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Add self query translator for weaviate vectorstore (#4804)

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# Add self query translator for weaviate vectorstore

Adds support for the EQ comparator and the AND/OR operators. 

Co-authored-by: Dominic Chan <dchan@cppib.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
searx_updates
domchan 1 year ago committed by GitHub
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277
docs/modules/indexes/retrievers/examples/weaviate_self_query.ipynb
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@ -0,0 +1,277 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "13afcae7",
"metadata": {},
"source": [
"# Self-querying with Weaviate"
]
},
{
"cell_type": "markdown",
"id": "68e75fb9",
"metadata": {},
"source": [
"## Creating a Weaviate vectorstore\n",
"First we'll want to create a Weaviate 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 `weaviate-client` package."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63a8af5b",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#!pip install lark weaviate-client"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cb4a5787",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.schema import Document\n",
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.vectorstores import Weaviate\n",
"import os\n",
"\n",
"embeddings = OpenAIEmbeddings()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "bcbe04d9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"docs = [\n",
" Document(page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\", metadata={\"year\": 1993, \"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={\"year\": 2010, \"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={\"year\": 2006, \"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={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3}),\n",
" Document(page_content=\"Toys come alive and have a blast doing so\", metadata={\"year\": 1995, \"genre\": \"animated\"}),\n",
" Document(page_content=\"Three men walk into the Zone, three men walk out of the Zone\", metadata={\"year\": 1979, \"rating\": 9.9, \"director\": \"Andrei Tarkovsky\", \"genre\": \"science fiction\", \"rating\": 9.9})\n",
"]\n",
"vectorstore = Weaviate.from_documents(\n",
" docs, embeddings, weaviate_url=\"http://127.0.0.1:8080\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "5ecaab6d",
"metadata": {},
"source": [
"## Creating our self-querying retriever\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 23,
"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 genre of the movie\", \n",
" type=\"string or list[string]\", \n",
" ),\n",
" AttributeInfo(\n",
" name=\"year\",\n",
" description=\"The year the movie was released\", \n",
" type=\"integer\", \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)"
]
},
{
"cell_type": "markdown",
"id": "ea9df8d4",
"metadata": {},
"source": [
"## Testing it out\n",
"And now we can try actually using our retriever!"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "38a126e9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"query='dinosaur' filter=None limit=None\n"
]
},
{
"data": {
"text/plain": [
"[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': 'science fiction', 'rating': 7.7, 'year': 1993}),\n",
" Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'rating': None, 'year': 1995}),\n",
" Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'genre': 'science fiction', 'rating': 9.9, 'year': 1979}),\n",
" Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'genre': None, 'rating': 8.6, 'year': 2006})]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This example only specifies a relevant query\n",
"retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "b19d4da0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None\n"
]
},
{
"data": {
"text/plain": [
"[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'genre': None, 'rating': 8.3, 'year': 2019})]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This example specifies a query and a filter\n",
"retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
]
},
{
"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": 27,
"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": 28,
"id": "2758d229-4f97-499c-819f-888acaf8ee10",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"query='dinosaur' filter=None limit=2\n"
]
},
{
"data": {
"text/plain": [
"[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': 'science fiction', 'rating': 7.7, 'year': 1993}),\n",
" Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'rating': None, 'year': 1995})]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"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": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

4
langchain/retrievers/self_query/base.py
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@ -10,8 +10,9 @@ from langchain.chains.query_constructor.ir import StructuredQuery, Visitor
from langchain.chains.query_constructor.schema import AttributeInfo
from langchain.retrievers.self_query.chroma import ChromaTranslator
from langchain.retrievers.self_query.pinecone import PineconeTranslator
from langchain.retrievers.self_query.weaviate import WeaviateTranslator
from langchain.schema import BaseRetriever, Document
from langchain.vectorstores import Chroma, Pinecone, VectorStore
from langchain.vectorstores import Chroma, Pinecone, VectorStore, Weaviate
def _get_builtin_translator(vectorstore_cls: Type[VectorStore]) -> Visitor:
@ -19,6 +20,7 @@ def _get_builtin_translator(vectorstore_cls: Type[VectorStore]) -> Visitor:
BUILTIN_TRANSLATORS: Dict[Type[VectorStore], Type[Visitor]] = {
Pinecone: PineconeTranslator,
Chroma: ChromaTranslator,
Weaviate: WeaviateTranslator,
}
if vectorstore_cls not in BUILTIN_TRANSLATORS:
raise ValueError(

60
langchain/retrievers/self_query/weaviate.py
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"""Logic for converting internal query language to a valid Weaviate query."""
from typing import Dict, Tuple, Union
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
class WeaviateTranslator(Visitor):
"""Logic for converting internal query language elements to valid filters."""
allowed_operators = [Operator.AND, Operator.OR]
"""Subset of allowed logical operators."""
allowed_comparators = [Comparator.EQ]
def _map_func(self, func: Union[Operator, Comparator]) -> str:
# https://weaviate.io/developers/weaviate/api/graphql/filters
map_dict = {Operator.AND: "And", Operator.OR: "Or", Comparator.EQ: "Equal"}
return map_dict[func]
def _format_func(self, func: Union[Operator, Comparator]) -> str:
if isinstance(func, Operator) and self.allowed_operators is not None:
if func not in self.allowed_operators:
raise ValueError(
f"Received disallowed operator {func}. Allowed "
f"comparators are {self.allowed_operators}"
)
if isinstance(func, Comparator) and self.allowed_comparators is not None:
if func not in self.allowed_comparators:
raise ValueError(
f"Received disallowed comparator {func}. Allowed "
f"comparators are {self.allowed_comparators}"
)
return self._map_func(func)
def visit_operation(self, operation: Operation) -> Dict:
args = [arg.accept(self) for arg in operation.arguments]
return {"operator": self._format_func(operation.operator), "operands": args}
def visit_comparison(self, comparison: Comparison) -> Dict:
return {
"path": [comparison.attribute],
"operator": self._format_func(comparison.comparator),
"valueText": comparison.value,
}
def visit_structured_query(
self, structured_query: StructuredQuery
) -> Tuple[str, dict]:
if structured_query.filter is None:
kwargs = {}
else:
kwargs = {"where_filter": structured_query.filter.accept(self)}
return structured_query.query, kwargs
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