diff --git a/docs/docs/integrations/vectorstores/coherence.ipynb b/docs/docs/integrations/vectorstores/coherence.ipynb new file mode 100644 index 00000000000..8a23d8c5e60 --- /dev/null +++ b/docs/docs/integrations/vectorstores/coherence.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6eb5f05c-0488-4563-a81f-d8e6c2c6b7d6", + "metadata": {}, + "source": [ + "# Coherence\n", + "\n", + "This notebook covers how to get started with the `Coherence` vector store.\n", + "\n", + ">[Coherence](https://www.oracle.com/java/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)](https://github.com/oracle/coherence)\n" + ] + }, + { + "cell_type": "markdown", + "id": "59238b02-359e-4ac3-939d-e88735183f28", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "To access `Coherence` vector stores you'll need to install the `langchain-coherence` integration package.\n", + "\n", + "```\n", + "pip install langchain_coherence\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "35761315-a34e-4f03-9246-459c1c36331b", + "metadata": {}, + "source": [ + "## Initialization" + ] + }, + { + "cell_type": "markdown", + "id": "592b5db4-807c-4a04-ac64-f0077c44edfa", + "metadata": {}, + "source": [ + "### Usage\n", + "\n", + "Before using LangChain's CoherenceVectorStore you must ensure that a Coherence server ([Coherence CE](https://github.com/oracle/coherence) 25.03+ or [Oracle Coherence](https://www.oracle.com/java/coherence/) 14.1.2+) is running \n", + "\n", + "For local development, we recommend using the Coherence CE container image:\n", + "```\n", + "docker run -d -p 1408:1408 ghcr.io/oracle/coherence-ce:25.03.2\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "86544bf7-8459-40cd-813c-cdbbcde9084f", + "metadata": {}, + "source": [ + "### Basic Initialization" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1bdd7da5-c050-47b0-a08c-52d1bd7b6948", + "metadata": {}, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "Unexpected error, , 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)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAioRpcError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/work/coherence/github/dhirupandey/langchain/libs/partners/coherence/.venv/lib/python3.9/site-packages/coherence/client.py:113\u001b[0m, in \u001b[0;36m_Handshake.handshake\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 113\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m stream\u001b[38;5;241m.\u001b[39mwrite(RequestFactoryV1\u001b[38;5;241m.\u001b[39minit_sub_channel())\n\u001b[1;32m 114\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mwait_for(stream\u001b[38;5;241m.\u001b[39mread(), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_session\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mrequest_timeout_seconds)\n", + "File \u001b[0;32m~/work/coherence/github/dhirupandey/langchain/libs/partners/coherence/.venv/lib/python3.9/site-packages/grpc/aio/_call.py:526\u001b[0m, in \u001b[0;36m_StreamRequestMixin.write\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 525\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_for_different_style(_APIStyle\u001b[38;5;241m.\u001b[39mREADER_WRITER)\n\u001b[0;32m--> 526\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_write(request)\n", + "File \u001b[0;32m~/work/coherence/github/dhirupandey/langchain/libs/partners/coherence/.venv/lib/python3.9/site-packages/grpc/aio/_call.py:495\u001b[0m, in \u001b[0;36m_StreamRequestMixin._write\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 494\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdone():\n\u001b[0;32m--> 495\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_for_status()\n\u001b[1;32m 497\u001b[0m serialized_request \u001b[38;5;241m=\u001b[39m _common\u001b[38;5;241m.\u001b[39mserialize(\n\u001b[1;32m 498\u001b[0m request, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_request_serializer\n\u001b[1;32m 499\u001b[0m )\n", + "File \u001b[0;32m~/work/coherence/github/dhirupandey/langchain/libs/partners/coherence/.venv/lib/python3.9/site-packages/grpc/aio/_call.py:272\u001b[0m, in \u001b[0;36mCall._raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m code \u001b[38;5;241m!=\u001b[39m grpc\u001b[38;5;241m.\u001b[39mStatusCode\u001b[38;5;241m.\u001b[39mOK:\n\u001b[0;32m--> 272\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m _create_rpc_error(\n\u001b[1;32m 273\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minitial_metadata(), \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_call\u001b[38;5;241m.\u001b[39mstatus()\n\u001b[1;32m 274\u001b[0m )\n", + "\u001b[0;31mAioRpcError\u001b[0m: ", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mcoherence\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m NamedMap, Session\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mlangchain_coherence\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m CoherenceVectorStore\n\u001b[0;32m----> 8\u001b[0m session: Session \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m Session\u001b[38;5;241m.\u001b[39mcreate()\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 10\u001b[0m named_map: NamedMap[\u001b[38;5;28mstr\u001b[39m, Document] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m session\u001b[38;5;241m.\u001b[39mget_map(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmy-map\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/work/coherence/github/dhirupandey/langchain/libs/partners/coherence/.venv/lib/python3.9/site-packages/coherence/client.py:1932\u001b[0m, in \u001b[0;36mSession.create\u001b[0;34m(session_options)\u001b[0m\n\u001b[1;32m 1930\u001b[0m session: Session \u001b[38;5;241m=\u001b[39m Session(session_options)\n\u001b[1;32m 1931\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m session\u001b[38;5;241m.\u001b[39m_set_ready(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m-> 1932\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m session\u001b[38;5;241m.\u001b[39m_handshake\u001b[38;5;241m.\u001b[39mhandshake()\n\u001b[1;32m 1933\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m session\u001b[38;5;241m.\u001b[39m_protocol_version \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 1934\u001b[0m COH_LOG\u001b[38;5;241m.\u001b[39minfo(\n\u001b[1;32m 1935\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSession(id=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msession\u001b[38;5;241m.\u001b[39msession_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, connected to [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msession\u001b[38;5;241m.\u001b[39m_session_options\u001b[38;5;241m.\u001b[39maddress\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1936\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m proxy-version=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msession\u001b[38;5;241m.\u001b[39m_proxy_version\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, protocol-version=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msession\u001b[38;5;241m.\u001b[39m_protocol_version\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1937\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m proxy-member-id=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msession\u001b[38;5;241m.\u001b[39m_proxy_member_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1938\u001b[0m )\n", + "File \u001b[0;32m~/work/coherence/github/dhirupandey/langchain/libs/partners/coherence/.venv/lib/python3.9/site-packages/coherence/client.py:129\u001b[0m, in \u001b[0;36m_Handshake.handshake\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 129\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 130\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnexpected error, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, when attempting to handshake with proxy: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;241m.\u001b[39mdetails()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 131\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mTimeoutError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHandshake with proxy timed out\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n", + "\u001b[0;31mRuntimeError\u001b[0m: Unexpected error, , 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)" + ] + } + ], + "source": [ + "from langchain_core.documents import Document\n", + "from langchain_core.embeddings import Embeddings\n", + "from langchain_huggingface.embeddings import HuggingFaceEmbeddings\n", + "\n", + "from coherence import NamedMap, Session\n", + "from langchain_coherence import CoherenceVectorStore\n", + "\n", + "session: Session = await Session.create()\n", + "try:\n", + " named_map: NamedMap[str, Document] = await session.get_map(\"my-map\")\n", + " embedding :Embeddings = HuggingFaceEmbeddings(\n", + " model_name=\"sentence-transformers/all-MiniLM-l6-v2\")\n", + " # this embedding generates vectors of dimension 384\n", + " cvs :CoherenceVectorStore = await CoherenceVectorStore.create(\n", + " named_map,embedding,384)\n", + " # other operations on the CoherenceVectorStore can be done\n", + "finally:\n", + " await session.close()" + ] + }, + { + "cell_type": "markdown", + "id": "5ca1dbd0-4fec-4907-8918-0a438a1b2535", + "metadata": {}, + "source": [ + "## Manage vector store" + ] + }, + { + "cell_type": "raw", + "id": "e7171672-4453-4b6f-afe6-071980908a5f", + "metadata": {}, + "source": [ + "### Add Documents and retrieve them:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d52c0187-f5c4-4c69-9052-0b8998945680", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.documents import Document\n", + "from langchain_core.embeddings import Embeddings\n", + "from langchain_huggingface.embeddings import HuggingFaceEmbeddings\n", + "\n", + "from coherence import NamedMap, Session\n", + "from langchain_core.vectorstores.coherence_store import CoherenceVectorStore\n", + "\n", + "session: Session = await Session.create()\n", + "try:\n", + " named_map: NamedMap[str, Document] = await session.get_map(\"my-map\")\n", + " embedding :Embeddings = HuggingFaceEmbeddings(\n", + " model_name=\"sentence-transformers/all-MiniLM-l6-v2\")\n", + " # this embedding generates vectors of dimension 384\n", + " cvs :CoherenceVectorStore = await CoherenceVectorStore.create(\n", + " named_map,embedding,384)\n", + " d1 :Document = Document(id=\"1\", page_content=\"apple\")\n", + " d2 :Document = Document(id=\"2\", page_content=\"orange\")\n", + " documents = [d1, d2]\n", + " await cvs.aadd_documents(documents)\n", + "\n", + " ids = [doc.id for doc in documents]\n", + " l = await cvs.aget_by_ids(ids)\n", + " assert len(l) == len(ids)\n", + " print(\"====\")\n", + " for e in l:\n", + " print(e)\n", + "finally:\n", + " await session.close()" + ] + }, + { + "cell_type": "raw", + "id": "f0f7215e-16a4-4fe7-8070-34c385aeeead", + "metadata": {}, + "source": [ + "### Delete Documents:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11328dac-633e-4155-8f19-95ef2bfa3d06", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.documents import Document\n", + "from langchain_core.embeddings import Embeddings\n", + "from langchain_huggingface.embeddings import HuggingFaceEmbeddings\n", + "\n", + "from coherence import NamedMap, Session\n", + "from langchain_core.vectorstores.coherence_store import CoherenceVectorStore\n", + "\n", + "session: Session = await Session.create()\n", + "try:\n", + " named_map: NamedMap[str, Document] = await session.get_map(\"my-map\")\n", + " embedding :Embeddings = HuggingFaceEmbeddings(\n", + " model_name=\"sentence-transformers/all-MiniLM-l6-v2\")\n", + " # this embedding generates vectors of dimension 384\n", + " cvs :CoherenceVectorStore = await CoherenceVectorStore.create(\n", + " named_map,embedding,384)\n", + " d1 :Document = Document(id=\"1\", page_content=\"apple\")\n", + " d2 :Document = Document(id=\"2\", page_content=\"orange\")\n", + " documents = [d1, d2]\n", + " await cvs.aadd_documents(documents)\n", + "\n", + " ids = [doc.id for doc in documents]\n", + " await cvs.adelete(ids)\n", + "finally:\n", + " await session.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8241b585-64ae-447a-b1a7-860d3c51f823", + "metadata": {}, + "outputs": [], + "source": [ + "## Query vector store" + ] + }, + { + "cell_type": "markdown", + "id": "a8c6ad91-ee54-486a-888f-cbfc89be75fd", + "metadata": {}, + "source": [ + "### Similarity Search:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffe24b05-7bf1-4eaa-a030-6ac3a0446f29", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.documents import Document\n", + "from langchain_core.embeddings import Embeddings\n", + "from langchain_huggingface.embeddings import HuggingFaceEmbeddings\n", + "\n", + "from coherence import NamedMap, Session\n", + "from langchain_core.vectorstores.coherence_store import CoherenceVectorStore\n", + "\n", + "def test_data():\n", + " d1 :Document = Document(id=\"1\", page_content=\"apple\")\n", + " d2 :Document = Document(id=\"2\", page_content=\"orange\")\n", + " d3 :Document = Document(id=\"3\", page_content=\"tiger\")\n", + " d4 :Document = Document(id=\"4\", page_content=\"cat\")\n", + " d5 :Document = Document(id=\"5\", page_content=\"dog\")\n", + " d6 :Document = Document(id=\"6\", page_content=\"fox\")\n", + " d7 :Document = Document(id=\"7\", page_content=\"pear\")\n", + " d8 :Document = Document(id=\"8\", page_content=\"banana\")\n", + " d9 :Document = Document(id=\"9\", page_content=\"plum\")\n", + " d10 :Document = Document(id=\"10\", page_content=\"lion\")\n", + "\n", + " documents = [d1, d2, d3, d4, d5, d6, d7, d8, d9, d10]\n", + " return documents\n", + "\n", + "async def test_asimilarity_search():\n", + " documents = test_data()\n", + " session: Session = await Session.create()\n", + " try:\n", + " named_map: NamedMap[str, Document] = await session.get_map(\"my-map\")\n", + " embedding :Embeddings = HuggingFaceEmbeddings(\n", + " model_name=\"sentence-transformers/all-MiniLM-l6-v2\")\n", + " # this embedding generates vectors of dimension 384\n", + " cvs :CoherenceVectorStore = await CoherenceVectorStore.create(\n", + " named_map,embedding,384)\n", + " await cvs.aadd_documents(documents)\n", + " ids = [doc.id for doc in documents]\n", + " l = await cvs.aget_by_ids(ids)\n", + " assert len(l) == 10\n", + "\n", + " result = await cvs.asimilarity_search(\"fruit\")\n", + " assert len(result) == 4\n", + " print(\"====\")\n", + " for e in result:\n", + " print(e)\n", + " finally:\n", + " await session.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6862ae65-a3d6-46e4-9e85-f3f64f2add5c", + "metadata": {}, + "outputs": [], + "source": [ + "## Usage for retrieval-augmented generation" + ] + }, + { + "cell_type": "markdown", + "id": "5411477e-5905-4a96-80d8-b8d2238c4bc4", + "metadata": {}, + "source": [ + "### Similarity Search by vector :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbb07be8-b13e-4bd5-a9ea-94603ff2a6e4", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.documents import Document\n", + "from langchain_core.embeddings import Embeddings\n", + "from langchain_huggingface.embeddings import HuggingFaceEmbeddings\n", + "\n", + "from coherence import NamedMap, Session\n", + "from langchain_core.vectorstores.coherence_store import CoherenceVectorStore\n", + "\n", + "def test_data():\n", + " d1 :Document = Document(id=\"1\", page_content=\"apple\")\n", + " d2 :Document = Document(id=\"2\", page_content=\"orange\")\n", + " d3 :Document = Document(id=\"3\", page_content=\"tiger\")\n", + " d4 :Document = Document(id=\"4\", page_content=\"cat\")\n", + " d5 :Document = Document(id=\"5\", page_content=\"dog\")\n", + " d6 :Document = Document(id=\"6\", page_content=\"fox\")\n", + " d7 :Document = Document(id=\"7\", page_content=\"pear\")\n", + " d8 :Document = Document(id=\"8\", page_content=\"banana\")\n", + " d9 :Document = Document(id=\"9\", page_content=\"plum\")\n", + " d10 :Document = Document(id=\"10\", page_content=\"lion\")\n", + "\n", + " documents = [d1, d2, d3, d4, d5, d6, d7, d8, d9, d10]\n", + " return documents\n", + "\n", + "async def test_asimilarity_search_by_vector():\n", + " documents = test_data()\n", + " session: Session = await Session.create()\n", + " try:\n", + " named_map: NamedMap[str, Document] = await session.get_map(\"my-map\")\n", + " embedding :Embeddings = HuggingFaceEmbeddings(\n", + " model_name=\"sentence-transformers/all-MiniLM-l6-v2\")\n", + " # this embedding generates vectors of dimension 384\n", + " cvs :CoherenceVectorStore = await CoherenceVectorStore.create(\n", + " named_map,embedding,384)\n", + " await cvs.aadd_documents(documents)\n", + " ids = [doc.id for doc in documents]\n", + " l = await cvs.aget_by_ids(ids)\n", + " assert len(l) == 10\n", + "\n", + " vector = cvs.embeddings.embed_query(\"fruit\")\n", + " result = await cvs.asimilarity_search_by_vector(vector)\n", + " assert len(result) == 4\n", + " print(\"====\")\n", + " for e in result:\n", + " print(e)\n", + " finally:\n", + " await session.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "803d5c9c-0df5-4a27-b179-b97c0ca0c27a", + "metadata": {}, + "outputs": [], + "source": [ + "## API reference" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.21" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/libs/partners/coherence/.gitignore b/libs/partners/coherence/.gitignore new file mode 100644 index 00000000000..bc65049590d --- /dev/null +++ b/libs/partners/coherence/.gitignore @@ -0,0 +1,36 @@ +# Python +__pycache__/ +*.py[cod] +*.egg +*.egg-info/ +dist/ +build/ +.eggs/ + +# Virtual environments +.venv/ +.env/ + +# uv cache +.uv/ + +# Testing +htmlcov/ +.cache/ +.coverage +coverage.xml + +# IDE +.idea/ +.vscode/ + +# Logs +*.log + +# OCA +.oca + +# OS +.DS_Store +Thumbs.db + diff --git a/libs/partners/coherence/LICENSE b/libs/partners/coherence/LICENSE new file mode 100644 index 00000000000..771f1479bc2 --- /dev/null +++ b/libs/partners/coherence/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/partners/coherence/Makefile b/libs/partners/coherence/Makefile new file mode 100644 index 00000000000..b7dae9e5e64 --- /dev/null +++ b/libs/partners/coherence/Makefile @@ -0,0 +1,84 @@ +# Makefile for LangChain-Coherence Integration +.PHONY: install update-dev update-core lock sync lint format fix check test clean docs-ipynb readme-ipynb create-ipynb help + +# Paths to common tools (adjust if using .uv or other env) +PYTHON := .venv/bin/python +PIP := .venv/bin/pip +RUFF := .venv/bin/ruff +PYTEST := .venv/bin/pytest +MYPY := .venv/bin/mypy + +PACKAGE_NAME=langchain-coherence +DIST_DIR=dist + +install: + @echo "๐Ÿ”ง Installing all dependencies..." + uv venv + uv pip install -e .[lint,typing,test,docs,publish] + +update-dev: + @echo "๐Ÿ”„ Updating development dependencies..." + uv pip install -e .[lint,typing,test,docs] --upgrade + +update-core: + @echo "๐Ÿ”„ Updating core dependencies..." + uv pip install --upgrade langchain-core coherence-client + +lock: + @echo "๐Ÿ” Locking all dependencies to uv.lock..." + uv pip compile --all-extras + +sync: + @echo "๐Ÿ“ฆ Syncing dependencies from uv.lock..." + uv pip install -r uv.lock + +lint: check + +check: + @echo "๐Ÿ” Running linter and type checker..." + $(RUFF) check langchain_coherence tests + $(MYPY) --explicit-package-bases langchain_coherence + +format: + @echo "๐ŸŽจ Formatting code with Ruff..." + $(RUFF) format langchain_coherence tests + +fix: + @echo "๐Ÿ”ง Fixing lint issues..." + $(MAKE) format + $(RUFF) check langchain_coherence tests --fix + +test: + @echo "๐Ÿงช Running tests..." + $(PYTEST) + +clean: + @echo "๐Ÿงน Cleaning build/test artifacts..." + rm -rf .pytest_cache .mypy_cache .ruff_cache __pycache__ *.egg-info dist build + +build: + @echo "๐Ÿงฑ Building distribution using local virtualenv" + $(PYTHON) -m build --no-isolation + +upload-pypi: + @echo "๐Ÿš€ Uploading to PyPI" + $(PYTHON) -m twine upload dist/* + +publish: build upload-pypi + +help: + @echo "๐Ÿ›  Available Make targets:" + @echo " install - Install all dependencies into .venv" + @echo " update-dev - Upgrade dev dependencies (ruff, pytest, etc.)" + @echo " update-core - Upgrade core runtime deps (langchain-core, coherence-client)" + @echo " lock - Generate uv.lock with pinned versions" + @echo " sync - Install from uv.lock (repeatable builds)" + @echo " lint - Run linter and mypy" + @echo " fix - Autoformat and fix issues" + @echo " test - Run all tests" + @echo " build - Building distribution using local virtualenv" + @echo " upload-pypi - Uploading to PyPI" + @echo " publish - calls build, upload-pypi" + @echo " clean - Remove temp and build files" + + diff --git a/libs/partners/coherence/README.md b/libs/partners/coherence/README.md new file mode 100644 index 00000000000..034c4f6fcdd --- /dev/null +++ b/libs/partners/coherence/README.md @@ -0,0 +1,103 @@ +# LangChain Coherence Integration + +This package integrates Oracle Coherence as a vector store in LangChain. + +## Installation + +```bash +pip install langchain_coherence +``` + +## Usage + +Before using LangChain's CoherenceVectorStore you must ensure that a Coherence server ([Coherence CE](https://github.com/oracle/coherence) 25.03+ or [Oracle Coherence](https://www.oracle.com/java/coherence/) 14.1.2+) is running + +For local development, we recommend using the Coherence CE container image: +```aiignore +docker run -d -p 1408:1408 ghcr.io/oracle/coherence-ce:25.03.2 +``` + +### Adding and retrieving Documents + +```python +import asyncio + +from langchain_coherence import CoherenceVectorStore +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_huggingface.embeddings import HuggingFaceEmbeddings +from coherence import NamedMap, Session + +async def do_run(): + 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() + +asyncio.run(do_run()) +``` +### SimilaritySearch on Documents + +```python +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() +``` \ No newline at end of file diff --git a/libs/partners/coherence/langchain_coherence/__init__.py b/libs/partners/coherence/langchain_coherence/__init__.py new file mode 100644 index 00000000000..35309f92865 --- /dev/null +++ b/libs/partners/coherence/langchain_coherence/__init__.py @@ -0,0 +1,9 @@ +"""Public interface for the LangChain Coherence integration.""" + +from __future__ import annotations + +__version__ = "0.0.1" + +from langchain_coherence.coherence_store import CoherenceVectorStore + +__all__ = ["CoherenceVectorStore"] diff --git a/libs/partners/coherence/langchain_coherence/coherence_store.py b/libs/partners/coherence/langchain_coherence/coherence_store.py new file mode 100644 index 00000000000..125eba6dff6 --- /dev/null +++ b/libs/partners/coherence/langchain_coherence/coherence_store.py @@ -0,0 +1,600 @@ +"""Coherence vector store.""" + +from __future__ import annotations + +import asyncio +import json +import uuid +from typing import ( + TYPE_CHECKING, + Any, + Final, + Optional, + cast, +) + +from typing_extensions import override + +if TYPE_CHECKING: + from collections.abc import Iterator, Sequence + +import jsonpickle # type: ignore[import-untyped] +from coherence import ( # type: ignore[import-untyped] + Extractors, + Filters, + NamedCache, +) +from coherence.ai import ( # type: ignore[import-untyped] + CosineDistance, + DistanceAlgorithm, + FloatVector, + HnswIndex, + QueryResult, + SimilaritySearch, + Vector, + Vectors, +) +from coherence.extractor import ( # type: ignore[import-untyped] + ValueExtractor, +) +from coherence.filter import ( # type: ignore[import-untyped] + Filter, +) +from coherence.serialization import ( # type: ignore[import-untyped] + JSONSerializer, + SerializerRegistry, +) +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.vectorstores import VectorStore + + +class CoherenceVectorStore(VectorStore): + """Coherence VectorStore implementation. + + Uses Coherence NamedCache, for similarity search. + + Setup: + Install ``langchain-core``. + + .. code-block:: bash + + pip install -U langchain-core + + Add Documents and retrieve them: + .. code-block:: python + + 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 + 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() + + Delete Documents: + .. code-block:: python + + 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) + 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() + + Similarity Search: + .. code-block:: python + + 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) + 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() + + Similarity Search by vector : + .. code-block:: python + + 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) + 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() + + """ + + VECTOR_FIELD: Final[str] = "__dict__.metadata.vector" + """The name of the field containing the vector embeddings.""" + + VECTOR_EXTRACTOR: Final[ValueExtractor] = Extractors.extract(VECTOR_FIELD) + """The ValueExtractor to extract the embeddings vector.""" + + def __init__(self, coherence_cache: NamedCache, embedding: Embeddings): + """Initialize with Coherence cache and embedding function. + + Args: + coherence_cache: Coherence NamedCache to use + embedding: embedding function to use. + """ + self.cache = coherence_cache + self.embedding = embedding + + @staticmethod + async def create( + coherence_cache: NamedCache, + embedding: Embeddings, + ) -> CoherenceVectorStore: + """Create an instance of CoherenceVectorStore. + + Args: + coherence_cache: Coherence NamedCache to use + embedding: embedding function to use. + """ + coh_store: CoherenceVectorStore = CoherenceVectorStore( + coherence_cache, embedding + ) + return coh_store + + async def add_index(self, dimensions: int) -> None: + """Creates index on the Coherence cache on the VECTOR_FIELD. + + Args: + dimensions: size of the vector created by the embedding function + """ + await self.cache.add_index( + HnswIndex(CoherenceVectorStore.VECTOR_EXTRACTOR, dimensions) + ) + + async def remove_index(self) -> None: + """Removes index on the Coherence cache on the VECTOR_FIELD.""" + await self.cache.remove_index(CoherenceVectorStore.VECTOR_EXTRACTOR) + + @property + @override + def embeddings(self) -> Embeddings: + return self.embedding + + @override + def add_documents( + self, documents: list[Document], ids: Optional[list[str]] = None, **kwargs: Any + ) -> list[str]: + raise NotImplementedError + + @override + async def aadd_documents( + self, documents: list[Document], ids: Optional[list[str]] = None, **kwargs: Any + ) -> list[str]: + """Async run more documents through the embeddings and add to the vectorstore. + + Args: + documents: Documents to add to the vectorstore. + ids: Optional list of IDs of the documents. + kwargs: Additional keyword arguments. + + Returns: + List of IDs of the added texts. + + Raises: + ValueError: If the number of IDs does not match the number of documents. + """ + texts = [doc.page_content for doc in documents] + vectors = await self.embedding.aembed_documents(texts) + + # Apply normalization and wrap in FloatVector + float_vectors = [FloatVector(Vectors.normalize(vector)) for vector in vectors] + + if ids and len(ids) != len(texts): + msg = ( + f"ids must be the same length as texts. " + f"Got {len(ids)} ids and {len(texts)} texts." + ) + raise ValueError(msg) + + id_iterator: Iterator[Optional[str]] = ( + iter(ids) if ids else iter(doc.id for doc in documents) + ) + ids_: list[str] = [] + + doc_map: dict[str, Document] = {} + for doc, vector in zip(documents, float_vectors): + doc_id = next(id_iterator) + doc_id_ = doc_id or str(uuid.uuid4()) + ids_.append(doc_id_) + doc.metadata["vector"] = vector + doc_map[doc_id_] = doc + + await self.cache.put_all(doc_map) + + return ids_ + + @override + def get_by_ids(self, ids: Sequence[str], /) -> list[Document]: + raise NotImplementedError + + @override + async def aget_by_ids(self, ids: Sequence[str], /) -> list[Document]: + """Get documents by their ids. + + Args: + ids: The ids of the documents to get. + + Returns: + A list of Document objects. + """ + return [e.value async for e in await self.cache.get_all(set(ids))] + + @override + async def adelete(self, ids: Optional[Sequence[str]] = None, **kwargs: Any) -> None: + """Async delete by Documeny ID or other criteria. + + Args: + ids: List of ids to delete. If None, delete all. Default is None. + **kwargs: Other keyword arguments that subclasses might use. + + Returns: + Optional[bool]: True if deletion is successful, + False otherwise, None if not implemented. + """ + if ids is None: + await self.cache.clear() + else: + # Efficient parallel delete + await asyncio.gather(*(self.cache.remove(i) for i in ids)) + + def _parse_coherence_kwargs( + self, **kwargs: Any + ) -> tuple[DistanceAlgorithm, Filter, bool]: + allowed_keys = {"algorithm", "filter", "brute_force"} + extra_keys = set(kwargs) - allowed_keys + if extra_keys: + # Silently ignore or log if needed + for key in extra_keys: + kwargs.pop(key) + + algorithm: DistanceAlgorithm = kwargs.get("algorithm", CosineDistance()) + filter_: Filter = kwargs.get("filter", Filters.always()) + brute_force: bool = kwargs.get("brute_force", False) + + return (algorithm, filter_, brute_force) + + @override + async def asimilarity_search( + self, query: str, k: int = 4, **kwargs: Any + ) -> list[Document]: + """Async method return list of docs most similar to query. + + Args: + query: Input text. + k: Number of Documents to return. Defaults to 4. + **kwargs: Optional arguments: + - algorithm: DistanceAlgorithm to use.(default CosineDistance) + https://oracle.github.io/coherence-py-client/api_reference/ai.html#cosinedistance + - filter: filter to use to limit the set of entries to search. + (default Filters.always()) + https://oracle.github.io/coherence-py-client/api_reference/filter.html + - brute_force: Force brute force search, ignoring any available indexes. + (default False) + https://oracle.github.io/coherence-py-client/api_reference/ai.html#similaritysearch + + Returns: + List of Documents most similar to the query. + """ + algorithm, filter_, brute_force = self._parse_coherence_kwargs(**kwargs) + + query_vector = self.embedding.embed_query(query) + float_query_vector = FloatVector(Vectors.normalize(query_vector)) + + search: SimilaritySearch = SimilaritySearch( + CoherenceVectorStore.VECTOR_EXTRACTOR, + float_query_vector, + k, + algorithm=algorithm, + filter=filter_, + brute_force=brute_force, + ) + query_results = await self.cache.aggregate(search) + + return [e.value for e in query_results] + + @override + def similarity_search( + self, query: str, k: int = 4, **kwargs: Any + ) -> list[Document]: + raise NotImplementedError + + @override + async def asimilarity_search_by_vector( + self, embedding: list[float], k: int = 4, **kwargs: Any + ) -> list[Document]: + """Async method return list of docs most similar to passed embedding vector. + + Args: + embedding: Input vector. + k: Number of Documents to return. Defaults to 4. + **kwargs: Optional arguments: + - algorithm: DistanceAlgorithm to use.(default CosineDistance) + https://oracle.github.io/coherence-py-client/api_reference/ai.html#cosinedistance + - filter: filter to use to limit the set of entries to search. + (default Filters.always()) + https://oracle.github.io/coherence-py-client/api_reference/filter.html + - brute_force: Force brute force search, ignoring any available indexes. + (default False) + https://oracle.github.io/coherence-py-client/api_reference/ai.html#similaritysearch + + Returns: + List of Documents most similar to the query. + """ + algorithm, filter_, brute_force = self._parse_coherence_kwargs(**kwargs) + float_query_vector = FloatVector(Vectors.normalize(embedding)) + + search: SimilaritySearch = SimilaritySearch( + CoherenceVectorStore.VECTOR_EXTRACTOR, + float_query_vector, + k, + algorithm=algorithm, + filter=filter_, + brute_force=brute_force, + ) + query_results = await self.cache.aggregate(search, filter=Filters.always()) + + return [e.value for e in query_results] + + @override + def similarity_search_by_vector( + self, embedding: list[float], k: int = 4, **kwargs: Any + ) -> list[Document]: + raise NotImplementedError + + @override + async def asimilarity_search_with_score( + self, query: str, k: int = 4, **kwargs: Any + ) -> list[tuple[Document, float]]: + """Async method return list of tuple(Document, score) most similar to query. + + Args: + query: Input text. + k: Number of Documents to return. Defaults to 4. + **kwargs: Optional arguments: + - algorithm: DistanceAlgorithm to use.(default CosineDistance) + https://oracle.github.io/coherence-py-client/api_reference/ai.html#cosinedistance + - filter: filter to use to limit the set of entries to search. + (default Filters.always()) + https://oracle.github.io/coherence-py-client/api_reference/filter.html + - brute_force: Force brute force search, ignoring any available indexes. + (default False) + https://oracle.github.io/coherence-py-client/api_reference/ai.html#similaritysearch + + Returns: + List of tuple(Document, score) most similar to the query. + """ + algorithm, filter_, brute_force = self._parse_coherence_kwargs(**kwargs) + query_vector = self.embedding.embed_query(query) + float_query_vector = FloatVector(Vectors.normalize(query_vector)) + + search: SimilaritySearch = SimilaritySearch( + CoherenceVectorStore.VECTOR_EXTRACTOR, + float_query_vector, + k, + algorithm=algorithm, + filter=filter_, + brute_force=brute_force, + ) + query_results: list[QueryResult] = await self.cache.aggregate( + search, filter=Filters.always() + ) + + return [(e.value, e.distance) for e in query_results] + + @override + def similarity_search_with_score( + self, query: str, k: int = 4, **kwargs: Any + ) -> list[tuple[Document, float]]: + raise NotImplementedError + + @classmethod + @override + def from_texts( + cls, + texts: list[str], + embedding: Embeddings, + metadatas: Optional[list[dict[Any, Any]]] = None, + **kwargs: Any, + ) -> CoherenceVectorStore: + msg = "Use `afrom_texts()` instead; sync context is not supported." + raise NotImplementedError(msg) + + @classmethod + @override + async def afrom_texts( + cls, + texts: list[str], + embedding: Embeddings, + metadatas: Optional[list[dict[str, Any]]] = None, + **kwargs: Any, + ) -> CoherenceVectorStore: + """Asynchronously initialize the CoherenceVectorStore from texts and embeddings. + + Args: + texts: List of input text strings. + embedding: Embedding function to use. + metadatas: Optional list of metadata dicts corresponding to each text. + kwargs: Additional keyword arguments. + - cache: Required Coherence NamedCache[str, Document] instance. + - ids: Optional list of document IDs. + + Returns: + CoherenceVectorStore: An initialized and populated vector store. + + Raises: + ValueError: If `cache` is not provided. + """ + # Extract and validate required Coherence cache + cache = kwargs.get("cache") + if cache is None: + msg = "Missing required 'cache' parameter in afrom_texts" + raise ValueError(msg) + + # Optionally use caller-supplied document IDs + ids: Optional[list[str]] = kwargs.get("ids") + if ids is not None and len(ids) != len(texts): + msg = "Length of 'ids' must match length of 'texts'" + raise ValueError(msg) + + # Create store instance + store = await cls.create(cache, embedding) + + # Construct Document objects + documents = [] + for i, text in enumerate(texts): + metadata = metadatas[i] if metadatas and i < len(metadatas) else {} + doc_id = ids[i] if ids else str(uuid.uuid4()) + documents.append(Document(page_content=text, metadata=metadata, id=doc_id)) + + # Add documents to vector store + await store.aadd_documents(documents) + return store + + +@jsonpickle.handlers.register(Document) +class _LangChainDocumentHandler(jsonpickle.handlers.BaseHandler): # type: ignore[misc] + def flatten(self, obj: object, data: dict[str, Any]) -> dict[str, Any]: + """Flatten object to a dictionary for handler to use.""" + ser = SerializerRegistry.serializer(JSONSerializer.SER_FORMAT) + json_ser = cast("JSONSerializer", ser) + o = cast("Document", obj) + vector = o.metadata["vector"] + if vector is not None and isinstance(vector, Vector): + s = json_ser.serialize(vector) + d = json.loads(s[1:]) + o.metadata["vector"] = json_ser.flatten_to_dict(d) + + data["__dict__"] = obj.__dict__ + return data + + def restore(self, obj: dict[str, Any]) -> Document: + """Convert dictionary to an object for handler to use.""" + ser = SerializerRegistry.serializer(JSONSerializer.SER_FORMAT) + json_ser = cast("JSONSerializer", ser) + d = Document(page_content="") + d.__dict__ = obj["__dict__"] + vector = d.metadata["vector"] + if vector is not None and isinstance(vector, dict): + d.metadata["vector"] = json_ser.restore_to_object(vector) + return d diff --git a/libs/partners/coherence/pyproject.toml b/libs/partners/coherence/pyproject.toml new file mode 100644 index 00000000000..efcf48461ae --- /dev/null +++ b/libs/partners/coherence/pyproject.toml @@ -0,0 +1,95 @@ +[project] +name = "langchain-coherence" +version = "0.0.1" +description = "LangChain integration for Oracle Coherence as a vector store." +authors = [{ name = "Your Name", email = "you@example.com" }] +license = {text = "MIT"} +readme = "README.md" +dependencies = [ + "langchain-core>=0.1.20", + "coherence-client>=2.0.4", +] +requires-python = ">=3.9" + +[project.urls] +"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/coherence" +repository = "https://github.com/langchain-ai/langchain" + +[project.optional-dependencies] +lint = [ + "ruff<0.12.0,>=0.11.2", +] +typing = [ + "mypy<1.16,>=1.15", +] +test = [ + "pytest<9,>=8", + "pytest-asyncio<1.0.0,>=0.21.1", + "langchain_huggingface", + "sentence_transformers" +] +docs = [ + "jupytext>=1.16", + "nbdoc>=0.0.29", +] +publish = [ + "build", + "twine" +] +[tool.mypy] +strict = "True" +disallow_untyped_defs = "True" + +[tool.ruff] +target-version = "py39" + +[tool.ruff.lint] +select = [ "ALL",] +ignore = [ + "C90", # McCabe complexity + "COM812", # Messes with the formatter + "FA100", # Can't activate since we exclude UP007 for now + "FIX002", # Line contains TODO + "ISC001", # Messes with the formatter + "PERF203", # Rarely useful + "PLR09", # Too many something (arg, statements, etc) + "RUF012", # Doesn't play well with Pydantic + "TC001", # Doesn't play well with Pydantic + "TC002", # Doesn't play well with Pydantic + "TC003", # Doesn't play well with Pydantic + "TD002", # Missing author in TODO + "TD003", # Missing issue link in TODO + "UP007", # Doesn't play well with Pydantic in Python 3.9 + + # TODO rules + "ANN401", + "BLE", + "ERA", + "PLR2004", +] +flake8-annotations.allow-star-arg-any = true +flake8-annotations.mypy-init-return = true +pydocstyle.convention = "google" +pydocstyle.ignore-var-parameters = true + +[tool.pytest.ini_options] +asyncio_default_fixture_loop_scope = "function" +testpaths = ["tests"] +filterwarnings = [ + "ignore::UserWarning:pkg_resources" +] +markers = [ + "compile: marker used to test compilation-only tests" +] + +[tool.ruff.lint.per-file-ignores] +"tests/**" = [ + "D", # docstring rules + "ANN", # missing type annotations + "T201", # use of `print` + "S101", # use of `assert` + "E741", # ambiguous variable name like `l` + "RET504", # unnecessary assignment before return + "I001", # import sorting + "UP035" # import from collections.abc instead of typing +] \ No newline at end of file diff --git a/libs/partners/coherence/tests/__init__.py b/libs/partners/coherence/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/coherence/tests/integration_tests/__init__.py b/libs/partners/coherence/tests/integration_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/coherence/tests/integration_tests/test_coherence_store.py b/libs/partners/coherence/tests/integration_tests/test_coherence_store.py new file mode 100644 index 00000000000..9f068f7ef96 --- /dev/null +++ b/libs/partners/coherence/tests/integration_tests/test_coherence_store.py @@ -0,0 +1,153 @@ +import pytest +import pytest_asyncio +import inspect + +from typing import AsyncGenerator +from langchain_core.embeddings import Embeddings +from langchain_core.documents import Document +from langchain_huggingface.embeddings import HuggingFaceEmbeddings + +from coherence import NamedCache, Session +from langchain_coherence import CoherenceVectorStore + + +@pytest_asyncio.fixture +async def store() -> AsyncGenerator[CoherenceVectorStore, None]: + session: Session = await Session.create() + named_cache: NamedCache[str, Document] = await session.get_cache("my-map") + embedding: Embeddings = HuggingFaceEmbeddings( + model_name="sentence-transformers/all-MiniLM-l6-v2" + ) + cvs: CoherenceVectorStore = await CoherenceVectorStore.create( + named_cache, embedding + ) + yield cvs + await cvs.cache.destroy() + await session.close() + + +def get_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 + + +@pytest.mark.asyncio +async def test_aget_by_id(store: CoherenceVectorStore): + print() + print(f"=======: {inspect.currentframe().f_code.co_name}") + documents = get_test_data() + await store.aadd_documents(documents) + ids = [doc.id for doc in documents] + l = await store.aget_by_ids(ids) + assert len(l) == 10 + print("====") + for e in l: + print(e) + + +@pytest.mark.asyncio +async def test_adelete(store: CoherenceVectorStore): + print() + print(f"=======: {inspect.currentframe().f_code.co_name}") + documents = get_test_data() + await store.aadd_documents(documents) + ids = [doc.id for doc in documents] + l = await store.aget_by_ids(ids) + assert len(l) == 10 + await store.adelete(["1", "2"]) + l = await store.aget_by_ids(ids) + assert len(l) == 8 + await store.adelete() + l = await store.aget_by_ids(ids) + assert len(l) == 0 + + +@pytest.mark.asyncio +async def test_asimilarity_search(store: CoherenceVectorStore): + print() + print(f"=======: {inspect.currentframe().f_code.co_name}") + documents = get_test_data() + await store.aadd_documents(documents) + ids = [doc.id for doc in documents] + l = await store.aget_by_ids(ids) + assert len(l) == 10 + + # result = await coherence_store.asimilarity_search("animal") + result = await store.asimilarity_search("fruit") + assert len(result) == 4 + print("====") + for e in result: + print(e) + + +@pytest.mark.asyncio +async def test_asimilarity_search_by_vector(store: CoherenceVectorStore): + print() + print(f"=======: {inspect.currentframe().f_code.co_name}") + documents = get_test_data() + await store.aadd_documents(documents) + ids = [doc.id for doc in documents] + l = await store.aget_by_ids(ids) + assert len(l) == 10 + + vector = store.embeddings.embed_query("animal") + result = await store.asimilarity_search_by_vector(vector) + assert len(result) == 4 + print("====") + for e in result: + print(e) + + +@pytest.mark.asyncio +async def test_asimilarity_search_with_score(store: CoherenceVectorStore): + print() + print(f"=======: {inspect.currentframe().f_code.co_name}") + documents = get_test_data() + await store.aadd_documents(documents) + ids = [doc.id for doc in documents] + l = await store.aget_by_ids(ids) + assert len(l) == 10 + + # result = await coherence_store.asimilarity_search("animal") + result = await store.asimilarity_search_with_score("fruit") + assert len(result) == 4 + print("====") + for e in result: + print(e) + + +@pytest.mark.asyncio +async def test_afrom_texts(): + session = await Session.create() + try: + cache = await session.get_cache("test-map-async") + embedding = HuggingFaceEmbeddings( + model_name="sentence-transformers/all-MiniLM-l6-v2" + ) + texts = ["apple", "banana"] + metadatas = [{"cat": "fruit"}, {"cat": "fruit"}] + ids = ["id1", "id2"] + + store = await CoherenceVectorStore.afrom_texts( + texts=texts, + embedding=embedding, + cache=cache, + metadatas=metadatas, + ids=ids, + ) + + results = await store.aget_by_ids(ids) + assert len(results) == 2 + finally: + await session.close() diff --git a/libs/partners/coherence/tests/integration_tests/test_compile.py b/libs/partners/coherence/tests/integration_tests/test_compile.py new file mode 100644 index 00000000000..f315e45f521 --- /dev/null +++ b/libs/partners/coherence/tests/integration_tests/test_compile.py @@ -0,0 +1,6 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" diff --git a/libs/partners/coherence/tests/unit_tests/__init__.py b/libs/partners/coherence/tests/unit_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/coherence/uv.lock b/libs/partners/coherence/uv.lock new file mode 100644 index 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