Simple-RAG-Agent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "264f53b9-2e4b-4a39-88ac-d1e33e703f3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b534b64d-e56c-4c63-bb93-62e048d6280a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename = 'Simple-RAG-Agent.jpeg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d69a9cad-8b72-49f7-8eca-37f695594dff",
   "metadata": {},
   "source": [
    "# Simple RAG Agent\n",
    "\n",
    "Author: [Kevin Thomas](mailto:ket189@pitt.edu)\n",
    "\n",
    "Source: [LlamaIndex](https://docs.llamaindex.ai/en/stable/understanding/rag)\n",
    "\n",
    "License: [Apache-2.0](https://github.com/mytechnotalent/Simple-RAG-Agent/blob/main/LICENSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80449fba-118d-4c25-93aa-0c463351385b",
   "metadata": {},
   "source": [
    "## Introduction to RAG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde35edc-d783-42e5-aa77-c2ce4edac41a",
   "metadata": {},
   "source": [
    "LLMs are trained on enormous bodies of data but they aren't trained on your data. Retrieval-Augmented Generation (RAG) solves this problem by adding your data to the data LLMs already have access to. You will see references to RAG frequently in this documentation. Query engines, chat engines and agents often use RAG to complete their tasks.\n",
    "\n",
    "In RAG, your data is loaded and prepared for queries or \"indexed\". User queries act on the index, which filters your data down to the most relevant context. This context and your query then go to the LLM along with a prompt, and the LLM provides a response.\n",
    "\n",
    "Even if what you're building is a chatbot or an agent, you'll want to know RAG techniques for getting data into your application."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6511fe3-e633-427b-8e57-b56fc6fca6c5",
   "metadata": {},
   "source": [
    "### Stages within RAG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61fd80b1-292a-43a5-aee6-86f75eb9dcb1",
   "metadata": {},
   "source": [
    "There are five key stages within RAG, which in turn will be a part of most larger applications you build. These are:\n",
    "\n",
    "* **Loading**: this refers to getting your data from where it lives -- whether it's text files, PDFs, another website, a database, or an API -- into your workflow. LlamaHub provides hundreds of connectors to choose from.\n",
    "* **Indexing**: this means creating a data structure that allows for querying the data. For LLMs this nearly always means creating vector embeddings, numerical representations of the meaning of your data, as well as numerous other metadata strategies to make it easy to accurately find contextually relevant data.\n",
    "* **Storing**: once your data is indexed you will almost always want to store your index, as well as other metadata, to avoid having to re-index it.\n",
    "* **Querying**: for any given indexing strategy there are many ways you can utilize LLMs and LlamaIndex data structures to query, including sub-queries, multi-step queries and hybrid strategies.\n",
    "* **Evaluation**: a critical step in any flow is checking how effective it is relative to other strategies, or when you make changes. Evaluation provides objective measures of how accurate, faithful and fast your responses to queries are."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d8397d5-50c1-4072-bc54-cdfb0f4355b2",
   "metadata": {},
   "source": [
    "### Important Concepts within RAG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "973b4cce-a315-449d-aed2-aaf3c1e185da",
   "metadata": {},
   "source": [
    "There are also some terms you'll encounter that refer to steps within each of these stages.\n",
    "\n",
    "**Loading Stage**\n",
    "1. **Nodes and Documents**: A Document is a container around any data source - for instance, a PDF, an API output, or retrieve data from a database. A Node is the atomic unit of data in LlamaIndex and represents a \"chunk\" of a source Document. Nodes have metadata that relate them to the document they are in and to other nodes.\n",
    "2. **Connectors**: A data connector (often called a Reader) ingests data from different data sources and data formats into Documents and Nodes.\n",
    "\n",
    "**Indexing Stage**\n",
    "1. **Indexes**: Once you've ingested your data, LlamaIndex will help you index the data into a structure that's easy to retrieve. This usually involves generating vector embeddings which are stored in a specialized database called a vector store. Indexes can also store a variety of metadata about your data.\n",
    "2. **Embeddings**: LLMs generate numerical representations of data called embeddings. When filtering your data for relevance, LlamaIndex will convert queries into embeddings, and your vector store will find data that is numerically similar to the embedding of your query.\n",
    "\n",
    "**Querying Stage**\n",
    "1. **Retrievers**: A retriever defines how to efficiently retrieve relevant context from an index when given a query. Your retrieval strategy is key to the relevancy of the data retrieved and the efficiency with which it's done.\n",
    "2. **Routers**: A router determines which retriever will be used to retrieve relevant context from the knowledge base. More specifically, the RouterRetriever class, is responsible for selecting one or multiple candidate retrievers to execute a query. They use a selector to choose the best option based on each candidate's metadata and the query.\n",
    "3. **Node Postprocessors**: A node postprocessor takes in a set of retrieved nodes and applies transformations, filtering, or re-ranking logic to them.\n",
    "4. **Response Synthesizers**: A response synthesizer generates a response from an LLM, using a user query and a given set of retrieved text chunks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ea0373-a2bb-4bc4-82d9-60ed7e54b14c",
   "metadata": {},
   "source": [
    "## Loading Data (Ingestion)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9833af29-c95b-4b1c-9797-ff48fcc74eee",
   "metadata": {},
   "source": [
    "Before your chosen LLM can act on your data, you first need to process the data and load it. This has parallels to data cleaning/feature engineering pipelines in the ML world, or ETL pipelines in the traditional data setting.\n",
    "\n",
    "This ingestion pipeline typically consists of three main stages:\n",
    "1. Load the data\n",
    "2. Transform the data\n",
    "3. Index and store the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a368dc70-2b24-4016-b29a-ef819e6be720",
   "metadata": {},
   "source": [
    "### Loaders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b62a0d6a-6bd4-43b9-a3af-84846adb81b1",
   "metadata": {},
   "source": [
    "Before your chosen LLM can act on your data you need to load it. The way LlamaIndex does this is via data connectors, also called `Reader`. Data connectors ingest data from different data sources and format the data into Document objects. A `Document` is a collection of data (currently text, and in future, images and audio) and metadata about that data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a80ecc2-d217-4875-a30d-91ce723493d6",
   "metadata": {},
   "source": [
    "#### Loading using `SimpleDirectoryReader`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937a48b9-05c1-4966-bc60-f272042b5060",
   "metadata": {},
   "source": [
    "The easiest reader to use is our `SimpleDirectoryReader`, which creates documents out of every file in a given directory. It is built in to LlamaIndex and can read a variety of formats including Markdown, PDFs, Word documents, PowerPoint decks, images, audio and video."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d60d77f1-c68b-4285-85d2-1b208ee6a606",
   "metadata": {},
   "source": [
    "```python\n",
    "documents = SimpleDirectoryReader(\"./data\").load_data()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff8cbbe4-9542-4d87-b572-1bc79db4cfba",
   "metadata": {},
   "source": [
    "### Transformations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "218da89e-a5b0-4104-aba4-09f5c79d0fd0",
   "metadata": {},
   "source": [
    "After the data is loaded, you then need to process and transform your data before putting it into a storage system. These transformations include chunking, extracting metadata, and embedding each chunk. This is necessary to make sure that the data can be retrieved, and used optimally by the LLM.\n",
    "\n",
    "Transformation input/outputs are `Node` objects (a `Document` is a subclass of a `Node`). Transformations can also be stacked and reordered.\n",
    "\n",
    "We have both a high-level and lower-level API for transforming documents."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c1175bc-6a17-4e81-9551-46ae928b8de4",
   "metadata": {},
   "source": [
    "#### High-Level Transformation API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0833256e-a458-4045-b1b4-0d35919efbe8",
   "metadata": {},
   "source": [
    "Indexes have a `.from_documents()` method which accepts an array of Document objects and will correctly parse and chunk them up. However, sometimes you will want greater control over how your documents are split up."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a159a05-e1e2-44b7-a1fe-d47591b23595",
   "metadata": {},
   "source": [
    "```python\n",
    "vector_index = VectorStoreIndex.from_documents(documents)\n",
    "query_engine = vector_index.as_query_engine()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "484d66fd-8f9f-42b0-8a72-e68ee9b14786",
   "metadata": {},
   "source": [
    "Under the hood, this splits your Document into Node objects, which are similar to Documents (they contain text and metadata) but have a relationship to their parent Document."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d48797f-b449-4c3b-969f-c8114d61be77",
   "metadata": {},
   "source": [
    "## Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbf11a28-e108-4dad-a768-75c30cf86ac6",
   "metadata": {},
   "source": [
    "With your data loaded, you now have a list of Document objects (or a list of Nodes). It's time to build an Index over these objects so you can start querying them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0404b422-734d-4daf-b421-d9e41690b187",
   "metadata": {},
   "source": [
    "### What is an Index?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb1dde3-ee5c-4560-b793-7e62e6205021",
   "metadata": {},
   "source": [
    "In LlamaIndex terms, an `Index` is a data structure composed of `Document` objects, designed to enable querying by an LLM. Your Index is designed to be complementary to your querying strategy.\n",
    "\n",
    "LlamaIndex offers several different index types. We'll cover the two most common here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d66a95a-a33e-4ea0-b1e6-9b52809c469f",
   "metadata": {},
   "source": [
    "### Vector Store Index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca43c34-3be9-48ce-bcc7-bbcaa296fa80",
   "metadata": {},
   "source": [
    "A `VectorStoreIndex` is by far the most frequent type of Index you'll encounter. The Vector Store Index takes your Documents and splits them up into Nodes. It then creates `vector` embeddings of the text of every node, ready to be queried by an LLM."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae3fb10-08e9-4bad-8326-0dd2408b0e7e",
   "metadata": {},
   "source": [
    "#### What is an embedding?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db732090-2d3e-4191-8c9f-f48ab1e507d3",
   "metadata": {},
   "source": [
    "A `vector embedding`, often just called an embedding, is a **numerical representation of the semantics, or meaning of your text**. Two pieces of text with similar meanings will have mathematically similar embeddings, even if the actual text is quite different.\n",
    "\n",
    "This mathematical relationship enables **semantic search**, where a user provides query terms and LlamaIndex can locate text that is related to the **meaning of the query terms** rather than simple keyword matching. This is a big part of how Retrieval-Augmented Generation works, and how LLMs function in general.\n",
    "\n",
    "There are many types of embeddings, and they vary in efficiency, effectiveness and computational cost. By default LlamaIndex uses `text-embedding-ada-002`, which is the default embedding used by OpenAI. If you are using different LLMs you will often want to use different embeddings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cadce10-79dc-49bf-8607-2eab0610d5f9",
   "metadata": {},
   "source": [
    "#### Vector Store Index Embeds Your Documents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9de7bbce-7b2a-493e-8375-51e1754d5894",
   "metadata": {},
   "source": [
    "Vector Store Index turns all of your text into embeddings using an API from your LLM; this is what is meant when we say it \"embeds your text\". If you have a lot of text, generating embeddings can take a long time since it involves many round-trip API calls.\n",
    "\n",
    "When you want to search your embeddings, your query is itself turned into a vector embedding, and then a mathematical operation is carried out by VectorStoreIndex to rank all the embeddings by how semantically similar they are to your query."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b26d0c8-a0f1-4b1b-9d47-7b1dcc1fb75d",
   "metadata": {},
   "source": [
    "#### Top K Retrieval"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9916c107-b25f-4971-ae89-c9148a0da8d2",
   "metadata": {},
   "source": [
    "Once the ranking is complete, VectorStoreIndex returns the most-similar embeddings as their corresponding chunks of text. The number of embeddings it returns is known as `k`, so the parameter controlling how many embeddings to return is known as `top_k`. This whole type of search is often referred to as \"top-k semantic retrieval\" for this reason.\n",
    "\n",
    "Top-k retrieval is the simplest form of querying a vector index."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c17f44a-8c80-4e5c-936c-1e26eaccf06c",
   "metadata": {},
   "source": [
    "#### Using Vector Store Index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c473ed0b-1048-4875-96a5-ffa57a45d7a7",
   "metadata": {},
   "source": [
    "To use the Vector Store Index, pass it the list of Documents you created during the loading stage:\n",
    "```python\n",
    "index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)\n",
    "```\n",
    "With your text indexed, it is now technically ready for querying! However, embedding all your text can be time-consuming and, if you are using a hosted LLM, it can also be expensive. To save time and money you will want to store your embeddings first."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b94688e9-0eca-42cc-8a1a-e0761ddbd9c6",
   "metadata": {},
   "source": [
    "### Summary Index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "587bed9f-b01d-4e1a-b808-b3e45ff8f51d",
   "metadata": {},
   "source": [
    "A Summary Index is a simpler form of Index best suited to queries where, as the name suggests, you are trying to generate a summary of the text in your Documents. It simply stores all of the Documents and returns all of them to your query engine."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2846f7f2-7ae7-4cea-a01b-3332aa601ae5",
   "metadata": {},
   "source": [
    "## Querying"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6efc700-c23a-40b3-ad51-2ec347fe3da6",
   "metadata": {},
   "source": [
    "Now you've loaded your data, built an index, you're ready to get to the most significant part of an LLM application: querying.\n",
    "\n",
    "At its simplest, querying is just a prompt call to an LLM: it can be a question and get an answer, or a request for summarization, or a much more complex instruction.\n",
    "\n",
    "More complex querying could involve repeated/chained prompt + LLM calls, or even a reasoning loop across multiple components."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c27b7e7d-ff7e-47ee-bbbf-4b6b41a67532",
   "metadata": {},
   "source": [
    "### Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "968fadab-648d-4ec8-8727-92c6a974ae6d",
   "metadata": {},
   "source": [
    "The basis of all querying is the QueryEngine. The simplest way to get a `QueryEngine` is to get your index to create one for you, like this:\n",
    "```python\n",
    "query_engine = index.as_query_engine()\n",
    "\n",
    "response = query_engine.query(\n",
    "    \"What are the signs of Lung Cancer?\"\n",
    ")\n",
    "print(response)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b07d7542-647c-4180-a09e-61a72e30fd4a",
   "metadata": {},
   "source": [
    "### Stages of Querying"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "430b1bf4-d0c5-411e-a792-e813859a771d",
   "metadata": {},
   "source": [
    "However, there is more to querying than initially meets the eye. Querying consists of three distinct stages:\n",
    "\n",
    "* **Retrieval** is when you find and return the most relevant documents for your query from your `Index`. As previously discussed in indexing, the most common type of retrieval is \"top-k\" semantic retrieval, but there are many other retrieval strategies.\n",
    "* **Postprocessing** is when the Nodes retrieved are optionally reranked, transformed, or filtered, for instance by requiring that they have specific metadata such as keywords attached.\n",
    "* **Response Synthesis** is when your query, your most-relevant data and your prompt are combined and sent to your LLM to return a response."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c0fb46-4c21-4226-b884-9c503eac6ad6",
   "metadata": {},
   "source": [
    "## Create Basic Tools"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c91da31f-42ad-467d-8d84-4efcc03e90b2",
   "metadata": {},
   "source": [
    "For this simple example we'll be creating two tools: one that calculates the factorial of a number and the other to check if a number is prime."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deafe4bf-e816-431c-b6bf-7e657764edd5",
   "metadata": {},
   "source": [
    "```python\n",
    "def factorial(n: int) -> int:\n",
    "    \"\"\"Calculate the factorial of a number.\"\"\"\n",
    "    if n == 0:\n",
    "        return 1\n",
    "    return n * factorial(n - 1)\n",
    "\n",
    "\n",
    "def is_prime(n: int) -> bool:\n",
    "    \"\"\"Check if a number is prime.\"\"\"\n",
    "    if n <= 1:\n",
    "        return False\n",
    "    for i in range(2, int(n**0.5) + 1):\n",
    "        if n % i == 0:\n",
    "            return False\n",
    "    return True\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a2630aa-949a-4fa0-91e8-14b2ee790461",
   "metadata": {},
   "source": [
    "As you can see, these are regular vanilla Python functions. The docstring comments provide metadata to the agent about what the tool does: if your LLM is having trouble figuring out which tool to use, these docstrings are what you should tweak first.\n",
    "\n",
    "After each function is defined we create `FunctionTool` objects from these functions, which wrap them in a way that the agent can understand."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ce6238-00ed-482d-92bd-ec5bbf39b41f",
   "metadata": {},
   "source": [
    "```python\n",
    "factorial_tool = FunctionTool.from_defaults(fn=factorial)\n",
    "is_prime_tool = FunctionTool.from_defaults(fn=is_prime)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7590a9a2-9ee3-4077-9197-c128b1fb786f",
   "metadata": {},
   "source": [
    "## Initialize the LLM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c65786fc-f431-4c16-b7b1-65a28f44f643",
   "metadata": {},
   "source": [
    "We will use the `mixtral:8x7b` open-source model with Ollama."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33911ff8-900b-4b35-9afb-3716ba2daded",
   "metadata": {},
   "source": [
    "```python\n",
    "llm = Ollama(model=\"mixtral:8x7b\", request_timeout=300.0)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1fc511-93b0-4df5-9d93-f060018312d8",
   "metadata": {},
   "source": [
    "## Initialize the Agent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eedc9cef-39aa-4f5a-ae26-1c0d59f91503",
   "metadata": {},
   "source": [
    "Now we create our agent. In this case, this is a `ReAct` agent, a relatively simple but powerful agent. We give it an array containing our two tools, the LLM we just created, and set `verbose=True` so we can see what's going on."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "233146cc-f2ad-4380-8ef2-0fa088472a0b",
   "metadata": {},
   "source": [
    "```python\n",
    "agent = ReActAgent.from_tools(\n",
    "    [\n",
    "        factorial_tool, \n",
    "        is_prime_tool\n",
    "    ], \n",
    "    llm=llm, \n",
    "    verbose=True, \n",
    "    max_iterations=10  # increase from the default (usually 3-5)\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a9b9276-c191-4c87-be1c-b2747e4ae406",
   "metadata": {},
   "source": [
    "## Ask a Question"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c474bfd6-f458-4d83-a2bf-31e3e128689a",
   "metadata": {},
   "source": [
    "We specify that it should use a tool, as this is pretty simple and `mixtral:8x7b` doesn't really need this tool to get the answer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11f8c236-b179-407e-ab5d-f0ce3e578eb9",
   "metadata": {},
   "source": [
    "```python\n",
    "response = agent.chat(\"Lung cancer is the leading cause of cancer deaths worldwide. If a patient diagnosed with lung cancer has undergone 5 treatment cycles, what is the factorial of the number of cycles completed? Also, is the number of lung cancer stages (usually 4) a prime number? Calculate step by step detailing all of your thought process and make sure you use the tools provided.\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991fca17-88ba-4415-b3b4-0ef06e1840df",
   "metadata": {},
   "source": [
    "```python\n",
    "response = agent.chat(\"Lung cancer treatments often involve a series of diagnostic tests to determine the most effective approach. If there are 6 diagnostic tests available and the oncologist wants to explore all possible ways to sequence them, what is the total number of permutations? Also, given that lung cancer affects approximately 1 in 14 people during their lifetime, is the number 14 a prime number? Calculate step by step detailing all of your thought process and make sure you use the tools provided.\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "259fedfc-1a77-4d3b-aff9-1f9d3493a120",
   "metadata": {},
   "source": [
    "## Install Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f5773a5f-66f7-4e9e-86fd-f305c114919d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Requirement already satisfied: pypdf<6.0.0,>=5.1.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from llama-index-readers-file) (5.1.0)\n",
      "Requirement already satisfied: striprtf<0.0.27,>=0.0.26 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from llama-index-readers-file) (0.0.26)\n",
      "Requirement already satisfied: ollama>=0.3.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from llama-index-llms-ollama) (0.4.2)\n",
      "Requirement already satisfied: huggingface-hub>=0.19.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (0.26.2)\n",
      "Requirement already satisfied: sentence-transformers>=2.6.1 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from llama-index-embeddings-huggingface) (3.3.0)\n",
      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from aiohttp<4.0.0,>=3.8.6->llama-index-core) (2.4.0)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from aiohttp<4.0.0,>=3.8.6->llama-index-core) (1.2.0)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from aiohttp<4.0.0,>=3.8.6->llama-index-core) (23.1.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from aiohttp<4.0.0,>=3.8.6->llama-index-core) (1.4.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from aiohttp<4.0.0,>=3.8.6->llama-index-core) (6.0.4)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from aiohttp<4.0.0,>=3.8.6->llama-index-core) (1.11.0)\n",
      "Requirement already satisfied: soupsieve>1.2 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from beautifulsoup4<5.0.0,>=4.12.3->llama-index-readers-file) (2.5)\n",
      "Requirement already satisfied: filelock in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from huggingface-hub>=0.19.0->huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (3.13.1)\n",
      "Requirement already satisfied: packaging>=20.9 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from huggingface-hub>=0.19.0->huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (24.1)\n",
      "Requirement already satisfied: click in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from nltk>3.8.1->llama-index-core) (8.1.7)\n",
      "Requirement already satisfied: joblib in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from nltk>3.8.1->llama-index-core) (1.4.2)\n",
      "Requirement already satisfied: regex>=2021.8.3 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from nltk>3.8.1->llama-index-core) (2024.9.11)\n",
      "Requirement already satisfied: anyio in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from httpx->llama-index-core) (4.2.0)\n",
      "Requirement already satisfied: certifi in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from httpx->llama-index-core) (2024.8.30)\n",
      "Requirement already satisfied: httpcore==1.* in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from httpx->llama-index-core) (1.0.2)\n",
      "Requirement already satisfied: idna in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from httpx->llama-index-core) (3.7)\n",
      "Requirement already satisfied: sniffio in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from httpx->llama-index-core) (1.3.0)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from httpcore==1.*->httpx->llama-index-core) (0.14.0)\n",
      "Requirement already satisfied: annotated-types>=0.6.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from pydantic<2.10.0,>=2.7.0->llama-index-core) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.23.4 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from pydantic<2.10.0,>=2.7.0->llama-index-core) (2.23.4)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from requests>=2.31.0->llama-index-core) (3.3.2)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from requests>=2.31.0->llama-index-core) (2.2.3)\n",
      "Requirement already satisfied: transformers<5.0.0,>=4.41.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (4.46.2)\n",
      "Requirement already satisfied: torch>=1.11.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (2.5.1)\n",
      "Requirement already satisfied: scikit-learn in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (1.5.1)\n",
      "Requirement already satisfied: scipy in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (1.13.1)\n",
      "Requirement already satisfied: greenlet!=0.4.17 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from SQLAlchemy[asyncio]>=1.4.49->llama-index-core) (3.0.1)\n",
      "Requirement already satisfied: mypy-extensions>=0.3.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from typing-inspect>=0.8.0->llama-index-core) (1.0.0)\n",
      "Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from dataclasses-json->llama-index-core) (3.23.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from pandas->llama-index-readers-file) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from pandas->llama-index-readers-file) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from pandas->llama-index-readers-file) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas->llama-index-readers-file) (1.16.0)\n",
      "Requirement already satisfied: jinja2 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch>=1.11.0->sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (3.1.4)\n",
      "Requirement already satisfied: setuptools in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch>=1.11.0->sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (75.1.0)\n",
      "Requirement already satisfied: sympy==1.13.1 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from torch>=1.11.0->sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (1.13.1)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from sympy==1.13.1->torch>=1.11.0->sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (1.3.0)\n",
      "Requirement already satisfied: safetensors>=0.4.1 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (0.4.5)\n",
      "Requirement already satisfied: tokenizers<0.21,>=0.20 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (0.20.3)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from scikit-learn->sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (3.5.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/anaconda3/envs/prod/lib/python3.12/site-packages (from jinja2->torch>=1.11.0->sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (2.1.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install llama-index-core llama-index-readers-file llama-index-llms-ollama llama-index-embeddings-huggingface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ffa9ba6-eb8c-4d8d-baa6-883e515ce66d",
   "metadata": {},
   "source": [
    "## Install & Run Ollama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d08c3a99-da97-4216-b0c9-e6f46bbe5732",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected macOS. Checking if Ollama is installed...\n",
      "Ollama is already installed.\n",
      "Managing Ollama process...\n",
      "Starting ollama serve in the background...\n",
      "ollama serve is now running in the background.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import platform\n",
    "import subprocess\n",
    "\n",
    "\n",
    "def install_and_manage_ollama():\n",
    "    \"\"\"Install and manage Ollama on various OS platforms.\"\"\"\n",
    "\n",
    "    # detect system\n",
    "    system = platform.system()\n",
    "\n",
    "    try:\n",
    "        # check if Ollama is installed\n",
    "        if system == \"Darwin\":\n",
    "            print(\"Detected macOS. Checking if Ollama is installed...\")\n",
    "            if subprocess.run(['which', 'ollama'], stdout=subprocess.PIPE, stderr=subprocess.PIPE).returncode != 0:\n",
    "                print(\"Installing Ollama on macOS using Homebrew...\")\n",
    "                os.system(\"brew install ollama\")\n",
    "            else:\n",
    "                print(\"Ollama is already installed.\")\n",
    "        elif system == \"Linux\":\n",
    "            print(\"Detected Linux. Checking if Ollama is installed...\")\n",
    "            if subprocess.run(['which', 'ollama'], stdout=subprocess.PIPE, stderr=subprocess.PIPE).returncode != 0:\n",
    "                print(\"Installing Ollama on Linux...\")\n",
    "                os.system(\"curl -sSL https://ollama.com/install | sh\")\n",
    "            else:\n",
    "                print(\"Ollama is already installed.\")\n",
    "        elif system == \"Windows\":\n",
    "            print(\"Detected Windows.\")\n",
    "            print(\"Please download and install Ollama manually from https://ollama.com.\")\n",
    "            return\n",
    "        else:\n",
    "            print(\"Unsupported operating system. Exiting.\")\n",
    "            return\n",
    "\n",
    "        # start a new Ollama process if Mac or Linux\n",
    "        print(\"Managing Ollama process...\")\n",
    "        if system in [\"Darwin\", \"Linux\"]:\n",
    "            print(\"Starting ollama serve in the background...\")\n",
    "            subprocess.Popen(['ollama', 'serve'], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, preexec_fn=os.setpgrp)\n",
    "            print(\"ollama serve is now running in the background.\")\n",
    "        else:\n",
    "            print(\"Automatic management of Ollama is not supported on Windows. Please run Ollama manually.\")\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred: {e}\")\n",
    "        return\n",
    "\n",
    "\n",
    "# run the function\n",
    "install_and_manage_ollama()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf35e15e-e19d-4c09-9022-c67b1b8a87fd",
   "metadata": {},
   "source": [
    "## Obtain `mixtral:8x7b` Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1c56cb55-98a6-4676-803e-c802ee4dde8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected Darwin. Running ollama mixtral:8x7b...\n",
      "Command executed successfully.\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "import subprocess\n",
    "\n",
    "\n",
    "def run_ollama_mixtral():\n",
    "    \"\"\"Obtain mixtral:8x7b model from Ollama.\"\"\"\n",
    "\n",
    "    # detect system\n",
    "    system = platform.system()\n",
    "\n",
    "    # detect and obtain mixtral:8x7b model on respective OS platform\n",
    "    try:\n",
    "        if system in [\"Darwin\", \"Linux\"]:  \n",
    "            print(f\"Detected {system}. Running ollama mixtral:8x7b...\")\n",
    "            if subprocess.run(['which', 'ollama'], stdout=subprocess.PIPE, stderr=subprocess.PIPE).returncode != 0:\n",
    "                print(\"Ollama is not installed. Please install it and try again.\")\n",
    "                return\n",
    "            result = subprocess.run(['ollama', 'run', 'mixtral:8x7b'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)\n",
    "            if result.returncode == 0:\n",
    "                print(\"Command executed successfully.\")\n",
    "            else:\n",
    "                print(\"Failed to execute the command:\")\n",
    "                print(result.stderr)\n",
    "        elif system == \"Windows\":\n",
    "            print(\"Detected Windows.\")\n",
    "            print(\"Please run the following command manually in your terminal:\")\n",
    "            print(\"`ollama run mixtral:8x7b`\")\n",
    "            return\n",
    "        else:\n",
    "            print(\"Unsupported operating system. Exiting.\")\n",
    "            return\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred: {e}\")\n",
    "        return\n",
    "\n",
    "\n",
    "# run the function\n",
    "run_ollama_mixtral()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1eece2-25c6-4298-b926-aa89a427c2ff",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "55680921-347a-4bb6-8a2c-9b73bfc862d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, Settings\n",
    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
    "from llama_index.llms.ollama import Ollama"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f236b302-3b7c-407e-8ebe-651847c90ff5",
   "metadata": {},
   "source": [
    "## Declare LLM `mixtral:8x7b`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b70d55cc-0f7b-4241-bd3a-d4fff49c45aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = Ollama(model=\"mixtral:8x7b\", request_timeout=360.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422a86b5-b017-4a92-88f1-5bfc4aaf41ab",
   "metadata": {},
   "source": [
    "## Declare Embedding Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "536fb918-aae4-4630-9d9a-c33a422f62ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_model = HuggingFaceEmbedding(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267aef09-a15b-45ee-bf68-c45f3d9068c1",
   "metadata": {},
   "source": [
    "## Set Global Settings for the LLM and Embedding Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ae5e215-35af-4f54-a610-7dfe2df99928",
   "metadata": {},
   "outputs": [],
   "source": [
    "Settings.llm = llm\n",
    "Settings.embed_model = embed_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5b513b-55ef-4864-9467-846e80c7a7d7",
   "metadata": {},
   "source": [
    "## Read Documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7db80282-36ab-4592-9541-a2c4cbc87202",
   "metadata": {},
   "outputs": [],
   "source": [
    "documents = SimpleDirectoryReader(\"./data\").load_data()\n",
    "index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)\n",
    "query_engine = index.as_query_engine()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f207190-78f7-476b-b9e4-5eef45dc9f29",
   "metadata": {},
   "source": [
    "## Initial Query Documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8790df5a-566d-4e43-84ce-99ee952cd081",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The signs of lung cancer include cough, hemoptysis, dyspnea, chest pain, recurrent or slowly resolving pneumonia, lethargy, anorexia, and weight loss. Additionally, a person with lung cancer may experience cachexia, anemia, clubbing, hypertrophic pulmonary osteoarthropathy (HPOA), supraclavicular or axillary nodes, and chest signs such as none, consolidation, collapse, pleural effusion. Metastases can also occur, leading to bone tenderness, hepatomegaly, confusion, seizures, focal CNS signs, cerebellar syndrome, proximal myopathy, and peripheral neuropathy.\n"
     ]
    }
   ],
   "source": [
    "response = query_engine.query(\n",
    "    \"What are the signs of Lung Cancer?\"\n",
    ")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b257c3-0267-4977-8793-182035d969b4",
   "metadata": {},
   "source": [
    "## Instantiate LLM, Register Tools w/ `ReactAgent`, Instantiate `agent` Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "63f59683-c55b-4f8e-b4a1-2789bf317d7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.agent import ReActAgent\n",
    "from llama_index.llms.ollama import Ollama\n",
    "from llama_index.core.tools import FunctionTool\n",
    "\n",
    "\n",
    "def factorial(n: int) -> int:\n",
    "    \"\"\"Calculate the factorial of a number.\"\"\"\n",
    "\n",
    "    if n == 0:\n",
    "        return 1\n",
    "    return n * factorial(n - 1)\n",
    "\n",
    "\n",
    "def is_prime(n: int) -> bool:\n",
    "    \"\"\"Check if a number is prime.\"\"\"\n",
    "\n",
    "    if n <= 1:\n",
    "        return False\n",
    "    for i in range(2, int(n**0.5) + 1):\n",
    "        if n % i == 0:\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "\n",
    "factorial_tool = FunctionTool.from_defaults(fn=factorial)\n",
    "is_prime_tool = FunctionTool.from_defaults(fn=is_prime)\n",
    "\n",
    "llm = Ollama(model=\"mixtral:8x7b\", request_timeout=300.0)\n",
    "\n",
    "agent = ReActAgent.from_tools(\n",
    "    [\n",
    "        factorial_tool, \n",
    "        is_prime_tool\n",
    "    ], \n",
    "    llm=llm, \n",
    "    verbose=True, \n",
    "    max_iterations=10  # increase from the default (usually 3-5)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d484e6a6-b76a-47a4-a4d5-7fd4b139906f",
   "metadata": {},
   "source": [
    "## Chat w/ RAG Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f66f5a5f-c7a9-410b-a93c-f3e650dc09b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> Running step 0c690c36-173f-43dd-8a49-b05489065f45. Step input: Lung cancer is the leading cause of cancer deaths worldwide. If a patient diagnosed with lung cancer has undergone 5 treatment cycles, what is the factorial of the number of cycles completed? Also, is the number of lung cancer stages (usually 4) a prime number? Calculate step by step detailing all of your thought process and make sure you use the tools provided.\n",
      "\u001b[1;3;38;5;200mThought: The user has asked for two pieces of information. First, they want to know the factorial of the number of lung cancer treatment cycles a patient has undergone, which is 5. Second, they want to know if the number of lung cancer stages (usually 4) is a prime number. I will use the 'factorial' tool to find the factorial of 5 and the 'is_prime' tool to check if 4 is a prime number.\n",
      "Action: factorial\n",
      "Action Input: {'n': 5}\n",
      "\u001b[0m\u001b[1;3;34mObservation: 120\n",
      "\u001b[0m> Running step a6682c95-2f43-4793-b8a6-a3c69b119459. Step input: None\n",
      "\u001b[1;3;38;5;200mThought: The 'factorial' tool has returned 120, which is the factorial of 5. So, the factorial of the number of lung cancer treatment cycles a patient has undergone, which is 5, is 120.\n",
      "Action: is_prime\n",
      "Action Input: {'n': 4}\n",
      "\u001b[0m\u001b[1;3;34mObservation: False\n",
      "\u001b[0m> Running step 7660fab9-af86-4a98-8eed-a91b94034af7. Step input: None\n",
      "\u001b[1;3;38;5;200mThought: The 'is_prime' tool has returned False, which means 4 is not a prime number. So, the number of lung cancer stages (usually 4) is not a prime number.\n",
      "Answer: The factorial of the number of lung cancer treatment cycles a patient has undergone, which is 5, is 120. The number of lung cancer stages, usually 4, is not a prime number.\n",
      "\u001b[0mThe factorial of the number of lung cancer treatment cycles a patient has undergone, which is 5, is 120. The number of lung cancer stages, usually 4, is not a prime number.\n"
     ]
    }
   ],
   "source": [
    "response = agent.chat(\"Lung cancer is the leading cause of cancer deaths worldwide. If a patient diagnosed with lung cancer has undergone 5 treatment cycles, what is the factorial of the number of cycles completed? Also, is the number of lung cancer stages (usually 4) a prime number? Calculate step by step detailing all of your thought process and make sure you use the tools provided.\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb43970e-5011-4f0e-825e-265038b60f55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> Running step cf5f43cf-dfd9-43ca-896a-b2908df0affb. Step input: Lung cancer treatments often involve a series of diagnostic tests to determine the most effective approach. If there are 6 diagnostic tests available and the oncologist wants to explore all possible ways to sequence them, what is the total number of permutations? Also, given that lung cancer affects approximately 1 in 14 people during their lifetime, is the number 14 a prime number? Calculate step by step detailing all of your thought process and make sure you use the tools provided.\n",
      "\u001b[1;3;38;5;200mThought: I need to find the factorial of a number using the tool 'factorial'.\n",
      "Action: factorial\n",
      "Action Input: {'n': 6}\n",
      "\u001b[0m\u001b[1;3;34mObservation: 720\n",
      "\u001b[0m> Running step 1f0d52c1-0177-445a-bd70-4767ff74218b. Step input: None\n",
      "\u001b[1;3;38;5;200mThought: The total number of permutations for sequencing 6 diagnostic tests is 720. Now, I need to determine if the number 14 is prime using the tool 'is_prime'.\n",
      "Action: is\\_prime\n",
      "Action Input: {'n': 14}\n",
      "\u001b[0m\u001b[1;3;34mObservation: Error: No such tool named `is\\_prime`.\n",
      "\u001b[0m> Running step 9d54656b-1286-4299-bb2e-669d9f3cca7b. Step input: None\n",
      "\u001b[1;3;38;5;200mThought: It seems there is no tool with the name 'is\\_prime' to check if a number is prime. I will now determine if the number 14 is prime manually. A prime number is only divisible by 1 and itself, but 14 can be divided by 2 and 7 without leaving a remainder. Therefore, the number 14 is not a prime number.\n",
      "Answer: The total number of permutations for sequencing 6 diagnostic tests is 720. However, the tool to check if a number is prime is not available, but I can manually determine that the number 14 is not a prime number as it's divisible by 2 and 7 without leaving a remainder.\n",
      "\u001b[0mThe total number of permutations for sequencing 6 diagnostic tests is 720. However, the tool to check if a number is prime is not available, but I can manually determine that the number 14 is not a prime number as it's divisible by 2 and 7 without leaving a remainder.\n"
     ]
    }
   ],
   "source": [
    "response = agent.chat(\"Lung cancer treatments often involve a series of diagnostic tests to determine the most effective approach. If there are 6 diagnostic tests available and the oncologist wants to explore all possible ways to sequence them, what is the total number of permutations? Also, given that lung cancer affects approximately 1 in 14 people during their lifetime, is the number 14 a prime number? Calculate step by step detailing all of your thought process and make sure you use the tools provided.\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb9ba5e4-fb3a-46af-a2f8-5874d9417550",
   "metadata": {},
   "source": [
    "## Terminate Ollama Instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "013cd991-81c3-4b3d-bab5-0ee477eaaa31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found running Ollama processes with PIDs: 94849, 95452. Killing them...\n",
      "Killed process with PID: 94849\n",
      "Killed process with PID: 95452\n",
      "Waiting for processes to terminate...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import platform\n",
    "import subprocess\n",
    "import time\n",
    "\n",
    "\n",
    "def kill_existing_ollama():\n",
    "    \"\"\"Kill all existing Ollama processes on macOS or Linux.\"\"\"\n",
    "\n",
    "    # detect system\n",
    "    system = platform.system()\n",
    "\n",
    "    # handle Windows OS\n",
    "    if system not in [\"Darwin\", \"Linux\"]:\n",
    "        print(\"This script is designed to run on macOS or Linux. Skipping process termination.\")\n",
    "        return\n",
    "\n",
    "    # terminate all Ollama instances\n",
    "    result = subprocess.run(['pgrep', '-f', 'ollama'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)\n",
    "    if result.stdout:\n",
    "        pids = result.stdout.strip().splitlines()  # get all PIDs\n",
    "        print(f\"Found running Ollama processes with PIDs: {', '.join(pids)}. Killing them...\")\n",
    "        for pid in pids:\n",
    "            try:\n",
    "                os.kill(int(pid), 9)  # kill each PID individually\n",
    "                print(f\"Killed process with PID: {pid}\")\n",
    "            except Exception as e:\n",
    "                print(f\"Failed to kill process with PID: {pid}. Error: {e}\")\n",
    "        print(\"Waiting for processes to terminate...\")\n",
    "        time.sleep(10)  # allow time for processes to shut down\n",
    "    else:\n",
    "        print(\"No Ollama process found running.\")\n",
    "\n",
    "\n",
    "# run the function\n",
    "kill_existing_ollama()"
   ]
  }
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