PGVector

An implementation of LangChain vectorstore abstraction using postgres as the backend and utilizing the pgvector 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://... to postgresql+psycopg://langchain:langchain@... (yes, it's the driver name is psycopg not psycopg3, but it'll use psycopg3.
• 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(model="embed-english-v3.0")

vectorstore = PGVector(
    embeddings=embeddings,
    collection_name=collection_name,
    connection=connection,
    use_jsonb=True,
)

API Reference:CohereEmbeddings | Document

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'})]