mirror of https://github.com/hwchase17/langchain
Add dashvector self query retriever (#9684)
## Description
Add `Dashvector` retriever and self-query retriever
## How to use
```python
from langchain.vectorstores.dashvector import DashVector
vectorstore = DashVector.from_documents(docs, embeddings)
retriever = SelfQueryRetriever.from_llm(
llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)
```
---------
Co-authored-by: smallrain.xuxy <smallrain.xuxy@alibaba-inc.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
pull/9754/head^2
Xiaoyu Xee
1 year ago
committed by
GitHub
parent
056e59672b
commit
9bcfd58580
@ -0,0 +1,434 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# DashVector self-querying\n",
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"\n",
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"> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.\n",
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"\n",
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"In this notebook we'll demo the `SelfQueryRetriever` with a `DashVector` vector store."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "59895c73d1a0f3ca"
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Create DashVector vectorstore\n",
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"\n",
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"First we'll want to create a `DashVector` VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
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"\n",
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"To use DashVector, you have to have `dashvector` package installed, and you must have an API key and an Environment. Here are the [installation instructions](https://help.aliyun.com/document_detail/2510223.html).\n",
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"\n",
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"NOTE: The self-query retriever requires you to have `lark` package installed."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "539ae9367e45a178"
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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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"outputs": [],
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"source": [
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"# !pip install lark dashvector"
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],
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"metadata": {
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"collapsed": false
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},
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"id": "67df7e1f8dc8cdd0"
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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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"outputs": [],
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"source": [
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"import os\n",
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"import dashvector\n",
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"\n",
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"client = dashvector.Client(api_key=os.environ[\"DASHVECTOR_API_KEY\"])"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-08-24T02:58:46.905337Z",
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"start_time": "2023-08-24T02:58:46.252566Z"
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}
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},
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"id": "ff61eaf13973b5fe"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"from langchain.schema import Document\n",
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"from langchain.embeddings import DashScopeEmbeddings\n",
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"from langchain.vectorstores import DashVector\n",
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"\n",
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"embeddings = DashScopeEmbeddings()\n",
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"\n",
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"# create DashVector collection\n",
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"client.create(\"langchain-self-retriever-demo\", dimension=1536)"
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],
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"metadata": {
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"collapsed": false
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},
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"id": "de5c77957ee42d14"
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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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"outputs": [],
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"source": [
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"docs = [\n",
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" Document(\n",
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" page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
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" metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"action\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
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" metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
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" metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
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" metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Toys come alive and have a blast doing so\",\n",
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" metadata={\"year\": 1995, \"genre\": \"animated\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
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" metadata={\n",
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" \"year\": 1979,\n",
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" \"director\": \"Andrei Tarkovsky\",\n",
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" \"genre\": \"science fiction\",\n",
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" \"rating\": 9.9,\n",
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" },\n",
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" ),\n",
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"]\n",
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"vectorstore = DashVector.from_documents(\n",
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" docs, embeddings, collection_name=\"langchain-self-retriever-demo\"\n",
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")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:08.090031Z",
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"start_time": "2023-08-24T02:59:05.660295Z"
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}
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},
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"id": "8f40605548a4550"
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Create your self-querying retriever\n",
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"\n",
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"Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "eb1340adafac8993"
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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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"outputs": [],
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"source": [
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"from langchain.llms import Tongyi\n",
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"from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
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"from langchain.chains.query_constructor.base import AttributeInfo\n",
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"\n",
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"metadata_field_info = [\n",
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" AttributeInfo(\n",
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" name=\"genre\",\n",
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" description=\"The genre of the movie\",\n",
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" type=\"string or list[string]\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"year\",\n",
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" description=\"The year the movie was released\",\n",
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" type=\"integer\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"director\",\n",
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" description=\"The name of the movie director\",\n",
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" type=\"string\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
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" ),\n",
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"]\n",
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"document_content_description = \"Brief summary of a movie\"\n",
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"llm = Tongyi(temperature=0)\n",
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"retriever = SelfQueryRetriever.from_llm(\n",
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" llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
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")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:11.003940Z",
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"start_time": "2023-08-24T02:59:10.476722Z"
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}
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},
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"id": "d65233dc044f95a7"
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Testing it out\n",
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"\n",
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"And now we can try actually using our retriever!"
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],
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"metadata": {
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"collapsed": false
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},
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"id": "a54af0d67b473db6"
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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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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"query='dinosaurs' filter=None limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}),\n Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\n Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.199999809265137}),\n Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]"
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example only specifies a relevant query\n",
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"retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:28.577901Z",
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"start_time": "2023-08-24T02:59:26.780184Z"
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}
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},
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"id": "dad9da670a267fe7"
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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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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"query=' ' filter=Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5) limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'}),\n Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]"
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example only specifies a filter\n",
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"retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:32.370774Z",
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"start_time": "2023-08-24T02:59:30.614252Z"
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}
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},
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"id": "d486a64316153d52"
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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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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"query='Greta Gerwig' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.300000190734863})]"
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example specifies a query and a filter\n",
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"retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:35.353439Z",
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"start_time": "2023-08-24T02:59:33.278255Z"
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}
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},
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"id": "e05919cdead7bd4a"
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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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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"query='science fiction' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)]) limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'})]"
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example specifies a composite filter\n",
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"retriever.get_relevant_documents(\"What's a highly rated (above 8.5) science fiction film?\")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:38.913707Z",
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"start_time": "2023-08-24T02:59:36.659271Z"
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}
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},
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"id": "ac2c7012379e918e"
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Filter k\n",
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"\n",
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"We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
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"\n",
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"We can do this by passing `enable_limit=True` to the constructor."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "af6aa93ae44af414"
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"outputs": [],
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"source": [
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"retriever = SelfQueryRetriever.from_llm(\n",
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" llm,\n",
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" vectorstore,\n",
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" document_content_description,\n",
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" metadata_field_info,\n",
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" enable_limit=True,\n",
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" verbose=True,\n",
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")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:41.594073Z",
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"start_time": "2023-08-24T02:59:41.563323Z"
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}
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},
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"id": "a8c8f09bf5702767"
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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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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"query='dinosaurs' filter=None limit=2\n"
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]
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},
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{
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"data": {
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"text/plain": "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}),\n Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example only specifies a relevant query\n",
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"retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-08-24T02:59:48.450506Z",
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"start_time": "2023-08-24T02:59:46.252944Z"
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}
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},
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"id": "b1089a6043980b84"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false
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},
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"id": "6d2d64e2ebb17d30"
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,64 @@
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"""Logic for converting internal query language to a valid DashVector query."""
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from typing import Tuple, Union
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from langchain.chains.query_constructor.ir import (
|
||||
Comparator,
|
||||
Comparison,
|
||||
Operation,
|
||||
Operator,
|
||||
StructuredQuery,
|
||||
Visitor,
|
||||
)
|
||||
|
||||
|
||||
class DashvectorTranslator(Visitor):
|
||||
"""Logic for converting internal query language elements to valid filters."""
|
||||
|
||||
allowed_operators = [Operator.AND, Operator.OR]
|
||||
allowed_comparators = [
|
||||
Comparator.EQ,
|
||||
Comparator.GT,
|
||||
Comparator.GTE,
|
||||
Comparator.LT,
|
||||
Comparator.LTE,
|
||||
Comparator.LIKE,
|
||||
]
|
||||
|
||||
map_dict = {
|
||||
Operator.AND: " AND ",
|
||||
Operator.OR: " OR ",
|
||||
Comparator.EQ: " = ",
|
||||
Comparator.GT: " > ",
|
||||
Comparator.GTE: " >= ",
|
||||
Comparator.LT: " < ",
|
||||
Comparator.LTE: " <= ",
|
||||
Comparator.LIKE: " LIKE ",
|
||||
}
|
||||
|
||||
def _format_func(self, func: Union[Operator, Comparator]) -> str:
|
||||
self._validate_func(func)
|
||||
return self.map_dict[func]
|
||||
|
||||
def visit_operation(self, operation: Operation) -> str:
|
||||
args = [arg.accept(self) for arg in operation.arguments]
|
||||
return self._format_func(operation.operator).join(args)
|
||||
|
||||
def visit_comparison(self, comparison: Comparison) -> str:
|
||||
value = comparison.value
|
||||
if isinstance(value, str):
|
||||
if comparison.comparator == Comparator.LIKE:
|
||||
value = f"'%{value}%'"
|
||||
else:
|
||||
value = f"'{value}'"
|
||||
return (
|
||||
f"{comparison.attribute}{self._format_func(comparison.comparator)}{value}"
|
||||
)
|
||||
|
||||
def visit_structured_query(
|
||||
self, structured_query: StructuredQuery
|
||||
) -> Tuple[str, dict]:
|
||||
if structured_query.filter is None:
|
||||
kwargs = {}
|
||||
else:
|
||||
kwargs = {"filter": structured_query.filter.accept(self)}
|
||||
return structured_query.query, kwargs
|
||||
@ -0,0 +1,52 @@
|
||||
from typing import Any, Tuple
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain.chains.query_constructor.ir import (
|
||||
Comparator,
|
||||
Comparison,
|
||||
Operation,
|
||||
Operator,
|
||||
)
|
||||
from langchain.retrievers.self_query.dashvector import DashvectorTranslator
|
||||
|
||||
DEFAULT_TRANSLATOR = DashvectorTranslator()
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"triplet",
|
||||
[
|
||||
(Comparator.EQ, 2, "foo = 2"),
|
||||
(Comparator.LT, 2, "foo < 2"),
|
||||
(Comparator.LTE, 2, "foo <= 2"),
|
||||
(Comparator.GT, 2, "foo > 2"),
|
||||
(Comparator.GTE, 2, "foo >= 2"),
|
||||
(Comparator.LIKE, "bar", "foo LIKE '%bar%'"),
|
||||
],
|
||||
)
|
||||
def test_visit_comparison(triplet: Tuple[Comparator, Any, str]) -> None:
|
||||
comparator, value, expected = triplet
|
||||
actual = DEFAULT_TRANSLATOR.visit_comparison(
|
||||
Comparison(comparator=comparator, attribute="foo", value=value)
|
||||
)
|
||||
assert expected == actual
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"triplet",
|
||||
[
|
||||
(Operator.AND, "foo < 2 AND bar = 'baz'"),
|
||||
(Operator.OR, "foo < 2 OR bar = 'baz'"),
|
||||
],
|
||||
)
|
||||
def test_visit_operation(triplet: Tuple[Operator, str]) -> None:
|
||||
operator, expected = triplet
|
||||
op = Operation(
|
||||
operator=operator,
|
||||
arguments=[
|
||||
Comparison(comparator=Comparator.LT, attribute="foo", value=2),
|
||||
Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
|
||||
],
|
||||
)
|
||||
actual = DEFAULT_TRANSLATOR.visit_operation(op)
|
||||
assert expected == actual
|
||||
Loading…
Reference in New Issue