diff --git a/.github/scripts/check_diff.py b/.github/scripts/check_diff.py index ddf351d..0b278e5 100644 --- a/.github/scripts/check_diff.py +++ b/.github/scripts/check_diff.py @@ -2,7 +2,7 @@ import sys from typing import Dict -LIB_DIRS = ["libs/ibm"] +LIB_DIRS = ["libs/ibm", "libs/langchain-db2"] if __name__ == "__main__": files = sys.argv[1:] diff --git a/README.md b/README.md index a33a021..fba68de 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@ -# 🦜️🔗 LangChain IBM Watsonx +# 🦜️🔗 LangChain IBM -This repository contains 1 package with IBM integrations with LangChain: +This repository contains 2 package with IBM integrations with LangChain: - [langchain-ibm](https://pypi.org/project/langchain-ibm/) integrates [IBM Watsonx](https://www.ibm.com/watsonx). + +- [langchain-db2](https://pypi.org/project/langchain-db2/)(will be uploaded very soon) integrates IBM Db2 database vector store and vector search. \ No newline at end of file diff --git a/libs/langchain-db2/.gitignore b/libs/langchain-db2/.gitignore new file mode 100644 index 0000000..bee8a64 --- /dev/null +++ b/libs/langchain-db2/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/libs/langchain-db2/LICENSE b/libs/langchain-db2/LICENSE new file mode 100644 index 0000000..771f147 --- /dev/null +++ b/libs/langchain-db2/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/langchain-db2/Makefile b/libs/langchain-db2/Makefile new file mode 100644 index 0000000..14c9280 --- /dev/null +++ b/libs/langchain-db2/Makefile @@ -0,0 +1,64 @@ +.PHONY: all format lint test tests integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ +integration_test integration_tests: TEST_FILE = tests/integration_tests/ + + +# unit tests are run with the --disable-socket flag to prevent network calls +test tests: + poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE) + +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + +# integration tests are run without the --disable-socket flag to allow network calls +integration_test integration_tests: + poetry run pytest $(TEST_FILE) + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/db2 --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_db2 +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +check_imports: $(shell find langchain_db2 -name '*.py') + poetry run python ./scripts/check_imports.py $^ + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'check_imports - check imports' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/langchain-db2/README.md b/libs/langchain-db2/README.md new file mode 100644 index 0000000..5133295 --- /dev/null +++ b/libs/langchain-db2/README.md @@ -0,0 +1,12 @@ +# langchain-db2 + +This package contains the LangChain integration with the connector to Db2 vector store. This will allow user to leverage the vector store and vector search ablility of the IBM Db2 relational datebase. + +## Installation + +```bash +pip install -U langchain-db2 +``` + +See more details of how to use it in the Jupyter Notebook: langchain-db2/docs/db2.ipynb (This link will be updated https://github.com/langchain-ai/langchain-ibm/blob/756f43fe392f70f6c4b755d7966ea8ee1cc42759/libs/langchain-db2/docs/db2.ipynb) + diff --git a/libs/langchain-db2/docs/db2.ipynb b/libs/langchain-db2/docs/db2.ipynb new file mode 100644 index 0000000..2511ac6 --- /dev/null +++ b/libs/langchain-db2/docs/db2.ipynb @@ -0,0 +1,404 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dd33e9d5-9dba-4aac-9f7f-4cf9e6686593", + "metadata": {}, + "source": [ + "# IBM Db2 Vector Store and Vector Search\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "67520bec", + "metadata": {}, + "source": [ + "LangChain's Db2 integration (langchain-db2) provides vector store and vector search capabilities for working with IBM relational database Db2 version v12.1.2 and above, distributed under the MIT license. Users can use the provided implementations as-is or customize them for specific needs.\n", + " Key features include:\n", + "\n", + " * Vector storage with metadata\n", + " * Vector similarity search and max marginal relevance search, with metadata filtering options\n", + " * Support for dot production, cosine, and euclidean distance metrics\n", + " * Performance optimization by index creation and Approximate nearest neighbors search. (Will be added shortly)" + ] + }, + { + "cell_type": "markdown", + "id": "7bd80054-c803-47e1-a259-c40ed073c37d", + "metadata": {}, + "source": [ + "### Prerequisites for using Langchain with Db2 Vector Store and Search\n", + "\n", + "Install package `langchain-db2` which is the integration package for the db2 LangChain Vector Store and Search.\n", + "\n", + "The installation of the package should also install its dependencies like `langchain-core` and `ibm_db`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bbb989d-c6fb-4ab9-bafd-a95fd48538d0", + "metadata": {}, + "outputs": [], + "source": [ + "# pip install langchain-db2" + ] + }, + { + "cell_type": "markdown", + "id": "0fceaa5a-95da-4ebd-8b8d-5e73bb653172", + "metadata": {}, + "source": [ + "### Connect to Db2 Vector Store\n", + "\n", + "The following sample code will show how to connect to Db2 Database. Besides the dependencies above, you will need a Db2 database instance (with version v12.1.2+, which has the vector datatype support) running." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4421e4b7-2c7e-4bcd-82b3-9576595edd0f", + "metadata": {}, + "outputs": [], + "source": [ + "import ibm_db\n", + "import ibm_db_dbi\n", + "\n", + "database = \"\"\n", + "username = \"\"\n", + "password = \"\"\n", + "\n", + "try:\n", + " connection = ibm_db_dbi.connect(database, username, password)\n", + " print(\"Connection successful!\")\n", + "except Exception as e:\n", + " print(\"Connection failed!\")" + ] + }, + { + "cell_type": "markdown", + "id": "b11cf362-01b0-485d-8527-31b0fbb5028e", + "metadata": {}, + "source": [ + "### Import the required dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43ea59e3-2910-45a6-b195-5f06094bb7c9", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.embeddings import HuggingFaceEmbeddings\n", + "from langchain_community.vectorstores.utils import DistanceStrategy\n", + "from langchain_core.documents import Document\n", + "\n", + "from langchain_db2 import db2vs\n", + "from langchain_db2.db2vs import DB2VS" + ] + }, + { + "cell_type": "markdown", + "id": "0aac10dc-a9cc-4fdb-901c-1b7a4bbbe5a7", + "metadata": {}, + "source": [ + "### Create Documents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70ac6982-b13a-4e8c-9c47-57c6d136ac60", + "metadata": {}, + "outputs": [], + "source": [ + "# Define a list of documents\n", + "documents_json_list = [\n", + " {\n", + " \"id\": \"doc_1_2_P4\",\n", + " \"text\": \"Db2 handles LOB data differently than other kinds of data. As a result, you sometimes need to take additional actions when you define LOB columns and insert the LOB data.\",\n", + " \"link\": \"https://www.ibm.com/docs/en/db2-for-zos/12?topic=programs-storing-lob-data-in-tables\",\n", + " },\n", + " {\n", + " \"id\": \"doc_11.1.0_P1\",\n", + " \"text\": \"Db2® column-organized tables add columnar capabilities to Db2 databases, which include data that is stored with column organization and vector processing of column data. Using this table format with star schema data marts provides significant improvements to storage, query performance, and ease of use through simplified design and tuning.\",\n", + " \"link\": \"https://www.ibm.com/docs/en/db2/11.1.0?topic=organization-column-organized-tables\",\n", + " },\n", + " {\n", + " \"id\": \"id_22.3.4.3.1_P2\",\n", + " \"text\": \"Data structures are elements that are required to use Db2®. You can access and use these elements to organize your data. Examples of data structures include tables, table spaces, indexes, index spaces, keys, views, and databases.\",\n", + " \"link\": \"https://www.ibm.com/docs/en/zos-basic-skills?topic=concepts-db2-data-structures\",\n", + " },\n", + " {\n", + " \"id\": \"id_3.4.3.1_P3\",\n", + " \"text\": \"Db2® maintains a set of tables that contain information about the data that Db2 controls. These tables are collectively known as the catalog. The catalog tables contain information about Db2 objects such as tables, views, and indexes. When you create, alter, or drop an object, Db2 inserts, updates, or deletes rows of the catalog that describe the object.\",\n", + " \"link\": \"https://www.ibm.com/docs/en/zos-basic-skills?topic=objects-db2-catalog\",\n", + " },\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eaa942d6-5954-4898-8c32-3627b923a3a5", + "metadata": {}, + "outputs": [], + "source": [ + "# Create Langchain Documents\n", + "\n", + "documents_langchain = []\n", + "\n", + "for doc in documents_json_list:\n", + " metadata = {\"id\": doc[\"id\"], \"link\": doc[\"link\"]}\n", + " doc_langchain = Document(page_content=doc[\"text\"], metadata=metadata)\n", + " documents_langchain.append(doc_langchain)" + ] + }, + { + "cell_type": "markdown", + "id": "6823f5e6-997c-4f15-927b-bd44c61f105f", + "metadata": {}, + "source": [ + "### Create Vector Stores with different distance metrics\n", + "\n", + "First we will create three vector stores each with different distance strategies. \n", + "\n", + "(You can manually connect to the Db2 Database and will see three tables : \n", + "Documents_DOT, Documents_COSINE and Documents_EUCLIDEAN. )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed1b253e-5f5c-4a81-983c-74645213a170", + "metadata": {}, + "outputs": [], + "source": [ + "# Create Db2 Vector Stores using different distance strategies\n", + "\n", + "# When using our API calls, start by initializing your vector store with a subset of your documents\n", + "# through from_documents(), then incrementally add more documents using add_texts().\n", + "# This approach prevents system overload and ensures efficient document processing.\n", + "\n", + "model = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\")\n", + "\n", + "vector_store_dot = DB2VS.from_documents(\n", + " documents_langchain,\n", + " model,\n", + " client=connection,\n", + " table_name=\"Documents_DOT\",\n", + " distance_strategy=DistanceStrategy.DOT_PRODUCT,\n", + ")\n", + "vector_store_max = DB2VS.from_documents(\n", + " documents_langchain,\n", + " model,\n", + " client=connection,\n", + " table_name=\"Documents_COSINE\",\n", + " distance_strategy=DistanceStrategy.COSINE,\n", + ")\n", + "vector_store_euclidean = DB2VS.from_documents(\n", + " documents_langchain,\n", + " model,\n", + " client=connection,\n", + " table_name=\"Documents_EUCLIDEAN\",\n", + " distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "77c29505-8688-4b87-9a99-e648fbb2d425", + "metadata": {}, + "source": [ + "### Demonstrating add and delete operations for texts, along with basic similarity search\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "306563ae-577b-4bc7-8a92-3dd6a59310f5", + "metadata": {}, + "outputs": [], + "source": [ + "def manage_texts(vector_stores):\n", + " \"\"\"\n", + " Adds texts to each vector store, demonstrates error handling for duplicate additions,\n", + " and performs deletion of texts. Showcases similarity searches and index creation for each vector store.\n", + "\n", + " Args:\n", + " - vector_stores (list): A list of DB2VS instances.\n", + " \"\"\"\n", + " texts = [\"Rohan\", \"Shailendra\"]\n", + " metadata = [\n", + " {\"id\": \"100\", \"link\": \"Document Example Test 1\"},\n", + " {\"id\": \"101\", \"link\": \"Document Example Test 2\"},\n", + " ]\n", + "\n", + " for i, vs in enumerate(vector_stores, start=1):\n", + " # Adding texts\n", + " try:\n", + " vs.add_texts(texts, metadata)\n", + " print(f\"\\n\\n\\nAdd texts complete for vector store {i}\\n\\n\\n\")\n", + " except Exception as ex:\n", + " print(f\"\\n\\n\\nExpected error on duplicate add for vector store {i}\\n\\n\\n\")\n", + "\n", + " # Deleting texts using the value of 'id'\n", + " vs.delete([metadata[0][\"id\"], metadata[1][\"id\"]])\n", + " print(f\"\\n\\n\\nDelete texts complete for vector store {i}\\n\\n\\n\")\n", + "\n", + " # Similarity search\n", + " results = vs.similarity_search(\"How are LOBS stored in Db2 Database\", 2)\n", + " print(f\"\\n\\n\\nSimilarity search results for vector store {i}: {results}\\n\\n\\n\")\n", + "\n", + "\n", + "vector_store_list = [\n", + " vector_store_dot,\n", + " vector_store_max,\n", + " vector_store_euclidean,\n", + "]\n", + "manage_texts(vector_store_list)" + ] + }, + { + "cell_type": "markdown", + "id": "0980cb33-69cf-4547-842a-afdc4d6fa7d3", + "metadata": {}, + "source": [ + "### Demonstrating index creation with specific parameters for each distance strategy\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46298a27-e309-456e-b2b8-771d9cb3be29", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: enable this part when the create index is implemented in db2:\n", + "\n", + "# def create_search_indices(connection):\n", + "# \"\"\"\n", + "# Creates search indices for the vector stores, each with specific parameters tailored to their distance strategy.\n", + "# \"\"\"\n", + "# # Index for DOT_PRODUCT strategy\n", + "# # Notice we are creating a HNSW index with default parameters\n", + "# # This will default to creating a HNSW index with 8 Parallel Workers and use the Default Accuracy used by Db2 AI Vector Search\n", + "# db2vs.create_index(\n", + "# connection,\n", + "# vector_store_dot,\n", + "# params={\"idx_name\": \"hnsw_idx1\", \"idx_type\": \"HNSW\"},\n", + "# )\n", + "\n", + "# # Index for COSINE strategy with specific parameters\n", + "# # Notice we are creating a HNSW index with parallel 16 and Target Accuracy Specification as 97 percent\n", + "# db2vs.create_index(\n", + "# connection,\n", + "# vector_store_max,\n", + "# params={\n", + "# \"idx_name\": \"hnsw_idx2\",\n", + "# \"idx_type\": \"HNSW\",\n", + "# \"accuracy\": 97,\n", + "# \"parallel\": 16,\n", + "# },\n", + "# )\n", + "\n", + "# # Index for EUCLIDEAN_DISTANCE strategy with specific parameters\n", + "# # Notice we are creating a HNSW index by specifying Power User Parameters which are neighbors = 64 and efConstruction = 100\n", + "# db2vs.create_index(\n", + "# connection,\n", + "# vector_store_euclidean,\n", + "# params={\n", + "# \"idx_name\": \"hnsw_idx3\",\n", + "# \"idx_type\": \"HNSW\",\n", + "# \"neighbors\": 64,\n", + "# \"efConstruction\": 100,\n", + "# },\n", + "# )\n", + "\n", + "\n", + "# create_search_indices(connection)" + ] + }, + { + "cell_type": "markdown", + "id": "7223d048-5c0b-4e91-a91b-a7daa9f86758", + "metadata": {}, + "source": [ + "### Demonstrate advanced searches on vector stores, with and without attribute filtering \n", + "\n", + "With filtering, we only select the document id 101 and nothing else" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37ca2e7d-9803-4260-95e7-62776d4fb820", + "metadata": {}, + "outputs": [], + "source": [ + "# Conduct advanced searches\n", + "def conduct_advanced_searches(vector_stores):\n", + " query = \"How are LOBS stored in Db2 Database\"\n", + " # Constructing a filter for direct comparison against document metadata\n", + " # This filter aims to include documents whose metadata 'id' is exactly '101'\n", + " filter_criteria = {\"id\": [\"101\"]} # Direct comparison filter\n", + "\n", + " for i, vs in enumerate(vector_stores, start=1):\n", + " print(f\"\\n--- Vector Store {i} Advanced Searches ---\")\n", + " # Similarity search without a filter\n", + " print(\"\\nSimilarity search results without filter:\")\n", + " print(vs.similarity_search(query, 2))\n", + "\n", + " # Similarity search with a filter\n", + " print(\"\\nSimilarity search results with filter:\")\n", + " print(vs.similarity_search(query, 2, filter=filter_criteria))\n", + "\n", + " # Similarity search with relevance score\n", + " print(\"\\nSimilarity search with relevance score:\")\n", + " print(vs.similarity_search_with_score(query, 2))\n", + "\n", + " # Similarity search with relevance score with filter\n", + " print(\"\\nSimilarity search with relevance score with filter:\")\n", + " print(vs.similarity_search_with_score(query, 2, filter=filter_criteria))\n", + "\n", + " # Max marginal relevance search\n", + " print(\"\\nMax marginal relevance search results:\")\n", + " print(vs.max_marginal_relevance_search(query, 2, fetch_k=20, lambda_mult=0.5))\n", + "\n", + " # Max marginal relevance search with filter\n", + " print(\"\\nMax marginal relevance search results with filter:\")\n", + " print(\n", + " vs.max_marginal_relevance_search(\n", + " query, 2, fetch_k=20, lambda_mult=0.5, filter=filter_criteria\n", + " )\n", + " )\n", + "\n", + "\n", + "conduct_advanced_searches(vector_store_list)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pythonEnv", + "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/langchain-db2/langchain_db2/__init__.py b/libs/langchain-db2/langchain_db2/__init__.py new file mode 100644 index 0000000..12a24de --- /dev/null +++ b/libs/langchain-db2/langchain_db2/__init__.py @@ -0,0 +1,15 @@ +from importlib import metadata + +from langchain_db2.db2vs import DB2VS + +try: + __version__ = metadata.version(__package__) +except metadata.PackageNotFoundError: + # Case where package metadata is not available. + __version__ = "" +del metadata # optional, avoids polluting the results of dir(__package__) + +__all__ = [ + "DB2VS", + "__version__", +] diff --git a/libs/langchain-db2/langchain_db2/db2vs.py b/libs/langchain-db2/langchain_db2/db2vs.py new file mode 100644 index 0000000..0db1b24 --- /dev/null +++ b/libs/langchain-db2/langchain_db2/db2vs.py @@ -0,0 +1,713 @@ +from __future__ import annotations + +import functools +import hashlib +import json +import logging +import os +import uuid +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Dict, + Iterable, + List, + Optional, + Tuple, + Type, + TypeVar, + Union, + cast, +) + +import ibm_db_dbi # type: ignore + +if TYPE_CHECKING: + from ibm_db_dbi import Connection + +import numpy as np +from langchain_community.vectorstores.utils import ( + DistanceStrategy, + maximal_marginal_relevance, +) +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.vectorstores import VectorStore + +logger = logging.getLogger(__name__) +log_level = os.getenv("LOG_LEVEL", "ERROR").upper() +logging.basicConfig( + level=getattr(logging, log_level), + format="%(asctime)s - %(levelname)s - %(message)s", +) + + +# Define a type variable that can be any kind of function +T = TypeVar("T", bound=Callable[..., Any]) + + +def _handle_exceptions(func: T) -> T: + @functools.wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + try: + return func(*args, **kwargs) + except RuntimeError as db_err: + # Handle a known type of error (e.g., DB-related) specifically + logger.exception("DB-related error occurred.") + raise RuntimeError( + "Failed due to a DB issue: {}".format(db_err) + ) from db_err + except ValueError as val_err: + # Handle another known type of error specifically + logger.exception("Validation error.") + raise ValueError("Validation failed: {}".format(val_err)) from val_err + except Exception as e: + # Generic handler for all other exceptions + logger.exception("An unexpected error occurred: {}".format(e)) + raise RuntimeError("Unexpected error: {}".format(e)) from e + + return cast(T, wrapper) + + +def _table_exists(client: Connection, table_name: str) -> bool: + try: + cursor = client.cursor() + cursor.execute(f"SELECT COUNT(*) FROM {table_name}") + except Exception as ex: + if "SQL0204N" in str(ex): + return False + raise + finally: + cursor.close() + return True + + +def _get_distance_function(distance_strategy: DistanceStrategy) -> str: + # Dictionary to map distance strategies to their corresponding function + # names + distance_strategy2function = { + DistanceStrategy.EUCLIDEAN_DISTANCE: "EUCLIDEAN", + DistanceStrategy.DOT_PRODUCT: "DOT", + DistanceStrategy.COSINE: "COSINE", + } + + # Attempt to return the corresponding distance function + if distance_strategy in distance_strategy2function: + return distance_strategy2function[distance_strategy] + + # If it's an unsupported distance strategy, raise an error + raise ValueError(f"Unsupported distance strategy: {distance_strategy}") + + +@_handle_exceptions +def _create_table(client: Connection, table_name: str, embedding_dim: int) -> None: + cols_dict = { + "id": "CHAR(16) PRIMARY KEY NOT NULL", + "text": "CLOB", + "metadata": "BLOB", + "embedding": f"vector({embedding_dim}, FLOAT32)", + } + + if not _table_exists(client, table_name): + cursor = client.cursor() + ddl_body = ", ".join( + f"{col_name} {col_type}" for col_name, col_type in cols_dict.items() + ) + ddl = f"CREATE TABLE {table_name} ({ddl_body})" + try: + cursor.execute(ddl) + cursor.execute("COMMIT") + logger.info(f"Table {table_name} created successfully...") + finally: + cursor.close() + else: + logger.info(f"Table {table_name} already exists...") + + +@_handle_exceptions +def drop_table(client: Connection, table_name: str) -> None: + """Drop a table from the database. + + Args: + client: The ibm_db_dbi connection object. + table_name: The name of the table to drop. + + Raises: + RuntimeError: If an error occurs while dropping the table. + """ + if _table_exists(client, table_name): + cursor = client.cursor() + ddl = f"DROP TABLE {table_name}" + try: + cursor.execute(ddl) + cursor.execute("COMMIT") + logger.info(f"Table {table_name} dropped successfully...") + finally: + cursor.close() + else: + logger.info(f"Table {table_name} not found...") + return + + +class DB2VS(VectorStore): + """`DB2VS` vector store. + + To use, you should have: + - the ``ibm_db`` python package installed + - a connection to db2 database with vector store feature (v12.1.2+) + """ + + def __init__( + self, + client: Connection, + embedding_function: Union[ + Callable[[str], List[float]], + Embeddings, + ], + table_name: str, + distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, + query: Optional[str] = "What is a Db2 database", + params: Optional[Dict[str, Any]] = None, + ): + try: + """Initialize with ibm_db_dbi client.""" + self.client = client + """Initialize with necessary components.""" + if not isinstance(embedding_function, Embeddings): + logger.warning( + "`embedding_function` is expected to be an Embeddings " + "object, support for passing in a function will soon " + "be removed." + ) + self.embedding_function = embedding_function + self.query = query + embedding_dim = self.get_embedding_dimension() + + self.table_name = table_name + self.distance_strategy = distance_strategy + self.params = params + _create_table(client, table_name, embedding_dim) + except ibm_db_dbi.DatabaseError as db_err: + logger.exception(f"Database error occurred while create table: {db_err}") + raise RuntimeError( + "Failed to create table due to a database error." + ) from db_err + except ValueError as val_err: + logger.exception(f"Validation error: {val_err}") + raise RuntimeError( + "Failed to create table due to a validation error." + ) from val_err + except Exception as ex: + logger.exception("An unexpected error occurred while creating the table.") + raise RuntimeError( + "Failed to create table due to an unexpected error." + ) from ex + + @property + def embeddings(self) -> Optional[Embeddings]: + """ + A property that returns an Embeddings instance if embedding_function + is an instance of Embeddings, otherwise returns None. + + Returns: + Optional[Embeddings]: Embeddings instance if embedding_function + is an instance of Embeddings, otherwise returns None. + """ + return ( + self.embedding_function + if isinstance(self.embedding_function, Embeddings) + else None + ) + + def get_embedding_dimension(self) -> int: + # Embed the single document by wrapping it in a list + embedded_document = self._embed_documents( + [self.query if self.query is not None else ""] + ) + + # Get the first (and only) embedding's dimension + return len(embedded_document[0]) + + def _embed_documents(self, texts: List[str]) -> List[List[float]]: + if isinstance(self.embedding_function, Embeddings): + return self.embedding_function.embed_documents(texts) + elif callable(self.embedding_function): + return [self.embedding_function(text) for text in texts] + else: + raise TypeError( + "The embedding_function is neither Embeddings nor callable." + ) + + def _embed_query(self, text: str) -> List[float]: + if isinstance(self.embedding_function, Embeddings): + return self.embedding_function.embed_query(text) + else: + return self.embedding_function(text) + + @_handle_exceptions + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[Dict[Any, Any]]] = None, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> List[str]: + """Add more texts to the vectorstore. + Args: + texts: Iterable of strings to add to the vectorstore. + metadatas: Optional list of metadatas associated with the texts. + ids: Optional list of ids for the texts that are being added to + the vector store. + kwargs: vectorstore specific parameters + Return: + List of ids from adding the texts into the vectorstore. + """ + + texts = list(texts) + + if metadatas and len(metadatas) != len(texts): + msg = ( + f"metadatas must be the same length as texts. " + f"Got {len(metadatas)} metadatas and {len(texts)} texts." + ) + raise ValueError(msg) + + if ids: + if 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) + # If ids are provided, hash them to maintain consistency + processed_ids = [ + hashlib.sha256(_id.encode()).hexdigest()[:16].upper() for _id in ids + ] + elif metadatas: + if all("id" in metadata for metadata in metadatas): + # If no ids are provided but metadatas with ids are, generate + # ids from metadatas + processed_ids = [ + hashlib.sha256(metadata["id"].encode()).hexdigest()[:16].upper() + for metadata in metadatas + ] + else: + # In the case partial metadata has id, generate new id if metadate + # doesn't have it. + processed_ids = [] + for metadata in metadatas: + if "id" in metadata: + processed_ids.append( + hashlib.sha256(metadata["id"].encode()) + .hexdigest()[:16] + .upper() + ) + else: + processed_ids.append( + hashlib.sha256(str(uuid.uuid4()).encode()) + .hexdigest()[:16] + .upper() + ) + else: + # Generate new ids if none are provided + generated_ids = [ + str(uuid.uuid4()) for _ in texts + ] # uuid4 is more standard for random UUIDs + processed_ids = [ + hashlib.sha256(_id.encode()).hexdigest()[:16].upper() + for _id in generated_ids + ] + + embeddings = self._embed_documents(texts) + if not metadatas: + metadatas = [{} for _ in texts] + + embeddingLen = self.get_embedding_dimension() + docs: List[Tuple[Any, Any, Any, Any]] + docs = [ + (id_, f"{embedding}", json.dumps(metadata), text) + for id_, embedding, metadata, text in zip( + processed_ids, embeddings, metadatas, texts + ) + ] + + SQL_INSERT = ( + f"INSERT INTO {self.table_name} (id, embedding, metadata, text) " + f"VALUES (?, VECTOR(?, {embeddingLen}, FLOAT32), SYSTOOLS.JSON2BSON(?), ?)" + ) + + cursor = self.client.cursor() + try: + cursor.executemany(SQL_INSERT, docs) + cursor.execute("COMMIT") + finally: + cursor.close() + return processed_ids + + def similarity_search( + self, + query: str, + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs most similar to query. + Args: + query: str, + k: int, the number for documents to retrieve + filter: Optional, the filter to apply + Return: + List[Document]: documents most similar to a query + """ + if isinstance(self.embedding_function, Embeddings): + embedding = self.embedding_function.embed_query(query) + documents = self.similarity_search_by_vector( + embedding=embedding, k=k, filter=filter, **kwargs + ) + return documents + + def similarity_search_by_vector( + self, + embedding: List[float], + k: int = 4, + filter: Optional[dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Document]: + docs_and_scores = self.similarity_search_by_vector_with_relevance_scores( + embedding=embedding, k=k, filter=filter, **kwargs + ) + return [doc for doc, _ in docs_and_scores] + + def similarity_search_with_score( + self, + query: str, + k: int = 4, + filter: Optional[dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """Return docs most similar to query.""" + if isinstance(self.embedding_function, Embeddings): + embedding = self.embedding_function.embed_query(query) + docs_and_scores = self.similarity_search_by_vector_with_relevance_scores( + embedding=embedding, k=k, filter=filter, **kwargs + ) + return docs_and_scores + + @_handle_exceptions + def similarity_search_by_vector_with_relevance_scores( + self, + embedding: List[float], + k: int = 4, + filter: Optional[dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + docs_and_scores = [] + embeddingLen = self.get_embedding_dimension() + + query = f""" + SELECT id, + text, + SYSTOOLS.BSON2JSON(metadata), + vector_distance(embedding, VECTOR('{embedding}', {embeddingLen}, FLOAT32), + {_get_distance_function(self.distance_strategy)}) as distance + FROM {self.table_name} + ORDER BY distance + FETCH FIRST {k} ROWS ONLY + """ + # TODO: No APPROX in "FETCH APPROX FIRST" now. This will be added once + # approximate nearest neighbors search in db2 is implemented. + + # Execute the query + cursor = self.client.cursor() + try: + cursor.execute(query) + results = cursor.fetchall() + + # Filter results if filter is provided + for result in results: + metadata = json.loads(result[2] if result[2] is not None else "{}") + + # Apply filtering based on the 'filter' dictionary + if filter: + if all(metadata.get(key) in value for key, value in filter.items()): + doc = Document( + page_content=(result[1] if result[1] is not None else ""), + metadata=metadata, + ) + distance = result[3] + docs_and_scores.append((doc, distance)) + else: + doc = Document( + page_content=(result[1] if result[1] is not None else ""), + metadata=metadata, + ) + distance = result[3] + docs_and_scores.append((doc, distance)) + finally: + cursor.close() + return docs_and_scores + + @_handle_exceptions + def similarity_search_by_vector_returning_embeddings( + self, + embedding: List[float], + k: int, + filter: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float, np.ndarray]]: + documents = [] + embeddingLen = self.get_embedding_dimension() + + query = f""" + SELECT id, + text, + SYSTOOLS.BSON2JSON(metadata), + vector_distance(embedding, VECTOR('{embedding}', {embeddingLen}, FLOAT32), + {_get_distance_function(self.distance_strategy)}) as distance, + embedding + FROM {self.table_name} + ORDER BY distance + FETCH FIRST {k} ROWS ONLY + """ + # TODO: No APPROX in "FETCH APPROX FIRST" now. This will be added once + # approximate nearest neighbors search in db2 is implemented. + + # Execute the query + cursor = self.client.cursor() + try: + cursor.execute(query) + results = cursor.fetchall() + + for result in results: + page_content_str = result[1] if result[1] is not None else "" + metadata = json.loads(result[2] if result[2] is not None else "{}") + + # Apply filter if provided and matches; otherwise, add all + # documents + if not filter or all( + metadata.get(key) in value for key, value in filter.items() + ): + document = Document( + page_content=page_content_str, metadata=metadata + ) + distance = result[3] + + # Assuming result[4] is already in the correct format; + # adjust if necessary + current_embedding = ( + np.array(json.loads(result[4]), dtype=np.float32) + if result[4] + else np.empty(0, dtype=np.float32) + ) + + documents.append((document, distance, current_embedding)) + finally: + cursor.close() + return documents # type: ignore + + @_handle_exceptions + def max_marginal_relevance_search_with_score_by_vector( + self, + embedding: List[float], + *, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, Any]] = None, + ) -> List[Tuple[Document, float]]: + """Return docs and their similarity scores selected using the + maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND + diversity among selected documents. + + Args: + self: An instance of the class + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. The default value is 4. + fetch_k: Number of Documents to fetch before filtering to + pass to MMR algorithm. + filter: (Optional[Dict[str, str]]): Filter by metadata. Defaults + to None. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + The default value is 0.5. + Returns: + List of Documents and similarity scores selected by maximal + marginal relevance and score for each. + """ + + # Fetch documents and their scores + docs_scores_embeddings = self.similarity_search_by_vector_returning_embeddings( + embedding, fetch_k, filter=filter + ) + # Assuming documents_with_scores is a list of tuples (Document, score) + + # If you need to split documents and scores for processing (e.g., + # for MMR calculation) + documents, scores, embeddings = ( + zip(*docs_scores_embeddings) if docs_scores_embeddings else ([], [], []) + ) + + # Assume maximal_marginal_relevance method accepts embeddings and + # scores, and returns indices of selected docs + mmr_selected_indices = maximal_marginal_relevance( + np.array(embedding, dtype=np.float32), + list(embeddings), + k=k, + lambda_mult=lambda_mult, + ) + + # Filter documents based on MMR-selected indices and map scores + mmr_selected_documents_with_scores = [ + (documents[i], scores[i]) for i in mmr_selected_indices + ] + + return mmr_selected_documents_with_scores + + @_handle_exceptions + def max_marginal_relevance_search_by_vector( + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND + diversity among selected documents. + + Args: + self: An instance of the class + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + filter: Optional[Dict[str, Any]] + **kwargs: Any + Returns: + List of Documents selected by maximal marginal relevance. + """ + docs_and_scores = self.max_marginal_relevance_search_with_score_by_vector( + embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, filter=filter + ) + return [doc for doc, _ in docs_and_scores] + + @_handle_exceptions + def max_marginal_relevance_search( + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND + diversity among selected documents. + + Args: + self: An instance of the class + query: Text to look up documents similar to. + k: Number of Documents to return. The default value is 4. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + The default value is 0.5. + filter: Optional[Dict[str, Any]] + **kwargs + Returns: + List of Documents selected by maximal marginal relevance. + + `max_marginal_relevance_search` requires that `query` returns matched + embeddings alongside the match documents. + """ + embedding = self._embed_query(query) + documents = self.max_marginal_relevance_search_by_vector( + embedding, + k=k, + fetch_k=fetch_k, + lambda_mult=lambda_mult, + filter=filter, + **kwargs, + ) + return documents + + @_handle_exceptions + def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None: + """Delete by vector IDs. + Args: + self: An instance of the class + ids: List of ids to delete. + **kwargs + """ + + if ids is None: + raise ValueError("No ids provided to delete.") + + # Compute SHA-256 hashes of the ids and truncate them + hashed_ids = [ + hashlib.sha256(_id.encode()).hexdigest()[:16].upper() for _id in ids + ] + + # Constructing the SQL statement with individual placeholders + placeholders = ", ".join(["?" for i in range(len(hashed_ids))]) + + ddl = f"DELETE FROM {self.table_name} WHERE id IN ({placeholders})" + cursor = self.client.cursor() + try: + cursor.execute(ddl, hashed_ids) + cursor.execute("COMMIT") + finally: + cursor.close() + + @classmethod + @_handle_exceptions + def from_texts( + cls: Type[DB2VS], + texts: Iterable[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + **kwargs: Any, + ) -> DB2VS: + """Return VectorStore initialized from texts and embeddings.""" + client = kwargs.get("client") + if client is None: + raise ValueError("client parameter is required...") + params = kwargs.get("params", {}) + + table_name = str(kwargs.get("table_name", "langchain")) + + distance_strategy = cast( + DistanceStrategy, kwargs.get("distance_strategy", None) + ) + if not isinstance(distance_strategy, DistanceStrategy): + raise TypeError( + f"Expected DistanceStrategy got {type(distance_strategy).__name__} " + ) + + query = kwargs.get("query", "What is a Db2 database") + + drop_table(client, table_name) + + vss = cls( + client=client, + embedding_function=embedding, + table_name=table_name, + distance_strategy=distance_strategy, + query=query, + params=params, + ) + vss.add_texts(texts=list(texts), metadatas=metadatas) + return vss diff --git a/libs/langchain-db2/langchain_db2/py.typed b/libs/langchain-db2/langchain_db2/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/libs/langchain-db2/poetry.lock b/libs/langchain-db2/poetry.lock new file mode 100644 index 0000000..ef424df --- /dev/null +++ b/libs/langchain-db2/poetry.lock @@ -0,0 +1,4067 @@ +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. + 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+[tool.poetry.urls] +"Source Code" = "https://github.com/langchain-ai/langchain-ibm/tree/main/libs/langchain-db2" + +[tool.poetry.dependencies] +python = ">=3.9,<4.0" +langchain-core = "^0.3.15" +ibm-db = "^3.2.6" +langchain-community = "^0.3.20" + +[tool.ruff.lint] +select = ["E", "F", "I", "T201"] + +[tool.coverage.run] +omit = ["tests/*"] + +[tool.pytest.ini_options] +addopts = "--strict-markers --strict-config --durations=5" +markers = [ + "compile: mark placeholder test used to compile integration tests without running them", +] +asyncio_mode = "auto" + +[tool.poetry.group.test] +optional = true + +[tool.poetry.group.codespell] +optional = true + +[tool.poetry.group.test_integration] +optional = true + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] + +[tool.poetry.group.test.dependencies] +pytest = "^7.4.3" +pytest-asyncio = "^0.23.2" +pytest-socket = "^0.7.0" +pytest-watcher = "^0.3.4" 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b/libs/langchain-db2/scripts/lint_imports.sh new file mode 100755 index 0000000..19ccec1 --- /dev/null +++ b/libs/langchain-db2/scripts/lint_imports.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain, langchain_experimental, or langchain_community +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1)) + +# Decide on an exit status based on the errors +if [ "$errors" -gt 0 ]; then + exit 1 +else + exit 0 +fi diff --git a/libs/langchain-db2/tests/__init__.py b/libs/langchain-db2/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/langchain-db2/tests/integration_tests/__init__.py b/libs/langchain-db2/tests/integration_tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/langchain-db2/tests/integration_tests/test_compile.py b/libs/langchain-db2/tests/integration_tests/test_compile.py new file mode 100644 index 0000000..33ecccd --- /dev/null +++ b/libs/langchain-db2/tests/integration_tests/test_compile.py @@ -0,0 +1,7 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" + pass diff --git a/libs/langchain-db2/tests/integration_tests/test_db2vs.py b/libs/langchain-db2/tests/integration_tests/test_db2vs.py new file mode 100644 index 0000000..103884f --- /dev/null +++ b/libs/langchain-db2/tests/integration_tests/test_db2vs.py @@ -0,0 +1,516 @@ +"""Test Db2 AI Vector Search functionality.""" + +# import required modules +import threading + +from langchain_community.embeddings import HuggingFaceEmbeddings +from langchain_community.vectorstores.utils import DistanceStrategy + +from langchain_db2.db2vs import ( + DB2VS, + _create_table, + _table_exists, + drop_table, +) + +database = "" +username = "" +password = "" + + +############################ +####### table_exists ####### +############################ +def test_table_exists_test() -> None: + try: + import ibm_db_dbi # type: ignore + except ImportError: + return + + try: + connection = ibm_db_dbi.connect(database, username, password) + except Exception: + return + + # 1. Create a Table + _create_table(connection, "TB1", 8148) + + # 2. Existing Table + # expectation:true + assert _table_exists(connection, "TB1") + + # 3. Non-Existing Table + # expectation:false + assert not _table_exists(connection, "TableNonExist") + + # 4. Invalid Table Name + # Expectation: SQL0104N + try: + _table_exists(connection, "123") + except Exception: + pass + + # 5. Empty String + # Expectation: SQL0104N + try: + _table_exists(connection, "") + except Exception: + pass + + # 6. Special Character + # Expectation: SQL0007N + try: + _table_exists(connection, "!!4") + except Exception: + pass + + # 7. Table name length > 128 + # Expectation: SQL0107N The name is too long. The maximum length is "128". + try: + _table_exists(connection, "x" * 129) + except Exception: + pass + + # 8. Toggle Upper/Lower Case (like TaBlE) + # Expectation:True + assert _table_exists(connection, "Tb1") + drop_table(connection, "TB1") + + # 9. Table_Name→ "表格" + # Expectation:True + _create_table(connection, '"表格"', 545) + assert _table_exists(connection, '"表格"') + drop_table(connection, '"表格"') + + connection.commit() + + +############################ +####### create_table ####### +############################ +def test_create_table_test() -> None: + try: + import ibm_db_dbi + except ImportError: + return + + try: + connection = ibm_db_dbi.connect(database, username, password) + except Exception: + return + + # 1. New table - HELLO + # Dimension - 100 + # Expectation: table is created + _create_table(connection, "HELLO", 100) + + # 2. Existing table name - HELLO + # Dimension - 110 + # Expectation: Log message table already exists + _create_table(connection, "HELLO", 110) + drop_table(connection, "HELLO") + + # 3. New Table - 123 + # Dimension - 100 + # Expectation: SQL0104N invalid table name + try: + _create_table(connection, "123", 100) + drop_table(connection, "123") + except Exception: + pass + + # 4. New Table - Hello123 + # Dimension - 8148 + # Expectation: table is created + _create_table(connection, "Hello123", 8148) + drop_table(connection, "Hello123") + + # 5. New Table - T1 + # Dimension - 65536 + # Expectation: SQL0604N VECTOR column exceed the supported + # dimension length. + try: + _create_table(connection, "T1", 65536) + drop_table(connection, "T1") + except Exception: + pass + + # 6. New Table - T1 + # Dimension - 0 + # Expectation: SQL0604N VECTOR column unsupported dimension length 0. + try: + _create_table(connection, "T1", 0) + drop_table(connection, "T1") + except Exception: + pass + + # 7. New Table - T1 + # Dimension - -1 + # Expectation: SQL0104N An unexpected token "-" was found + try: + _create_table(connection, "T1", -1) + drop_table(connection, "T1") + except Exception: + pass + + # 8. New Table - T2 + # Dimension - '1000' + # Expectation: table is created + _create_table(connection, "T2", int("1000")) + drop_table(connection, "T2") + + # 9. New Table - T3 + # Dimension - 100 passed as a variable + # Expectation: table is created + val = 100 + _create_table(connection, "T3", val) + drop_table(connection, "T3") + + # 10. + # Expectation: SQL0104N An unexpected token + val2 = """H + ello""" + try: + _create_table(connection, val2, 545) + drop_table(connection, val2) + except Exception: + pass + + # 11. New Table - 表格 + # Dimension - 545 + # Expectation: table is created + _create_table(connection, '"表格"', 545) + drop_table(connection, '"表格"') + + # 12. + # Expectation: table with schema is created + _create_table(connection, "U1.TB4", 128) + drop_table(connection, "U1.TB4") + + # 13. + # Expectation: table is created + _create_table(connection, '"T5"', 128) + drop_table(connection, '"T5"') + + # 14. Toggle Case + # Expectation: table is created + _create_table(connection, "TaBlE", 128) + drop_table(connection, "TaBlE") + + # 15. table_name as empty_string + # Expectation: SQL0104N An unexpected token + try: + _create_table(connection, "", 128) + drop_table(connection, "") + _create_table(connection, '""', 128) + drop_table(connection, '""') + except Exception: + pass + + # 16. Arithmetic Operations in dimension parameter + # Expectation: table is created + n = 1 + _create_table(connection, "T10", n + 500) + drop_table(connection, "T10") + + # 17. String Operations in table_name parameter + # Expectation: table is created + _create_table(connection, "YaSh".replace("aS", "ok"), 500) + drop_table(connection, "YaSh".replace("aS", "ok")) + + connection.commit() + + +################################## +####### add_texts ################ +################################## +def test_add_texts_test() -> None: + try: + import ibm_db_dbi + except ImportError: + return + + try: + connection = ibm_db_dbi.connect(database, username, password) + except Exception: + return + + # 1. Add 2 records to table + # Expectation: Successful + texts = ["David", "Vectoria"] + metadata = [ + {"id": "100", "link": "Document Example Test 1"}, + {"id": "101", "link": "Document Example Test 2"}, + ] + model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") + vs_obj = DB2VS(connection, model, "TB1", DistanceStrategy.EUCLIDEAN_DISTANCE) + vs_obj.add_texts(texts, metadata) + drop_table(connection, "TB1") + + # 2-1. Add 2 records to table with metadata but no id inside it + # Expectation: Successful, new ID will be generated + metadataNoID = [ + {"link": "Document Example Test 1"}, + {"link": "Document Example Test 2"}, + ] + model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") + vs_obj = DB2VS(connection, model, "TB2", DistanceStrategy.EUCLIDEAN_DISTANCE) + vs_obj.add_texts(texts, metadataNoID) + drop_table(connection, "TB2") + + # 2-2. Add 3 records to table with metadata but partial metadata has id + # Expectation: Successful, new ID will be generated for the missing ones + texts1 = ["David", "Vectoria", "John"] + metadataPartialID = [ + {"id": "100", "link": "Document Example Test 1"}, + {"link": "Document Example Test 2"}, + {"link": "Document Example Test 3"}, + ] + vs_obj = DB2VS(connection, model, "TB2", DistanceStrategy.EUCLIDEAN_DISTANCE) + vs_obj.add_texts(texts1, metadataPartialID) + drop_table(connection, "TB2") + + # 3. Add record but neither metadata nor ids are there + # Expectation: Successful, new ID will be generated + model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") + vs_obj = DB2VS(connection, model, "TB3", DistanceStrategy.EUCLIDEAN_DISTANCE) + texts2 = ["Sam", "John"] + vs_obj.add_texts(texts2) + drop_table(connection, "TB3") + + # 4. Add record with ids option + # ids are passed as string + # ids are passed as empty string + # ids are passed as multi-line string + # ids are passed as "" + # Expectations: + # Successful + # Successful + # Successful + # Successful + + vs_obj = DB2VS(connection, model, "TB4", DistanceStrategy.EUCLIDEAN_DISTANCE) + ids4 = ["114", "124"] + vs_obj.add_texts(texts2, ids=ids4) + drop_table(connection, "TB4") + + vs_obj = DB2VS(connection, model, "TB5", DistanceStrategy.EUCLIDEAN_DISTANCE) + ids5 = ["", "134"] + vs_obj.add_texts(texts2, ids=ids5) + drop_table(connection, "TB5") + + vs_obj = DB2VS(connection, model, "TB6", DistanceStrategy.EUCLIDEAN_DISTANCE) + ids6 = [ + """Good afternoon + my friends""", + "India", + ] + vs_obj.add_texts(texts2, ids=ids6) + drop_table(connection, "TB6") + + vs_obj = DB2VS(connection, model, "TB7", DistanceStrategy.EUCLIDEAN_DISTANCE) + ids7 = ['"Good afternoon"', '"India"'] + vs_obj.add_texts(texts2, ids=ids7) + drop_table(connection, "TB7") + + # 5. Add record with ids option but the id are duplicated + # Expectations: SQL0803N having duplicate values for the index key + try: + vs_obj = DB2VS(connection, model, "TB8", DistanceStrategy.EUCLIDEAN_DISTANCE) + ids8 = ["118", "118"] + vs_obj.add_texts(texts2, ids=ids8) + drop_table(connection, "TB8") + except Exception: + pass + + # 6. Add records with both ids and metadatas + # Expectation: Successful, the ID will be generated based on ids + vs_obj = DB2VS(connection, model, "TB9", DistanceStrategy.EUCLIDEAN_DISTANCE) + texts3 = ["Sam 6", "John 6"] + ids9 = ["1", "2"] + metadata = [ + {"id": "102", "link": "Document Example", "stream": "Science"}, + {"id": "104", "link": "Document Example 45"}, + ] + vs_obj.add_texts(texts3, metadata, ids=ids9) + drop_table(connection, "TB9") + + # This one may run slow before using executemany() <<<<<<<<<< + # 7. Add 10000 records + # Expectation:Successful + vs_obj = DB2VS(connection, model, "TB10", DistanceStrategy.EUCLIDEAN_DISTANCE) + texts4 = ["Sam{0}".format(i) for i in range(1, 10000)] + ids10 = ["Hello{0}".format(i) for i in range(1, 10000)] + vs_obj.add_texts(texts4, ids=ids10) + drop_table(connection, "TB10") + + # 8. Add 2 different record concurrently + # Expectation:Successful + def add(val: str) -> None: + model = HuggingFaceEmbeddings( + model_name="sentence-transformers/all-mpnet-base-v2" + ) + vs_obj = DB2VS(connection, model, "TB11", DistanceStrategy.EUCLIDEAN_DISTANCE) + texts5 = [val] + ids11 = texts5 + vs_obj.add_texts(texts5, ids=ids11) + + thread_1 = threading.Thread(target=add, args=("Sam",)) + thread_2 = threading.Thread(target=add, args=("John",)) + thread_1.start() + thread_2.start() + thread_1.join() + thread_2.join() + drop_table(connection, "TB11") + + # 9. Add 2 same record concurrently + # Expectation: Successful, For one of the insert,get primary key violation error + def add1(val: str) -> None: + model = HuggingFaceEmbeddings( + model_name="sentence-transformers/all-mpnet-base-v2" + ) + vs_obj = DB2VS(connection, model, "TB12", DistanceStrategy.EUCLIDEAN_DISTANCE) + texts = [val] + ids12 = texts + vs_obj.add_texts(texts, ids=ids12) + + try: + thread_1 = threading.Thread(target=add1, args=("Sam",)) + thread_2 = threading.Thread(target=add1, args=("Sam",)) + thread_1.start() + thread_2.start() + thread_1.join() + thread_2.join() + except Exception: + pass + drop_table(connection, "TB12") + + connection.commit() + + +################################## +####### embed_documents(texts) #### +################################## +def test_embed_documents_test() -> None: + try: + import ibm_db_dbi + except ImportError: + return + + try: + connection = ibm_db_dbi.connect(database, username, password) + except Exception: + return + # 1. Embed String Example-'Sam' + # Expectation: Successful. + model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") + vs_obj = DB2VS(connection, model, "TB7", DistanceStrategy.EUCLIDEAN_DISTANCE) + vs_obj._embed_documents( + [ + "Sam", + ] + ) + + # 2. Embed List of string + # Expectation: Successful. + vs_obj._embed_documents(["hello", "yash"]) + drop_table(connection, "TB7") + + connection.commit() + + +################################## +####### embed_query(text) ######## +################################## +def test_embed_query_test() -> None: + try: + import ibm_db_dbi + except ImportError: + return + + try: + connection = ibm_db_dbi.connect(database, username, password) + except Exception: + return + + # 1. Embed String + # Expectation: Successful. + model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") + vs_obj = DB2VS(connection, model, "TB8", DistanceStrategy.EUCLIDEAN_DISTANCE) + vs_obj._embed_query("Sam") + + # 2. Embed Empty string + # Expectation: Successful. + vs_obj._embed_query("") + drop_table(connection, "TB8") + + connection.commit() + + +################################## +####### perform_search ########### +################################## +def test_perform_search_test() -> None: + try: + import ibm_db_dbi + except ImportError: + return + + try: + connection = ibm_db_dbi.connect(database, username, password) + except Exception: + return + + model1 = HuggingFaceEmbeddings( + model_name="sentence-transformers/paraphrase-mpnet-base-v2" + ) + vs_1 = DB2VS(connection, model1, "TB10", DistanceStrategy.EUCLIDEAN_DISTANCE) + vs_2 = DB2VS(connection, model1, "TB11", DistanceStrategy.DOT_PRODUCT) + vs_3 = DB2VS(connection, model1, "TB12", DistanceStrategy.COSINE) + + # vector store lists: + vs_list = [vs_1, vs_2, vs_3] + + for i, vs in enumerate(vs_list, start=1): + # insert data + texts = ["Yash", "Varanasi", "Yashaswi", "Mumbai", "BengaluruYash"] + metadatas = [ + {"id": "hello"}, + {"id": "105"}, + {"id": "106"}, + {"id": "yash"}, + {"id": "108"}, + ] + + vs.add_texts(texts, metadatas) + + # perform search + query = "YashB" + + filter = {"id": ["106", "108", "yash"]} + + # similarity_search without filter + vs.similarity_search(query, 2) + + # similarity_search with filter + vs.similarity_search(query, 2, filter=filter) + + # Similarity search with relevance score + vs.similarity_search_with_score(query, 2) + + # Similarity search with relevance score with filter + vs.similarity_search_with_score(query, 2, filter=filter) + + # Max marginal relevance search + vs.max_marginal_relevance_search(query, 2, fetch_k=20, lambda_mult=0.5) + + # Max marginal relevance search with filter + vs.max_marginal_relevance_search( + query, 2, fetch_k=20, lambda_mult=0.5, filter=filter + ) + + drop_table(connection, "TB10") + drop_table(connection, "TB11") + drop_table(connection, "TB12") + + connection.commit() diff --git a/libs/langchain-db2/tests/unit_tests/__init__.py b/libs/langchain-db2/tests/unit_tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/langchain-db2/tests/unit_tests/test_db2vs.py b/libs/langchain-db2/tests/unit_tests/test_db2vs.py new file mode 100644 index 0000000..0f75dbc --- /dev/null +++ b/libs/langchain-db2/tests/unit_tests/test_db2vs.py @@ -0,0 +1,16 @@ +from unittest.mock import MagicMock + +from langchain_core.embeddings.fake import DeterministicFakeEmbedding + +from langchain_db2.db2vs import DB2VS + + +def test_init() -> None: + """Test that the DB2VS class can be initialized.""" + client = MagicMock() + embedding = DeterministicFakeEmbedding(size=100) + table_name = "foo" + db2vs = DB2VS(client, embedding, table_name) + assert db2vs is not None + assert isinstance(db2vs, DB2VS) + assert len(client.mock_calls) == 3