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DashVector

DashVector is a fully managed vector DB 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. The vector retrieval service DashVector is based on the Proxima core of the efficient vector engine independently developed by DAMO Academy, and provides a cloud-native, fully managed vector retrieval service with horizontal expansion capabilities. DashVector exposes its powerful vector management, vector query and other diversified capabilities through a simple and easy-to-use SDK/API interface, which can be quickly integrated by upper-layer AI applications, thereby providing services including large model ecology, multi-modal AI search, molecular structure A variety of application scenarios, including analysis, provide the required efficient vector retrieval capabilities.

In this notebook, we’ll demo the SelfQueryRetriever with a DashVector vector store.

Create DashVector vectorstore​

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.

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.

NOTE: The self-query retriever requires you to have lark package installed.

%pip install --upgrade --quiet  lark dashvector
import os

import dashvector

client = dashvector.Client(api_key=os.environ["DASHVECTOR_API_KEY"])
from langchain_community.embeddings import DashScopeEmbeddings
from langchain_community.vectorstores import DashVector
from langchain_core.documents import Document

embeddings = DashScopeEmbeddings()

# create DashVector collection
client.create("langchain-self-retriever-demo", dimension=1536)
docs = [
Document(
page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",
metadata={"year": 1993, "rating": 7.7, "genre": "action"},
),
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},
),
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},
),
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},
),
Document(
page_content="Toys come alive and have a blast doing so",
metadata={"year": 1995, "genre": "animated"},
),
Document(
page_content="Three men walk into the Zone, three men walk out of the Zone",
metadata={
"year": 1979,
"director": "Andrei Tarkovsky",
"genre": "science fiction",
"rating": 9.9,
},
),
]
vectorstore = DashVector.from_documents(
docs, embeddings, collection_name="langchain-self-retriever-demo"
)

Create your self-querying retriever​

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.

from langchain.chains.query_constructor.base import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_community.llms import Tongyi

metadata_field_info = [
AttributeInfo(
name="genre",
description="The genre of the movie",
type="string or list[string]",
),
AttributeInfo(
name="year",
description="The year the movie was released",
type="integer",
),
AttributeInfo(
name="director",
description="The name of the movie director",
type="string",
),
AttributeInfo(
name="rating", description="A 1-10 rating for the movie", type="float"
),
]
document_content_description = "Brief summary of a movie"
llm = Tongyi(temperature=0)
retriever = SelfQueryRetriever.from_llm(
llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)

Testing it out​

And now we can try actually using our retriever!

# This example only specifies a relevant query
retriever.get_relevant_documents("What are some movies about dinosaurs")
query='dinosaurs' filter=None limit=None
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}),
Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),
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}),
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})]
# This example only specifies a filter
retriever.get_relevant_documents("I want to watch a movie rated higher than 8.5")
query=' ' filter=Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5) limit=None
[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'}),
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})]
# This example specifies a query and a filter
retriever.get_relevant_documents("Has Greta Gerwig directed any movies about women")
query='Greta Gerwig' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None
[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})]
# This example specifies a composite filter
retriever.get_relevant_documents(
"What's a highly rated (above 8.5) science fiction film?"
)
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
[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'})]

Filter k​

We can also use the self query retriever to specify k: the number of documents to fetch.

We can do this by passing enable_limit=True to the constructor.

retriever = SelfQueryRetriever.from_llm(
llm,
vectorstore,
document_content_description,
metadata_field_info,
enable_limit=True,
verbose=True,
)
# This example only specifies a relevant query
retriever.get_relevant_documents("what are two movies about dinosaurs")
query='dinosaurs' filter=None limit=2
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}),
Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]