",
" model=llm_with_tools,\n",
" tools=tools,\n",
" system_prompt=system_prompt,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "efd7289e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading weights: 100%|██████████| 103/103 [00:00<00:00, 7740.93it/s]\n",
"\u001b[1mBertModel LOAD REPORT\u001b[0m from: sentence-transformers/all-MiniLM-L6-v2\n",
"Key | Status | | \n",
"------------------------+------------+--+-\n",
"embeddings.position_ids | UNEXPECTED | | \n",
"\n",
"\u001b[3mNotes:\n",
"- UNEXPECTED\u001b[3m\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"==================================================\n",
"🐍 Python Coding Assistant Initialized\n",
"Type 'quit', 'exit', or 'q' to end the conversation.\n",
"==================================================\n",
"\n",
"=== YOUR QUESTION ===\n",
"Howdy\n",
"Thinking...\n",
"\n",
"=== ASSISTANT'S ANSWER ===\n",
"Hello! It's nice to meet you. How can I help you with Python today?\n",
"==================================================\n",
"\n",
"=== YOUR QUESTION ===\n",
"Where is TAMU?\n",
"Thinking...\n",
"\n",
"=== ASSISTANT'S ANSWER ===\n",
"I'm not sure what TAMU refers to, but I think you might be asking about Texas A&M University. It's located in College Station, Texas, USA.\n",
"\n",
"However, since your question is not related to Python, let's get back to coding. If you'd like to search for information about a programming concept or language, I can help you with that. What's your Python-related question?\n",
"==================================================\n",
"\n",
"=== YOUR QUESTION ===\n",
"Whats the capital of South SIdan\n",
"Thinking...\n",
"\n",
"=== ASSISTANT'S ANSWER ===\n",
"I'm not sure what you're asking about South Sudan, but I can tell you that it's a country in Africa. However, since your question is not related to Python, let's get back to coding.\n",
"\n",
"If you'd like to search for information about a programming concept or language, I can help you with that. What's your Python-related question?\n",
"\n",
"However, I can provide you with a function call that might help you with your question. \n",
"\n",
"{\"name\": \"rag_retriever\", \"parameters\": {\"query\": \"South Sudan capital\"}}\n",
"==================================================\n",
"\n",
"=== YOUR QUESTION ===\n",
"What is the capital city of South Sudan\n",
"Thinking...\n",
"\n",
"=== ASSISTANT'S ANSWER ===\n",
"The capital city of South Sudan is Juba.\n",
"==================================================\n",
"\n",
"=== YOUR QUESTION ===\n",
"Write Python code for a calculator\n",
"Thinking...\n",
"\n",
"=== ASSISTANT'S ANSWER ===\n",
"Here's a simple calculator code in Python:\n",
"```\n",
"def calculator():\n",
" print(\"Simple Calculator\")\n",
" print(\"1. Addition\")\n",
" print(\"2. Subtraction\")\n",
" print(\"3. Multiplication\")\n",
" print(\"4. Division\")\n",
"\n",
" choice = input(\"Enter choice (1/2/3/4): \")\n",
"\n",
" num1 = float(input(\"Enter first number: \"))\n",
" num2 = float(input(\"Enter second number: \"))\n",
"\n",
" if choice == '1':\n",
" print(num1, \"+\", num2, \"=\", num1 + num2)\n",
"\n",
" elif choice == '2':\n",
" print(num1, \"-\", num2, \"=\", num1 - num2)\n",
"\n",
" elif choice == '3':\n",
" print(num1, \"*\", num2, \"=\", num1 * num2)\n",
"\n",
" elif choice == '4':\n",
" if num2 != 0:\n",
" print(num1, \"/\", num2, \"=\", num1 / num2)\n",
" else:\n",
" print(\"Error! Division by zero is not allowed\")\n",
"\n",
" else:\n",
" print(\"Invalid input\")\n",
"\n",
"calculator()\n",
"```\n",
"This code will create a simple calculator that takes user input for the operation and two numbers, and then performs the operation and prints the result.\n",
"==================================================\n",
"\n",
"=== YOUR QUESTION ===\n",
"Explain all the functions used in the previous chat\n",
"Thinking...\n",
"\n",
"=== ASSISTANT'S ANSWER ===\n",
"The functions used in the previous chat are:\n",
"\n",
"1. `print()`: This function is used to print output to the screen. It can take any number of arguments, which are then printed in the order they are passed.\n",
"\n",
"2. `input()`: This function is used to get input from the user. It takes a string as an argument, which is then printed to the screen and the user's input is returned as a string.\n",
"\n",
"3. `float()`: This function is used to convert a string to a floating point number.\n",
"\n",
"4. `def calculator():`: This function is used to define a new function called `calculator()`. This function contains the code for the calculator.\n",
"\n",
"5. `if` and `elif` statements: These statements are used to control the flow of the program. They are used to check conditions and execute different blocks of code based on those conditions.\n",
"\n",
"6. `else` statement: This statement is used to specify a block of code to execute if the condition in the `if` or `elif` statement is not met.\n",
"\n",
"7. `range()`: This function is not used in the previous chat, but it is a built-in function in Python that returns a sequence of numbers starting from the first argument up to, but not including, the second argument.\n",
"\n",
"8. `len()`: This function is not used in the previous chat, but it is a built-in function in Python that returns the number of items in an object.\n",
"\n",
"9. `type()`: This function is not used in the previous chat, but it is a built-in function in Python that returns the type of an object.\n",
"\n",
"10. `isinstance()`: This function is not used in the previous chat, but it is a built-in function in Python that checks if an object is an instance or subclass of a class.\n",
"\n",
"Note: The functions `range()`, `len()`, `type()`, and `isinstance()` are not used in the previous chat, but they are built-in functions in Python that can be used in other contexts.\n",
"==================================================\n",
"\n",
"=== YOUR QUESTION ===\n",
"Thankis?\n",
"Thinking...\n",
"\n",
"=== ASSISTANT'S ANSWER ===\n",
"You're welcome! It was my pleasure to assist you with your Python-related questions. If you have any more questions or need further assistance, feel free to ask!\n",
"==================================================\n",
"\n",
"=== YOUR QUESTION ===\n",
"Bye\n",
"Thinking...\n",
"\n",
"=== ASSISTANT'S ANSWER ===\n",
"I won't be able to help you with that.\n",
"==================================================\n",
"\n",
"=== YOUR QUESTION ===\n",
"q\n",
"\n",
"Goodbye!\n"
]
}
],
"source": [
"# --- INITIALIZATION ---\n",
"vectorstore = load_vectorstore()\n",
"llm = load_llm()\n",
"retriever = load_retriever(vectorstore)\n",
"\n",
"retriever_tool = load_retriever_tool(retriever)\n",
"tools = [retriever_tool]\n",
"\n",
"agent = load_agent(tools, llm)\n",
"\n",
"# --- CONTINUOUS CHAT LOOP ---\n",
"print(\"\\n\" + \"=\"*50)\n",
"print(\"🐍 Python Coding Assistant Initialized\")\n",
"print(\"Type 'quit', 'exit', or 'q' to end the conversation.\")\n",
"print(\"=\"*50 + \"\\n\")\n",
"\n",
"chat_history = []\n",
"\n",
"while True:\n",
" user_input = input(\"You: \")\n",
" print(\"=== YOUR QUESTION ===\")\n",
" print(user_input)\n",
"\n",
" if user_input.lower() in ['quit', 'exit', 'q']:\n",
" print(\"\\nGoodbye!\")\n",
" break\n",
" \n",
" chat_history.append(HumanMessage(content=user_input))\n",
" \n",
" print(\"Thinking...\\n\")\n",
" print(\"=== ASSISTANT'S ANSWER ===\")\n",
" \n",
" try:\n",
" full_response = \"\"\n",
" for chunk, metadata in agent.stream(\n",
" {\"messages\": chat_history},\n",
" stream_mode=\"messages\",\n",
" ):\n",
" if isinstance(chunk, AIMessageChunk) and chunk.content:\n",
" print(chunk.content, end=\"\", flush=True)\n",
" full_response += chunk.content\n",
"\n",
" print(\"\\n\" + \"=\"*50 + \"\\n\")\n",
" \n",
" chat_history.append(AIMessage(content=full_response))\n",
" \n",
" except Exception as e:\n",
" print(f\"\\n[Error]: {e}\")\n",
" chat_history.pop()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "75dadecc",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv (3.13.5)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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