Archives
/
langchain
mirror of https://github.com/hwchase17/langchain
2
0
Fork 1
Code Issues Packages Projects Releases Wiki Activity

community[minor]: Add SelfQueryRetriever support to PGVector (#16991)

Browse Source 
- **Description:** Add SelfQueryRetriever support to PGVector
  - **Issue:** -
  - **Dependencies:** -
  - **Twitter handle:** -

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
pull/17120/head
Junyoung Park 4 months ago committed by GitHub
parent cd945e3a5b
commit 1ed73f1992
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 450 additions and 0 deletions
Show Stats Download Patch File Download Diff File

308
docs/docs/integrations/retrievers/self_query/pgvector_self_query.ipynb
Unescape Escape View File

@ -0,0 +1,308 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "13afcae7",
"metadata": {},
"source": [
"# PGVector\n",
"\n",
">[PGVector](https://github.com/pgvector/pgvector) is a vector similarity search for Postgres.\n",
"\n",
"In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `PGVector` vector store."
]
},
{
"cell_type": "markdown",
"id": "68e75fb9",
"metadata": {},
"source": [
"## Creating a PGVector vector store\n",
"First we'll want to create a PGVector vector store 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 `` package."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63a8af5b",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%pip install --upgrade --quiet lark pgvector psycopg2-binary"
]
},
{
"cell_type": "markdown",
"id": "83811610-7df3-4ede-b268-68a6a83ba9e2",
"metadata": {},
"source": [
"We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dd01b61b-7d32-4a55-85d6-b2d2d4f18840",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb4a5787",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.schema import Document\n",
"from langchain_community.vectorstores import PGVector\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"collection = \"Name of your collection\"\n",
"embeddings = OpenAIEmbeddings()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bcbe04d9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"docs = [\n",
" Document(\n",
" page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
" metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},\n",
" ),\n",
" Document(\n",
" page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
" metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n",
" ),\n",
" Document(\n",
" page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
" metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n",
" ),\n",
" Document(\n",
" page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
" metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n",
" ),\n",
" Document(\n",
" page_content=\"Toys come alive and have a blast doing so\",\n",
" metadata={\"year\": 1995, \"genre\": \"animated\"},\n",
" ),\n",
" Document(\n",
" page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
" metadata={\n",
" \"year\": 1979,\n",
" \"director\": \"Andrei Tarkovsky\",\n",
" \"genre\": \"science fiction\",\n",
" \"rating\": 9.9,\n",
" },\n",
" ),\n",
"]\n",
"vectorstore = PGVector.from_documents(\n",
" docs,\n",
" embeddings,\n",
" collection_name=collection,\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": 6,
"id": "86e34dbf",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chains.query_constructor.base import AttributeInfo\n",
"from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
"from langchain_openai import OpenAI\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\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
" ),\n",
"]\n",
"document_content_description = \"Brief summary of a movie\"\n",
"llm = OpenAI(temperature=0)\n",
"retriever = SelfQueryRetriever.from_llm(\n",
" llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
")"
]
},
{
"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": 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": {},
"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(\n",
" \"What's a highly rated (above 8.5) science fiction film?\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "12a51522",
"metadata": {},
"outputs": [],
"source": [
"# This example specifies a query and composite filter\n",
"retriever.get_relevant_documents(\n",
" \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
")"
]
},
{
"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": 7,
"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": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

3
libs/langchain/langchain/retrievers/self_query/base.py
Unescape Escape View File

@ -12,6 +12,7 @@ from langchain_community.vectorstores import (
MongoDBAtlasVectorSearch,
MyScale,
OpenSearchVectorSearch,
PGVector,
Pinecone,
Qdrant,
Redis,
@ -43,6 +44,7 @@ from langchain.retrievers.self_query.milvus import MilvusTranslator
from langchain.retrievers.self_query.mongodb_atlas import MongoDBAtlasTranslator
from langchain.retrievers.self_query.myscale import MyScaleTranslator
from langchain.retrievers.self_query.opensearch import OpenSearchTranslator
from langchain.retrievers.self_query.pgvector import PGVectorTranslator
from langchain.retrievers.self_query.pinecone import PineconeTranslator
from langchain.retrievers.self_query.qdrant import QdrantTranslator
from langchain.retrievers.self_query.redis import RedisTranslator
@ -58,6 +60,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
"""Get the translator class corresponding to the vector store class."""
BUILTIN_TRANSLATORS: Dict[Type[VectorStore], Type[Visitor]] = {
AstraDB: AstraDBTranslator,
PGVector: PGVectorTranslator,
Pinecone: PineconeTranslator,
Chroma: ChromaTranslator,
DashVector: DashvectorTranslator,

52
libs/langchain/langchain/retrievers/self_query/pgvector.py
Unescape Escape View File

@ -0,0 +1,52 @@
from typing import Dict, Tuple, Union
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
class PGVectorTranslator(Visitor):
"""Translate `PGVector` internal query language elements to valid filters."""
allowed_operators = [Operator.AND, Operator.OR]
"""Subset of allowed logical operators."""
allowed_comparators = [
Comparator.EQ,
Comparator.NE,
Comparator.GT,
Comparator.LT,
Comparator.IN,
Comparator.NIN,
Comparator.CONTAIN,
Comparator.LIKE,
]
"""Subset of allowed logical comparators."""
def _format_func(self, func: Union[Operator, Comparator]) -> str:
self._validate_func(func)
return f"{func.value}"
def visit_operation(self, operation: Operation) -> Dict:
args = [arg.accept(self) for arg in operation.arguments]
return {self._format_func(operation.operator): args}
def visit_comparison(self, comparison: Comparison) -> Dict:
return {
comparison.attribute: {
self._format_func(comparison.comparator): comparison.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

87
libs/langchain/tests/unit_tests/retrievers/self_query/test_pgvector.py
Unescape Escape View File

@ -0,0 +1,87 @@
from typing import Dict, Tuple
import pytest as pytest
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
)
from langchain.retrievers.self_query.pgvector import PGVectorTranslator
DEFAULT_TRANSLATOR = PGVectorTranslator()
def test_visit_comparison() -> None:
comp = Comparison(comparator=Comparator.LT, attribute="foo", value=1)
expected = {"foo": {"lt": 1}}
actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
assert expected == actual
@pytest.mark.skip("Not implemented")
def test_visit_operation() -> None:
op = Operation(
operator=Operator.AND,
arguments=[
Comparison(comparator=Comparator.LT, attribute="foo", value=2),
Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
Comparison(comparator=Comparator.GT, attribute="abc", value=2.0),
],
)
expected = {
"foo": {"lt": 2},
"bar": {"eq": "baz"},
"abc": {"gt": 2.0},
}
actual = DEFAULT_TRANSLATOR.visit_operation(op)
assert expected == actual
def test_visit_structured_query() -> None:
query = "What is the capital of France?"
structured_query = StructuredQuery(
query=query,
filter=None,
)
expected: Tuple[str, Dict] = (query, {})
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
assert expected == actual
comp = Comparison(comparator=Comparator.LT, attribute="foo", value=1)
structured_query = StructuredQuery(
query=query,
filter=comp,
)
expected = (query, {"filter": {"foo": {"lt": 1}}})
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
assert expected == actual
op = Operation(
operator=Operator.AND,
arguments=[
Comparison(comparator=Comparator.LT, attribute="foo", value=2),
Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
Comparison(comparator=Comparator.GT, attribute="abc", value=2.0),
],
)
structured_query = StructuredQuery(
query=query,
filter=op,
)
expected = (
query,
{
"filter": {
"and": [
{"foo": {"lt": 2}},
{"bar": {"eq": "baz"}},
{"abc": {"gt": 2.0}},
]
}
},
)
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
assert expected == actual
Write Preview
Loading…
Cancel
Save