PGVector
An implementation of LangChain vectorstore abstraction using
postgres
as the backend and utilizing thepgvector
extension.
The code lives in an integration package called: langchain_postgres.
You can run the following command to spin up a a postgres container with the pgvector
extension:
docker run --name pgvector-container -e POSTGRES_USER=langchain -e POSTGRES_PASSWORD=langchain -e POSTGRES_DB=langchain -p 6024:5432 -d pgvector/pgvector:pg16
Status
This code has been ported over from langchain_community
into a dedicated package called langchain-postgres
. The following changes have been made:
- langchain_postgres works only with psycopg3. Please update your connnecion strings from
postgresql+psycopg2://...
topostgresql+psycopg://langchain:langchain@...
(yes, it's the driver name ispsycopg
notpsycopg3
, but it'll usepsycopg3
. - The schema of the embedding store and collection have been changed to make add_documents work correctly with user specified ids.
- One has to pass an explicit connection object now.
Currently, there is no mechanism that supports easy data migration on schema changes. So any schema changes in the vectorstore will require the user to recreate the tables and re-add the documents. If this is a concern, please use a different vectorstore. If not, this implementation should be fine for your use case.
Install dependencies
Here, we're using langchain_cohere
for embeddings, but you can use other embeddings providers.
!pip install --quiet -U langchain_cohere
!pip install --quiet -U langchain_postgres
Initialize the vectorstore
from langchain_cohere import CohereEmbeddings
from langchain_core.documents import Document
from langchain_postgres import PGVector
from langchain_postgres.vectorstores import PGVector
# See docker command above to launch a postgres instance with pgvector enabled.
connection = "postgresql+psycopg://langchain:langchain@localhost:6024/langchain" # Uses psycopg3!
collection_name = "my_docs"
embeddings = CohereEmbeddings()
vectorstore = PGVector(
embeddings=embeddings,
collection_name=collection_name,
connection=connection,
use_jsonb=True,
)
API Reference:
Drop tables
If you need to drop tables (e.g., updating the embedding to a different dimension or just updating the embedding provider):
vectorstore.drop_tables()
Add documents
Add documents to the vectorstore
docs = [
Document(
page_content="there are cats in the pond",
metadata={"id": 1, "location": "pond", "topic": "animals"},
),
Document(
page_content="ducks are also found in the pond",
metadata={"id": 2, "location": "pond", "topic": "animals"},
),
Document(
page_content="fresh apples are available at the market",
metadata={"id": 3, "location": "market", "topic": "food"},
),
Document(
page_content="the market also sells fresh oranges",
metadata={"id": 4, "location": "market", "topic": "food"},
),
Document(
page_content="the new art exhibit is fascinating",
metadata={"id": 5, "location": "museum", "topic": "art"},
),
Document(
page_content="a sculpture exhibit is also at the museum",
metadata={"id": 6, "location": "museum", "topic": "art"},
),
Document(
page_content="a new coffee shop opened on Main Street",
metadata={"id": 7, "location": "Main Street", "topic": "food"},
),
Document(
page_content="the book club meets at the library",
metadata={"id": 8, "location": "library", "topic": "reading"},
),
Document(
page_content="the library hosts a weekly story time for kids",
metadata={"id": 9, "location": "library", "topic": "reading"},
),
Document(
page_content="a cooking class for beginners is offered at the community center",
metadata={"id": 10, "location": "community center", "topic": "classes"},
),
]
vectorstore.add_documents(docs, ids=[doc.metadata["id"] for doc in docs])
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
vectorstore.similarity_search("kitty", k=10)
[Document(page_content='there are cats in the pond', metadata={'id': 1, 'topic': 'animals', 'location': 'pond'}),
Document(page_content='the book club meets at the library', metadata={'id': 8, 'topic': 'reading', 'location': 'library'}),
Document(page_content='the library hosts a weekly story time for kids', metadata={'id': 9, 'topic': 'reading', 'location': 'library'}),
Document(page_content='the new art exhibit is fascinating', metadata={'id': 5, 'topic': 'art', 'location': 'museum'}),
Document(page_content='ducks are also found in the pond', metadata={'id': 2, 'topic': 'animals', 'location': 'pond'}),
Document(page_content='the market also sells fresh oranges', metadata={'id': 4, 'topic': 'food', 'location': 'market'}),
Document(page_content='a cooking class for beginners is offered at the community center', metadata={'id': 10, 'topic': 'classes', 'location': 'community center'}),
Document(page_content='fresh apples are available at the market', metadata={'id': 3, 'topic': 'food', 'location': 'market'}),
Document(page_content='a sculpture exhibit is also at the museum', metadata={'id': 6, 'topic': 'art', 'location': 'museum'}),
Document(page_content='a new coffee shop opened on Main Street', metadata={'id': 7, 'topic': 'food', 'location': 'Main Street'})]
Adding documents by ID will over-write any existing documents that match that ID.
docs = [
Document(
page_content="there are cats in the pond",
metadata={"id": 1, "location": "pond", "topic": "animals"},
),
Document(
page_content="ducks are also found in the pond",
metadata={"id": 2, "location": "pond", "topic": "animals"},
),
Document(
page_content="fresh apples are available at the market",
metadata={"id": 3, "location": "market", "topic": "food"},
),
Document(
page_content="the market also sells fresh oranges",
metadata={"id": 4, "location": "market", "topic": "food"},
),
Document(
page_content="the new art exhibit is fascinating",
metadata={"id": 5, "location": "museum", "topic": "art"},
),
Document(
page_content="a sculpture exhibit is also at the museum",
metadata={"id": 6, "location": "museum", "topic": "art"},
),
Document(
page_content="a new coffee shop opened on Main Street",
metadata={"id": 7, "location": "Main Street", "topic": "food"},
),
Document(
page_content="the book club meets at the library",
metadata={"id": 8, "location": "library", "topic": "reading"},
),
Document(
page_content="the library hosts a weekly story time for kids",
metadata={"id": 9, "location": "library", "topic": "reading"},
),
Document(
page_content="a cooking class for beginners is offered at the community center",
metadata={"id": 10, "location": "community center", "topic": "classes"},
),
]
Filtering Support
The vectorstore supports a set of filters that can be applied against the metadata fields of the documents.
Operator | Meaning/Category |
---|---|
\$eq | Equality (==) |
\$ne | Inequality (!=) |
\$lt | Less than (<) |
\$lte | Less than or equal (<=) |
\$gt | Greater than (>) |
\$gte | Greater than or equal (>=) |
\$in | Special Cased (in) |
\$nin | Special Cased (not in) |
\$between | Special Cased (between) |
\$like | Text (like) |
\$ilike | Text (case-insensitive like) |
\$and | Logical (and) |
\$or | Logical (or) |
vectorstore.similarity_search("kitty", k=10, filter={"id": {"$in": [1, 5, 2, 9]}})
[Document(page_content='there are cats in the pond', metadata={'id': 1, 'topic': 'animals', 'location': 'pond'}),
Document(page_content='the library hosts a weekly story time for kids', metadata={'id': 9, 'topic': 'reading', 'location': 'library'}),
Document(page_content='the new art exhibit is fascinating', metadata={'id': 5, 'topic': 'art', 'location': 'museum'}),
Document(page_content='ducks are also found in the pond', metadata={'id': 2, 'topic': 'animals', 'location': 'pond'})]
If you provide a dict with multiple fields, but no operators, the top level will be interpreted as a logical AND filter
vectorstore.similarity_search(
"ducks",
k=10,
filter={"id": {"$in": [1, 5, 2, 9]}, "location": {"$in": ["pond", "market"]}},
)
[Document(page_content='ducks are also found in the pond', metadata={'id': 2, 'topic': 'animals', 'location': 'pond'}),
Document(page_content='there are cats in the pond', metadata={'id': 1, 'topic': 'animals', 'location': 'pond'})]
vectorstore.similarity_search(
"ducks",
k=10,
filter={
"$and": [
{"id": {"$in": [1, 5, 2, 9]}},
{"location": {"$in": ["pond", "market"]}},
]
},
)
[Document(page_content='ducks are also found in the pond', metadata={'id': 2, 'topic': 'animals', 'location': 'pond'}),
Document(page_content='there are cats in the pond', metadata={'id': 1, 'topic': 'animals', 'location': 'pond'})]
vectorstore.similarity_search("bird", k=10, filter={"location": {"$ne": "pond"}})
[Document(page_content='the book club meets at the library', metadata={'id': 8, 'topic': 'reading', 'location': 'library'}),
Document(page_content='the new art exhibit is fascinating', metadata={'id': 5, 'topic': 'art', 'location': 'museum'}),
Document(page_content='the library hosts a weekly story time for kids', metadata={'id': 9, 'topic': 'reading', 'location': 'library'}),
Document(page_content='a sculpture exhibit is also at the museum', metadata={'id': 6, 'topic': 'art', 'location': 'museum'}),
Document(page_content='the market also sells fresh oranges', metadata={'id': 4, 'topic': 'food', 'location': 'market'}),
Document(page_content='a cooking class for beginners is offered at the community center', metadata={'id': 10, 'topic': 'classes', 'location': 'community center'}),
Document(page_content='a new coffee shop opened on Main Street', metadata={'id': 7, 'topic': 'food', 'location': 'Main Street'}),
Document(page_content='fresh apples are available at the market', metadata={'id': 3, 'topic': 'food', 'location': 'market'})]