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Harrison/myscale self query (#6376)

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Co-authored-by: Fangrui Liu <fangruil@moqi.ai>
Co-authored-by: 刘 方瑞 <fangrui.liu@outlook.com>
Co-authored-by: Fangrui.Liu <fangrui.liu@ubc.ca>
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Harrison Chase
2023-06-18 16:53:10 -07:00
committed by GitHub
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commit 9bf5b0defa
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docs/extras/modules/data_connection/retrievers/how_to/self_query/myscale_self_query.ipynb Normal file
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "13afcae7",
"metadata": {},
"source": [
"# Self-querying with MyScale\n",
"\n",
">[MyScale](https://docs.myscale.com/en/) is an integrated vector database. You can access your database in SQL and also from here, LangChain. MyScale can make a use of [various data types and functions for filters](https://blog.myscale.com/2023/06/06/why-integrated-database-solution-can-boost-your-llm-apps/#filter-on-anything-without-constraints). It will boost up your LLM app no matter if you are scaling up your data or expand your system to broader application.\n",
"\n",
"In the notebook we'll demo the `SelfQueryRetriever` wrapped around a MyScale vector store with some extra piece we contributed to LangChain. In short, it can be concluded into 4 points:\n",
"1. Add `contain` comparator to match list of any if there is more than one element matched\n",
"2. Add `timestamp` data type for datetime match (ISO-format, or YYYY-MM-DD)\n",
"3. Add `like` comparator for string pattern search\n",
"4. Add arbitrary function capability"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "68e75fb9",
"metadata": {},
"source": [
"## Creating a MyScale vectorstore\n",
"MyScale has already been integrated to LangChain for a while. So you can follow [this notebook](../../vectorstores/examples/myscale.ipynb) to create your own vectorstore for a self-query retriever.\n",
"\n",
"NOTE: All self-query retrievers requires you to have `lark` installed (`pip install lark`). We use `lark` for grammar definition. Before you proceed to the next step, we also want to remind you that `clickhouse-connect` is also needed to interact with your MyScale backend."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63a8af5b",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"! pip install lark clickhouse-connect"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "83811610-7df3-4ede-b268-68a6a83ba9e2",
"metadata": {},
"source": [
"In this tutorial we follow other example's setting and use `OpenAIEmbeddings`. Remember to get a OpenAI API Key for valid accesss to LLMs."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dd01b61b-7d32-4a55-85d6-b2d2d4f18840",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"import getpass\n",
"\n",
"os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')\n",
"os.environ['MYSCALE_HOST'] = getpass.getpass('MyScale URL:')\n",
"os.environ['MYSCALE_PORT'] = getpass.getpass('MyScale Port:')\n",
"os.environ['MYSCALE_USERNAME'] = getpass.getpass('MyScale Username:')\n",
"os.environ['MYSCALE_PASSWORD'] = getpass.getpass('MyScale Password:')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb4a5787",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.schema import Document\n",
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.vectorstores import MyScale\n",
"\n",
"embeddings = OpenAIEmbeddings()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "bf7f6fc4",
"metadata": {},
"source": [
"## Create some sample data\n",
"As you can see, the data we created has some difference to other self-query retrievers. We replaced keyword `year` to `date` which gives you a finer control on timestamps. We also altered the type of keyword `gerne` to list of strings, where LLM can use a new `contain` comparator to construct filters. We also provides comparator `like` and arbitrary function support to filters, which will be introduced in next few cells.\n",
"\n",
"Now let's look at the data first."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bcbe04d9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"docs = [\n",
" Document(page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\", metadata={\"date\": \"1993-07-02\", \"rating\": 7.7, \"genre\": [\"science fiction\"]}),\n",
" Document(page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\", metadata={\"date\": \"2010-12-30\", \"director\": \"Christopher Nolan\", \"rating\": 8.2}),\n",
" Document(page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\", metadata={\"date\": \"2006-04-23\", \"director\": \"Satoshi Kon\", \"rating\": 8.6}),\n",
" Document(page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\", metadata={\"date\": \"2019-08-22\", \"director\": \"Greta Gerwig\", \"rating\": 8.3}),\n",
" Document(page_content=\"Toys come alive and have a blast doing so\", metadata={\"date\": \"1995-02-11\", \"genre\": [\"animated\"]}),\n",
" Document(page_content=\"Three men walk into the Zone, three men walk out of the Zone\", metadata={\"date\": \"1979-09-10\", \"rating\": 9.9, \"director\": \"Andrei Tarkovsky\", \"genre\": [\"science fiction\", \"adventure\"], \"rating\": 9.9})\n",
"]\n",
"vectorstore = MyScale.from_documents(\n",
" docs, \n",
" embeddings, \n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "5ecaab6d",
"metadata": {},
"source": [
"## Creating our self-querying retriever\n",
"Just like other retrievers... Simple and nice."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "86e34dbf",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.llms import OpenAI\n",
"from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
"from langchain.chains.query_constructor.base import AttributeInfo\n",
"\n",
"metadata_field_info=[\n",
" AttributeInfo(\n",
" name=\"genre\",\n",
" description=\"The genres of the movie\", \n",
" type=\"list[string]\", \n",
" ),\n",
" # If you want to include length of a list, just define it as a new column\n",
" # This will teach the LLM to use it as a column when constructing filter.\n",
" AttributeInfo(\n",
" name=\"length(genre)\",\n",
" description=\"The lenth of genres of the movie\", \n",
" type=\"integer\", \n",
" ),\n",
" # Now you can define a column as timestamp. By simply set the type to timestamp.\n",
" AttributeInfo(\n",
" name=\"date\",\n",
" description=\"The date the movie was released\", \n",
" type=\"timestamp\", \n",
" ),\n",
" AttributeInfo(\n",
" name=\"director\",\n",
" description=\"The name of the movie director\", \n",
" type=\"string\", \n",
" ),\n",
" AttributeInfo(\n",
" name=\"rating\",\n",
" description=\"A 1-10 rating for the movie\",\n",
" type=\"float\"\n",
" ),\n",
"]\n",
"document_content_description = \"Brief summary of a movie\"\n",
"llm = OpenAI(temperature=0)\n",
"retriever = SelfQueryRetriever.from_llm(llm, vectorstore, document_content_description, metadata_field_info, verbose=True)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "ea9df8d4",
"metadata": {},
"source": [
"## Testing it out with self-query retriever's existing functionalities\n",
"And now we can try actually using our retriever!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38a126e9",
"metadata": {},
"outputs": [],
"source": [
"# This example only specifies a relevant query\n",
"retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fc3f1e6e",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# This example only specifies a filter\n",
"retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b19d4da0",
"metadata": {},
"outputs": [],
"source": [
"# This example specifies a query and a filter\n",
"retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f900e40e",
"metadata": {},
"outputs": [],
"source": [
"# This example specifies a composite filter\n",
"retriever.get_relevant_documents(\"What's a highly rated (above 8.5) science fiction film?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "12a51522",
"metadata": {},
"outputs": [],
"source": [
"# This example specifies a query and composite filter\n",
"retriever.get_relevant_documents(\"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "86371ac8",
"metadata": {},
"source": [
"# Wait a second... What else?\n",
"\n",
"Self-query retriever with MyScale can do more! Let's find out."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1d043096",
"metadata": {},
"outputs": [],
"source": [
"# You can use length(genres) to do anything you want\n",
"retriever.get_relevant_documents(\"What's a movie that have more than 1 genres?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d570d33c",
"metadata": {},
"outputs": [],
"source": [
"# Fine-grained datetime? You got it already.\n",
"retriever.get_relevant_documents(\"What's a movie that release after feb 1995?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fbe0b21b",
"metadata": {},
"outputs": [],
"source": [
"# Don't know what your exact filter should be? Use string pattern match!\n",
"retriever.get_relevant_documents(\"What's a movie whose name is like Andrei?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6a514104",
"metadata": {},
"outputs": [],
"source": [
"# Contain works for lists: so you can match a list with contain comparator!\n",
"retriever.get_relevant_documents(\"What's a movie who has genres science fiction and adventure?\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
"metadata": {},
"source": [
"## Filter k\n",
"\n",
"We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
"\n",
"We can do this by passing `enable_limit=True` to the constructor."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bff36b88-b506-4877-9c63-e5a1a8d78e64",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"retriever = SelfQueryRetriever.from_llm(\n",
" llm, \n",
" vectorstore, \n",
" document_content_description, \n",
" metadata_field_info, \n",
" enable_limit=True,\n",
" verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2758d229-4f97-499c-819f-888acaf8ee10",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# This example only specifies a relevant query\n",
"retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

8
docs/extras/modules/data_connection/retrievers/how_to/self_query/qdrant_self_query.ipynb
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@@ -1,6 +1,7 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "13afcae7",
"metadata": {},
@@ -13,12 +14,13 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "68e75fb9",
"metadata": {},
"source": [
"## Creating a Qdrant vectorstore\n",
"First we'll want to create a Chroma VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
"First we'll want to create a Qdrant VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
"\n",
"NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `qdrant-client` package."
]
@@ -36,6 +38,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "83811610-7df3-4ede-b268-68a6a83ba9e2",
"metadata": {},
@@ -124,6 +127,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "5ecaab6d",
"metadata": {},
@@ -173,6 +177,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "ea9df8d4",
"metadata": {},
@@ -337,6 +342,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
"metadata": {},

69
docs/extras/modules/data_connection/vectorstores/integrations/myscale.ipynb
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@@ -67,7 +67,7 @@
"1. Environment Variables\n",
"\n",
" Before you run the app, please set the environment variable with `export`:\n",
" `export MYSCALE_URL='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`\n",
" `export MYSCALE_HOST='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`\n",
"\n",
" You can easily find your account, password and other info on our SaaS. For details please refer to [this document](https://docs.myscale.com/en/cluster-management/)\n",
"\n",
@@ -120,18 +120,10 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"id": "6e104aee",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Inserting data...: 100%|██████████| 42/42 [00:18<00:00, 2.21it/s]\n"
]
}
],
"outputs": [],
"source": [
"for d in docs:\n",
" d.metadata = {\"some\": \"metadata\"}\n",
@@ -143,32 +135,10 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"id": "9c608226",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"As Frances Haugen, who is here with us tonight, has shown, we must hold social media platforms accountable for the national experiment they’re conducting on our children for profit. \n",
"\n",
"It’s time to strengthen privacy protections, ban targeted advertising to children, demand tech companies stop collecting personal data on our children. \n",
"\n",
"And let’s get all Americans the mental health services they need. More people they can turn to for help, and full parity between physical and mental health care. \n",
"\n",
"Third, support our veterans. \n",
"\n",
"Veterans are the best of us. \n",
"\n",
"I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home. \n",
"\n",
"My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free. \n",
"\n",
"Our troops in Iraq and Afghanistan faced many dangers.\n"
]
}
],
"outputs": [],
"source": [
"print(docs[0].page_content)"
]
@@ -209,18 +179,10 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"id": "232055f6",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Inserting data...: 100%|██████████| 42/42 [00:15<00:00, 2.69it/s]\n"
]
}
],
"outputs": [],
"source": [
"from langchain.vectorstores import MyScale, MyScaleSettings\n",
"from langchain.document_loaders import TextLoader\n",
@@ -258,21 +220,10 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": null,
"id": "ddbcee77",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.252379834651947 {'doc_id': 6, 'some': ''} And I’m taking robus...\n",
"0.25022566318511963 {'doc_id': 1, 'some': ''} Groups of citizens b...\n",
"0.2469480037689209 {'doc_id': 8, 'some': ''} And so many families...\n",
"0.2428302764892578 {'doc_id': 0, 'some': 'metadata'} As Frances Haugen, w...\n"
]
}
],
"outputs": [],
"source": [
"meta = docsearch.metadata_column\n",
"output = docsearch.similarity_search_with_relevance_scores(\n",
@@ -328,7 +279,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
"version": "3.8.8"
}
},
"nbformat": 4,