Feature/vectorstore myscale

docs/ecosystem/myscale.md

@@ -0,0 +1,65 @@
+# MyScale
+
+This page covers how to use MyScale vector database within LangChain.
+It is broken into two parts: installation and setup, and then references to specific MyScale wrappers.
+
+With MyScale, you can manage both structured and unstructured (vectorized) data, and perform joint queries and analytics on both types of data using SQL. Plus, MyScale's cloud-native OLAP architecture, built on top of ClickHouse, enables lightning-fast data processing even on massive datasets.
+
+## Introduction
+
+[Overview to MyScale and High performance vector search](https://docs.myscale.com/en/overview/)
+
+You can now register on our SaaS and [start a cluster now!](https://docs.myscale.com/en/quickstart/)
+
+If you are also interested in how we managed to integrate SQL and vector, please refer to [this document](https://docs.myscale.com/en/vector-reference/) for further syntax reference.
+
+We also deliver with live demo on huggingface! Please checkout our [huggingface space](https://huggingface.co/myscale)! They search millions of vector within a blink!
+
+## Installation and Setup
+- Install the Python SDK with `pip install clickhouse-connect`
+
+### Setting up envrionments
+
+There are two ways to set up parameters for myscale index.
+
+1. Environment Variables
+
+    Before you run the app, please set the environment variable with `export`:
+    `export MYSCALE_URL='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`
+
+    You can easily find your account, password and other info on our SaaS. For details please refer to [this document](https://docs.myscale.com/en/cluster-management/)
+    Every attributes under `MyScaleSettings` can be set with prefix `MYSCALE_` and is case insensitive.
+
+2. Create `MyScaleSettings` object with parameters
+
+
+    ```python
+    from langchain.vectorstores import MyScale, MyScaleSettings
+    config = MyScaleSetting(host="<your-backend-url>", port=8443, ...)
+    index = MyScale(embedding_function, config)
+    index.add_documents(...)
+    ```
+
+## Wrappers
+supported functions:
+- `add_texts`
+- `add_documents`
+- `from_texts`
+- `from_documents`
+- `similarity_search`
+- `asimilarity_search`
+- `similarity_search_by_vector`
+- `asimilarity_search_by_vector`
+- `similarity_search_with_relevance_scores`
+
+### VectorStore
+
+There exists a wrapper around MyScale database, allowing you to use it as a vectorstore,
+whether for semantic search or similar example retrieval.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import MyScale
+```
+
+For a more detailed walkthrough of the MyScale wrapper, see [this notebook](../modules/indexes/vectorstores/examples/myscale.ipynb)

docs/modules/indexes/vectorstores/examples/myscale.ipynb

@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "683953b3",
+   "metadata": {},
+   "source": [
+    "# MyScale\n",
+    "\n",
+    "This notebook shows how to use functionality related to the MyScale vector database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aac9563e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import MyScale\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "a9d16fa3",
+   "metadata": {},
+   "source": [
+    "## Setting up envrionments\n",
+    "\n",
+    "There are two ways to set up parameters for myscale index.\n",
+    "\n",
+    "1. Environment Variables\n",
+    "\n",
+    "    Before you run the app, please set the environment variable with `export`:\n",
+    "    `export MYSCALE_URL='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`\n",
+    "\n",
+    "    You can easily find your account, password and other info on our SaaS. For details please refer to [this document](https://docs.myscale.com/en/cluster-management/)\n",
+    "\n",
+    "\n",
+    "\n",
+    "    Every attributes under `MyScaleSettings` can be set with prefix `MYSCALE_` and is case insensitive.\n",
+    "\n",
+    "2. Create `MyScaleSettings` object with parameters\n",
+    "\n",
+    "\n",
+    "    ```python\n",
+    "    from langchain.vectorstores import MyScale, MyScaleSettings\n",
+    "    config = MyScaleSetting(host=\"<your-backend-url>\", port=8443, ...)\n",
+    "    index = MyScale(embedding_function, config)\n",
+    "    index.add_documents(...)\n",
+    "    ```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a3c3999a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "loader = TextLoader('../../../state_of_the_union.txt')\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6e104aee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Inserting data...: 100%|██████████| 42/42 [00:18<00:00,  2.21it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for d in docs:\n",
+    "    d.metadata = {'some': 'metadata'}\n",
+    "docsearch = MyScale.from_documents(docs, embeddings)\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9c608226",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "As Frances Haugen, who is here with us tonight, has shown, we must hold social media platforms accountable for the national experiment they’re conducting on our children for profit. \n",
+      "\n",
+      "It’s time to strengthen privacy protections, ban targeted advertising to children, demand tech companies stop collecting personal data on our children. \n",
+      "\n",
+      "And let’s get all Americans the mental health services they need. More people they can turn to for help, and full parity between physical and mental health care. \n",
+      "\n",
+      "Third, support our veterans. \n",
+      "\n",
+      "Veterans are the best of us. \n",
+      "\n",
+      "I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home. \n",
+      "\n",
+      "My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free.  \n",
+      "\n",
+      "Our troops in Iraq and Afghanistan faced many dangers.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "e3a8b105",
+   "metadata": {},
+   "source": [
+    "## Show data schema"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69996818",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(str(docsearch))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "f59360c0",
+   "metadata": {},
+   "source": [
+    "## Filtering\n",
+    "\n",
+    "You can have direct access to myscale SQL where statement. You can write `WHERE` clause following standard SQL.\n",
+    "\n",
+    "**NOTE**: Please be aware of SQL injection, this interface must not be directly called by end-user.\n",
+    "\n",
+    "If you custimized your `column_map` under your setting, you search with filter like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "232055f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Inserting data...: 100%|██████████| 42/42 [00:15<00:00,  2.69it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.vectorstores import MyScale, MyScaleSettings\n",
+    "from langchain.document_loaders import TextLoader\n",
+    "\n",
+    "loader = TextLoader('../../../state_of_the_union.txt')\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "\n",
+    "for i, d in enumerate(docs):\n",
+    "    d.metadata = {'doc_id': i}\n",
+    "\n",
+    "docsearch = MyScale.from_documents(docs, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ddbcee77",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.252379834651947 {'doc_id': 6, 'some': ''} And I’m taking robus...\n",
+      "0.25022566318511963 {'doc_id': 1, 'some': ''} Groups of citizens b...\n",
+      "0.2469480037689209 {'doc_id': 8, 'some': ''} And so many families...\n",
+      "0.2428302764892578 {'doc_id': 0, 'some': 'metadata'} As Frances Haugen, w...\n"
+     ]
+    }
+   ],
+   "source": [
+    "meta = docsearch.metadata_column\n",
+    "output = docsearch.similarity_search_with_relevance_scores('What did the president say about Ketanji Brown Jackson?', \n",
+    "                                                           k=4, where_str=f\"{meta}.doc_id<10\")\n",
+    "for d, dist in output:\n",
+    "    print(dist, d.metadata, d.page_content[:20] + '...')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "a359ed74",
+   "metadata": {},
+   "source": [
+    "## Deleting your data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb6a9d36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docsearch.drop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48dbd8e0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/reference/integrations.md

@@ -45,6 +45,8 @@ The following use cases require specific installs and api keys:
   - Set up Elasticsearch backend. If you want to do locally, [this](https://www.elastic.co/guide/en/elasticsearch/reference/7.17/getting-started.html) is a good guide.
 - _FAISS_:
   - Install requirements with `pip install faiss` for Python 3.7 and `pip install faiss-cpu` for Python 3.10+.
+- _MyScale_
+  - Install requirements with `pip install clickhouse-connect`. For documentations, please refer to [this document](https://docs.myscale.com/en/overview/).
 - _Manifest_:
   - Install requirements with `pip install manifest-ml` (Note: this is only available in Python 3.8+ currently).
 - _OpenSearch_:

langchain/vectorstores/__init__.py

@@ -7,6 +7,7 @@
 from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch
 from langchain.vectorstores.faiss import FAISS
 from langchain.vectorstores.milvus import Milvus
+from langchain.vectorstores.myscale import MyScale, MyScaleSettings
 from langchain.vectorstores.opensearch_vector_search import OpenSearchVectorSearch
 from langchain.vectorstores.pinecone import Pinecone
 from langchain.vectorstores.qdrant import Qdrant
@@ -26,5 +27,7 @@
     "AtlasDB",
     "DeepLake",
     "Annoy",
+    "MyScale",
+    "MyScaleSettings",
     "SupabaseVectorStore",
 ]