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

Initialization

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

Basic Initialization

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_coherence 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
    )
    # other operations on the CoherenceVectorStore can be done
finally:
    await session.close()

API Reference:Document | Embeddings

---------------------------------------------------------------------------
``````output
AioRpcError                               Traceback (most recent call last)
``````output
File ~/work/coherence/github/dhirupandey/langchain/libs/partners/coherence/.venv/lib/python3.9/site-packages/coherence/client.py:113, in _Handshake.handshake(self)
    112 try:
--> 113     await stream.write(RequestFactoryV1.init_sub_channel())
    114     response = await asyncio.wait_for(stream.read(), self._session.options.request_timeout_seconds)
``````output
File ~/work/coherence/github/dhirupandey/langchain/libs/partners/coherence/.venv/lib/python3.9/site-packages/grpc/aio/_call.py:526, in _StreamRequestMixin.write(self, request)
    525 self._raise_for_different_style(_APIStyle.READER_WRITER)
--> 526 await self._write(request)
``````output
File ~/work/coherence/github/dhirupandey/langchain/libs/partners/coherence/.venv/lib/python3.9/site-packages/grpc/aio/_call.py:495, in _StreamRequestMixin._write(self, request)
    494     if self.done():
--> 495         await self._raise_for_status()
    497 serialized_request = _common.serialize(
    498     request, self._request_serializer
    499 )
``````output
File ~/work/coherence/github/dhirupandey/langchain/libs/partners/coherence/.venv/lib/python3.9/site-packages/grpc/aio/_call.py:272, in Call._raise_for_status(self)
    271 if code != grpc.StatusCode.OK:
--> 272     raise _create_rpc_error(
    273         await self.initial_metadata(), await self._cython_call.status()
    274     )
``````output
AioRpcError: <AioRpcError of RPC that terminated with:
	status = StatusCode.UNAVAILABLE
	details = "connections to all backends failing; last error: UNKNOWN: ipv4:127.0.0.1:1408: Failed to connect to remote host: connect: Connection refused (61)"
	debug_error_string = "UNKNOWN:Error received from peer  {grpc_status:14, grpc_message:"connections to all backends failing; last error: UNKNOWN: ipv4:127.0.0.1:1408: Failed to connect to remote host: connect: Connection refused (61)"}"
>
``````output

The above exception was the direct cause of the following exception:
``````output
RuntimeError                              Traceback (most recent call last)
``````output
Cell In[5], line 8
      5 from coherence import NamedMap, Session
      6 from langchain_coherence import CoherenceVectorStore
----> 8 session: Session = await Session.create()
      9 try:
     10     named_map: NamedMap[str, Document] = await session.get_map("my-map")
``````output
File ~/work/coherence/github/dhirupandey/langchain/libs/partners/coherence/.venv/lib/python3.9/site-packages/coherence/client.py:1932, in Session.create(session_options)
   1930 session: Session = Session(session_options)
   1931 await session._set_ready(False)
-> 1932 await session._handshake.handshake()
   1933 if session._protocol_version > 0:
   1934     COH_LOG.info(
   1935         f"Session(id={session.session_id}, connected to [{session._session_options.address}]"
   1936         f" proxy-version={session._proxy_version}, protocol-version={session._protocol_version}"
   1937         f" proxy-member-id={session._proxy_member_id})"
   1938     )
``````output
File ~/work/coherence/github/dhirupandey/langchain/libs/partners/coherence/.venv/lib/python3.9/site-packages/coherence/client.py:129, in _Handshake.handshake(self)
    127         return
    128     else:
--> 129         raise RuntimeError(
    130             f"Unexpected error, {e}, when attempting to handshake with proxy: {e.details()}"
    131         ) from e
    132 except asyncio.TimeoutError as e:
    133     raise RuntimeError("Handshake with proxy timed out") from e
``````output
RuntimeError: Unexpected error, <AioRpcError of RPC that terminated with:
	status = StatusCode.UNAVAILABLE
	details = "connections to all backends failing; last error: UNKNOWN: ipv4:127.0.0.1:1408: Failed to connect to remote host: connect: Connection refused (61)"
	debug_error_string = "UNKNOWN:Error received from peer  {grpc_status:14, grpc_message:"connections to all backends failing; last error: UNKNOWN: ipv4:127.0.0.1:1408: Failed to connect to remote host: connect: Connection refused (61)"}"
>, when attempting to handshake with proxy: connections to all backends failing; last error: UNKNOWN: ipv4:127.0.0.1:1408: Failed to connect to remote host: connect: Connection refused (61)

Manage vector store

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

## Query vector store

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

## Usage for retrieval-augmented generation

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

## API reference

Vector store conceptual guide
Vector store how-to guides

Setup​

Initialization​

Usage​

Basic Initialization​

Manage vector store​

Add Documents and retrieve them:​

Delete Documents:​

Similarity Search:​

Similarity Search by vector :​

Related​

Setup

Initialization

Usage

Basic Initialization

Manage vector store

Add Documents and retrieve them:

Delete Documents:

Similarity Search:

Similarity Search by vector :

Related