Tencent Cloud VectorDB
Tencent Cloud VectorDB is a fully managed, self-developed, enterprise-level distributed database service designed for storing, retrieving, and analyzing multi-dimensional vector data.
In the walkthrough, we'll demo the SelfQueryRetriever
with a Tencent Cloud VectorDB.
create a TencentVectorDB instance
First we'll want to create a TencentVectorDB and seed it with some data. We've created a small demo set of documents that contain summaries of movies.
Note: The self-query retriever requires you to have lark
installed (pip install lark
) along with integration-specific requirements.
%pip install --upgrade --quiet tcvectordb langchain-openai tiktoken lark
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.2.1[0m[39;49m -> [0m[32;49m24.0[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m
Note: you may need to restart the kernel to use updated packages.
We want to use OpenAIEmbeddings
so we have to get the OpenAI API Key.
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
create a TencentVectorDB instance and seed it with some data:
from langchain_community.vectorstores.tencentvectordb import (
ConnectionParams,
MetaField,
TencentVectorDB,
)
from langchain_core.documents import Document
from tcvectordb.model.enum import FieldType
meta_fields = [
MetaField(name="year", data_type="uint64", index=True),
MetaField(name="rating", data_type="string", index=False),
MetaField(name="genre", data_type=FieldType.String, index=True),
MetaField(name="director", data_type=FieldType.String, index=True),
]
docs = [
Document(
page_content="The Shawshank Redemption is a 1994 American drama film written and directed by Frank Darabont.",
metadata={
"year": 1994,
"rating": "9.3",
"genre": "drama",
"director": "Frank Darabont",
},
),
Document(
page_content="The Godfather is a 1972 American crime film directed by Francis Ford Coppola.",
metadata={
"year": 1972,
"rating": "9.2",
"genre": "crime",
"director": "Francis Ford Coppola",
},
),
Document(
page_content="The Dark Knight is a 2008 superhero film directed by Christopher Nolan.",
metadata={
"year": 2008,
"rating": "9.0",
"genre": "science fiction",
"director": "Christopher Nolan",
},
),
Document(
page_content="Inception is a 2010 science fiction action film written and directed by Christopher Nolan.",
metadata={
"year": 2010,
"rating": "8.8",
"genre": "science fiction",
"director": "Christopher Nolan",
},
),
Document(
page_content="The Avengers is a 2012 American superhero film based on the Marvel Comics superhero team of the same name.",
metadata={
"year": 2012,
"rating": "8.0",
"genre": "science fiction",
"director": "Joss Whedon",
},
),
Document(
page_content="Black Panther is a 2018 American superhero film based on the Marvel Comics character of the same name.",
metadata={
"year": 2018,
"rating": "7.3",
"genre": "science fiction",
"director": "Ryan Coogler",
},
),
]
vector_db = TencentVectorDB.from_documents(
docs,
None,
connection_params=ConnectionParams(
url="http://10.0.X.X",
key="eC4bLRy2va******************************",
username="root",
timeout=20,
),
collection_name="self_query_movies",
meta_fields=meta_fields,
drop_old=True,
)
API Reference:
Creating our 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_openai import ChatOpenAI
metadata_field_info = [
AttributeInfo(
name="genre",
description="The genre of the movie",
type="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="string"
),
]
document_content_description = "Brief summary of a movie"
API Reference:
llm = ChatOpenAI(temperature=0, model="gpt-4", max_tokens=4069)
retriever = SelfQueryRetriever.from_llm(
llm, vector_db, document_content_description, metadata_field_info, verbose=True
)
Test it out
And now we can try actually using our retriever!
# This example only specifies a relevant query
retriever.invoke("movies about a superhero")
[Document(page_content='The Dark Knight is a 2008 superhero film directed by Christopher Nolan.', metadata={'year': 2008, 'rating': '9.0', 'genre': 'science fiction', 'director': 'Christopher Nolan'}),
Document(page_content='The Avengers is a 2012 American superhero film based on the Marvel Comics superhero team of the same name.', metadata={'year': 2012, 'rating': '8.0', 'genre': 'science fiction', 'director': 'Joss Whedon'}),
Document(page_content='Black Panther is a 2018 American superhero film based on the Marvel Comics character of the same name.', metadata={'year': 2018, 'rating': '7.3', 'genre': 'science fiction', 'director': 'Ryan Coogler'}),
Document(page_content='The Godfather is a 1972 American crime film directed by Francis Ford Coppola.', metadata={'year': 1972, 'rating': '9.2', 'genre': 'crime', 'director': 'Francis Ford Coppola'})]
# This example only specifies a filter
retriever.invoke("movies that were released after 2010")
[Document(page_content='The Avengers is a 2012 American superhero film based on the Marvel Comics superhero team of the same name.', metadata={'year': 2012, 'rating': '8.0', 'genre': 'science fiction', 'director': 'Joss Whedon'}),
Document(page_content='Black Panther is a 2018 American superhero film based on the Marvel Comics character of the same name.', metadata={'year': 2018, 'rating': '7.3', 'genre': 'science fiction', 'director': 'Ryan Coogler'})]
# This example specifies both a relevant query and a filter
retriever.invoke("movies about a superhero which were released after 2010")
[Document(page_content='The Avengers is a 2012 American superhero film based on the Marvel Comics superhero team of the same name.', metadata={'year': 2012, 'rating': '8.0', 'genre': 'science fiction', 'director': 'Joss Whedon'}),
Document(page_content='Black Panther is a 2018 American superhero film based on the Marvel Comics character of the same name.', metadata={'year': 2018, 'rating': '7.3', 'genre': 'science fiction', 'director': 'Ryan Coogler'})]
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,
vector_db,
document_content_description,
metadata_field_info,
verbose=True,
enable_limit=True,
)
retriever.invoke("what are two movies about a superhero")
[Document(page_content='The Dark Knight is a 2008 superhero film directed by Christopher Nolan.', metadata={'year': 2008, 'rating': '9.0', 'genre': 'science fiction', 'director': 'Christopher Nolan'}),
Document(page_content='The Avengers is a 2012 American superhero film based on the Marvel Comics superhero team of the same name.', metadata={'year': 2012, 'rating': '8.0', 'genre': 'science fiction', 'director': 'Joss Whedon'})]