diff --git a/docs/community/integrations/vector_stores.md b/docs/community/integrations/vector_stores.md index 7b8c13ecc42fb..f41476aa96f54 100644 --- a/docs/community/integrations/vector_stores.md +++ b/docs/community/integrations/vector_stores.md @@ -16,6 +16,7 @@ as the storage backend for `VectorStoreIndex`. - Astra DB (`AstraDBVectorStore`). [Quickstart](https://docs.datastax.com/en/astra/home/astra.html). - Azure AI Search (`AzureAISearchVectorStore`). [Quickstart](https://learn.microsoft.com/en-us/azure/search/search-get-started-vector) - Chroma (`ChromaVectorStore`) [Installation](https://docs.trychroma.com/getting-started) +- ClickHouse (`ClickHouseVectorStore`) [Installation](https://clickhouse.com/docs/en/install) - DashVector (`DashVectorStore`). [Installation](https://help.aliyun.com/document_detail/2510230.html). - DeepLake (`DeepLakeVectorStore`) [Installation](https://docs.deeplake.ai/en/latest/Installation.html) - DocArray (`DocArrayHnswVectorStore`, `DocArrayInMemoryVectorStore`). [Installation/Python Client](https://github.com/docarray/docarray#installation). @@ -171,6 +172,24 @@ vector_store = ChromaVectorStore( ) ``` +**ClickHouse** + +```python +import clickhouse_connect +from llama_index.vector_stores import ClickHouseVectorStore + +# Creating a ClickHouse client +client = clickhouse_connect.get_client( + host="YOUR_CLUSTER_HOST", + port=8123, + username="YOUR_USERNAME", + password="YOUR_CLUSTER_PASSWORD", +) + +# construct vector store +vector_store = ClickHouseVectorStore(clickhouse_client=client) +``` + **DashVector** ```python diff --git a/docs/examples/data_connectors/MakeDemo.ipynb b/docs/examples/data_connectors/MakeDemo.ipynb index f8aa7eaaab014..3b0cc3fd1797a 100644 --- a/docs/examples/data_connectors/MakeDemo.ipynb +++ b/docs/examples/data_connectors/MakeDemo.ipynb @@ -126,11 +126,7 @@ "source": [ "# Send response to Make.com webhook\n", "wrapper = MakeWrapper()\n", - "wrapper.pass_response_to_webhook(\n", - " \",\n", - " response,\n", - " query_str\n", - ")" + "wrapper.pass_response_to_webhook(\"\", response, query_str)" ] } ], diff --git a/docs/examples/ingestion/async_ingestion_pipeline.ipynb b/docs/examples/ingestion/async_ingestion_pipeline.ipynb index b8bc0405b801c..960fa5ad6bf01 100644 --- a/docs/examples/ingestion/async_ingestion_pipeline.ipynb +++ b/docs/examples/ingestion/async_ingestion_pipeline.ipynb @@ -39,9 +39,7 @@ "source": [ "import os\n", "\n", - "os.environ[\n", - " \"OPENAI_API_KEY\"\n", - "] = \"sk-..." + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"" ] }, { diff --git a/docs/examples/vector_stores/ClickHouseIndexDemo.ipynb b/docs/examples/vector_stores/ClickHouseIndexDemo.ipynb new file mode 100644 index 0000000000000..f52e059d5b277 --- /dev/null +++ b/docs/examples/vector_stores/ClickHouseIndexDemo.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "e0c2f11f", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "307804a3-c02b-4a57-ac0d-172c30ddc851", + "metadata": {}, + "source": [ + "# ClickHouse Vector Store\n", + "In this notebook we are going to show a quick demo of using the ClickHouseVectorStore." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c12f55a9", + "metadata": {}, + "source": [ + "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1edec46", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index\n", + "!pip install clickhouse_connect" + ] + }, + { + "cell_type": "markdown", + "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396", + "metadata": {}, + "source": [ + "#### Creating a ClickHouse Client" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d48af8e1", + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "import sys\n", + "\n", + "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n", + "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50ad978c", + "metadata": {}, + "outputs": [], + "source": [ + "from os import environ\n", + "import clickhouse_connect\n", + "\n", + "environ[\"OPENAI_API_KEY\"] = \"sk-*\"\n", + "\n", + "# initialize client\n", + "client = clickhouse_connect.get_client(\n", + " host=\"localhost\",\n", + " port=8123,\n", + " username=\"default\",\n", + " password=\"\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8ee4473a-094f-4d0a-a825-e1213db07240", + "metadata": {}, + "source": [ + "#### Load documents, build and store the VectorStoreIndex with ClickHouseVectorStore\n", + "\n", + "Here we will use a set of Paul Graham essays to provide the text to turn into embeddings, store in a ``ClickHouseVectorStore`` and query to find context for our LLM QnA loop." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a2bcc07", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n", + "from llama_index.vector_stores.clickhouse import ClickHouseVectorStore" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68cbd239-880e-41a3-98d8-dbb3fab55431", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Document ID: d03ac7db-8dae-4199-bc38-445dec51a534\n", + "Number of Documents: 1\n" + ] + } + ], + "source": [ + "# load documents\n", + "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n", + "print(\"Document ID:\", documents[0].doc_id)\n", + "print(\"Number of Documents: \", len(documents))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b6afe88c", + "metadata": {}, + "source": [ + "Download Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d09a78f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-02-13 10:08:31-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt\r\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.110.133, ...\r\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\r\n", + "HTTP request sent, awaiting response... 200 OK\r\n", + "Length: 75042 (73K) [text/plain]\r\n", + "Saving to: ‘data/paul_graham/paul_graham_essay.txt’\r\n", + "\r\n", + "data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.003s \r\n", + "\r\n", + "2024-02-13 10:08:31 (23.9 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\r\n", + "\r\n" + ] + } + ], + "source": [ + "!mkdir -p 'data/paul_graham/'\n", + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'" + ] + }, + { + "cell_type": "markdown", + "id": "4fe3dc84", + "metadata": {}, + "source": [ + "You can process your files individually using [SimpleDirectoryReader](/examples/data_connectors/simple_directory_reader.ipynb):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4febd54a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data/paul_graham/paul_graham_essay.txt\n" + ] + } + ], + "source": [ + "loader = SimpleDirectoryReader(\"./data/paul_graham/\")\n", + "documents = loader.load_data()\n", + "for file in loader.input_files:\n", + " print(file)\n", + " # Here is where you would do any preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba1558b3", + "metadata": {}, + "outputs": [], + "source": [ + "# initialize with metadata filter and store indexes\n", + "from llama_index.core import StorageContext\n", + "\n", + "for document in documents:\n", + " document.metadata = {\"user_id\": \"123\", \"favorite_color\": \"blue\"}\n", + "vector_store = ClickHouseVectorStore(clickhouse_client=client)\n", + "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", + "index = VectorStoreIndex.from_documents(\n", + " documents, storage_context=storage_context\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "04304299-fc3e-40a0-8600-f50c3292767e", + "metadata": {}, + "source": [ + "#### Query Index\n", + "\n", + "Now ClickHouse vector store supports filter search and hybrid search\n", + "\n", + "You can learn more about [query_engine](/module_guides/deploying/query_engine/root.md) and [retriever](/module_guides/querying/retriever/root.md)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35369eda", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The author learned several things during their time at Interleaf, including the importance of having\n", + "technology companies run by product people rather than sales people, the drawbacks of having too\n", + "many people edit code, the value of corridor conversations over planned meetings, the challenges of\n", + "dealing with big bureaucratic customers, and the importance of being the \"entry level\" option in a\n", + "market.\n" + ] + } + ], + "source": [ + "import textwrap\n", + "\n", + "from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n", + "\n", + "# set Logging to DEBUG for more detailed outputs\n", + "query_engine = index.as_query_engine(\n", + " filters=MetadataFilters(\n", + " filters=[\n", + " ExactMatchFilter(key=\"user_id\", value=\"123\"),\n", + " ]\n", + " ),\n", + " similarity_top_k=2,\n", + " vector_store_query_mode=\"hybrid\",\n", + ")\n", + "response = query_engine.query(\"What did the author learn?\")\n", + "print(textwrap.fill(str(response), 100))" + ] + }, + { + "cell_type": "markdown", + "id": "a732d16f0a29f8ab", + "metadata": {}, + "source": [ + "#### Clear All Indexes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "552c203fd054d771", + "metadata": {}, + "outputs": [], + "source": [ + "for document in documents:\n", + " index.delete_ref_doc(document.doc_id)" + ] + } + ], + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/llama-index-core/llama_index/core/data_structs/struct_type.py b/llama-index-core/llama_index/core/data_structs/struct_type.py index 8be98f4ef067e..801db812c5bbd 100644 --- a/llama-index-core/llama_index/core/data_structs/struct_type.py +++ b/llama-index-core/llama_index/core/data_structs/struct_type.py @@ -45,6 +45,9 @@ class IndexStructType(str, Enum): MYSCALE ("myscale"): MyScale Vector Store Index. See :ref:`Ref-Indices-VectorStore` for more information on the MyScale vector store index. + CLICKHOUSE ("clickhouse"): ClickHouse Vector Store Index. + See :ref:`Ref-Indices-VectorStore` + for more information on the ClickHouse vector store index. EPSILLA ("epsilla"): Epsilla Vector Store Index. See :ref:`Ref-Indices-VectorStore` for more information on the Epsilla vector store index. @@ -81,6 +84,7 @@ class IndexStructType(str, Enum): MILVUS = "milvus" CHROMA = "chroma" MYSCALE = "myscale" + CLICKHOUSE = "clickhouse" VECTOR_STORE = "vector_store" OPENSEARCH = "opensearch" DASHVECTOR = "dashvector" diff --git a/llama-index-integrations/readers/llama-index-readers-clickhouse/.gitignore b/llama-index-integrations/readers/llama-index-readers-clickhouse/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-clickhouse/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/readers/llama-index-readers-clickhouse/BUILD b/llama-index-integrations/readers/llama-index-readers-clickhouse/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-clickhouse/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/readers/llama-index-readers-clickhouse/Makefile b/llama-index-integrations/readers/llama-index-readers-clickhouse/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-clickhouse/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/readers/llama-index-readers-clickhouse/README.md b/llama-index-integrations/readers/llama-index-readers-clickhouse/README.md new file mode 100644 index 0000000000000..88234c17f70c9 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-clickhouse/README.md @@ -0,0 +1 @@ +# LlamaIndex Readers Integration: ClickHouse diff --git a/llama-index-integrations/readers/llama-index-readers-clickhouse/llama_index/readers/clickhouse/BUILD b/llama-index-integrations/readers/llama-index-readers-clickhouse/llama_index/readers/clickhouse/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-clickhouse/llama_index/readers/clickhouse/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/readers/llama-index-readers-clickhouse/llama_index/readers/clickhouse/__init__.py b/llama-index-integrations/readers/llama-index-readers-clickhouse/llama_index/readers/clickhouse/__init__.py new file mode 100644 index 0000000000000..9b8443909fe59 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-clickhouse/llama_index/readers/clickhouse/__init__.py @@ -0,0 +1,7 @@ +from llama_index.readers.clickhouse.base import ( + ClickHouseReader, + escape_str, + format_list_to_string, +) + +__all__ = ["ClickHouseReader", "escape_str", "format_list_to_string"] diff --git a/llama-index-integrations/readers/llama-index-readers-clickhouse/llama_index/readers/clickhouse/base.py b/llama-index-integrations/readers/llama-index-readers-clickhouse/llama_index/readers/clickhouse/base.py new file mode 100644 index 0000000000000..57abcbfee144c --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-clickhouse/llama_index/readers/clickhouse/base.py @@ -0,0 +1,165 @@ +"""ClickHouse reader.""" +import logging +from typing import Any, List, Optional +import clickhouse_connect +from llama_index.core.readers.base import BaseReader +from llama_index.core.schema import Document + +logger = logging.getLogger(__name__) + + +def escape_str(value: str) -> str: + BS = "\\" + must_escape = (BS, "'") + return ( + "".join(f"{BS}{c}" if c in must_escape else c for c in value) if value else "" + ) + + +def format_list_to_string(lst: List) -> str: + return "[" + ",".join(str(item) for item in lst) + "]" + + +DISTANCE_MAPPING = { + "l2": "L2Distance", + "cosine": "cosineDistance", + "dot": "dotProduct", +} + + +class ClickHouseSettings: + """ClickHouse Client Configuration. + + Attributes: + table (str): Table name to operate on. + database (str): Database name to find the table. + engine (str): Engine. Options are "MergeTree" and "Memory". Default is "MergeTree". + index_type (str): Index type string. + metric (str): Metric type to compute distance e.g., cosine, l3, or dot. + batch_size (int): The size of documents to insert. + index_params (dict, optional): Index build parameter. + search_params (dict, optional): Index search parameters for ClickHouse query. + """ + + def __init__( + self, + table: str, + database: str, + engine: str, + index_type: str, + metric: str, + batch_size: int, + index_params: Optional[dict] = None, + search_params: Optional[dict] = None, + **kwargs: Any, + ) -> None: + self.table = table + self.database = database + self.engine = engine + self.index_type = index_type + self.metric = metric + self.batch_size = batch_size + self.index_params = index_params + self.search_params = search_params + + def build_query_statement( + self, + query_embed: List[float], + where_str: Optional[str] = None, + limit: Optional[int] = None, + ) -> str: + query_embed_str = format_list_to_string(query_embed) + where_str = f"WHERE {where_str}" if where_str else "" + distance = DISTANCE_MAPPING[self.metric] + return f""" + SELECT id, doc_id, text, node_info, metadata, + {distance}(vector, {query_embed_str}) AS score + FROM {self.database}.{self.table} {where_str} + ORDER BY score ASC + LIMIT {limit} + """ + + +class ClickHouseReader(BaseReader): + """ClickHouse reader. + + Args: + clickhouse_host (str) : An URL to connect to ClickHouse backend. Default to "localhost". + username (str) : Username to login. Defaults to "default". + password (str) : Password to login. Defaults to "". + clickhouse_port (int) : URL port to connect with HTTP. Defaults to 8123. + database (str) : Database name to find the table. Defaults to 'default'. + engine (str) : Engine. Options are "MergeTree" and "Memory". Default is "MergeTree". + table (str) : Table name to operate on. Defaults to 'vector_table'. + index_type (str): index type string. Default to "NONE", supported are ("NONE", "HNSW", "ANNOY") + metric (str) : Metric to compute distance, supported are ('l2', 'cosine', 'dot'). + Defaults to 'cosine' + batch_size (int, optional): the size of documents to insert. Defaults to 1000. + index_params (dict, optional): The index parameters for ClickHouse. + Defaults to None. + search_params (dict, optional): The search parameters for a ClicKHouse query. + Defaults to None. + """ + + def __init__( + self, + clickhouse_host: str = "localhost", + username: str = "default", + password: str = "", + clickhouse_port: Optional[int] = 8123, + database: str = "default", + engine: str = "MergeTree", + table: str = "llama_index", + index_type: str = "NONE", + metric: str = "cosine", + batch_size: int = 1000, + index_params: Optional[dict] = None, + search_params: Optional[dict] = None, + **kwargs: Any, + ) -> None: + self.client = clickhouse_connect.get_client( + host=clickhouse_host, + port=clickhouse_port, + username=username, + password=password, + ) + + self.config = ClickHouseSettings( + table=table, + database=database, + engine=engine, + index_type=index_type, + metric=metric, + batch_size=batch_size, + index_params=index_params, + search_params=search_params, + **kwargs, + ) + + def load_data( + self, + query_vector: List[float], + where_str: Optional[str] = None, + limit: int = 10, + ) -> List[Document]: + """Load data from ClickHouse. + + Args: + query_vector (List[float]): Query vector. + where_str (Optional[str], optional): where condition string. + Defaults to None. + limit (int): Number of results to return. + + Returns: + List[Document]: A list of documents. + """ + query_statement = self.config.build_query_statement( + query_embed=query_vector, + where_str=where_str, + limit=limit, + ) + + return [ + Document(id_=r["doc_id"], text=r["text"], metadata=r["metadata"]) + for r in self.client.query(query_statement).named_results() + ] diff --git a/llama-index-integrations/readers/llama-index-readers-clickhouse/poetry.lock b/llama-index-integrations/readers/llama-index-readers-clickhouse/poetry.lock new file mode 100644 index 0000000000000..2e45952d1ecb5 --- /dev/null +++ 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names_of_base_classes diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/.gitignore b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/Makefile b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/README.md b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/README.md new file mode 100644 index 0000000000000..67208253d6471 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/README.md @@ -0,0 +1 @@ +# LlamaIndex Vector_Stores Integration: ClickHouse diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/docker-compose.yml b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/docker-compose.yml new file mode 100644 index 0000000000000..d9781737f65c0 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/docker-compose.yml @@ -0,0 +1,15 @@ +version: "3" + +services: + clickhouse: + image: clickhouse/clickhouse-server:24.1 + user: "101:101" + container_name: clickhouse-server + hostname: clickhouse-server + ports: + - "8123:8123" + - "9000:9000" + ulimits: + nofile: + soft: 262144 + hard: 262144 diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/__init__.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/__init__.py new file mode 100644 index 0000000000000..741d81b7da6b7 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/__init__.py @@ -0,0 +1,3 @@ +from llama_index.vector_stores.clickhouse.base import ClickHouseVectorStore + +__all__ = ["ClickHouseVectorStore"] diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/base.py new file mode 100644 index 0000000000000..2979787845239 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/base.py @@ -0,0 +1,405 @@ +"""ClickHouse vector store. + +An index that is built on top of an existing ClickHouse cluster. + +""" +import importlib +import json +import logging +import re +from typing import Any, Dict, List, Optional, cast + +from pydantic import PrivateAttr + +from llama_index.core import ServiceContext +from llama_index.core.schema import ( + BaseNode, + MetadataMode, + NodeRelationship, + RelatedNodeInfo, + TextNode, +) +from llama_index.core.utils import iter_batch +from llama_index.core.vector_stores.types import ( + VectorStoreQuery, + VectorStoreQueryMode, + VectorStoreQueryResult, + BasePydanticVectorStore, +) +from llama_index.readers.clickhouse.base import ( + DISTANCE_MAPPING, + ClickHouseSettings, + escape_str, + format_list_to_string, +) + +logger = logging.getLogger(__name__) + + +def _default_tokenizer(text: str) -> List[str]: + """Default tokenizer.""" + tokens = re.split(r"[ \n]", text) # split by space or newline + result = [] + for token in tokens: + if token.strip() == "": + continue + result.append(token.strip()) + return result + + +class ClickHouseVectorStore(BasePydanticVectorStore): + """ClickHouse Vector Store. + In this vector store, embeddings and docs are stored within an existing + ClickHouse cluster. + During query time, the index uses ClickHouse to query for the top + k most similar nodes. + + Args: + clickhouse_client (httpclient): clickhouse-connect httpclient of + an existing ClickHouse cluster. + table (str, optional): The name of the ClickHouse table + where data will be stored. Defaults to "llama_index". + database (str, optional): The name of the ClickHouse database + where data will be stored. Defaults to "default". + index_type (str, optional): The type of the ClickHouse vector index. + Defaults to "NONE", supported are ("NONE", "HNSW", "ANNOY") + metric (str, optional): The metric type of the ClickHouse vector index. + Defaults to "cosine". + batch_size (int, optional): the size of documents to insert. Defaults to 1000. + index_params (dict, optional): The index parameters for ClickHouse. + Defaults to None. + search_params (dict, optional): The search parameters for a ClickHouse query. + Defaults to None. + service_context (ServiceContext, optional): Vector store service context. + Defaults to None + """ + + stores_text = True + flat_metadata = False + _table_existed: bool = PrivateAttr(default=False) + _client: Any = PrivateAttr() + _config: Any = PrivateAttr() + _dim: Any = PrivateAttr() + _column_config: Any = PrivateAttr() + _column_names: List[str] = PrivateAttr() + _column_type_names: List[str] = PrivateAttr() + metadata_column: str = "metadata" + AMPLIFY_RATIO_LE5 = 100 + AMPLIFY_RATIO_GT5 = 20 + AMPLIFY_RATIO_GT50 = 10 + + def __init__( + self, + clickhouse_client: Optional[Any] = None, + table: str = "llama_index", + database: str = "default", + engine: str = "MergeTree", + index_type: str = "NONE", + metric: str = "cosine", + batch_size: int = 1000, + index_params: Optional[dict] = None, + search_params: Optional[dict] = None, + service_context: Optional[ServiceContext] = None, + **kwargs: Any, + ) -> None: + """Initialize params.""" + import_err_msg = """ + `clickhouse_connect` package not found, + please run `pip install clickhouse-connect` + """ + clickhouse_connect_spec = importlib.util.find_spec( + "clickhouse_connect.driver.httpclient" + ) + if clickhouse_connect_spec is None: + raise ImportError(import_err_msg) + + if clickhouse_client is None: + raise ValueError("Missing ClickHouse client!") + self._client = clickhouse_client + self._config = ClickHouseSettings( + table=table, + database=database, + engine=engine, + index_type=index_type, + metric=metric, + batch_size=batch_size, + index_params=index_params, + search_params=search_params, + **kwargs, + ) + + # schema column name, type, and construct format method + self._column_config: Dict = { + "id": {"type": "String", "extract_func": lambda x: x.node_id}, + "doc_id": {"type": "String", "extract_func": lambda x: x.ref_doc_id}, + "text": { + "type": "String", + "extract_func": lambda x: escape_str( + x.get_content(metadata_mode=MetadataMode.NONE) or "" + ), + }, + "vector": { + "type": "Array(Float32)", + "extract_func": lambda x: x.get_embedding(), + }, + "node_info": { + "type": "Tuple(start Nullable(UInt64), end Nullable(UInt64))", + "extract_func": lambda x: x.get_node_info(), + }, + "metadata": { + "type": "String", + "extract_func": lambda x: json.dumps(x.metadata), + }, + } + self._column_names = list(self._column_config.keys()) + self._column_type_names = [ + self._column_config[column_name]["type"] + for column_name in self._column_names + ] + + if service_context is not None: + service_context = cast(ServiceContext, service_context) + dimension = len( + service_context.embed_model.get_query_embedding("try this out") + ) + self.create_table(dimension) + super().__init__( + clickhouse_client=clickhouse_client, + table=table, + database=database, + engine=engine, + index_type=index_type, + metric=metric, + batch_size=batch_size, + index_params=index_params, + search_params=search_params, + service_context=service_context, + ) + + @property + def client(self) -> Any: + """Get client.""" + return self._client + + def create_table(self, dimension: int) -> None: + index = "" + settings = {"allow_experimental_object_type": "1"} + if self._config.index_type.lower() == "hnsw": + scalarKind = "f32" + if self._config.index_params and "ScalarKind" in self._config.index_params: + scalarKind = self._config.index_params["ScalarKind"] + index = f"INDEX hnsw_indx vector TYPE usearch('{DISTANCE_MAPPING[self._config.metric]}', '{scalarKind}')" + settings["allow_experimental_usearch_index"] = "1" + elif self._config.index_type.lower() == "annoy": + numTrees = 100 + if self._config.index_params and "NumTrees" in self._config.index_params: + numTrees = self._config.index_params["NumTrees"] + index = f"INDEX annoy_indx vector TYPE annoy('{DISTANCE_MAPPING[self._config.metric]}', {numTrees})" + settings["allow_experimental_annoy_index"] = "1" + schema_ = f""" + CREATE TABLE IF NOT EXISTS {self._config.database}.{self._config.table}( + {",".join([f'{k} {v["type"]}' for k, v in self._column_config.items()])}, + CONSTRAINT vector_length CHECK length(vector) = {dimension}, + {index} + ) ENGINE = MergeTree ORDER BY id + """ + self._dim = dimension + self._client.command(schema_, settings=settings) + self._table_existed = True + + def _upload_batch( + self, + batch: List[BaseNode], + ) -> None: + _data = [] + # we assume all rows have all columns + for idx, item in enumerate(batch): + _row = [] + for column_name in self._column_names: + _row.append(self._column_config[column_name]["extract_func"](item)) + _data.append(_row) + + self._client.insert( + f"{self._config.database}.{self._config.table}", + data=_data, + column_names=self._column_names, + column_type_names=self._column_type_names, + ) + + def _build_text_search_statement( + self, query_str: str, similarity_top_k: int + ) -> str: + # TODO: We could make this overridable + tokens = _default_tokenizer(query_str) + terms_pattern = [f"\\b(?i){x}\\b" for x in tokens] + column_keys = self._column_config.keys() + return ( + f"SELECT {','.join(filter(lambda k: k != 'vector', column_keys))}, " + f"score FROM {self._config.database}.{self._config.table} WHERE score > 0 " + f"ORDER BY length(multiMatchAllIndices(text, {terms_pattern})) " + f"AS score DESC, " + f"log(1 + countMatches(text, '\\b(?i)({'|'.join(tokens)})\\b')) " + f"AS d2 DESC limit {similarity_top_k}" + ) + + def _build_hybrid_search_statement( + self, stage_one_sql: str, query_str: str, similarity_top_k: int + ) -> str: + # TODO: We could make this overridable + tokens = _default_tokenizer(query_str) + terms_pattern = [f"\\b(?i){x}\\b" for x in tokens] + column_keys = self._column_config.keys() + return ( + f"SELECT {','.join(filter(lambda k: k != 'vector', column_keys))}, " + f"score FROM ({stage_one_sql}) tempt " + f"ORDER BY length(multiMatchAllIndices(text, {terms_pattern})) " + f"AS d1 DESC, " + f"log(1 + countMatches(text, '\\\\b(?i)({'|'.join(tokens)})\\\\b')) " + f"AS d2 DESC limit {similarity_top_k}" + ) + + def _append_meta_filter_condition( + self, where_str: Optional[str], exact_match_filter: list + ) -> str: + filter_str = " AND ".join( + f"JSONExtractString(" + f"{self.metadata_column}, '{filter_item.key}') " + f"= '{filter_item.value}'" + for filter_item in exact_match_filter + ) + if where_str is None: + where_str = filter_str + else: + where_str = f"{where_str} AND " + filter_str + return where_str + + def add( + self, + nodes: List[BaseNode], + **add_kwargs: Any, + ) -> List[str]: + """Add nodes to index. + + Args: + nodes: List[BaseNode]: list of nodes with embeddings + """ + if not nodes: + return [] + + if not self._table_existed: + self.create_table(len(nodes[0].get_embedding())) + + for batch in iter_batch(nodes, self._config.batch_size): + self._upload_batch(batch=batch) + + return [result.node_id for result in nodes] + + def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None: + """ + Delete nodes using with ref_doc_id. + + Args: + ref_doc_id (str): The doc_id of the document to delete. + """ + self._client.command( + f"DELETE FROM {self._config.database}.{self._config.table} WHERE doc_id='{ref_doc_id}'" + ) + + def drop(self) -> None: + """Drop ClickHouse table.""" + self._client.command( + f"DROP TABLE IF EXISTS {self._config.database}.{self._config.table}" + ) + + def query( + self, query: VectorStoreQuery, where: Optional[str] = None, **kwargs: Any + ) -> VectorStoreQueryResult: + """Query index for top k most similar nodes. + + Args: + query (VectorStoreQuery): query + where (str): additional where filter + """ + query_embedding = cast(List[float], query.query_embedding) + where_str = where + if query.doc_ids: + if where_str is not None: + where_str = f"{where_str} AND {f'doc_id IN {format_list_to_string(query.doc_ids)}'}" + else: + where_str = f"doc_id IN {format_list_to_string(query.doc_ids)}" + + # TODO: Support other filter types + if query.filters is not None and len(query.filters.legacy_filters()) > 0: + where_str = self._append_meta_filter_condition( + where_str, query.filters.legacy_filters() + ) + + # build query sql + if query.mode == VectorStoreQueryMode.DEFAULT: + query_statement = self._config.build_query_statement( + query_embed=query_embedding, + where_str=where_str, + limit=query.similarity_top_k, + ) + elif query.mode == VectorStoreQueryMode.HYBRID: + if query.query_str is not None: + amplify_ratio = self.AMPLIFY_RATIO_LE5 + if 5 < query.similarity_top_k < 50: + amplify_ratio = self.AMPLIFY_RATIO_GT5 + if query.similarity_top_k > 50: + amplify_ratio = self.AMPLIFY_RATIO_GT50 + query_statement = self._build_hybrid_search_statement( + self._config.build_query_statement( + query_embed=query_embedding, + where_str=where_str, + limit=query.similarity_top_k * amplify_ratio, + ), + query.query_str, + query.similarity_top_k, + ) + logger.debug(f"hybrid query_statement={query_statement}") + else: + raise ValueError("query_str must be specified for a hybrid query.") + elif query.mode == VectorStoreQueryMode.TEXT_SEARCH: + if query.query_str is not None: + query_statement = self._build_text_search_statement( + query.query_str, + query.similarity_top_k, + ) + logger.debug(f"text query_statement={query_statement}") + else: + raise ValueError("query_str must be specified for a text query.") + else: + raise ValueError(f"query mode {query.mode!s} not supported") + nodes = [] + ids = [] + similarities = [] + response = self._client.query(query_statement) + column_names = response.column_names + id_idx = column_names.index("id") + text_idx = column_names.index("text") + metadata_idx = column_names.index("metadata") + node_info_idx = column_names.index("node_info") + score_idx = column_names.index("score") + for r in response.result_rows: + start_char_idx = None + end_char_idx = None + + if isinstance(r[node_info_idx], dict): + start_char_idx = r[node_info_idx].get("start", None) + end_char_idx = r[node_info_idx].get("end", None) + node = TextNode( + id_=r[id_idx], + text=r[text_idx], + metadata=json.loads(r[metadata_idx]), + start_char_idx=start_char_idx, + end_char_idx=end_char_idx, + relationships={ + NodeRelationship.SOURCE: RelatedNodeInfo(node_id=r[id_idx]) + }, + ) + + nodes.append(node) + similarities.append(r[score_idx]) + ids.append(r[id_idx]) + return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/pyproject.toml new file mode 100644 index 0000000000000..ad6f47e726cde --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/pyproject.toml @@ -0,0 +1,56 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.mypy] +disallow_untyped_defs = true +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Your Name dale@clickhouse.com"] +description = "llama-index vector_stores clickhouse integration" +license = "MIT" +name = "llama-index-vector-stores-clickhouse" +readme = "README.md" +version = "0.1.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<3.12" +llama-index-core = "0.10.0" +llama-index-readers-clickhouse = "0.1.0" +clickhouse-connect = "^0.7.0" + +[tool.poetry.group.dev.dependencies] +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" +types-setuptools = "67.1.0.0" + +[tool.poetry.group.dev.dependencies.black] +extras = ["jupyter"] +version = "<=23.9.1,>=23.7.0" + +[tool.poetry.group.dev.dependencies.codespell] +extras = ["toml"] +version = ">=v2.2.6" + +[[tool.poetry.packages]] +include = "llama_index/" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/tests/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/tests/__init__.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/tests/test_clickhouse.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/tests/test_clickhouse.py new file mode 100644 index 0000000000000..f8fdac79ea971 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/tests/test_clickhouse.py @@ -0,0 +1,401 @@ +import logging +import sys +import uuid +from typing import Any, Generator, List +import clickhouse_connect +import pytest +from llama_index.core.schema import ( + NodeRelationship, + RelatedNodeInfo, + TextNode, +) +from llama_index.core.vector_stores.types import ( + ExactMatchFilter, + MetadataFilters, + VectorStoreQuery, + VectorStoreQueryMode, +) +from llama_index.vector_stores.clickhouse import ClickHouseVectorStore + +## +# Start ClickHouse locally +# cd llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse +# docker-compose up +# +# Run tests +# cd tests +# pytest test_clickhouse.py + +logging.basicConfig(level=logging.DEBUG) + +TEST_DB = "test_vector_db" +clickhouse_not_available = True + +try: + client = clickhouse_connect.get_client( + host="localhost", port=8124, username="default", password="" + ) + client.ping() + clickhouse_not_available = False +except Exception: + clickhouse_not_available = True + + +@pytest.fixture() +def table_name() -> str: + """Return the table name.""" + return f"test_{uuid.uuid4().hex}" + + +@pytest.fixture(scope="session") +def clickhouse_client() -> Generator: + import clickhouse_connect + + clickhouse_client = clickhouse_connect.get_client( + host="localhost", port=8124, username="default", password="" + ) + clickhouse_client.command(f"DROP DATABASE IF EXISTS {TEST_DB}") + clickhouse_client.command(f"CREATE DATABASE {TEST_DB}") + yield clickhouse_client + clickhouse_client.command(f"DROP DATABASE {TEST_DB}") + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse not available") +@pytest.fixture() +def clickhouse_store(table_name: str, clickhouse_client: Any) -> ClickHouseVectorStore: + return ClickHouseVectorStore( + clickhouse_client, + database=TEST_DB, + table=table_name, + metric="l2", + ) + + +@pytest.fixture(scope="session") +def node_embeddings() -> List[TextNode]: + return [ + TextNode( + text="lorem ipsum", + id_="c330d77f-90bd-4c51-9ed2-57d8d693b3b0", + relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="test-0")}, + metadata={ + "author": "Stephen King", + "theme": "Friendship", + }, + embedding=[1.0, 0.0, 0.0], + ), + TextNode( + text="lorem ipsum", + id_="c3d1e1dd-8fb4-4b8f-b7ea-7fa96038d39d", + relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="test-1")}, + metadata={ + "director": "Francis Ford Coppola", + "theme": "Mafia", + }, + embedding=[0.0, 1.0, 0.0], + ), + TextNode( + text="lorem ipsum", + id_="c3ew11cd-8fb4-4b8f-b7ea-7fa96038d39d", + relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="test-2")}, + metadata={ + "director": "Christopher Nolan", + }, + embedding=[0.0, 0.0, 1.0], + ), + TextNode( + text="I was taught that the way of progress was neither swift nor easy.", + id_="0b31ae71-b797-4e88-8495-031371a7752e", + relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="text-3")}, + metadate={ + "author": "Marie Curie", + }, + embedding=[0.0, 0.0, 0.9], + ), + TextNode( + text=( + "The important thing is not to stop questioning." + + " Curiosity has its own reason for existing." + ), + id_="bd2e080b-159a-4030-acc3-d98afd2ba49b", + relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="text-4")}, + metadate={ + "author": "Albert Einstein", + }, + embedding=[0.0, 0.0, 0.5], + ), + TextNode( + text=( + "I am no bird; and no net ensnares me;" + + " I am a free human being with an independent will." + ), + id_="f658de3b-8cef-4d1c-8bed-9a263c907251", + relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="text-5")}, + metadate={ + "author": "Charlotte Bronte", + }, + embedding=[0.0, 0.0, 0.3], + ), + ] + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_instance_creation(table_name: str, clickhouse_client: Any) -> None: + ch_store = ClickHouseVectorStore( + clickhouse_client, + database=TEST_DB, + table=table_name, + ) + assert isinstance(ch_store, ClickHouseVectorStore) + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_table_creation(table_name: str, clickhouse_client: Any) -> None: + ch_store = ClickHouseVectorStore( + clickhouse_client, + database=TEST_DB, + table=table_name, + ) + ch_store.create_table(3) + ch_store.drop() + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_add_to_ch_and_query( + clickhouse_store: ClickHouseVectorStore, + node_embeddings: List[TextNode], +) -> None: + clickhouse_store.add(node_embeddings) + res = clickhouse_store.query( + VectorStoreQuery(query_embedding=[1.0, 0.0, 0.0], similarity_top_k=1) + ) + assert res.nodes + assert res.nodes[0].get_content() == "lorem ipsum" + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_add_to_ch_and_text_query( + clickhouse_store: ClickHouseVectorStore, + node_embeddings: List[TextNode], +) -> None: + clickhouse_store.add(node_embeddings) + res = clickhouse_store.query( + VectorStoreQuery( + query_str="lorem", + mode=VectorStoreQueryMode.TEXT_SEARCH, + similarity_top_k=1, + ) + ) + assert res.nodes + assert res.nodes[0].get_content() == "lorem ipsum" + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +@pytest.mark.skipif(sys.platform == "darwin", reason="annoy not supported on osx") +def test_add_to_ch_and_text_query_annoy( + clickhouse_client: Any, + table_name: str, + node_embeddings: List[TextNode], +) -> None: + clickhouse_store = ClickHouseVectorStore( + clickhouse_client, + database=TEST_DB, + table=table_name, + metric="l2", + index_type="ANNOY", + index_params={"NumTrees": 100}, + ) + clickhouse_store.add(node_embeddings) + res = clickhouse_store.query( + VectorStoreQuery( + query_str="lorem", + mode=VectorStoreQueryMode.TEXT_SEARCH, + similarity_top_k=1, + ) + ) + assert res.nodes + assert res.nodes[0].get_content() == "lorem ipsum" + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_add_to_ch_and_text_query_hnsw( + clickhouse_client: Any, + table_name: str, + node_embeddings: List[TextNode], +) -> None: + clickhouse_store = ClickHouseVectorStore( + clickhouse_client, + database=TEST_DB, + table=table_name, + metric="l2", + index_type="HNSW", + index_params={"ScalarKind": "f16"}, + ) + clickhouse_store.add(node_embeddings) + res = clickhouse_store.query( + VectorStoreQuery( + query_str="lorem", + mode=VectorStoreQueryMode.TEXT_SEARCH, + similarity_top_k=1, + ) + ) + assert res.nodes + assert res.nodes[0].get_content() == "lorem ipsum" + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_add_to_ch_and_hybrid_query( + clickhouse_store: ClickHouseVectorStore, + node_embeddings: List[TextNode], +) -> None: + clickhouse_store.add(node_embeddings) + res = clickhouse_store.query( + VectorStoreQuery( + query_str="lorem", + query_embedding=[1.0, 0.0, 0.0], + mode=VectorStoreQueryMode.HYBRID, + similarity_top_k=1, + ) + ) + assert res.nodes + assert res.nodes[0].get_content() == "lorem ipsum" + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_add_to_ch_query_with_filters( + clickhouse_store: ClickHouseVectorStore, + node_embeddings: List[TextNode], +) -> None: + filters = MetadataFilters( + filters=[ExactMatchFilter(key="author", value="Stephen King")] + ) + q = VectorStoreQuery( + query_embedding=[1.0, 0.0, 0.0], similarity_top_k=10, filters=filters + ) + clickhouse_store.add(node_embeddings) + res = clickhouse_store.query(q) + assert res.nodes + assert len(res.nodes) == 1 + assert res.nodes[0].node_id == "c330d77f-90bd-4c51-9ed2-57d8d693b3b0" + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_add_to_ch_query_with_where_filters( + clickhouse_store: ClickHouseVectorStore, + node_embeddings: List[TextNode], +) -> None: + filters = MetadataFilters( + filters=[ExactMatchFilter(key="author", value="Stephen King")] + ) + q = VectorStoreQuery( + query_embedding=[1.0, 0.0, 0.0], similarity_top_k=10, filters=filters + ) + clickhouse_store.add(node_embeddings) + res = clickhouse_store.query( + q, where="JSONExtractString(metadata, 'theme')='Friendship'" + ) + assert res.nodes + assert len(res.nodes) == 1 + assert res.nodes[0].node_id == "c330d77f-90bd-4c51-9ed2-57d8d693b3b0" + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_add_to_ch_query_and_delete( + clickhouse_store: ClickHouseVectorStore, + node_embeddings: List[TextNode], +) -> None: + q = VectorStoreQuery(query_embedding=[1.0, 0.0, 0.0], similarity_top_k=1) + + clickhouse_store.add(node_embeddings) + res = clickhouse_store.query(q) + assert res.nodes + assert len(res.nodes) == 1 + assert res.nodes[0].node_id == "c330d77f-90bd-4c51-9ed2-57d8d693b3b0" + + clickhouse_store.delete("test-0") + res = clickhouse_store.query(q) + assert res.nodes + assert len(res.nodes) == 1 + assert res.nodes[0].node_id == "f658de3b-8cef-4d1c-8bed-9a263c907251" + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_add_to_ch_and_embed_query_ranked( + clickhouse_store: ClickHouseVectorStore, + node_embeddings: List[TextNode], +) -> None: + einstein_bronte_curie = [ + "bd2e080b-159a-4030-acc3-d98afd2ba49b", + "f658de3b-8cef-4d1c-8bed-9a263c907251", + "0b31ae71-b797-4e88-8495-031371a7752e", + ] + query_get_1_first = VectorStoreQuery( + query_embedding=[0.0, 0.0, 0.5], similarity_top_k=3 + ) + clickhouse_store.add(node_embeddings) + check_top_match(clickhouse_store, query_get_1_first, *einstein_bronte_curie) + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_add_to_ch_and_text_query_ranked( + clickhouse_store: ClickHouseVectorStore, + node_embeddings: List[TextNode], +) -> None: + node1 = "0b31ae71-b797-4e88-8495-031371a7752e" + node2 = "f658de3b-8cef-4d1c-8bed-9a263c907251" + clickhouse_store.add(node_embeddings) + query_get_1_first = VectorStoreQuery( + query_str="I was", mode=VectorStoreQueryMode.TEXT_SEARCH, similarity_top_k=2 + ) + check_top_match(clickhouse_store, query_get_1_first, node1, node2) + + query_get_2_first = VectorStoreQuery( + query_str="I am", mode=VectorStoreQueryMode.TEXT_SEARCH, similarity_top_k=2 + ) + check_top_match(clickhouse_store, query_get_2_first, node2, node1) + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_add_to_ch_and_text_query_ranked_hybrid( + clickhouse_store: ClickHouseVectorStore, + node_embeddings: List[TextNode], +) -> None: + node1 = "0b31ae71-b797-4e88-8495-031371a7752e" + node2 = "f658de3b-8cef-4d1c-8bed-9a263c907251" + + query_get_1_first = VectorStoreQuery( + query_str="I was", + query_embedding=[0.0, 0.0, 0.5], + mode=VectorStoreQueryMode.HYBRID, + similarity_top_k=2, + ) + clickhouse_store.add(node_embeddings) + check_top_match(clickhouse_store, query_get_1_first, node1, node2) + + +@pytest.mark.skipif(clickhouse_not_available, reason="clickhouse is not available") +def test_invalid_query_modes( + clickhouse_store: ClickHouseVectorStore, +) -> None: + query_sparse = VectorStoreQuery( + query_str="I was", + query_embedding=[0.0, 0.0, 0.5], + mode=VectorStoreQueryMode.SPARSE, + similarity_top_k=2, + ) + clickhouse_store.add([]) + with pytest.raises(ValueError) as exc: + clickhouse_store.query(query_sparse) + assert str(exc.value) == "query mode VectorStoreQueryMode.SPARSE not supported" + + +def check_top_match( + clickhouse_store: ClickHouseVectorStore, + query: VectorStoreQuery, + *expected_nodes: str, +) -> None: + res = clickhouse_store.query(query) + assert res.nodes + # test the nodes are return in the expected order + for i, node in enumerate(expected_nodes): + assert res.nodes[i].node_id == node