mirror of
https://github.com/microsoft/semantic-kernel.git
synced 2026-10-12 02:19:06 +00:00
Add sample notebook to demo weaviate memory store (#1359)
### Motivation and Context
1. Why is this change required?
To show users how to use the WeaviateMemoryStore memory store
2. What problem does it solve?
Users don't know how to config and use the WeaviateMemoryStore memory
store
3. What scenario does it contribute to?
Users want to learn how to config and use the WeaviateMemoryStore memory
store
4. If it fixes an open issue, please link to the issue here.
NA
### Description
Add a notebook to show users how to config and use the
WeaviateMemoryStore memory store in a few examples.
---------
Co-authored-by: Dan Dascalescu <[email protected]>
This commit is contained in:
Shukri
@@ -0,0 +1,7 @@
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OPENAI_API_KEY=""
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OPENAI_ORG_ID=""
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AZURE_OPENAI_DEPLOYMENT_NAME=""
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AZURE_OPENAI_ENDPOINT=""
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AZURE_OPENAI_API_KEY=""
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WEAVIATE_URL="http://localhost:8080"
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# WEAVIATE_API_KEY=""
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@@ -0,0 +1,706 @@
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Introduction"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook shows how to replace the `VolatileMemoryStore` memory storage used in a [previous notebook](./06-memory-and-embeddings.ipynb) with a `WeaviateMemoryStore`.\n",
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"\n",
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"`WeaviateMemoryStore` is an example of a persistent (i.e. long-term) memory store backed by the Weaviate vector database."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# About Weaviate"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"[Weaviate](https://weaviate.io/) is an open-source vector database designed to scale seamlessly into billions of data objects. This implementation supports hybrid search out-of-the-box (meaning it will perform better for keyword searches)."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can run Weaviate in 5 ways:\n",
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"\n",
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"- **SaaS** – with [Weaviate Cloud Services (WCS)](https://weaviate.io/pricing).\n",
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"\n",
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" WCS is a fully managed service that takes care of hosting, scaling, and updating your Weaviate instance. You can try it out for free with a sandbox that lasts for 14 days.\n",
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"\n",
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" To set up a SaaS Weaviate instance with WCS:\n",
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"\n",
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" 1. Navigate to [Weaviate Cloud Console](https://console.weaviate.cloud/).\n",
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" 2. Register or sign in to your WCS account.\n",
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" 3. Create a new cluster with the following settings:\n",
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" - `Subscription Tier` – Free sandbox for a free trial, or contact [[email protected]](mailto:[email protected]) for other options.\n",
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" - `Cluster name` – a unique name for your cluster. The name will become part of the URL used to access this instance.\n",
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" - `Enable Authentication?` – Enabled by default. This will generate a static API key that you can use to authenticate.\n",
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" 4. Wait for a few minutes until your cluster is ready. You will see a green tick ✔️ when it's done. Copy your cluster URL.\n",
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"\n",
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"- **Hybrid SaaS**\n",
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"\n",
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" > If you need to keep your data on-premise for security or compliance reasons, Weaviate also offers a Hybrid SaaS option: Weaviate runs within your cloud instances, but the cluster is managed remotely by Weaviate. This gives you the benefits of a managed service without sending data to an external party.\n",
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"\n",
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" The Weaviate Hybrid SaaS is a custom solution. If you are interested in this option, please reach out to [[email protected]](mailto:[email protected]).\n",
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"\n",
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"- **Self-hosted** – with a Docker container\n",
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"\n",
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" To set up a Weaviate instance with Docker:\n",
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"\n",
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" 1. [Install Docker](https://docs.docker.com/engine/install/) on your local machine if it is not already installed.\n",
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" 2. [Install the Docker Compose Plugin](https://docs.docker.com/compose/install/)\n",
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" 3. Download a `docker-compose.yml` file with this `curl` command:\n",
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"\n",
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" ```\n",
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" curl -o docker-compose.yml \"https://configuration.weaviate.io/v2/docker-compose/docker-compose.yml?modules=standalone&runtime=docker-compose&weaviate_version=v1.19.6\"\n",
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" ```\n",
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"\n",
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" Alternatively, you can use Weaviate's docker compose [configuration tool](https://weaviate.io/developers/weaviate/installation/docker-compose) to generate your own `docker-compose.yml` file.\n",
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"\n",
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" 4. Run `docker compose up -d` to spin up a Weaviate instance.\n",
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"\n",
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" > To shut it down, run `docker compose down`.\n",
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"\n",
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"- **Self-hosted** – with a Kubernetes cluster\n",
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"\n",
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" To configure a self-hosted instance with Kubernetes, follow Weaviate's [documentation](https://weaviate.io/developers/weaviate/installation/kubernetes).|\n",
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"\n",
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"- **Embedded** - start a weaviate instance right from your application code using the client library\n",
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" \n",
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" This code snippet shows how to instantiate an embedded weaviate instance and upload a document:\n",
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"\n",
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" ```python\n",
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" import weaviate\n",
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" from weaviate.embedded import EmbeddedOptions\n",
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"\n",
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" client = weaviate.Client(\n",
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" embedded_options=EmbeddedOptions()\n",
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" )\n",
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"\n",
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" data_obj = {\n",
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" \"name\": \"Chardonnay\",\n",
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" \"description\": \"Goes with fish\"\n",
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" }\n",
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"\n",
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" client.data_object.create(data_obj, \"Wine\")\n",
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" ```\n",
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" \n",
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" Refer to the [documentation](https://weaviate.io/developers/weaviate/installation/embedded) for more details about this deployment method."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting semantic-kernel==0.3.0.dev0\n",
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" Downloading semantic_kernel-0.3.0.dev0-py3-none-any.whl (94 kB)\n",
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"\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.3/94.3 kB\u001b[0m \u001b[31m869.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m1m797.8 kB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: aiofiles<24.0.0,>=23.1.0 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from semantic-kernel==0.3.0.dev0) (23.1.0)\n",
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"Requirement already satisfied: numpy<2.0.0,>=1.24.2 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from semantic-kernel==0.3.0.dev0) (1.24.3)\n",
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"Requirement already satisfied: openai<0.28.0,>=0.27.0 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from semantic-kernel==0.3.0.dev0) (0.27.7)\n",
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"Requirement already satisfied: requests>=2.20 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (2.28.2)\n",
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"Requirement already satisfied: tqdm in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (4.65.0)\n",
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"Requirement already satisfied: aiohttp in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (3.8.4)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from requests>=2.20->openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (3.1.0)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from requests>=2.20->openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (3.4)\n",
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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from requests>=2.20->openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (1.26.16)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from requests>=2.20->openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (2023.5.7)\n",
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"Requirement already satisfied: attrs>=17.3.0 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from aiohttp->openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (23.1.0)\n",
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"Requirement already satisfied: multidict<7.0,>=4.5 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from aiohttp->openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (6.0.4)\n",
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"Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from aiohttp->openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (4.0.2)\n",
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"Requirement already satisfied: yarl<2.0,>=1.0 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from aiohttp->openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (1.9.2)\n",
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"Requirement already satisfied: frozenlist>=1.1.1 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from aiohttp->openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (1.3.3)\n",
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"Requirement already satisfied: aiosignal>=1.1.2 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from aiohttp->openai<0.28.0,>=0.27.0->semantic-kernel==0.3.0.dev0) (1.3.1)\n",
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"Installing collected packages: semantic-kernel\n",
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" Attempting uninstall: semantic-kernel\n",
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" Found existing installation: semantic-kernel 0.2.9.dev0\n",
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" Uninstalling semantic-kernel-0.2.9.dev0:\n",
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" Successfully uninstalled semantic-kernel-0.2.9.dev0\n",
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"Successfully installed semantic-kernel-0.3.0.dev0\n",
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"Requirement already satisfied: weaviate-client in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (3.19.2)\n",
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"Requirement already satisfied: requests<2.29.0,>=2.28.0 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from weaviate-client) (2.28.2)\n",
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"Requirement already satisfied: validators<=0.21.0,>=0.18.2 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from weaviate-client) (0.20.0)\n",
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"Requirement already satisfied: tqdm<5.0.0,>=4.59.0 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from weaviate-client) (4.65.0)\n",
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"Requirement already satisfied: authlib>=1.1.0 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from weaviate-client) (1.2.0)\n",
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"Requirement already satisfied: cryptography>=3.2 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from authlib>=1.1.0->weaviate-client) (41.0.1)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (3.4)\n",
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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (1.26.16)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (2023.5.7)\n",
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"Requirement already satisfied: decorator>=3.4.0 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from validators<=0.21.0,>=0.18.2->weaviate-client) (5.1.1)\n",
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"Requirement already satisfied: cffi>=1.12 in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from cryptography>=3.2->authlib>=1.1.0->weaviate-client) (1.15.1)\n",
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"Requirement already satisfied: pycparser in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (from cffi>=1.12->cryptography>=3.2->authlib>=1.1.0->weaviate-client) (2.21)\n",
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"Requirement already satisfied: python-dotenv in /Users/dandv/Library/Caches/pypoetry/virtualenvs/semantic-kernel-tJOEiq7p-py3.11/lib/python3.11/site-packages (1.0.0)\n"
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]
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}
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],
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"source": [
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"!pip install semantic-kernel==0.3.0.dev0\n",
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"!pip install weaviate-client\n",
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"!pip install python-dotenv"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## OS-specific notes:\n",
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"* if you run into SSL errors when connecting to OpenAI on macOS, see this issue for a [potential solution](https://github.com/microsoft/semantic-kernel/issues/627#issuecomment-1580912248)\n",
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"* on Windows, you may need to run Docker Desktop as administrator"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Tuple\n",
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"\n",
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"import semantic_kernel as sk\n",
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"from semantic_kernel.connectors.ai.open_ai import (\n",
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" OpenAITextCompletion,\n",
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" OpenAITextEmbedding,\n",
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")\n",
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"\n",
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"import os"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, we instantiate the Weaviate memory store. Uncomment ONE of the options below, depending on how you want to use Weaviate:\n",
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"* from a Docker instance\n",
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"* from WCS\n",
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"* directly from the client (embedded Weaviate), which works on Linux only at the moment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from semantic_kernel.connectors.memory import weaviate_memory_store\n",
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"from dotenv import load_dotenv\n",
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"\n",
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"load_dotenv(override=True)\n",
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"\n",
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"# Using Docker\n",
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"config = weaviate_memory_store.WeaviateConfig(url=\"http://localhost:8080\")\n",
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"\n",
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"# Using WCS. Make sure the environment variables `WEAVIATE_URL` and `WEAVIATE_API_KEY` were set in the `.env` file.\n",
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"#config = weaviate_memory_store.WeaviateConfig(\n",
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"# url=os.getenv(\"WEAVIATE_URL\"),\n",
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"# api_key=os.getenv(\"WEAVIATE_API_KEY\")\n",
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"#)\n",
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"\n",
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"# Using Embedded Weaviate\n",
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"#config = weaviate_memory_store.WeaviateConfig(use_embed=True)\n",
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"\n",
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"store = weaviate_memory_store.WeaviateMemoryStore(config=config)\n",
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"store.client.schema.delete_all()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Then, we register the memory store to the kernel:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'recall': <semantic_kernel.orchestration.sk_function.SKFunction at 0x103c21250>,\n",
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" 'save': <semantic_kernel.orchestration.sk_function.SKFunction at 0x105926710>}"
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]
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},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"kernel = sk.Kernel()\n",
|
||||
"\n",
|
||||
"api_key, org_id = sk.openai_settings_from_dot_env()\n",
|
||||
"kernel.add_text_completion_service(\n",
|
||||
" \"dv\", OpenAITextCompletion(\"text-davinci-003\", api_key, org_id)\n",
|
||||
")\n",
|
||||
"kernel.add_text_embedding_generation_service(\n",
|
||||
" \"ada\", OpenAITextEmbedding(\"text-embedding-ada-002\", api_key, org_id)\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"kernel.register_memory_store(memory_store=store)\n",
|
||||
"kernel.import_skill(sk.core_skills.TextMemorySkill())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Manually adding memories\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Let's create some initial memories \"About Me\". We can add memories to our weaviate memory store by using `save_information_async`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"COLLECTION = \"AboutMe\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"async def populate_memory(kernel: sk.Kernel) -> None:\n",
|
||||
" # Add some documents to the semantic memory\n",
|
||||
" await kernel.memory.save_information_async(\n",
|
||||
" COLLECTION, id=\"info1\", text=\"My name is Andrea\"\n",
|
||||
" )\n",
|
||||
" await kernel.memory.save_information_async(\n",
|
||||
" COLLECTION, id=\"info2\", text=\"I currently work as a tour guide\"\n",
|
||||
" )\n",
|
||||
" await kernel.memory.save_information_async(\n",
|
||||
" COLLECTION, id=\"info3\", text=\"I've been living in Seattle since 2005\"\n",
|
||||
" )\n",
|
||||
" await kernel.memory.save_information_async(\n",
|
||||
" COLLECTION, id=\"info4\", text=\"I visited France and Italy five times since 2015\"\n",
|
||||
" )\n",
|
||||
" await kernel.memory.save_information_async(\n",
|
||||
" COLLECTION, id=\"info5\", text=\"My family is from New York\"\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Searching is done through `search_async`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"async def search_memory_examples(kernel: sk.Kernel) -> None:\n",
|
||||
" questions = [\n",
|
||||
" \"what's my name\",\n",
|
||||
" \"where do I live?\",\n",
|
||||
" \"where's my family from?\",\n",
|
||||
" \"where have I traveled?\",\n",
|
||||
" \"what do I do for work\",\n",
|
||||
" ]\n",
|
||||
"\n",
|
||||
" for question in questions:\n",
|
||||
" print(f\"Question: {question}\")\n",
|
||||
" result = await kernel.memory.search_async(COLLECTION, question)\n",
|
||||
" print(f\"Answer: {result[0].text}\\n\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Let's see the results of the functions:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating memory...\n",
|
||||
"Asking questions... (manually)\n",
|
||||
"Question: what's my name\n",
|
||||
"Answer: My name is Andrea\n",
|
||||
"\n",
|
||||
"Question: where do I live?\n",
|
||||
"Answer: I've been living in Seattle since 2005\n",
|
||||
"\n",
|
||||
"Question: where's my family from?\n",
|
||||
"Answer: My family is from New York\n",
|
||||
"\n",
|
||||
"Question: where have I traveled?\n",
|
||||
"Answer: I visited France and Italy five times since 2015\n",
|
||||
"\n",
|
||||
"Question: what do I do for work\n",
|
||||
"Answer: I currently work as a tour guide\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(\"Populating memory...\")\n",
|
||||
"await populate_memory(kernel)\n",
|
||||
"\n",
|
||||
"print(\"Asking questions... (manually)\")\n",
|
||||
"await search_memory_examples(kernel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Here's how to use the weaviate memory store in a chat application:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"async def setup_chat_with_memory(\n",
|
||||
" kernel: sk.Kernel,\n",
|
||||
") -> Tuple[sk.SKFunctionBase, sk.SKContext]:\n",
|
||||
" sk_prompt = \"\"\"\n",
|
||||
" ChatBot can have a conversation with you about any topic.\n",
|
||||
" It can give explicit instructions or say 'I don't know' if\n",
|
||||
" it does not have an answer.\n",
|
||||
"\n",
|
||||
" Information about me, from previous conversations:\n",
|
||||
" - {{$fact1}} {{recall $fact1}}\n",
|
||||
" - {{$fact2}} {{recall $fact2}}\n",
|
||||
" - {{$fact3}} {{recall $fact3}}\n",
|
||||
" - {{$fact4}} {{recall $fact4}}\n",
|
||||
" - {{$fact5}} {{recall $fact5}}\n",
|
||||
"\n",
|
||||
" Chat:\n",
|
||||
" {{$chat_history}}\n",
|
||||
" User: {{$user_input}}\n",
|
||||
" ChatBot: \"\"\".strip()\n",
|
||||
"\n",
|
||||
" chat_func = kernel.create_semantic_function(\n",
|
||||
" sk_prompt, max_tokens=200, temperature=0.8\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" context = kernel.create_new_context()\n",
|
||||
" context[\"fact1\"] = \"what is my name?\"\n",
|
||||
" context[\"fact2\"] = \"where do I live?\"\n",
|
||||
" context[\"fact3\"] = \"where's my family from?\"\n",
|
||||
" context[\"fact4\"] = \"where have I traveled?\"\n",
|
||||
" context[\"fact5\"] = \"what do I do for work?\"\n",
|
||||
"\n",
|
||||
" context[sk.core_skills.TextMemorySkill.COLLECTION_PARAM] = COLLECTION\n",
|
||||
" context[sk.core_skills.TextMemorySkill.RELEVANCE_PARAM] = 0.8\n",
|
||||
"\n",
|
||||
" context[\"chat_history\"] = \"\"\n",
|
||||
"\n",
|
||||
" return chat_func, context"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"async def chat(\n",
|
||||
" kernel: sk.Kernel, chat_func: sk.SKFunctionBase, context: sk.SKContext\n",
|
||||
") -> bool:\n",
|
||||
" try:\n",
|
||||
" user_input = input(\"User:> \")\n",
|
||||
" context[\"user_input\"] = user_input\n",
|
||||
" print(f\"User:> {user_input}\")\n",
|
||||
" except KeyboardInterrupt:\n",
|
||||
" print(\"\\n\\nExiting chat...\")\n",
|
||||
" return False\n",
|
||||
" except EOFError:\n",
|
||||
" print(\"\\n\\nExiting chat...\")\n",
|
||||
" return False\n",
|
||||
"\n",
|
||||
" if user_input == \"exit\":\n",
|
||||
" print(\"\\n\\nExiting chat...\")\n",
|
||||
" return False\n",
|
||||
"\n",
|
||||
" answer = await kernel.run_async(chat_func, input_vars=context.variables)\n",
|
||||
" context[\"chat_history\"] += f\"\\nUser:> {user_input}\\nChatBot:> {answer}\\n\"\n",
|
||||
"\n",
|
||||
" print(f\"ChatBot:> {answer}\")\n",
|
||||
" return True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Setting up a chat (with memory!)\n",
|
||||
"Begin chatting (type 'exit' to exit):\n",
|
||||
"\n",
|
||||
"User:> hi\n",
|
||||
"ChatBot:> Hi there! How can I help you?\n",
|
||||
"User:> where is my family from?\n",
|
||||
"ChatBot:> Your family is from New York.\n",
|
||||
"User:> tell me a joke\n",
|
||||
"ChatBot:> Why did the chicken cross the playground?\n",
|
||||
" To get to the other slide!\n",
|
||||
"User:> bye!\n",
|
||||
"ChatBot:> Error: (<ErrorCodes.ServiceError: 6>, 'OpenAI service failed to complete the prompt', RateLimitError(message='The server had an error while processing your request. Sorry about that!', http_status=429, request_id=None))\n",
|
||||
"User:> exit\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Exiting chat...\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(\"Setting up a chat (with memory!)\")\n",
|
||||
"chat_func, context = await setup_chat_with_memory(kernel)\n",
|
||||
"\n",
|
||||
"print(\"Begin chatting (type 'exit' to exit):\\n\")\n",
|
||||
"chatting = True\n",
|
||||
"while chatting:\n",
|
||||
" chatting = await chat(kernel, chat_func, context)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Adding documents to your memory"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Create a dictionary to hold some files. The key is the hyperlink to the file and the value is the file's content:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"github_files = {}\n",
|
||||
"github_files[\n",
|
||||
" \"https://github.com/microsoft/semantic-kernel/blob/main/README.md\"\n",
|
||||
"] = \"README: Installation, getting started, and how to contribute\"\n",
|
||||
"github_files[\n",
|
||||
" \"https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/02-running-prompts-from-file.ipynb\"\n",
|
||||
"] = \"Jupyter notebook describing how to pass prompts from a file to a semantic skill or function\"\n",
|
||||
"github_files[\n",
|
||||
" \"https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/00-getting-started.ipynb\"\n",
|
||||
"] = \"Jupyter notebook describing how to get started with the Semantic Kernel\"\n",
|
||||
"github_files[\n",
|
||||
" \"https://github.com/microsoft/semantic-kernel/tree/main/samples/skills/ChatSkill/ChatGPT\"\n",
|
||||
"] = \"Sample demonstrating how to create a chat skill interfacing with ChatGPT\"\n",
|
||||
"github_files[\n",
|
||||
" \"https://github.com/microsoft/semantic-kernel/blob/main/dotnet/src/SemanticKernel/Memory/Volatile/VolatileMemoryStore.cs\"\n",
|
||||
"] = \"C# class that defines a volatile embedding store\"\n",
|
||||
"github_files[\n",
|
||||
" \"https://github.com/microsoft/semantic-kernel/tree/main/samples/dotnet/KernelHttpServer/README.md\"\n",
|
||||
"] = \"README: How to set up a Semantic Kernel Service API using Azure Function Runtime v4\"\n",
|
||||
"github_files[\n",
|
||||
" \"https://github.com/microsoft/semantic-kernel/tree/main/samples/apps/chat-summary-webapp-react/README.md\"\n",
|
||||
"] = \"README: README associated with a sample starter react-based chat summary webapp\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Use `save_reference_async` to save the file:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Adding some GitHub file URLs and their descriptions to a volatile Semantic Memory.\n",
|
||||
" URL 1 saved\n",
|
||||
" URL 2 saved\n",
|
||||
" URL 3 saved\n",
|
||||
" URL 4 saved\n",
|
||||
" URL 5 saved\n",
|
||||
" URL 6 saved\n",
|
||||
" URL 7 saved\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"COLLECTION = \"SKGitHub\"\n",
|
||||
"\n",
|
||||
"print(\n",
|
||||
" \"Adding some GitHub file URLs and their descriptions to a volatile Semantic Memory.\"\n",
|
||||
")\n",
|
||||
"i = 0\n",
|
||||
"for entry, value in github_files.items():\n",
|
||||
" await kernel.memory.save_reference_async(\n",
|
||||
" collection=COLLECTION,\n",
|
||||
" description=value,\n",
|
||||
" text=value,\n",
|
||||
" external_id=entry,\n",
|
||||
" external_source_name=\"GitHub\",\n",
|
||||
" )\n",
|
||||
" i += 1\n",
|
||||
" print(\" URL {} saved\".format(i))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Use `search_async` to ask a question:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"===========================\n",
|
||||
"Query: I love Jupyter notebooks, how should I get started?\n",
|
||||
"\n",
|
||||
"Result 1:\n",
|
||||
" URL: : https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/00-getting-started.ipynb\n",
|
||||
" Title : Jupyter notebook describing how to get started with the Semantic Kernel\n",
|
||||
" Relevance: 0.9338777661323547\n",
|
||||
"\n",
|
||||
"Result 2:\n",
|
||||
" URL: : https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/02-running-prompts-from-file.ipynb\n",
|
||||
" Title : Jupyter notebook describing how to pass prompts from a file to a semantic skill or function\n",
|
||||
" Relevance: 0.9082032442092896\n",
|
||||
"\n",
|
||||
"Result 3:\n",
|
||||
" URL: : https://github.com/microsoft/semantic-kernel/blob/main/README.md\n",
|
||||
" Title : README: Installation, getting started, and how to contribute\n",
|
||||
" Relevance: 0.9042205810546875\n",
|
||||
"\n",
|
||||
"Result 4:\n",
|
||||
" URL: : https://github.com/microsoft/semantic-kernel/tree/main/samples/apps/chat-summary-webapp-react/README.md\n",
|
||||
" Title : README: README associated with a sample starter react-based chat summary webapp\n",
|
||||
" Relevance: 0.8821048438549042\n",
|
||||
"\n",
|
||||
"Result 5:\n",
|
||||
" URL: : https://github.com/microsoft/semantic-kernel/tree/main/samples/skills/ChatSkill/ChatGPT\n",
|
||||
" Title : Sample demonstrating how to create a chat skill interfacing with ChatGPT\n",
|
||||
" Relevance: 0.8685075342655182\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ask = \"I love Jupyter notebooks, how should I get started?\"\n",
|
||||
"print(\"===========================\\n\" + \"Query: \" + ask + \"\\n\")\n",
|
||||
"\n",
|
||||
"memories = await kernel.memory.search_async(\n",
|
||||
" COLLECTION, ask, limit=5, min_relevance_score=0.77\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"i = 0\n",
|
||||
"for memory in memories:\n",
|
||||
" i += 1\n",
|
||||
" print(f\"Result {i}:\")\n",
|
||||
" print(\" URL: : \" + memory.id)\n",
|
||||
" print(\" Title : \" + memory.description)\n",
|
||||
" print(\" Relevance: \" + str(memory.relevance))\n",
|
||||
" print()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in New Issue
Block a user