Coherence

This notebook covers how to get started with the Coherence vector store.

Coherence is an in-memory data grid that provides a distributed, fault-tolerant, and scalable platform for managing and accessing data. It is primarily used for high-performance, mission-critical enterprise applications that require low-latency access to large datasets. In addition to the commercially available product, Oracle also offers Coherence CE (Community Edition)

Setup

To access Coherence vector stores you'll need to install the langchain-coherence integration package.

pip install langchain_coherence

Usage

Before using LangChain's CoherenceVectorStore you must ensure that a Coherence server (Coherence CE 25.03+ or Oracle Coherence 14.1.2+) is running

For local development, we recommend using the Coherence CE container image:

docker run -d -p 1408:1408 ghcr.io/oracle/coherence-ce:25.03.2

Add Documents and retrieve them:

from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_huggingface.embeddings import HuggingFaceEmbeddings

from coherence import NamedMap, Session
from langchain_core.vectorstores.coherence_store import CoherenceVectorStore

session: Session = await Session.create()
try:
    named_map: NamedMap[str, Document] = await session.get_map("my-map")
    embedding :Embeddings = HuggingFaceEmbeddings(
                model_name="sentence-transformers/all-MiniLM-l6-v2")
    # this embedding generates vectors of dimension 384
    cvs :CoherenceVectorStore = await CoherenceVectorStore.create(
                                        named_map,embedding,384)
    d1 :Document = Document(id="1", page_content="apple")
    d2 :Document = Document(id="2", page_content="orange")
    documents = [d1, d2]
    await cvs.aadd_documents(documents)

    ids = [doc.id for doc in documents]
    l = await cvs.aget_by_ids(ids)
    assert len(l) == len(ids)
    print("====")
    for e in l:
        print(e)
finally:
    await session.close()

API Reference:Document | Embeddings

Delete Documents:

from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_huggingface.embeddings import HuggingFaceEmbeddings

from coherence import NamedMap, Session
from langchain_core.vectorstores.coherence_store import CoherenceVectorStore

session: Session = await Session.create()
try:
    named_map: NamedMap[str, Document] = await session.get_map("my-map")
    embedding :Embeddings = HuggingFaceEmbeddings(
                model_name="sentence-transformers/all-MiniLM-l6-v2")
    # this embedding generates vectors of dimension 384
    cvs :CoherenceVectorStore = await CoherenceVectorStore.create(
                                        named_map,embedding,384)
    d1 :Document = Document(id="1", page_content="apple")
    d2 :Document = Document(id="2", page_content="orange")
    documents = [d1, d2]
    await cvs.aadd_documents(documents)

    ids = [doc.id for doc in documents]
    await cvs.adelete(ids)
finally:
    await session.close()

API Reference:Document | Embeddings

Similarity Search:

from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_huggingface.embeddings import HuggingFaceEmbeddings

from coherence import NamedMap, Session
from langchain_core.vectorstores.coherence_store import CoherenceVectorStore

def test_data():
    d1 :Document = Document(id="1", page_content="apple")
    d2 :Document = Document(id="2", page_content="orange")
    d3 :Document = Document(id="3", page_content="tiger")
    d4 :Document = Document(id="4", page_content="cat")
    d5 :Document = Document(id="5", page_content="dog")
    d6 :Document = Document(id="6", page_content="fox")
    d7 :Document = Document(id="7", page_content="pear")
    d8 :Document = Document(id="8", page_content="banana")
    d9 :Document = Document(id="9", page_content="plum")
    d10 :Document = Document(id="10", page_content="lion")

    documents = [d1, d2, d3, d4, d5, d6, d7, d8, d9, d10]
    return documents

async def test_asimilarity_search():
    documents = test_data()
    session: Session = await Session.create()
    try:
        named_map: NamedMap[str, Document] = await session.get_map("my-map")
        embedding :Embeddings = HuggingFaceEmbeddings(
                model_name="sentence-transformers/all-MiniLM-l6-v2")
        # this embedding generates vectors of dimension 384
        cvs :CoherenceVectorStore = await CoherenceVectorStore.create(
                                            named_map,embedding,384)
        await cvs.aadd_documents(documents)
        ids = [doc.id for doc in documents]
        l = await cvs.aget_by_ids(ids)
        assert len(l) == 10

        result = await cvs.asimilarity_search("fruit")
        assert len(result) == 4
        print("====")
        for e in result:
            print(e)
    finally:
        await session.close()

API Reference:Document | Embeddings

Similarity Search by vector :

from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_huggingface.embeddings import HuggingFaceEmbeddings

from coherence import NamedMap, Session
from langchain_core.vectorstores.coherence_store import CoherenceVectorStore

def test_data():
    d1 :Document = Document(id="1", page_content="apple")
    d2 :Document = Document(id="2", page_content="orange")
    d3 :Document = Document(id="3", page_content="tiger")
    d4 :Document = Document(id="4", page_content="cat")
    d5 :Document = Document(id="5", page_content="dog")
    d6 :Document = Document(id="6", page_content="fox")
    d7 :Document = Document(id="7", page_content="pear")
    d8 :Document = Document(id="8", page_content="banana")
    d9 :Document = Document(id="9", page_content="plum")
    d10 :Document = Document(id="10", page_content="lion")

    documents = [d1, d2, d3, d4, d5, d6, d7, d8, d9, d10]
    return documents

async def test_asimilarity_search_by_vector():
    documents = test_data()
    session: Session = await Session.create()
    try:
        named_map: NamedMap[str, Document] = await session.get_map("my-map")
        embedding :Embeddings = HuggingFaceEmbeddings(
                model_name="sentence-transformers/all-MiniLM-l6-v2")
        # this embedding generates vectors of dimension 384
        cvs :CoherenceVectorStore = await CoherenceVectorStore.create(
                                            named_map,embedding,384)
        await cvs.aadd_documents(documents)
        ids = [doc.id for doc in documents]
        l = await cvs.aget_by_ids(ids)
        assert len(l) == 10

        vector = cvs.embeddings.embed_query("fruit")
        result = await cvs.asimilarity_search_by_vector(vector)
        assert len(result) == 4
        print("====")
        for e in result:
            print(e)
    finally:
        await session.close()

API Reference:Document | Embeddings

Related

• Vector store conceptual guide
• Vector store how-to guides