octoai live data example, tested

demo_apps/OctoAI_API_example/LiveData.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "30eb1704-8d76-4bc9-9308-93243aeb69cb",
+   "metadata": {},
+   "source": [
+    "## This demo app shows:\n",
+    "* How to use LlamaIndex, an open source library to help you build custom data augmented LLM applications\n",
+    "* How to ask Llama questions about recent live data via the You.com live search API and LlamaIndex\n",
+    "\n",
+    "The LangChain package is used to facilitate the call to Llama2 hosted on OctoAI\n",
+    "\n",
+    "**Note** We will be using OctoAI to run the examples here. You will need to first sign into [OctoAI](https://octoai.cloud/) with your Github or Google account, then create a free API token [here](https://octo.ai/docs/getting-started/how-to-create-an-octoai-access-token) that you can use for a while (a month or $10 in OctoAI credits, whichever one runs out first).\n",
+    "After the free trial ends, you will need to enter billing info to continue to use Llama2 hosted on OctoAI."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68cf076e",
+   "metadata": {},
+   "source": [
+    "We start by installing the necessary packages:\n",
+    "- [langchain](https://python.langchain.com/docs/get_started/introduction) which provides RAG capabilities\n",
+    "- [llama-index](https://docs.llamaindex.ai/en/stable/) for data augmentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d0005d6-e928-4d1a-981b-534a40e19e56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install llama-index langchain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21fe3849",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use ServiceContext to configure the LLM used and the custom embeddings \n",
+    "from llama_index import ServiceContext\n",
+    "\n",
+    "# VectorStoreIndex is used to index custom data \n",
+    "from llama_index import VectorStoreIndex\n",
+    "\n",
+    "from langchain.llms.octoai_endpoint import OctoAIEndpoint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73e8e661",
+   "metadata": {},
+   "source": [
+    "Next we set up the OctoAI token."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9d76e33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from getpass import getpass\n",
+    "import os\n",
+    "\n",
+    "OCTOAI_API_TOKEN = getpass()\n",
+    "os.environ[\"OCTOAI_API_TOKEN\"] = OCTOAI_API_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8ff812b",
+   "metadata": {},
+   "source": [
+    "In this example we will use the [YOU.com](https://you.com/) search engine to augment the LLM's responses.\n",
+    "To use the You.com Search API, you can email api@you.com to request an API key. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75275628-5235-4b55-8033-601c76107528",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "YOUCOM_API_KEY = getpass()\n",
+    "os.environ[\"YOUCOM_API_KEY\"] = YOUCOM_API_KEY"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb210c7c",
+   "metadata": {},
+   "source": [
+    "We then call the Llama 2 model from OctoAI.\n",
+    "\n",
+    "We will use the Llama 2 13b chat FP16 model. You can find more on Llama 2 models on the [OctoAI text generation solution page](https://octoai.cloud/tools/text).\n",
+    "\n",
+    "At the time of writing this notebook the following Llama models are available on OctoAI:\n",
+    "* llama-2-13b-chat-fp16\n",
+    "* llama-2-70b-chat-int4\n",
+    "* llama-2-70b-chat-fp16\n",
+    "* codellama-7b-instruct-fp16\n",
+    "* codellama-13b-instruct-fp16\n",
+    "* codellama-34b-instruct-int4\n",
+    "* codellama-34b-instruct-fp16\n",
+    "* codellama-70b-instruct-fp16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c12fc2cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set llm to be using Llama2 hosted on OctoAI\n",
+    "llama2_13b = \"llama-2-13b-chat-fp16\"\n",
+    "\n",
+    "llm = OctoAIEndpoint(\n",
+    "    endpoint_url=\"https://text.octoai.run/v1/chat/completions\",\n",
+    "    model_kwargs={\n",
+    "        \"model\": llama2_13b,\n",
+    "        \"messages\": [\n",
+    "            {\n",
+    "                \"role\": \"system\",\n",
+    "                \"content\": \"You are a helpful, respectful and honest assistant.\"\n",
+    "            }\n",
+    "        ],\n",
+    "        \"max_tokens\": 500,\n",
+    "        \"top_p\": 1,\n",
+    "        \"temperature\": 0.01\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "476d72da",
+   "metadata": {},
+   "source": [
+    "Using our api key we set up earlier, we make a request from YOU.com for live data on a particular topic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "effc9656-b18d-4d24-a80b-6066564a838b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "\n",
+    "query = \"Meta Connect\" # you can try other live data query about sports score, stock market and weather info \n",
+    "headers = {\"X-API-Key\": os.environ[\"YOUCOM_API_KEY\"]}\n",
+    "data = requests.get(\n",
+    "    f\"https://api.ydc-index.io/search?query={query}\",\n",
+    "    headers=headers,\n",
+    ").json()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bed3baf-742e-473c-ada1-4459012a8a2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the query result in JSON\n",
+    "import json\n",
+    "\n",
+    "print(json.dumps(data, indent=2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b196e697",
+   "metadata": {},
+   "source": [
+    "We then use the [`JSONLoader`](https://llamahub.ai/l/file-json) to extract the text from the returned data. The `JSONLoader` gives us the ability to load the data into LamaIndex.\n",
+    "In the next cell we show how to load the JSON result with key info stored as \"snippets\".\n",
+    "\n",
+    "However, you can also add the snippets in the query result to documents like below:\n",
+    "```python \n",
+    "from llama_index import Document\n",
+    "snippets = [snippet for hit in data[\"hits\"] for snippet in hit[\"snippets\"]]\n",
+    "documents = [Document(text=s) for s in snippets]\n",
+    "```\n",
+    "This can be handy if you just need to add a list of text strings to doc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c40e73f-ca13-4f4a-a753-e613df3d389e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# one way to load the JSON result with key info stored as \"snippets\"\n",
+    "from llama_index import download_loader\n",
+    "\n",
+    "JsonDataReader = download_loader(\"JsonDataReader\")\n",
+    "loader = JsonDataReader()\n",
+    "documents = loader.load_data([hit[\"snippets\"] for hit in data[\"hits\"]])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e5e3b4e",
+   "metadata": {},
+   "source": [
+    "With the data set up, we create a vector store for the data and a query engine for it.\n",
+    "\n",
+    "For our embeddings we will use `HuggingFaceEmbeddings` whose default embedding model is sentence-transformers/all-mpnet-base-v2. This model provides a good balance between speed and performance.\n",
+    "To change the default model, call `HuggingFaceEmbeddings(model_name=<another_embedding_model>)`. \n",
+    "\n",
+    "For more info see https://huggingface.co/blog/mteb. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5de3080-2c4b-479c-baba-793b3bee36ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use HuggingFace embeddings \n",
+    "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
+    "from llama_index.embeddings import LangchainEmbedding\n",
+    "\n",
+    "\n",
+    "embeddings = LangchainEmbedding(HuggingFaceEmbeddings())\n",
+    "print(embeddings)\n",
+    "\n",
+    "# create a ServiceContext instance to use Llama2 and custom embeddings\n",
+    "service_context = ServiceContext.from_defaults(llm=llm, chunk_size=800, chunk_overlap=20, embed_model=embeddings)\n",
+    "\n",
+    "# create vector store index from the documents created above\n",
+    "index = VectorStoreIndex.from_documents(documents, service_context=service_context)\n",
+    "\n",
+    "# create query engine from the index\n",
+    "query_engine = index.as_query_engine(streaming=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c4ea012",
+   "metadata": {},
+   "source": [
+    "We are now ready to ask Llama 2 a question about the live data using our query engine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de91a191-d0f2-498e-88dc-b2b43423e0e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ask Llama2 a summary question about the search result\n",
+    "response = query_engine.query(\"give me a summary\")\n",
+    "print(str(response))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72814b20-06aa-4da8-b4dd-f0b0d74a2ea0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# more questions\n",
+    "print(str(query_engine.query(\"what products were announced\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a65bc037-a689-476d-b529-0059a27bc949",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(str(query_engine.query(\"tell me more about Meta AI assistant\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16a56542",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(str(query_engine.query(\"what are Generative AI stickers\")))"
+   ]
+  }
+ ],
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