diff --git a/.github/workflows/_integration_test.yml b/.github/workflows/_integration_test.yml index 1189907e96695..ee21aae7853b9 100644 --- a/.github/workflows/_integration_test.yml +++ b/.github/workflows/_integration_test.yml @@ -70,6 +70,9 @@ jobs: ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }} ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }} ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }} + ES_URL: ${{ secrets.ES_URL }} + ES_CLOUD_ID: ${{ secrets.ES_CLOUD_ID }} + ES_API_KEY: ${{ secrets.ES_API_KEY }} run: | make integration_tests diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index fbd053ec9c06e..2afb23a5a02ca 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -191,6 +191,9 @@ jobs: ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }} ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }} ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }} + ES_URL: ${{ secrets.ES_URL }} + ES_CLOUD_ID: ${{ secrets.ES_CLOUD_ID }} + ES_API_KEY: ${{ secrets.ES_API_KEY }} run: make integration_tests working-directory: ${{ inputs.working-directory }} diff --git a/cookbook/self_query_hotel_search.ipynb b/cookbook/self_query_hotel_search.ipynb index d38192c5a2cb3..6b93b19627983 100644 --- a/cookbook/self_query_hotel_search.ipynb +++ b/cookbook/self_query_hotel_search.ipynb @@ -1083,7 +1083,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain_community.vectorstores import ElasticsearchStore\n", + "from langchain_elasticsearch import ElasticsearchStore\n", "from langchain_openai import OpenAIEmbeddings\n", "\n", "embeddings = OpenAIEmbeddings()" diff --git a/docs/docs/integrations/providers/elasticsearch.mdx b/docs/docs/integrations/providers/elasticsearch.mdx index a7125b55b33ce..280066f467add 100644 --- a/docs/docs/integrations/providers/elasticsearch.mdx +++ b/docs/docs/integrations/providers/elasticsearch.mdx @@ -23,7 +23,7 @@ Elastic Cloud is a managed Elasticsearch service. Signup for a [free trial](http ### Install Client ```bash -pip install elasticsearch +pip install langchain-elasticsearch ``` ## Vector Store @@ -31,7 +31,7 @@ pip install elasticsearch The vector store is a simple wrapper around Elasticsearch. It provides a simple interface to store and retrieve vectors. ```python -from langchain_community.vectorstores import ElasticsearchStore +from langchain_elasticsearch import ElasticsearchStore from langchain_community.document_loaders import TextLoader from langchain.text_splitter import CharacterTextSplitter diff --git a/docs/docs/integrations/retrievers/self_query/elasticsearch_self_query.ipynb b/docs/docs/integrations/retrievers/self_query/elasticsearch_self_query.ipynb index 6bef6db0d0c24..165cd16f3c63e 100644 --- a/docs/docs/integrations/retrievers/self_query/elasticsearch_self_query.ipynb +++ b/docs/docs/integrations/retrievers/self_query/elasticsearch_self_query.ipynb @@ -60,8 +60,8 @@ "import getpass\n", "import os\n", "\n", - "from langchain_community.vectorstores import ElasticsearchStore\n", "from langchain_core.documents import Document\n", + "from langchain_elasticsearch import ElasticsearchStore\n", "from langchain_openai import OpenAIEmbeddings\n", "\n", "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n", diff --git a/docs/docs/integrations/text_embedding/elasticsearch.ipynb b/docs/docs/integrations/text_embedding/elasticsearch.ipynb index a50048363cee8..d19cdc89d6d2e 100644 --- a/docs/docs/integrations/text_embedding/elasticsearch.ipynb +++ b/docs/docs/integrations/text_embedding/elasticsearch.ipynb @@ -24,7 +24,7 @@ }, "outputs": [], "source": [ - "!pip -q install elasticsearch langchain" + "!pip -q install langchain-elasticsearch" ] }, { @@ -36,7 +36,7 @@ }, "outputs": [], "source": [ - "from langchain_community.embeddings.elasticsearch import ElasticsearchEmbeddings" + "from langchain_elasticsearch import ElasticsearchEmbeddings" ] }, { diff --git a/docs/docs/integrations/vectorstores/elasticsearch.ipynb b/docs/docs/integrations/vectorstores/elasticsearch.ipynb index c36c6f65ee632..3579ea8e6a2e1 100644 --- a/docs/docs/integrations/vectorstores/elasticsearch.ipynb +++ b/docs/docs/integrations/vectorstores/elasticsearch.ipynb @@ -21,7 +21,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install --upgrade --quiet elasticsearch langchain-openai tiktoken langchain" + "%pip install --upgrade --quiet langchain-elasticsearch langchain-openai tiktoken langchain" ] }, { @@ -64,7 +64,7 @@ "\n", "Example:\n", "```python\n", - " from langchain_community.vectorstores.elasticsearch import ElasticsearchStore\n", + " from langchain_elasticsearch import ElasticsearchStore\n", " from langchain_openai import OpenAIEmbeddings\n", "\n", " embedding = OpenAIEmbeddings()\n", @@ -79,7 +79,7 @@ "\n", "Example:\n", "```python\n", - " from langchain_community.vectorstores import ElasticsearchStore\n", + " from langchain_elasticsearch import ElasticsearchStore\n", " from langchain_openai import OpenAIEmbeddings\n", "\n", " embedding = OpenAIEmbeddings()\n", @@ -97,7 +97,7 @@ "Example:\n", "```python\n", " import elasticsearch\n", - " from langchain_community.vectorstores import ElasticsearchStore\n", + " from langchain_elasticsearch import ElasticsearchStore\n", "\n", " es_client= elasticsearch.Elasticsearch(\n", " hosts=[\"http://localhost:9200\"],\n", @@ -137,7 +137,7 @@ "\n", "Example:\n", "```python\n", - " from langchain_community.vectorstores.elasticsearch import ElasticsearchStore\n", + " from langchain_elasticsearch import ElasticsearchStore\n", " from langchain_openai import OpenAIEmbeddings\n", "\n", " embedding = OpenAIEmbeddings()\n", @@ -202,7 +202,7 @@ }, "outputs": [], "source": [ - "from langchain_community.vectorstores import ElasticsearchStore\n", + "from langchain_elasticsearch import ElasticsearchStore\n", "from langchain_openai import OpenAIEmbeddings" ] }, @@ -817,7 +817,7 @@ "source": [ "from typing import Dict\n", "\n", - "from langchain.docstore.document import Document\n", + "from langchain_core.documents import Document\n", "\n", "\n", "def custom_document_builder(hit: Dict) -> Document:\n", @@ -902,7 +902,7 @@ "\n", "```python\n", "\n", - "from langchain_community.vectorstores.elasticsearch import ElasticsearchStore\n", + "from langchain_elasticsearch import ElasticsearchStore\n", "\n", "db = ElasticsearchStore(\n", " es_url=\"http://localhost:9200\",\n", @@ -936,7 +936,7 @@ "\n", "```python\n", "\n", - "from langchain_community.vectorstores.elasticsearch import ElasticsearchStore\n", + "from langchain_elasticsearch import ElasticsearchStore\n", "\n", "db = ElasticsearchStore(\n", " es_url=\"http://localhost:9200\",\n", diff --git a/docs/docs/modules/data_connection/indexing.ipynb b/docs/docs/modules/data_connection/indexing.ipynb index 7767f931ddb36..ec252c99a1dc9 100644 --- a/docs/docs/modules/data_connection/indexing.ipynb +++ b/docs/docs/modules/data_connection/indexing.ipynb @@ -91,8 +91,8 @@ "outputs": [], "source": [ "from langchain.indexes import SQLRecordManager, index\n", - "from langchain_community.vectorstores import ElasticsearchStore\n", "from langchain_core.documents import Document\n", + "from langchain_elasticsearch import ElasticsearchStore\n", "from langchain_openai import OpenAIEmbeddings" ] }, diff --git a/libs/partners/elasticsearch/.gitignore b/libs/partners/elasticsearch/.gitignore new file mode 100644 index 0000000000000..bee8a64b79a99 --- /dev/null +++ b/libs/partners/elasticsearch/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/libs/partners/elasticsearch/LICENSE b/libs/partners/elasticsearch/LICENSE new file mode 100644 index 0000000000000..fc0602feecdd6 --- /dev/null +++ b/libs/partners/elasticsearch/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/partners/elasticsearch/Makefile b/libs/partners/elasticsearch/Makefile new file mode 100644 index 0000000000000..9ada9f6fc7327 --- /dev/null +++ b/libs/partners/elasticsearch/Makefile @@ -0,0 +1,60 @@ +.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 + +install: + poetry install + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ +integration_test integration_tests: TEST_FILE=tests/integration_tests/ + +test tests 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/elasticsearch --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_elasticsearch +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + poetry run ruff format $(PYTHON_FILES) --diff + poetry run ruff --select I $(PYTHON_FILES) + mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --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_elasticsearch -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/partners/elasticsearch/README.md b/libs/partners/elasticsearch/README.md new file mode 100644 index 0000000000000..722fa1caea44a --- /dev/null +++ b/libs/partners/elasticsearch/README.md @@ -0,0 +1,29 @@ +# langchain-elasticsearch + +This package contains the LangChain integration with Elasticsearch. + +## Installation + +```bash +pip install -U langchain-elasticsearch +``` + +TODO document how to get id and key + +## Usage + +The `ElasticsearchStore` class exposes the connection to the Pinecone vector store. + +```python +from langchain_elasticsearch import ElasticsearchStore + +embeddings = ... # use a LangChain Embeddings class + +vectorstore = ElasticsearchStore( + es_cloud_id="your-cloud-id", + es_api_key="your-api-key", + index_name="your-index-name", + embeddings=embeddings, +) +``` + diff --git a/libs/partners/elasticsearch/langchain_elasticsearch/__init__.py b/libs/partners/elasticsearch/langchain_elasticsearch/__init__.py new file mode 100644 index 0000000000000..bba1f3f6260bb --- /dev/null +++ b/libs/partners/elasticsearch/langchain_elasticsearch/__init__.py @@ -0,0 +1,17 @@ +from langchain_elasticsearch.chat_history import ElasticsearchChatMessageHistory +from langchain_elasticsearch.embeddings import ElasticsearchEmbeddings +from langchain_elasticsearch.vectorstores import ( + ApproxRetrievalStrategy, + ElasticsearchStore, + ExactRetrievalStrategy, + SparseRetrievalStrategy, +) + +__all__ = [ + "ApproxRetrievalStrategy", + "ElasticsearchChatMessageHistory", + "ElasticsearchEmbeddings", + "ElasticsearchStore", + "ExactRetrievalStrategy", + "SparseRetrievalStrategy", +] diff --git a/libs/partners/elasticsearch/langchain_elasticsearch/_utilities.py b/libs/partners/elasticsearch/langchain_elasticsearch/_utilities.py new file mode 100644 index 0000000000000..237d587bc0323 --- /dev/null +++ b/libs/partners/elasticsearch/langchain_elasticsearch/_utilities.py @@ -0,0 +1,82 @@ +from enum import Enum +from typing import List, Union + +import numpy as np + +Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray] + + +class DistanceStrategy(str, Enum): + """Enumerator of the Distance strategies for calculating distances + between vectors.""" + + EUCLIDEAN_DISTANCE = "EUCLIDEAN_DISTANCE" + MAX_INNER_PRODUCT = "MAX_INNER_PRODUCT" + DOT_PRODUCT = "DOT_PRODUCT" + JACCARD = "JACCARD" + COSINE = "COSINE" + + +def maximal_marginal_relevance( + query_embedding: np.ndarray, + embedding_list: list, + lambda_mult: float = 0.5, + k: int = 4, +) -> List[int]: + """Calculate maximal marginal relevance.""" + if min(k, len(embedding_list)) <= 0: + return [] + if query_embedding.ndim == 1: + query_embedding = np.expand_dims(query_embedding, axis=0) + similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0] + most_similar = int(np.argmax(similarity_to_query)) + idxs = [most_similar] + selected = np.array([embedding_list[most_similar]]) + while len(idxs) < min(k, len(embedding_list)): + best_score = -np.inf + idx_to_add = -1 + similarity_to_selected = cosine_similarity(embedding_list, selected) + for i, query_score in enumerate(similarity_to_query): + if i in idxs: + continue + redundant_score = max(similarity_to_selected[i]) + equation_score = ( + lambda_mult * query_score - (1 - lambda_mult) * redundant_score + ) + if equation_score > best_score: + best_score = equation_score + idx_to_add = i + idxs.append(idx_to_add) + selected = np.append(selected, [embedding_list[idx_to_add]], axis=0) + return idxs + + +def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: + """Row-wise cosine similarity between two equal-width matrices.""" + if len(X) == 0 or len(Y) == 0: + return np.array([]) + + X = np.array(X) + Y = np.array(Y) + if X.shape[1] != Y.shape[1]: + raise ValueError( + f"Number of columns in X and Y must be the same. X has shape {X.shape} " + f"and Y has shape {Y.shape}." + ) + try: + import simsimd as simd # type: ignore + + X = np.array(X, dtype=np.float32) + Y = np.array(Y, dtype=np.float32) + Z = 1 - simd.cdist(X, Y, metric="cosine") + if isinstance(Z, float): + return np.array([Z]) + return Z + except ImportError: + X_norm = np.linalg.norm(X, axis=1) + Y_norm = np.linalg.norm(Y, axis=1) + # Ignore divide by zero errors run time warnings as those are handled below. + with np.errstate(divide="ignore", invalid="ignore"): + similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm) + similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0 + return similarity diff --git a/libs/partners/elasticsearch/langchain_elasticsearch/chat_history.py b/libs/partners/elasticsearch/langchain_elasticsearch/chat_history.py new file mode 100644 index 0000000000000..026557c33fbd5 --- /dev/null +++ b/libs/partners/elasticsearch/langchain_elasticsearch/chat_history.py @@ -0,0 +1,201 @@ +import json +import logging +from time import time +from typing import TYPE_CHECKING, Any, Dict, List, Optional + +from langchain_core.chat_history import BaseChatMessageHistory +from langchain_core.messages import ( + BaseMessage, + message_to_dict, + messages_from_dict, +) + +if TYPE_CHECKING: + from elasticsearch import Elasticsearch + +logger = logging.getLogger(__name__) + + +class ElasticsearchChatMessageHistory(BaseChatMessageHistory): + """Chat message history that stores history in Elasticsearch. + + Args: + es_url: URL of the Elasticsearch instance to connect to. + es_cloud_id: Cloud ID of the Elasticsearch instance to connect to. + es_user: Username to use when connecting to Elasticsearch. + es_password: Password to use when connecting to Elasticsearch. + es_api_key: API key to use when connecting to Elasticsearch. + es_connection: Optional pre-existing Elasticsearch connection. + esnsure_ascii: Used to escape ASCII symbols in json.dumps. Defaults to True. + index: Name of the index to use. + session_id: Arbitrary key that is used to store the messages + of a single chat session. + """ + + def __init__( + self, + index: str, + session_id: str, + *, + es_connection: Optional["Elasticsearch"] = None, + es_url: Optional[str] = None, + es_cloud_id: Optional[str] = None, + es_user: Optional[str] = None, + es_api_key: Optional[str] = None, + es_password: Optional[str] = None, + esnsure_ascii: Optional[bool] = True, + ): + self.index: str = index + self.session_id: str = session_id + self.ensure_ascii = esnsure_ascii + + # Initialize Elasticsearch client from passed client arg or connection info + if es_connection is not None: + self.client = es_connection.options( + headers={"user-agent": self.get_user_agent()} + ) + elif es_url is not None or es_cloud_id is not None: + self.client = ElasticsearchChatMessageHistory.connect_to_elasticsearch( + es_url=es_url, + username=es_user, + password=es_password, + cloud_id=es_cloud_id, + api_key=es_api_key, + ) + else: + raise ValueError( + """Either provide a pre-existing Elasticsearch connection, \ + or valid credentials for creating a new connection.""" + ) + + if self.client.indices.exists(index=index): + logger.debug( + f"Chat history index {index} already exists, skipping creation." + ) + else: + logger.debug(f"Creating index {index} for storing chat history.") + + self.client.indices.create( + index=index, + mappings={ + "properties": { + "session_id": {"type": "keyword"}, + "created_at": {"type": "date"}, + "history": {"type": "text"}, + } + }, + ) + + @staticmethod + def get_user_agent() -> str: + from langchain_core import __version__ + + return f"langchain-py-ms/{__version__}" + + @staticmethod + def connect_to_elasticsearch( + *, + es_url: Optional[str] = None, + cloud_id: Optional[str] = None, + api_key: Optional[str] = None, + username: Optional[str] = None, + password: Optional[str] = None, + ) -> "Elasticsearch": + try: + import elasticsearch + except ImportError: + raise ImportError( + "Could not import elasticsearch python package. " + "Please install it with `pip install elasticsearch`." + ) + + if es_url and cloud_id: + raise ValueError( + "Both es_url and cloud_id are defined. Please provide only one." + ) + + connection_params: Dict[str, Any] = {} + + if es_url: + connection_params["hosts"] = [es_url] + elif cloud_id: + connection_params["cloud_id"] = cloud_id + else: + raise ValueError("Please provide either elasticsearch_url or cloud_id.") + + if api_key: + connection_params["api_key"] = api_key + elif username and password: + connection_params["basic_auth"] = (username, password) + + es_client = elasticsearch.Elasticsearch( + **connection_params, + headers={"user-agent": ElasticsearchChatMessageHistory.get_user_agent()}, + ) + try: + es_client.info() + except Exception as err: + logger.error(f"Error connecting to Elasticsearch: {err}") + raise err + + return es_client + + @property + def messages(self) -> List[BaseMessage]: # type: ignore[override] + """Retrieve the messages from Elasticsearch""" + try: + from elasticsearch import ApiError + + result = self.client.search( + index=self.index, + query={"term": {"session_id": self.session_id}}, + sort="created_at:asc", + ) + except ApiError as err: + logger.error(f"Could not retrieve messages from Elasticsearch: {err}") + raise err + + if result and len(result["hits"]["hits"]) > 0: + items = [ + json.loads(document["_source"]["history"]) + for document in result["hits"]["hits"] + ] + else: + items = [] + + return messages_from_dict(items) + + def add_message(self, message: BaseMessage) -> None: + """Add a message to the chat session in Elasticsearch""" + try: + from elasticsearch import ApiError + + self.client.index( + index=self.index, + document={ + "session_id": self.session_id, + "created_at": round(time() * 1000), + "history": json.dumps( + message_to_dict(message), + ensure_ascii=bool(self.ensure_ascii), + ), + }, + refresh=True, + ) + except ApiError as err: + logger.error(f"Could not add message to Elasticsearch: {err}") + raise err + + def clear(self) -> None: + """Clear session memory in Elasticsearch""" + try: + from elasticsearch import ApiError + + self.client.delete_by_query( + index=self.index, + query={"term": {"session_id": self.session_id}}, + refresh=True, + ) + except ApiError as err: + logger.error(f"Could not clear session memory in Elasticsearch: {err}") + raise err diff --git a/libs/partners/elasticsearch/langchain_elasticsearch/embeddings.py b/libs/partners/elasticsearch/langchain_elasticsearch/embeddings.py new file mode 100644 index 0000000000000..50e8705b17e1a --- /dev/null +++ b/libs/partners/elasticsearch/langchain_elasticsearch/embeddings.py @@ -0,0 +1,208 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, List, Optional + +from elasticsearch import Elasticsearch +from langchain_core.embeddings import Embeddings +from langchain_core.utils import get_from_env + +if TYPE_CHECKING: + from elasticsearch.client import MlClient + + +class ElasticsearchEmbeddings(Embeddings): + """Elasticsearch embedding models. + + This class provides an interface to generate embeddings using a model deployed + in an Elasticsearch cluster. It requires an Elasticsearch connection object + and the model_id of the model deployed in the cluster. + + In Elasticsearch you need to have an embedding model loaded and deployed. + - https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-trained-model.html + - https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-deploy-models.html + """ # noqa: E501 + + def __init__( + self, + client: MlClient, + model_id: str, + *, + input_field: str = "text_field", + ): + """ + Initialize the ElasticsearchEmbeddings instance. + + Args: + client (MlClient): An Elasticsearch ML client object. + model_id (str): The model_id of the model deployed in the Elasticsearch + cluster. + input_field (str): The name of the key for the input text field in the + document. Defaults to 'text_field'. + """ + self.client = client + self.model_id = model_id + self.input_field = input_field + + @classmethod + def from_credentials( + cls, + model_id: str, + *, + es_cloud_id: Optional[str] = None, + es_api_key: Optional[str] = None, + input_field: str = "text_field", + ) -> ElasticsearchEmbeddings: + """Instantiate embeddings from Elasticsearch credentials. + + Args: + model_id (str): The model_id of the model deployed in the Elasticsearch + cluster. + input_field (str): The name of the key for the input text field in the + document. Defaults to 'text_field'. + es_cloud_id: (str, optional): The Elasticsearch cloud ID to connect to. + es_user: (str, optional): Elasticsearch username. + es_password: (str, optional): Elasticsearch password. + + Example: + .. code-block:: python + + from langchain_elasticserach.embeddings import ElasticsearchEmbeddings + + # Define the model ID and input field name (if different from default) + model_id = "your_model_id" + # Optional, only if different from 'text_field' + input_field = "your_input_field" + + # Credentials can be passed in two ways. Either set the env vars + # ES_CLOUD_ID, ES_USER, ES_PASSWORD and they will be automatically + # pulled in, or pass them in directly as kwargs. + embeddings = ElasticsearchEmbeddings.from_credentials( + model_id, + input_field=input_field, + # es_cloud_id="foo", + # es_user="bar", + # es_password="baz", + ) + + documents = [ + "This is an example document.", + "Another example document to generate embeddings for.", + ] + embeddings_generator.embed_documents(documents) + """ + from elasticsearch.client import MlClient + + es_cloud_id = es_cloud_id or get_from_env("es_cloud_id", "ES_CLOUD_ID") + es_api_key = es_api_key or get_from_env("es_api_key", "ES_API_KEY") + + # Connect to Elasticsearch + es_connection = Elasticsearch(cloud_id=es_cloud_id, api_key=es_api_key) + client = MlClient(es_connection) + return cls(client, model_id, input_field=input_field) + + @classmethod + def from_es_connection( + cls, + model_id: str, + es_connection: Elasticsearch, + input_field: str = "text_field", + ) -> ElasticsearchEmbeddings: + """ + Instantiate embeddings from an existing Elasticsearch connection. + + This method provides a way to create an instance of the ElasticsearchEmbeddings + class using an existing Elasticsearch connection. The connection object is used + to create an MlClient, which is then used to initialize the + ElasticsearchEmbeddings instance. + + Args: + model_id (str): The model_id of the model deployed in the Elasticsearch cluster. + es_connection (elasticsearch.Elasticsearch): An existing Elasticsearch + connection object. input_field (str, optional): The name of the key for the + input text field in the document. Defaults to 'text_field'. + + Returns: + ElasticsearchEmbeddings: An instance of the ElasticsearchEmbeddings class. + + Example: + .. code-block:: python + + from elasticsearch import Elasticsearch + + from langchain_elasticsearch.embeddings import ElasticsearchEmbeddings + + # Define the model ID and input field name (if different from default) + model_id = "your_model_id" + # Optional, only if different from 'text_field' + input_field = "your_input_field" + + # Create Elasticsearch connection + es_connection = Elasticsearch( + hosts=["localhost:9200"], http_auth=("user", "password") + ) + + # Instantiate ElasticsearchEmbeddings using the existing connection + embeddings = ElasticsearchEmbeddings.from_es_connection( + model_id, + es_connection, + input_field=input_field, + ) + + documents = [ + "This is an example document.", + "Another example document to generate embeddings for.", + ] + embeddings_generator.embed_documents(documents) + """ + from elasticsearch.client import MlClient + + # Create an MlClient from the given Elasticsearch connection + client = MlClient(es_connection) + + # Return a new instance of the ElasticsearchEmbeddings class with + # the MlClient, model_id, and input_field + return cls(client, model_id, input_field=input_field) + + def _embedding_func(self, texts: List[str]) -> List[List[float]]: + """ + Generate embeddings for the given texts using the Elasticsearch model. + + Args: + texts (List[str]): A list of text strings to generate embeddings for. + + Returns: + List[List[float]]: A list of embeddings, one for each text in the input + list. + """ + response = self.client.infer_trained_model( + model_id=self.model_id, docs=[{self.input_field: text} for text in texts] + ) + + embeddings = [doc["predicted_value"] for doc in response["inference_results"]] + return embeddings + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """ + Generate embeddings for a list of documents. + + Args: + texts (List[str]): A list of document text strings to generate embeddings + for. + + Returns: + List[List[float]]: A list of embeddings, one for each document in the input + list. + """ + return self._embedding_func(texts) + + def embed_query(self, text: str) -> List[float]: + """ + Generate an embedding for a single query text. + + Args: + text (str): The query text to generate an embedding for. + + Returns: + List[float]: The embedding for the input query text. + """ + return self._embedding_func([text])[0] diff --git a/libs/partners/elasticsearch/langchain_elasticsearch/py.typed b/libs/partners/elasticsearch/langchain_elasticsearch/py.typed new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/elasticsearch/langchain_elasticsearch/vectorstores.py b/libs/partners/elasticsearch/langchain_elasticsearch/vectorstores.py new file mode 100644 index 0000000000000..3d2522b2d165f --- /dev/null +++ b/libs/partners/elasticsearch/langchain_elasticsearch/vectorstores.py @@ -0,0 +1,1285 @@ +import logging +import uuid +from abc import ABC, abstractmethod +from typing import ( + Any, + Callable, + Dict, + Iterable, + List, + Literal, + Optional, + Tuple, + Union, +) + +import numpy as np +from elasticsearch import Elasticsearch +from elasticsearch.helpers import BulkIndexError, bulk +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.vectorstores import VectorStore + +from langchain_elasticsearch._utilities import ( + DistanceStrategy, + maximal_marginal_relevance, +) + +logger = logging.getLogger(__name__) + + +class BaseRetrievalStrategy(ABC): + """Base class for `Elasticsearch` retrieval strategies.""" + + @abstractmethod + def query( + self, + query_vector: Union[List[float], None], + query: Union[str, None], + *, + k: int, + fetch_k: int, + vector_query_field: str, + text_field: str, + filter: List[dict], + similarity: Union[DistanceStrategy, None], + ) -> Dict: + """ + Executes when a search is performed on the store. + + Args: + query_vector: The query vector, + or None if not using vector-based query. + query: The text query, or None if not using text-based query. + k: The total number of results to retrieve. + fetch_k: The number of results to fetch initially. + vector_query_field: The field containing the vector + representations in the index. + text_field: The field containing the text data in the index. + filter: List of filter clauses to apply to the query. + similarity: The similarity strategy to use, or None if not using one. + + Returns: + Dict: The Elasticsearch query body. + """ + + @abstractmethod + def index( + self, + dims_length: Union[int, None], + vector_query_field: str, + similarity: Union[DistanceStrategy, None], + ) -> Dict: + """ + Executes when the index is created. + + Args: + dims_length: Numeric length of the embedding vectors, + or None if not using vector-based query. + vector_query_field: The field containing the vector + representations in the index. + similarity: The similarity strategy to use, + or None if not using one. + + Returns: + Dict: The Elasticsearch settings and mappings for the strategy. + """ + + def before_index_setup( + self, client: "Elasticsearch", text_field: str, vector_query_field: str + ) -> None: + """ + Executes before the index is created. Used for setting up + any required Elasticsearch resources like a pipeline. + + Args: + client: The Elasticsearch client. + text_field: The field containing the text data in the index. + vector_query_field: The field containing the vector + representations in the index. + """ + + def require_inference(self) -> bool: + """ + Returns whether or not the strategy requires inference + to be performed on the text before it is added to the index. + + Returns: + bool: Whether or not the strategy requires inference + to be performed on the text before it is added to the index. + """ + return True + + +class ApproxRetrievalStrategy(BaseRetrievalStrategy): + """Approximate retrieval strategy using the `HNSW` algorithm.""" + + def __init__( + self, + query_model_id: Optional[str] = None, + hybrid: Optional[bool] = False, + rrf: Optional[Union[dict, bool]] = True, + ): + self.query_model_id = query_model_id + self.hybrid = hybrid + + # RRF has two optional parameters + # 'rank_constant', 'window_size' + # https://www.elastic.co/guide/en/elasticsearch/reference/current/rrf.html + self.rrf = rrf + + def query( + self, + query_vector: Union[List[float], None], + query: Union[str, None], + k: int, + fetch_k: int, + vector_query_field: str, + text_field: str, + filter: List[dict], + similarity: Union[DistanceStrategy, None], + ) -> Dict: + knn = { + "filter": filter, + "field": vector_query_field, + "k": k, + "num_candidates": fetch_k, + } + + # Embedding provided via the embedding function + if query_vector and not self.query_model_id: + knn["query_vector"] = query_vector + + # Case 2: Used when model has been deployed to + # Elasticsearch and can infer the query vector from the query text + elif query and self.query_model_id: + knn["query_vector_builder"] = { + "text_embedding": { + "model_id": self.query_model_id, # use 'model_id' argument + "model_text": query, # use 'query' argument + } + } + + else: + raise ValueError( + "You must provide an embedding function or a" + " query_model_id to perform a similarity search." + ) + + # If hybrid, add a query to the knn query + # RRF is used to even the score from the knn query and text query + # RRF has two optional parameters: {'rank_constant':int, 'window_size':int} + # https://www.elastic.co/guide/en/elasticsearch/reference/current/rrf.html + if self.hybrid: + query_body = { + "knn": knn, + "query": { + "bool": { + "must": [ + { + "match": { + text_field: { + "query": query, + } + } + } + ], + "filter": filter, + } + }, + } + + if isinstance(self.rrf, dict): + query_body["rank"] = {"rrf": self.rrf} + elif isinstance(self.rrf, bool) and self.rrf is True: + query_body["rank"] = {"rrf": {}} + + return query_body + else: + return {"knn": knn} + + def index( + self, + dims_length: Union[int, None], + vector_query_field: str, + similarity: Union[DistanceStrategy, None], + ) -> Dict: + """Create the mapping for the Elasticsearch index.""" + + if similarity is DistanceStrategy.COSINE: + similarityAlgo = "cosine" + elif similarity is DistanceStrategy.EUCLIDEAN_DISTANCE: + similarityAlgo = "l2_norm" + elif similarity is DistanceStrategy.DOT_PRODUCT: + similarityAlgo = "dot_product" + elif similarity is DistanceStrategy.MAX_INNER_PRODUCT: + similarityAlgo = "max_inner_product" + else: + raise ValueError(f"Similarity {similarity} not supported.") + + return { + "mappings": { + "properties": { + vector_query_field: { + "type": "dense_vector", + "dims": dims_length, + "index": True, + "similarity": similarityAlgo, + }, + } + } + } + + +class ExactRetrievalStrategy(BaseRetrievalStrategy): + """Exact retrieval strategy using the `script_score` query.""" + + def query( + self, + query_vector: Union[List[float], None], + query: Union[str, None], + k: int, + fetch_k: int, + vector_query_field: str, + text_field: str, + filter: Union[List[dict], None], + similarity: Union[DistanceStrategy, None], + ) -> Dict: + if similarity is DistanceStrategy.COSINE: + similarityAlgo = ( + f"cosineSimilarity(params.query_vector, '{vector_query_field}') + 1.0" + ) + elif similarity is DistanceStrategy.EUCLIDEAN_DISTANCE: + similarityAlgo = ( + f"1 / (1 + l2norm(params.query_vector, '{vector_query_field}'))" + ) + elif similarity is DistanceStrategy.DOT_PRODUCT: + similarityAlgo = f""" + double value = dotProduct(params.query_vector, '{vector_query_field}'); + return sigmoid(1, Math.E, -value); + """ + else: + raise ValueError(f"Similarity {similarity} not supported.") + + queryBool: Dict = {"match_all": {}} + if filter: + queryBool = {"bool": {"filter": filter}} + + return { + "query": { + "script_score": { + "query": queryBool, + "script": { + "source": similarityAlgo, + "params": {"query_vector": query_vector}, + }, + }, + } + } + + def index( + self, + dims_length: Union[int, None], + vector_query_field: str, + similarity: Union[DistanceStrategy, None], + ) -> Dict: + """Create the mapping for the Elasticsearch index.""" + + return { + "mappings": { + "properties": { + vector_query_field: { + "type": "dense_vector", + "dims": dims_length, + "index": False, + }, + } + } + } + + +class SparseRetrievalStrategy(BaseRetrievalStrategy): + """Sparse retrieval strategy using the `text_expansion` processor.""" + + def __init__(self, model_id: Optional[str] = None): + self.model_id = model_id or ".elser_model_1" + + def query( + self, + query_vector: Union[List[float], None], + query: Union[str, None], + k: int, + fetch_k: int, + vector_query_field: str, + text_field: str, + filter: List[dict], + similarity: Union[DistanceStrategy, None], + ) -> Dict: + return { + "query": { + "bool": { + "must": [ + { + "text_expansion": { + f"{vector_query_field}.tokens": { + "model_id": self.model_id, + "model_text": query, + } + } + } + ], + "filter": filter, + } + } + } + + def _get_pipeline_name(self) -> str: + return f"{self.model_id}_sparse_embedding" + + def before_index_setup( + self, client: "Elasticsearch", text_field: str, vector_query_field: str + ) -> None: + # If model_id is provided, create a pipeline for the model + if self.model_id: + client.ingest.put_pipeline( + id=self._get_pipeline_name(), + description="Embedding pipeline for langchain vectorstore", + processors=[ + { + "inference": { + "model_id": self.model_id, + "target_field": vector_query_field, + "field_map": {text_field: "text_field"}, + "inference_config": { + "text_expansion": {"results_field": "tokens"} + }, + } + } + ], + ) + + def index( + self, + dims_length: Union[int, None], + vector_query_field: str, + similarity: Union[DistanceStrategy, None], + ) -> Dict: + return { + "mappings": { + "properties": { + vector_query_field: { + "properties": {"tokens": {"type": "rank_features"}} + } + } + }, + "settings": {"default_pipeline": self._get_pipeline_name()}, + } + + def require_inference(self) -> bool: + return False + + +class ElasticsearchStore(VectorStore): + """`Elasticsearch` vector store. + + Example: + .. code-block:: python + + from langchain_elasticsearch.vectorstores import ElasticsearchStore + from langchain_openai import OpenAIEmbeddings + + vectorstore = ElasticsearchStore( + embedding=OpenAIEmbeddings(), + index_name="langchain-demo", + es_url="http://localhost:9200" + ) + + Args: + index_name: Name of the Elasticsearch index to create. + es_url: URL of the Elasticsearch instance to connect to. + cloud_id: Cloud ID of the Elasticsearch instance to connect to. + es_user: Username to use when connecting to Elasticsearch. + es_password: Password to use when connecting to Elasticsearch. + es_api_key: API key to use when connecting to Elasticsearch. + es_connection: Optional pre-existing Elasticsearch connection. + vector_query_field: Optional. Name of the field to store + the embedding vectors in. + query_field: Optional. Name of the field to store the texts in. + strategy: Optional. Retrieval strategy to use when searching the index. + Defaults to ApproxRetrievalStrategy. Can be one of + ExactRetrievalStrategy, ApproxRetrievalStrategy, + or SparseRetrievalStrategy. + distance_strategy: Optional. Distance strategy to use when + searching the index. + Defaults to COSINE. Can be one of COSINE, + EUCLIDEAN_DISTANCE, MAX_INNER_PRODUCT or DOT_PRODUCT. + + If you want to use a cloud hosted Elasticsearch instance, you can pass in the + cloud_id argument instead of the es_url argument. + + Example: + .. code-block:: python + + from langchain_elasticsearch.vectorstores import ElasticsearchStore + from langchain_openai import OpenAIEmbeddings + + vectorstore = ElasticsearchStore( + embedding=OpenAIEmbeddings(), + index_name="langchain-demo", + es_cloud_id="" + es_user="elastic", + es_password="" + ) + + You can also connect to an existing Elasticsearch instance by passing in a + pre-existing Elasticsearch connection via the es_connection argument. + + Example: + .. code-block:: python + + from langchain_elasticsearch.vectorstores import ElasticsearchStore + from langchain_openai import OpenAIEmbeddings + + from elasticsearch import Elasticsearch + + es_connection = Elasticsearch("http://localhost:9200") + + vectorstore = ElasticsearchStore( + embedding=OpenAIEmbeddings(), + index_name="langchain-demo", + es_connection=es_connection + ) + + ElasticsearchStore by default uses the ApproxRetrievalStrategy, which uses the + HNSW algorithm to perform approximate nearest neighbor search. This is the + fastest and most memory efficient algorithm. + + If you want to use the Brute force / Exact strategy for searching vectors, you + can pass in the ExactRetrievalStrategy to the ElasticsearchStore constructor. + + Example: + .. code-block:: python + + from langchain_elasticsearch.vectorstores import ElasticsearchStore + from langchain_openai import OpenAIEmbeddings + + vectorstore = ElasticsearchStore( + embedding=OpenAIEmbeddings(), + index_name="langchain-demo", + es_url="http://localhost:9200", + strategy=ElasticsearchStore.ExactRetrievalStrategy() + ) + + Both strategies require that you know the similarity metric you want to use + when creating the index. The default is cosine similarity, but you can also + use dot product or euclidean distance. + + Example: + .. code-block:: python + + from langchain_elasticsearch.vectorstores import ElasticsearchStore + from langchain_openai import OpenAIEmbeddings + from langchain_community.vectorstores.utils import DistanceStrategy + + vectorstore = ElasticsearchStore( + "langchain-demo", + embedding=OpenAIEmbeddings(), + es_url="http://localhost:9200", + distance_strategy="DOT_PRODUCT" + ) + + """ + + def __init__( + self, + index_name: str, + *, + embedding: Optional[Embeddings] = None, + es_connection: Optional["Elasticsearch"] = None, + es_url: Optional[str] = None, + es_cloud_id: Optional[str] = None, + es_user: Optional[str] = None, + es_api_key: Optional[str] = None, + es_password: Optional[str] = None, + vector_query_field: str = "vector", + query_field: str = "text", + distance_strategy: Optional[ + Literal[ + DistanceStrategy.COSINE, + DistanceStrategy.DOT_PRODUCT, + DistanceStrategy.EUCLIDEAN_DISTANCE, + DistanceStrategy.MAX_INNER_PRODUCT, + ] + ] = None, + strategy: BaseRetrievalStrategy = ApproxRetrievalStrategy(), + es_params: Optional[Dict[str, Any]] = None, + ): + self.embedding = embedding + self.index_name = index_name + self.query_field = query_field + self.vector_query_field = vector_query_field + self.distance_strategy = ( + DistanceStrategy.COSINE + if distance_strategy is None + else DistanceStrategy[distance_strategy] + ) + self.strategy = strategy + + if es_connection is not None: + headers = dict(es_connection._headers) + headers.update({"user-agent": self.get_user_agent()}) + self.client = es_connection.options(headers=headers) + elif es_url is not None or es_cloud_id is not None: + self.client = ElasticsearchStore.connect_to_elasticsearch( + es_url=es_url, + username=es_user, + password=es_password, + cloud_id=es_cloud_id, + api_key=es_api_key, + es_params=es_params, + ) + else: + raise ValueError( + """Either provide a pre-existing Elasticsearch connection, \ + or valid credentials for creating a new connection.""" + ) + + @staticmethod + def get_user_agent() -> str: + from langchain_core import __version__ + + return f"langchain-py-vs/{__version__}" + + @staticmethod + def connect_to_elasticsearch( + *, + es_url: Optional[str] = None, + cloud_id: Optional[str] = None, + api_key: Optional[str] = None, + username: Optional[str] = None, + password: Optional[str] = None, + es_params: Optional[Dict[str, Any]] = None, + ) -> "Elasticsearch": + if es_url and cloud_id: + raise ValueError( + "Both es_url and cloud_id are defined. Please provide only one." + ) + + connection_params: Dict[str, Any] = {} + + if es_url: + connection_params["hosts"] = [es_url] + elif cloud_id: + connection_params["cloud_id"] = cloud_id + else: + raise ValueError("Please provide either elasticsearch_url or cloud_id.") + + if api_key: + connection_params["api_key"] = api_key + elif username and password: + connection_params["basic_auth"] = (username, password) + + if es_params is not None: + connection_params.update(es_params) + + es_client = Elasticsearch( + **connection_params, + headers={"user-agent": ElasticsearchStore.get_user_agent()}, + ) + try: + es_client.info() + except Exception as e: + logger.error(f"Error connecting to Elasticsearch: {e}") + raise e + + return es_client + + @property + def embeddings(self) -> Optional[Embeddings]: + return self.embedding + + def similarity_search( + self, + query: str, + k: int = 4, + fetch_k: int = 50, + filter: Optional[List[dict]] = None, + **kwargs: Any, + ) -> List[Document]: + """Return Elasticsearch documents most similar to query. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + fetch_k (int): Number of Documents to fetch to pass to knn num_candidates. + filter: Array of Elasticsearch filter clauses to apply to the query. + + Returns: + List of Documents most similar to the query, + in descending order of similarity. + """ + + results = self._search( + query=query, k=k, fetch_k=fetch_k, filter=filter, **kwargs + ) + return [doc for doc, _ in results] + + def max_marginal_relevance_search( + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + fields: Optional[List[str]] = 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: + query (str): Text to look up documents similar to. + k (int): Number of Documents to return. Defaults to 4. + fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. + lambda_mult (float): 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. + fields: Other fields to get from elasticsearch source. These fields + will be added to the document metadata. + + Returns: + List[Document]: A list of Documents selected by maximal marginal relevance. + """ + if self.embedding is None: + raise ValueError("You must provide an embedding function to perform MMR") + remove_vector_query_field_from_metadata = True + if fields is None: + fields = [self.vector_query_field] + elif self.vector_query_field not in fields: + fields.append(self.vector_query_field) + else: + remove_vector_query_field_from_metadata = False + + # Embed the query + query_embedding = self.embedding.embed_query(query) + + # Fetch the initial documents + got_docs = self._search( + query_vector=query_embedding, k=fetch_k, fields=fields, **kwargs + ) + + # Get the embeddings for the fetched documents + got_embeddings = [doc.metadata[self.vector_query_field] for doc, _ in got_docs] + + # Select documents using maximal marginal relevance + selected_indices = maximal_marginal_relevance( + np.array(query_embedding), got_embeddings, lambda_mult=lambda_mult, k=k + ) + selected_docs = [got_docs[i][0] for i in selected_indices] + + if remove_vector_query_field_from_metadata: + for doc in selected_docs: + del doc.metadata[self.vector_query_field] + + return selected_docs + + @staticmethod + def _identity_fn(score: float) -> float: + return score + + def _select_relevance_score_fn(self) -> Callable[[float], float]: + """ + The 'correct' relevance function + may differ depending on a few things, including: + - the distance / similarity metric used by the VectorStore + - the scale of your embeddings (OpenAI's are unit normed. Many others are not!) + - embedding dimensionality + - etc. + + Vectorstores should define their own selection based method of relevance. + """ + # All scores from Elasticsearch are already normalized similarities: + # https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#dense-vector-params + return self._identity_fn + + def similarity_search_with_score( + self, query: str, k: int = 4, filter: Optional[List[dict]] = None, **kwargs: Any + ) -> List[Tuple[Document, float]]: + """Return Elasticsearch documents most similar to query, along with scores. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + filter: Array of Elasticsearch filter clauses to apply to the query. + + Returns: + List of Documents most similar to the query and score for each + """ + if isinstance(self.strategy, ApproxRetrievalStrategy) and self.strategy.hybrid: + raise ValueError("scores are currently not supported in hybrid mode") + + return self._search(query=query, k=k, filter=filter, **kwargs) + + def similarity_search_by_vector_with_relevance_scores( + self, + embedding: List[float], + k: int = 4, + filter: Optional[List[Dict]] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """Return Elasticsearch documents most similar to query, along with scores. + + Args: + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + filter: Array of Elasticsearch filter clauses to apply to the query. + + Returns: + List of Documents most similar to the embedding and score for each + """ + if isinstance(self.strategy, ApproxRetrievalStrategy) and self.strategy.hybrid: + raise ValueError("scores are currently not supported in hybrid mode") + + return self._search(query_vector=embedding, k=k, filter=filter, **kwargs) + + def _search( + self, + query: Optional[str] = None, + k: int = 4, + query_vector: Union[List[float], None] = None, + fetch_k: int = 50, + fields: Optional[List[str]] = None, + filter: Optional[List[dict]] = None, + custom_query: Optional[Callable[[Dict, Union[str, None]], Dict]] = None, + doc_builder: Optional[Callable[[Dict], Document]] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """Return Elasticsearch documents most similar to query, along with scores. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + query_vector: Embedding to look up documents similar to. + fetch_k: Number of candidates to fetch from each shard. + Defaults to 50. + fields: List of fields to return from Elasticsearch. + Defaults to only returning the text field. + filter: Array of Elasticsearch filter clauses to apply to the query. + custom_query: Function to modify the Elasticsearch + query body before it is sent to Elasticsearch. + + Returns: + List of Documents most similar to the query and score for each + """ + if fields is None: + fields = [] + + if "metadata" not in fields: + fields.append("metadata") + + if self.query_field not in fields: + fields.append(self.query_field) + + if self.embedding and query is not None: + query_vector = self.embedding.embed_query(query) + + query_body = self.strategy.query( + query_vector=query_vector, + query=query, + k=k, + fetch_k=fetch_k, + vector_query_field=self.vector_query_field, + text_field=self.query_field, + filter=filter or [], + similarity=self.distance_strategy, + ) + + logger.debug(f"Query body: {query_body}") + + if custom_query is not None: + query_body = custom_query(query_body, query) + logger.debug(f"Calling custom_query, Query body now: {query_body}") + # Perform the kNN search on the Elasticsearch index and return the results. + response = self.client.search( + index=self.index_name, + **query_body, + size=k, + source=True, + source_includes=fields, + ) + + def default_doc_builder(hit: Dict) -> Document: + return Document( + page_content=hit["_source"].get(self.query_field, ""), + metadata=hit["_source"]["metadata"], + ) + + doc_builder = doc_builder or default_doc_builder + + docs_and_scores = [] + for hit in response["hits"]["hits"]: + for field in fields: + if field in hit["_source"] and field not in [ + "metadata", + self.query_field, + ]: + if "metadata" not in hit["_source"]: + hit["_source"]["metadata"] = {} + hit["_source"]["metadata"][field] = hit["_source"][field] + + docs_and_scores.append( + ( + doc_builder(hit), + hit["_score"], + ) + ) + return docs_and_scores + + def delete( + self, + ids: Optional[List[str]] = None, + refresh_indices: Optional[bool] = True, + **kwargs: Any, + ) -> Optional[bool]: + """Delete documents from the Elasticsearch index. + + Args: + ids: List of ids of documents to delete. + refresh_indices: Whether to refresh the index + after deleting documents. Defaults to True. + """ + body = [] + + if ids is None: + raise ValueError("ids must be provided.") + + for _id in ids: + body.append({"_op_type": "delete", "_index": self.index_name, "_id": _id}) + + if len(body) > 0: + try: + bulk(self.client, body, refresh=refresh_indices, ignore_status=404) + logger.debug(f"Deleted {len(body)} texts from index") + + return True + except BulkIndexError as e: + logger.error(f"Error deleting texts: {e}") + firstError = e.errors[0].get("index", {}).get("error", {}) + logger.error(f"First error reason: {firstError.get('reason')}") + raise e + + else: + logger.debug("No texts to delete from index") + return False + + def _create_index_if_not_exists( + self, index_name: str, dims_length: Optional[int] = None + ) -> None: + """Create the Elasticsearch index if it doesn't already exist. + + Args: + index_name: Name of the Elasticsearch index to create. + dims_length: Length of the embedding vectors. + """ + + if self.client.indices.exists(index=index_name): + logger.debug(f"Index {index_name} already exists. Skipping creation.") + + else: + if dims_length is None and self.strategy.require_inference(): + raise ValueError( + "Cannot create index without specifying dims_length " + "when the index doesn't already exist. We infer " + "dims_length from the first embedding. Check that " + "you have provided an embedding function." + ) + + self.strategy.before_index_setup( + client=self.client, + text_field=self.query_field, + vector_query_field=self.vector_query_field, + ) + + indexSettings = self.strategy.index( + vector_query_field=self.vector_query_field, + dims_length=dims_length, + similarity=self.distance_strategy, + ) + logger.debug( + f"Creating index {index_name} with mappings {indexSettings['mappings']}" + ) + self.client.indices.create(index=index_name, **indexSettings) + + def __add( + self, + texts: Iterable[str], + embeddings: Optional[List[List[float]]], + metadatas: Optional[List[Dict[Any, Any]]] = None, + ids: Optional[List[str]] = None, + refresh_indices: bool = True, + create_index_if_not_exists: bool = True, + bulk_kwargs: Optional[Dict] = None, + **kwargs: Any, + ) -> List[str]: + bulk_kwargs = bulk_kwargs or {} + ids = ids or [str(uuid.uuid4()) for _ in texts] + requests = [] + + if create_index_if_not_exists: + if embeddings: + dims_length = len(embeddings[0]) + else: + dims_length = None + + self._create_index_if_not_exists( + index_name=self.index_name, dims_length=dims_length + ) + + for i, text in enumerate(texts): + metadata = metadatas[i] if metadatas else {} + + request = { + "_op_type": "index", + "_index": self.index_name, + self.query_field: text, + "metadata": metadata, + "_id": ids[i], + } + if embeddings: + request[self.vector_query_field] = embeddings[i] + + requests.append(request) + + if len(requests) > 0: + try: + success, failed = bulk( + self.client, + requests, + stats_only=True, + refresh=refresh_indices, + **bulk_kwargs, + ) + logger.debug( + f"Added {success} and failed to add {failed} texts to index" + ) + + logger.debug(f"added texts {ids} to index") + return ids + except BulkIndexError as e: + logger.error(f"Error adding texts: {e}") + firstError = e.errors[0].get("index", {}).get("error", {}) + logger.error(f"First error reason: {firstError.get('reason')}") + raise e + + else: + logger.debug("No texts to add to index") + return [] + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[Dict[Any, Any]]] = None, + ids: Optional[List[str]] = None, + refresh_indices: bool = True, + create_index_if_not_exists: bool = True, + bulk_kwargs: Optional[Dict] = None, + **kwargs: Any, + ) -> List[str]: + """Run more texts through the embeddings and add 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 to associate with the texts. + refresh_indices: Whether to refresh the Elasticsearch indices + after adding the texts. + create_index_if_not_exists: Whether to create the Elasticsearch + index if it doesn't already exist. + *bulk_kwargs: Additional arguments to pass to Elasticsearch bulk. + - chunk_size: Optional. Number of texts to add to the + index at a time. Defaults to 500. + + Returns: + List of ids from adding the texts into the vectorstore. + """ + if self.embedding is not None: + # If no search_type requires inference, we use the provided + # embedding function to embed the texts. + embeddings = self.embedding.embed_documents(list(texts)) + else: + # the search_type doesn't require inference, so we don't need to + # embed the texts. + embeddings = None + + return self.__add( + texts, + embeddings, + metadatas=metadatas, + ids=ids, + refresh_indices=refresh_indices, + create_index_if_not_exists=create_index_if_not_exists, + bulk_kwargs=bulk_kwargs, + kwargs=kwargs, + ) + + def add_embeddings( + self, + text_embeddings: Iterable[Tuple[str, List[float]]], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + refresh_indices: bool = True, + create_index_if_not_exists: bool = True, + bulk_kwargs: Optional[Dict] = None, + **kwargs: Any, + ) -> List[str]: + """Add the given texts and embeddings to the vectorstore. + + Args: + text_embeddings: Iterable pairs of string and embedding to + add to the vectorstore. + metadatas: Optional list of metadatas associated with the texts. + ids: Optional list of unique IDs. + refresh_indices: Whether to refresh the Elasticsearch indices + after adding the texts. + create_index_if_not_exists: Whether to create the Elasticsearch + index if it doesn't already exist. + *bulk_kwargs: Additional arguments to pass to Elasticsearch bulk. + - chunk_size: Optional. Number of texts to add to the + index at a time. Defaults to 500. + + Returns: + List of ids from adding the texts into the vectorstore. + """ + texts, embeddings = zip(*text_embeddings) + return self.__add( + list(texts), + list(embeddings), + metadatas=metadatas, + ids=ids, + refresh_indices=refresh_indices, + create_index_if_not_exists=create_index_if_not_exists, + bulk_kwargs=bulk_kwargs, + kwargs=kwargs, + ) + + @classmethod + def from_texts( + cls, + texts: List[str], + embedding: Optional[Embeddings] = None, + metadatas: Optional[List[Dict[str, Any]]] = None, + bulk_kwargs: Optional[Dict] = None, + **kwargs: Any, + ) -> "ElasticsearchStore": + """Construct ElasticsearchStore wrapper from raw documents. + + Example: + .. code-block:: python + + from langchain_elasticsearch.vectorstores import ElasticsearchStore + from langchain_openai import OpenAIEmbeddings + + db = ElasticsearchStore.from_texts( + texts, + // embeddings optional if using + // a strategy that doesn't require inference + embeddings, + index_name="langchain-demo", + es_url="http://localhost:9200" + ) + + Args: + texts: List of texts to add to the Elasticsearch index. + embedding: Embedding function to use to embed the texts. + metadatas: Optional list of metadatas associated with the texts. + index_name: Name of the Elasticsearch index to create. + es_url: URL of the Elasticsearch instance to connect to. + cloud_id: Cloud ID of the Elasticsearch instance to connect to. + es_user: Username to use when connecting to Elasticsearch. + es_password: Password to use when connecting to Elasticsearch. + es_api_key: API key to use when connecting to Elasticsearch. + es_connection: Optional pre-existing Elasticsearch connection. + vector_query_field: Optional. Name of the field to + store the embedding vectors in. + query_field: Optional. Name of the field to store the texts in. + distance_strategy: Optional. Name of the distance + strategy to use. Defaults to "COSINE". + can be one of "COSINE", + "EUCLIDEAN_DISTANCE", "DOT_PRODUCT", + "MAX_INNER_PRODUCT". + bulk_kwargs: Optional. Additional arguments to pass to + Elasticsearch bulk. + """ + + elasticsearchStore = ElasticsearchStore._create_cls_from_kwargs( + embedding=embedding, **kwargs + ) + + # Encode the provided texts and add them to the newly created index. + elasticsearchStore.add_texts( + texts, metadatas=metadatas, bulk_kwargs=bulk_kwargs + ) + + return elasticsearchStore + + @staticmethod + def _create_cls_from_kwargs( + embedding: Optional[Embeddings] = None, **kwargs: Any + ) -> "ElasticsearchStore": + index_name = kwargs.get("index_name") + + if index_name is None: + raise ValueError("Please provide an index_name.") + + es_connection = kwargs.get("es_connection") + es_cloud_id = kwargs.get("es_cloud_id") + es_url = kwargs.get("es_url") + es_user = kwargs.get("es_user") + es_password = kwargs.get("es_password") + es_api_key = kwargs.get("es_api_key") + vector_query_field = kwargs.get("vector_query_field") + query_field = kwargs.get("query_field") + distance_strategy = kwargs.get("distance_strategy") + strategy = kwargs.get("strategy", ElasticsearchStore.ApproxRetrievalStrategy()) + + optional_args = {} + + if vector_query_field is not None: + optional_args["vector_query_field"] = vector_query_field + + if query_field is not None: + optional_args["query_field"] = query_field + + return ElasticsearchStore( + index_name=index_name, + embedding=embedding, + es_url=es_url, + es_connection=es_connection, + es_cloud_id=es_cloud_id, + es_user=es_user, + es_password=es_password, + es_api_key=es_api_key, + strategy=strategy, + distance_strategy=distance_strategy, + **optional_args, + ) + + @classmethod + def from_documents( + cls, + documents: List[Document], + embedding: Optional[Embeddings] = None, + bulk_kwargs: Optional[Dict] = None, + **kwargs: Any, + ) -> "ElasticsearchStore": + """Construct ElasticsearchStore wrapper from documents. + + Example: + .. code-block:: python + + from langchain_elasticsearch.vectorstores import ElasticsearchStore + from langchain_openai import OpenAIEmbeddings + + db = ElasticsearchStore.from_documents( + texts, + embeddings, + index_name="langchain-demo", + es_url="http://localhost:9200" + ) + + Args: + texts: List of texts to add to the Elasticsearch index. + embedding: Embedding function to use to embed the texts. + Do not provide if using a strategy + that doesn't require inference. + metadatas: Optional list of metadatas associated with the texts. + index_name: Name of the Elasticsearch index to create. + es_url: URL of the Elasticsearch instance to connect to. + cloud_id: Cloud ID of the Elasticsearch instance to connect to. + es_user: Username to use when connecting to Elasticsearch. + es_password: Password to use when connecting to Elasticsearch. + es_api_key: API key to use when connecting to Elasticsearch. + es_connection: Optional pre-existing Elasticsearch connection. + vector_query_field: Optional. Name of the field + to store the embedding vectors in. + query_field: Optional. Name of the field to store the texts in. + bulk_kwargs: Optional. Additional arguments to pass to + Elasticsearch bulk. + """ + + elasticsearchStore = ElasticsearchStore._create_cls_from_kwargs( + embedding=embedding, **kwargs + ) + # Encode the provided texts and add them to the newly created index. + elasticsearchStore.add_documents(documents, bulk_kwargs=bulk_kwargs) + + return elasticsearchStore + + @staticmethod + def ExactRetrievalStrategy() -> "ExactRetrievalStrategy": + """Used to perform brute force / exact + nearest neighbor search via script_score.""" + return ExactRetrievalStrategy() + + @staticmethod + def ApproxRetrievalStrategy( + query_model_id: Optional[str] = None, + hybrid: Optional[bool] = False, + rrf: Optional[Union[dict, bool]] = True, + ) -> "ApproxRetrievalStrategy": + """Used to perform approximate nearest neighbor search + using the HNSW algorithm. + + At build index time, this strategy will create a + dense vector field in the index and store the + embedding vectors in the index. + + At query time, the text will either be embedded using the + provided embedding function or the query_model_id + will be used to embed the text using the model + deployed to Elasticsearch. + + if query_model_id is used, do not provide an embedding function. + + Args: + query_model_id: Optional. ID of the model to use to + embed the query text within the stack. Requires + embedding model to be deployed to Elasticsearch. + hybrid: Optional. If True, will perform a hybrid search + using both the knn query and a text query. + Defaults to False. + rrf: Optional. rrf is Reciprocal Rank Fusion. + When `hybrid` is True, + and `rrf` is True, then rrf: {}. + and `rrf` is False, then rrf is omitted. + and isinstance(rrf, dict) is True, then pass in the dict values. + rrf could be passed for adjusting 'rank_constant' and 'window_size'. + """ + return ApproxRetrievalStrategy( + query_model_id=query_model_id, hybrid=hybrid, rrf=rrf + ) + + @staticmethod + def SparseVectorRetrievalStrategy( + model_id: Optional[str] = None, + ) -> "SparseRetrievalStrategy": + """Used to perform sparse vector search via text_expansion. + Used for when you want to use ELSER model to perform document search. + + At build index time, this strategy will create a pipeline that + will embed the text using the ELSER model and store the + resulting tokens in the index. + + At query time, the text will be embedded using the ELSER + model and the resulting tokens will be used to + perform a text_expansion query. + + Args: + model_id: Optional. Default is ".elser_model_1". + ID of the model to use to embed the query text + within the stack. Requires embedding model to be + deployed to Elasticsearch. + """ + return SparseRetrievalStrategy(model_id=model_id) diff --git a/libs/partners/elasticsearch/poetry.lock b/libs/partners/elasticsearch/poetry.lock new file mode 100644 index 0000000000000..018efc931442a --- /dev/null +++ b/libs/partners/elasticsearch/poetry.lock @@ -0,0 +1,1655 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.9.3" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:939677b61f9d72a4fa2a042a5eee2a99a24001a67c13da113b2e30396567db54"}, + {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1f5cd333fcf7590a18334c90f8c9147c837a6ec8a178e88d90a9b96ea03194cc"}, + {file = "aiohttp-3.9.3-cp310-cp310-macosx_11_0_arm64.whl", hash = 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= "0.1.0" +description = "An integration package connecting Elasticsearch and LangChain" +authors = [] +readme = "README.md" +repository = "https://github.com/langchain-ai/langchain" +license = "MIT" + +[tool.poetry.urls] +"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/elasticsearch" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain-core = "^0.1" +elasticsearch = "^8.12.0" +numpy = "^1" + +[tool.poetry.group.test] +optional = true + +[tool.poetry.group.test.dependencies] +pytest = "^7.3.0" +freezegun = "^1.2.2" +pytest-mock = "^3.10.0" +syrupy = "^4.0.2" +pytest-watcher = "^0.3.4" +pytest-asyncio = "^0.21.1" +langchain = { path = "../../langchain", develop = true } +langchain-community = { path = "../../community", develop = true } +langchain-core = { path = "../../core", develop = true } + +[tool.poetry.group.codespell] +optional = true + +[tool.poetry.group.codespell.dependencies] +codespell = "^2.2.0" + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.5" + +[tool.poetry.group.typing.dependencies] +mypy = "^0.991" +langchain-core = { path = "../../core", develop = true } + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +langchain-core = { path = "../../core", develop = true } + +[tool.poetry.group.test_integration] +optional = true + +[tool.poetry.group.test_integration.dependencies] + + +[tool.ruff] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort +] + +[tool.mypy] +disallow_untyped_defs = "True" + +[tool.coverage.run] +omit = ["tests/*"] + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +# +# https://github.com/tophat/syrupy +# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite. +addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" +# Registering custom markers. +# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers +markers = [ + "requires: mark tests as requiring a specific library", + "asyncio: mark tests as requiring asyncio", + "compile: mark placeholder test used to compile integration tests without running them", +] +asyncio_mode = "auto" diff --git a/libs/partners/elasticsearch/scripts/check_imports.py b/libs/partners/elasticsearch/scripts/check_imports.py new file mode 100644 index 0000000000000..fd21a4975b7f0 --- /dev/null +++ b/libs/partners/elasticsearch/scripts/check_imports.py @@ -0,0 +1,17 @@ +import sys +import traceback +from importlib.machinery import SourceFileLoader + +if __name__ == "__main__": + files = sys.argv[1:] + has_failure = False + for file in files: + try: + SourceFileLoader("x", file).load_module() + except Exception: + has_faillure = True + print(file) + traceback.print_exc() + print() + + sys.exit(1 if has_failure else 0) diff --git a/libs/partners/elasticsearch/scripts/check_pydantic.sh b/libs/partners/elasticsearch/scripts/check_pydantic.sh new file mode 100755 index 0000000000000..06b5bb81ae236 --- /dev/null +++ b/libs/partners/elasticsearch/scripts/check_pydantic.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# This script searches for lines starting with "import pydantic" or "from pydantic" +# in tracked files within a Git repository. +# +# Usage: ./scripts/check_pydantic.sh /path/to/repository + +# Check if a path argument is provided +if [ $# -ne 1 ]; then + echo "Usage: $0 /path/to/repository" + exit 1 +fi + +repository_path="$1" + +# Search for lines matching the pattern within the specified repository +result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') + +# Check if any matching lines were found +if [ -n "$result" ]; then + echo "ERROR: The following lines need to be updated:" + echo "$result" + echo "Please replace the code with an import from langchain_core.pydantic_v1." + echo "For example, replace 'from pydantic import BaseModel'" + echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + exit 1 +fi diff --git a/libs/partners/elasticsearch/scripts/lint_imports.sh b/libs/partners/elasticsearch/scripts/lint_imports.sh new file mode 100755 index 0000000000000..695613c7ba8fd --- /dev/null +++ b/libs/partners/elasticsearch/scripts/lint_imports.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain or langchain_experimental +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && 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/partners/elasticsearch/tests/__init__.py b/libs/partners/elasticsearch/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/elasticsearch/tests/fake_embeddings.py b/libs/partners/elasticsearch/tests/fake_embeddings.py new file mode 100644 index 0000000000000..d12df58d92b3f --- /dev/null +++ b/libs/partners/elasticsearch/tests/fake_embeddings.py @@ -0,0 +1,55 @@ +"""Fake Embedding class for testing purposes.""" + +from typing import List + +from langchain_core.embeddings import Embeddings + +fake_texts = ["foo", "bar", "baz"] + + +class FakeEmbeddings(Embeddings): + """Fake embeddings functionality for testing.""" + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Return simple embeddings. + Embeddings encode each text as its index.""" + return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))] + + async def aembed_documents(self, texts: List[str]) -> List[List[float]]: + return self.embed_documents(texts) + + def embed_query(self, text: str) -> List[float]: + """Return constant query embeddings. + Embeddings are identical to embed_documents(texts)[0]. + Distance to each text will be that text's index, + as it was passed to embed_documents.""" + return [float(1.0)] * 9 + [float(0.0)] + + async def aembed_query(self, text: str) -> List[float]: + return self.embed_query(text) + + +class ConsistentFakeEmbeddings(FakeEmbeddings): + """Fake embeddings which remember all the texts seen so far to return consistent + vectors for the same texts.""" + + def __init__(self, dimensionality: int = 10) -> None: + self.known_texts: List[str] = [] + self.dimensionality = dimensionality + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Return consistent embeddings for each text seen so far.""" + out_vectors = [] + for text in texts: + if text not in self.known_texts: + self.known_texts.append(text) + vector = [float(1.0)] * (self.dimensionality - 1) + [ + float(self.known_texts.index(text)) + ] + out_vectors.append(vector) + return out_vectors + + def embed_query(self, text: str) -> List[float]: + """Return consistent embeddings for the text, if seen before, or a constant + one if the text is unknown.""" + return self.embed_documents([text])[0] diff --git a/libs/partners/elasticsearch/tests/integration_tests/__init__.py b/libs/partners/elasticsearch/tests/integration_tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/elasticsearch/tests/integration_tests/test_chat_history.py b/libs/partners/elasticsearch/tests/integration_tests/test_chat_history.py new file mode 100644 index 0000000000000..5ddbcc4eb6b15 --- /dev/null +++ b/libs/partners/elasticsearch/tests/integration_tests/test_chat_history.py @@ -0,0 +1,89 @@ +import json +import os +import uuid +from typing import Generator, Union + +import pytest +from langchain.memory import ConversationBufferMemory +from langchain_core.messages import message_to_dict + +from langchain_elasticsearch.chat_history import ElasticsearchChatMessageHistory + +""" +cd tests/integration_tests/memory/docker-compose +docker-compose -f elasticsearch.yml up + +By default runs against local docker instance of Elasticsearch. +To run against Elastic Cloud, set the following environment variables: +- ES_CLOUD_ID +- ES_USERNAME +- ES_PASSWORD +""" + + +class TestElasticsearch: + @pytest.fixture(scope="class", autouse=True) + def elasticsearch_connection(self) -> Union[dict, Generator[dict, None, None]]: + # Run this integration test against Elasticsearch on localhost, + # or an Elastic Cloud instance + from elasticsearch import Elasticsearch + + es_url = os.environ.get("ES_URL", "http://localhost:9200") + es_cloud_id = os.environ.get("ES_CLOUD_ID") + es_api_key = os.environ.get("ES_API_KEY") + + if es_cloud_id: + es = Elasticsearch( + cloud_id=es_cloud_id, + api_key=es_api_key, + ) + yield { + "es_cloud_id": es_cloud_id, + "es_api_key": es_api_key, + } + + else: + # Running this integration test with local docker instance + es = Elasticsearch(hosts=es_url) + yield {"es_url": es_url} + + # Clear all indexes + index_names = es.indices.get(index="_all").keys() + for index_name in index_names: + if index_name.startswith("test_"): + es.indices.delete(index=index_name) + es.indices.refresh(index="_all") + + @pytest.fixture(scope="function") + def index_name(self) -> str: + """Return the index name.""" + return f"test_{uuid.uuid4().hex}" + + def test_memory_with_message_store( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test the memory with a message store.""" + # setup Elasticsearch as a message store + message_history = ElasticsearchChatMessageHistory( + **elasticsearch_connection, index=index_name, session_id="test-session" + ) + + memory = ConversationBufferMemory( + memory_key="baz", chat_memory=message_history, return_messages=True + ) + + # add some messages + memory.chat_memory.add_ai_message("This is me, the AI") + memory.chat_memory.add_user_message("This is me, the human") + + # get the message history from the memory store and turn it into a json + messages = memory.chat_memory.messages + messages_json = json.dumps([message_to_dict(msg) for msg in messages]) + + assert "This is me, the AI" in messages_json + assert "This is me, the human" in messages_json + + # remove the record from Elasticsearch, so the next test run won't pick it up + memory.chat_memory.clear() + + assert memory.chat_memory.messages == [] diff --git a/libs/partners/elasticsearch/tests/integration_tests/test_compile.py b/libs/partners/elasticsearch/tests/integration_tests/test_compile.py new file mode 100644 index 0000000000000..33ecccdfa0fbd --- /dev/null +++ b/libs/partners/elasticsearch/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/partners/elasticsearch/tests/integration_tests/test_embeddings.py b/libs/partners/elasticsearch/tests/integration_tests/test_embeddings.py new file mode 100644 index 0000000000000..c512bb741556f --- /dev/null +++ b/libs/partners/elasticsearch/tests/integration_tests/test_embeddings.py @@ -0,0 +1,48 @@ +"""Test elasticsearch_embeddings embeddings.""" + +import pytest +from langchain_core.utils import get_from_env + +from langchain_elasticsearch.embeddings import ElasticsearchEmbeddings + +# deployed with +# https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-text-emb-vector-search-example.html +DEFAULT_MODEL = "sentence-transformers__msmarco-minilm-l-12-v3" +DEFAULT_NUM_DIMENSIONS = "384" + + +@pytest.fixture +def model_id() -> str: + return get_from_env("model_id", "MODEL_ID", DEFAULT_MODEL) + + +@pytest.fixture +def expected_num_dimensions() -> int: + return int( + get_from_env( + "expected_num_dimensions", "EXPECTED_NUM_DIMENSIONS", DEFAULT_NUM_DIMENSIONS + ) + ) + + +def test_elasticsearch_embedding_documents( + model_id: str, expected_num_dimensions: int +) -> None: + """Test Elasticsearch embedding documents.""" + documents = ["foo bar", "bar foo", "foo"] + embedding = ElasticsearchEmbeddings.from_credentials(model_id) + output = embedding.embed_documents(documents) + assert len(output) == 3 + assert len(output[0]) == expected_num_dimensions + assert len(output[1]) == expected_num_dimensions + assert len(output[2]) == expected_num_dimensions + + +def test_elasticsearch_embedding_query( + model_id: str, expected_num_dimensions: int +) -> None: + """Test Elasticsearch embedding query.""" + document = "foo bar" + embedding = ElasticsearchEmbeddings.from_credentials(model_id) + output = embedding.embed_query(document) + assert len(output) == expected_num_dimensions diff --git a/libs/partners/elasticsearch/tests/integration_tests/test_vectorstores.py b/libs/partners/elasticsearch/tests/integration_tests/test_vectorstores.py new file mode 100644 index 0000000000000..c46fc8655952c --- /dev/null +++ b/libs/partners/elasticsearch/tests/integration_tests/test_vectorstores.py @@ -0,0 +1,931 @@ +"""Test ElasticsearchStore functionality.""" + +import logging +import os +import re +import uuid +from typing import Any, Dict, Generator, List, Union + +import pytest +from elastic_transport import Transport +from elasticsearch import Elasticsearch +from elasticsearch.helpers import BulkIndexError +from langchain_core.documents import Document + +from langchain_elasticsearch.vectorstores import ElasticsearchStore + +from ..fake_embeddings import ( + ConsistentFakeEmbeddings, + FakeEmbeddings, +) + +logging.basicConfig(level=logging.DEBUG) + +""" +cd tests/integration_tests/vectorstores/docker-compose +docker-compose -f elasticsearch.yml up + +By default runs against local docker instance of Elasticsearch. +To run against Elastic Cloud, set the following environment variables: +- ES_CLOUD_ID +- ES_API_KEY + +Some of the tests require the following models to be deployed in the ML Node: +- elser (can be downloaded and deployed through Kibana and trained models UI) +- sentence-transformers__all-minilm-l6-v2 (can be deployed + through API, loaded via eland) + +These tests that require the models to be deployed are skipped by default. +Enable them by adding the model name to the modelsDeployed list below. +""" + +modelsDeployed: List[str] = [ + # "elser", + # "sentence-transformers__all-minilm-l6-v2", +] + + +class TestElasticsearch: + @classmethod + def setup_class(cls) -> None: + if not os.getenv("OPENAI_API_KEY"): + raise ValueError("OPENAI_API_KEY environment variable is not set") + + @pytest.fixture(scope="class", autouse=True) + def elasticsearch_connection(self) -> Union[dict, Generator[dict, None, None]]: + es_url = os.environ.get("ES_URL", "http://localhost:9200") + cloud_id = os.environ.get("ES_CLOUD_ID") + api_key = os.environ.get("ES_API_KEY") + + if cloud_id: + # Running this integration test with Elastic Cloud + # Required for in-stack inference testing (ELSER + model_id) + es = Elasticsearch( + cloud_id=cloud_id, + api_key=api_key, + ) + yield { + "es_cloud_id": cloud_id, + "es_api_key": api_key, + } + + else: + # Running this integration test with local docker instance + es = Elasticsearch(hosts=es_url) + yield {"es_url": es_url} + + # Clear all indexes + index_names = es.indices.get(index="_all").keys() + for index_name in index_names: + if index_name.startswith("test_"): + es.indices.delete(index=index_name) + es.indices.refresh(index="_all") + + # clear all test pipelines + try: + response = es.ingest.get_pipeline(id="test_*,*_sparse_embedding") + + for pipeline_id, _ in response.items(): + try: + es.ingest.delete_pipeline(id=pipeline_id) + print(f"Deleted pipeline: {pipeline_id}") # noqa: T201 + except Exception as e: + print(f"Pipeline error: {e}") # noqa: T201 + except Exception: + pass + + @pytest.fixture(scope="function") + def es_client(self) -> Any: + class CustomTransport(Transport): + requests = [] + + def perform_request(self, *args, **kwargs): # type: ignore + self.requests.append(kwargs) + return super().perform_request(*args, **kwargs) + + es_url = os.environ.get("ES_URL", "http://localhost:9200") + cloud_id = os.environ.get("ES_CLOUD_ID") + api_key = os.environ.get("ES_API_KEY") + + if cloud_id: + # Running this integration test with Elastic Cloud + # Required for in-stack inference testing (ELSER + model_id) + es = Elasticsearch( + cloud_id=cloud_id, + api_key=api_key, + transport_class=CustomTransport, + ) + return es + else: + # Running this integration test with local docker instance + es = Elasticsearch(hosts=es_url, transport_class=CustomTransport) + return es + + @pytest.fixture(scope="function") + def index_name(self) -> str: + """Return the index name.""" + return f"test_{uuid.uuid4().hex}" + + def test_similarity_search_without_metadata( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test end to end construction and search without metadata.""" + + def assert_query(query_body: dict, query: str) -> dict: + assert query_body == { + "knn": { + "field": "vector", + "filter": [], + "k": 1, + "num_candidates": 50, + "query_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0], + } + } + return query_body + + texts = ["foo", "bar", "baz"] + docsearch = ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + ) + output = docsearch.similarity_search("foo", k=1, custom_query=assert_query) + assert output == [Document(page_content="foo")] + + async def test_similarity_search_without_metadata_async( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test end to end construction and search without metadata.""" + texts = ["foo", "bar", "baz"] + docsearch = ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + ) + output = await docsearch.asimilarity_search("foo", k=1) + assert output == [Document(page_content="foo")] + + def test_add_embeddings( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """ + Test add_embeddings, which accepts pre-built embeddings instead of + using inference for the texts. + This allows you to separate the embeddings text and the page_content + for better proximity between user's question and embedded text. + For example, your embedding text can be a question, whereas page_content + is the answer. + """ + embeddings = ConsistentFakeEmbeddings() + text_input = ["foo1", "foo2", "foo3"] + metadatas = [{"page": i} for i in range(len(text_input))] + + """In real use case, embedding_input can be questions for each text""" + embedding_input = ["foo2", "foo3", "foo1"] + embedding_vectors = embeddings.embed_documents(embedding_input) + + docsearch = ElasticsearchStore._create_cls_from_kwargs( + embeddings, + **elasticsearch_connection, + index_name=index_name, + ) + docsearch.add_embeddings(list(zip(text_input, embedding_vectors)), metadatas) + output = docsearch.similarity_search("foo1", k=1) + assert output == [Document(page_content="foo3", metadata={"page": 2})] + + def test_similarity_search_with_metadata( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test end to end construction and search with metadata.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = ElasticsearchStore.from_texts( + texts, + ConsistentFakeEmbeddings(), + metadatas=metadatas, + **elasticsearch_connection, + index_name=index_name, + ) + + output = docsearch.similarity_search("foo", k=1) + assert output == [Document(page_content="foo", metadata={"page": 0})] + + output = docsearch.similarity_search("bar", k=1) + assert output == [Document(page_content="bar", metadata={"page": 1})] + + def test_similarity_search_with_filter( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test end to end construction and search with metadata.""" + texts = ["foo", "foo", "foo"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + metadatas=metadatas, + **elasticsearch_connection, + index_name=index_name, + ) + + def assert_query(query_body: dict, query: str) -> dict: + assert query_body == { + "knn": { + "field": "vector", + "filter": [{"term": {"metadata.page": "1"}}], + "k": 3, + "num_candidates": 50, + "query_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0], + } + } + return query_body + + output = docsearch.similarity_search( + query="foo", + k=3, + filter=[{"term": {"metadata.page": "1"}}], + custom_query=assert_query, + ) + assert output == [Document(page_content="foo", metadata={"page": 1})] + + def test_similarity_search_with_doc_builder( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + texts = ["foo", "foo", "foo"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + metadatas=metadatas, + **elasticsearch_connection, + index_name=index_name, + ) + + def custom_document_builder(_: Dict) -> Document: + return Document( + page_content="Mock content!", + metadata={ + "page_number": -1, + "original_filename": "Mock filename!", + }, + ) + + output = docsearch.similarity_search( + query="foo", k=1, doc_builder=custom_document_builder + ) + assert output[0].page_content == "Mock content!" + assert output[0].metadata["page_number"] == -1 + assert output[0].metadata["original_filename"] == "Mock filename!" + + def test_similarity_search_exact_search( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test end to end construction and search with metadata.""" + texts = ["foo", "bar", "baz"] + docsearch = ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + strategy=ElasticsearchStore.ExactRetrievalStrategy(), + ) + + expected_query = { + "query": { + "script_score": { + "query": {"match_all": {}}, + "script": { + "source": "cosineSimilarity(params.query_vector, 'vector') + 1.0", # noqa: E501 + "params": { + "query_vector": [ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.0, + ] + }, + }, + } + } + } + + def assert_query(query_body: dict, query: str) -> dict: + assert query_body == expected_query + return query_body + + output = docsearch.similarity_search("foo", k=1, custom_query=assert_query) + assert output == [Document(page_content="foo")] + + def test_similarity_search_exact_search_with_filter( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test end to end construction and search with metadata.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + metadatas=metadatas, + strategy=ElasticsearchStore.ExactRetrievalStrategy(), + ) + + def assert_query(query_body: dict, query: str) -> dict: + expected_query = { + "query": { + "script_score": { + "query": {"bool": {"filter": [{"term": {"metadata.page": 0}}]}}, + "script": { + "source": "cosineSimilarity(params.query_vector, 'vector') + 1.0", # noqa: E501 + "params": { + "query_vector": [ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.0, + ] + }, + }, + } + } + } + assert query_body == expected_query + return query_body + + output = docsearch.similarity_search( + "foo", + k=1, + custom_query=assert_query, + filter=[{"term": {"metadata.page": 0}}], + ) + assert output == [Document(page_content="foo", metadata={"page": 0})] + + def test_similarity_search_exact_search_distance_dot_product( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test end to end construction and search with metadata.""" + texts = ["foo", "bar", "baz"] + docsearch = ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + strategy=ElasticsearchStore.ExactRetrievalStrategy(), + distance_strategy="DOT_PRODUCT", + ) + + def assert_query(query_body: dict, query: str) -> dict: + assert query_body == { + "query": { + "script_score": { + "query": {"match_all": {}}, + "script": { + "source": """ + double value = dotProduct(params.query_vector, 'vector'); + return sigmoid(1, Math.E, -value); + """, + "params": { + "query_vector": [ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.0, + ] + }, + }, + } + } + } + return query_body + + output = docsearch.similarity_search("foo", k=1, custom_query=assert_query) + assert output == [Document(page_content="foo")] + + def test_similarity_search_exact_search_unknown_distance_strategy( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test end to end construction and search with unknown distance strategy.""" + + with pytest.raises(KeyError): + texts = ["foo", "bar", "baz"] + ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + strategy=ElasticsearchStore.ExactRetrievalStrategy(), + distance_strategy="NOT_A_STRATEGY", + ) + + def test_max_marginal_relevance_search( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test max marginal relevance search.""" + texts = ["foo", "bar", "baz"] + docsearch = ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + strategy=ElasticsearchStore.ExactRetrievalStrategy(), + ) + + mmr_output = docsearch.max_marginal_relevance_search(texts[0], k=3, fetch_k=3) + sim_output = docsearch.similarity_search(texts[0], k=3) + assert mmr_output == sim_output + + mmr_output = docsearch.max_marginal_relevance_search(texts[0], k=2, fetch_k=3) + assert len(mmr_output) == 2 + assert mmr_output[0].page_content == texts[0] + assert mmr_output[1].page_content == texts[1] + + mmr_output = docsearch.max_marginal_relevance_search( + texts[0], + k=2, + fetch_k=3, + lambda_mult=0.1, # more diversity + ) + assert len(mmr_output) == 2 + assert mmr_output[0].page_content == texts[0] + assert mmr_output[1].page_content == texts[2] + + # if fetch_k < k, then the output will be less than k + mmr_output = docsearch.max_marginal_relevance_search(texts[0], k=3, fetch_k=2) + assert len(mmr_output) == 2 + + def test_similarity_search_approx_with_hybrid_search( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test end to end construction and search with metadata.""" + texts = ["foo", "bar", "baz"] + docsearch = ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + strategy=ElasticsearchStore.ApproxRetrievalStrategy(hybrid=True), + ) + + def assert_query(query_body: dict, query: str) -> dict: + assert query_body == { + "knn": { + "field": "vector", + "filter": [], + "k": 1, + "num_candidates": 50, + "query_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0], + }, + "query": { + "bool": { + "filter": [], + "must": [{"match": {"text": {"query": "foo"}}}], + } + }, + "rank": {"rrf": {}}, + } + return query_body + + output = docsearch.similarity_search("foo", k=1, custom_query=assert_query) + assert output == [Document(page_content="foo")] + + def test_similarity_search_approx_with_hybrid_search_rrf( + self, es_client: Any, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test end to end construction and rrf hybrid search with metadata.""" + from functools import partial + from typing import Optional + + # 1. check query_body is okay + rrf_test_cases: List[Optional[Union[dict, bool]]] = [ + True, + False, + {"rank_constant": 1, "window_size": 5}, + ] + for rrf_test_case in rrf_test_cases: + texts = ["foo", "bar", "baz"] + docsearch = ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + strategy=ElasticsearchStore.ApproxRetrievalStrategy( + hybrid=True, rrf=rrf_test_case + ), + ) + + def assert_query( + query_body: dict, + query: str, + rrf: Optional[Union[dict, bool]] = True, + ) -> dict: + cmp_query_body = { + "knn": { + "field": "vector", + "filter": [], + "k": 3, + "num_candidates": 50, + "query_vector": [ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.0, + ], + }, + "query": { + "bool": { + "filter": [], + "must": [{"match": {"text": {"query": "foo"}}}], + } + }, + } + + if isinstance(rrf, dict): + cmp_query_body["rank"] = {"rrf": rrf} + elif isinstance(rrf, bool) and rrf is True: + cmp_query_body["rank"] = {"rrf": {}} + + assert query_body == cmp_query_body + + return query_body + + ## without fetch_k parameter + output = docsearch.similarity_search( + "foo", k=3, custom_query=partial(assert_query, rrf=rrf_test_case) + ) + + # 2. check query result is okay + es_output = es_client.search( + index=index_name, + query={ + "bool": { + "filter": [], + "must": [{"match": {"text": {"query": "foo"}}}], + } + }, + knn={ + "field": "vector", + "filter": [], + "k": 3, + "num_candidates": 50, + "query_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0], + }, + size=3, + rank={"rrf": {"rank_constant": 1, "window_size": 5}}, + ) + + assert [o.page_content for o in output] == [ + e["_source"]["text"] for e in es_output["hits"]["hits"] + ] + + # 3. check rrf default option is okay + docsearch = ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + strategy=ElasticsearchStore.ApproxRetrievalStrategy(hybrid=True), + ) + + ## with fetch_k parameter + output = docsearch.similarity_search( + "foo", k=3, fetch_k=50, custom_query=assert_query + ) + + def test_similarity_search_approx_with_custom_query_fn( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """test that custom query function is called + with the query string and query body""" + + def my_custom_query(query_body: dict, query: str) -> dict: + assert query == "foo" + assert query_body == { + "knn": { + "field": "vector", + "filter": [], + "k": 1, + "num_candidates": 50, + "query_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0], + } + } + return {"query": {"match": {"text": {"query": "bar"}}}} + + """Test end to end construction and search with metadata.""" + texts = ["foo", "bar", "baz"] + docsearch = ElasticsearchStore.from_texts( + texts, FakeEmbeddings(), **elasticsearch_connection, index_name=index_name + ) + output = docsearch.similarity_search("foo", k=1, custom_query=my_custom_query) + assert output == [Document(page_content="bar")] + + @pytest.mark.skipif( + "sentence-transformers__all-minilm-l6-v2" not in modelsDeployed, + reason="Sentence Transformers model not deployed in ML Node, skipping test", + ) + def test_similarity_search_with_approx_infer_instack( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """test end to end with approx retrieval strategy and inference in-stack""" + docsearch = ElasticsearchStore( + index_name=index_name, + strategy=ElasticsearchStore.ApproxRetrievalStrategy( + query_model_id="sentence-transformers__all-minilm-l6-v2" + ), + query_field="text_field", + vector_query_field="vector_query_field.predicted_value", + **elasticsearch_connection, + ) + + # setting up the pipeline for inference + docsearch.client.ingest.put_pipeline( + id="test_pipeline", + processors=[ + { + "inference": { + "model_id": "sentence-transformers__all-minilm-l6-v2", + "field_map": {"query_field": "text_field"}, + "target_field": "vector_query_field", + } + } + ], + ) + + # creating a new index with the pipeline, + # not relying on langchain to create the index + docsearch.client.indices.create( + index=index_name, + mappings={ + "properties": { + "text_field": {"type": "text"}, + "vector_query_field": { + "properties": { + "predicted_value": { + "type": "dense_vector", + "dims": 384, + "index": True, + "similarity": "l2_norm", + } + } + }, + } + }, + settings={"index": {"default_pipeline": "test_pipeline"}}, + ) + + # adding documents to the index + texts = ["foo", "bar", "baz"] + + for i, text in enumerate(texts): + docsearch.client.create( + index=index_name, + id=str(i), + document={"text_field": text, "metadata": {}}, + ) + + docsearch.client.indices.refresh(index=index_name) + + def assert_query(query_body: dict, query: str) -> dict: + assert query_body == { + "knn": { + "filter": [], + "field": "vector_query_field.predicted_value", + "k": 1, + "num_candidates": 50, + "query_vector_builder": { + "text_embedding": { + "model_id": "sentence-transformers__all-minilm-l6-v2", + "model_text": "foo", + } + }, + } + } + return query_body + + output = docsearch.similarity_search("foo", k=1, custom_query=assert_query) + assert output == [Document(page_content="foo")] + + output = docsearch.similarity_search("bar", k=1) + assert output == [Document(page_content="bar")] + + @pytest.mark.skipif( + "elser" not in modelsDeployed, + reason="ELSER not deployed in ML Node, skipping test", + ) + def test_similarity_search_with_sparse_infer_instack( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """test end to end with sparse retrieval strategy and inference in-stack""" + texts = ["foo", "bar", "baz"] + docsearch = ElasticsearchStore.from_texts( + texts, + **elasticsearch_connection, + index_name=index_name, + strategy=ElasticsearchStore.SparseVectorRetrievalStrategy(), + ) + output = docsearch.similarity_search("foo", k=1) + assert output == [Document(page_content="foo")] + + def test_elasticsearch_with_relevance_score( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test to make sure the relevance score is scaled to 0-1.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + embeddings = FakeEmbeddings() + + docsearch = ElasticsearchStore.from_texts( + index_name=index_name, + texts=texts, + embedding=embeddings, + metadatas=metadatas, + **elasticsearch_connection, + ) + + embedded_query = embeddings.embed_query("foo") + output = docsearch.similarity_search_by_vector_with_relevance_scores( + embedding=embedded_query, k=1 + ) + assert output == [(Document(page_content="foo", metadata={"page": "0"}), 1.0)] + + def test_elasticsearch_with_relevance_threshold( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test to make sure the relevance threshold is respected.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + embeddings = FakeEmbeddings() + + docsearch = ElasticsearchStore.from_texts( + index_name=index_name, + texts=texts, + embedding=embeddings, + metadatas=metadatas, + **elasticsearch_connection, + ) + + # Find a good threshold for testing + query_string = "foo" + embedded_query = embeddings.embed_query(query_string) + top3 = docsearch.similarity_search_by_vector_with_relevance_scores( + embedding=embedded_query, k=3 + ) + similarity_of_second_ranked = top3[1][1] + assert len(top3) == 3 + + # Test threshold + retriever = docsearch.as_retriever( + search_type="similarity_score_threshold", + search_kwargs={"score_threshold": similarity_of_second_ranked}, + ) + output = retriever.get_relevant_documents(query=query_string) + + assert output == [ + top3[0][0], + top3[1][0], + # third ranked is out + ] + + def test_elasticsearch_delete_ids( + self, elasticsearch_connection: dict, index_name: str + ) -> None: + """Test delete methods from vector store.""" + texts = ["foo", "bar", "baz", "gni"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = ElasticsearchStore( + embedding=ConsistentFakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + ) + + ids = docsearch.add_texts(texts, metadatas) + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 4 + + docsearch.delete(ids[1:3]) + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 2 + + docsearch.delete(["not-existing"]) + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 2 + + docsearch.delete([ids[0]]) + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 1 + + docsearch.delete([ids[3]]) + output = docsearch.similarity_search("gni", k=10) + assert len(output) == 0 + + def test_elasticsearch_indexing_exception_error( + self, + elasticsearch_connection: dict, + index_name: str, + caplog: pytest.LogCaptureFixture, + ) -> None: + """Test bulk exception logging is giving better hints.""" + + docsearch = ElasticsearchStore( + embedding=ConsistentFakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + ) + + docsearch.client.indices.create( + index=index_name, + mappings={"properties": {}}, + settings={"index": {"default_pipeline": "not-existing-pipeline"}}, + ) + + texts = ["foo"] + + with pytest.raises(BulkIndexError): + docsearch.add_texts(texts) + + error_reason = "pipeline with id [not-existing-pipeline] does not exist" + log_message = f"First error reason: {error_reason}" + + assert log_message in caplog.text + + def test_elasticsearch_with_user_agent( + self, es_client: Any, index_name: str + ) -> None: + """Test to make sure the user-agent is set correctly.""" + + texts = ["foo", "bob", "baz"] + ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + es_connection=es_client, + index_name=index_name, + ) + + user_agent = es_client.transport.requests[0]["headers"]["User-Agent"] + pattern = r"^langchain-py-vs/\d+\.\d+\.\d+$" + match = re.match(pattern, user_agent) + + assert ( + match is not None + ), f"The string '{user_agent}' does not match the expected pattern." + + def test_elasticsearch_with_internal_user_agent( + self, elasticsearch_connection: Dict, index_name: str + ) -> None: + """Test to make sure the user-agent is set correctly.""" + + texts = ["foo"] + store = ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + **elasticsearch_connection, + index_name=index_name, + ) + + user_agent = store.client._headers["User-Agent"] + pattern = r"^langchain-py-vs/\d+\.\d+\.\d+$" + match = re.match(pattern, user_agent) + + assert ( + match is not None + ), f"The string '{user_agent}' does not match the expected pattern." + + def test_bulk_args(self, es_client: Any, index_name: str) -> None: + """Test to make sure the user-agent is set correctly.""" + + texts = ["foo", "bob", "baz"] + ElasticsearchStore.from_texts( + texts, + FakeEmbeddings(), + es_connection=es_client, + index_name=index_name, + bulk_kwargs={"chunk_size": 1}, + ) + + # 1 for index exist, 1 for index create, 3 for index docs + assert len(es_client.transport.requests) == 5 # type: ignore diff --git a/libs/partners/elasticsearch/tests/unit_tests/__init__.py b/libs/partners/elasticsearch/tests/unit_tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/elasticsearch/tests/unit_tests/test_imports.py b/libs/partners/elasticsearch/tests/unit_tests/test_imports.py new file mode 100644 index 0000000000000..915ba491788c9 --- /dev/null +++ b/libs/partners/elasticsearch/tests/unit_tests/test_imports.py @@ -0,0 +1,14 @@ +from langchain_elasticsearch import __all__ + +EXPECTED_ALL = [ + "ApproxRetrievalStrategy", + "ElasticsearchChatMessageHistory", + "ElasticsearchEmbeddings", + "ElasticsearchStore", + "ExactRetrievalStrategy", + "SparseRetrievalStrategy", +] + + +def test_all_imports() -> None: + assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/elasticsearch/tests/unit_tests/test_vectorstores.py b/libs/partners/elasticsearch/tests/unit_tests/test_vectorstores.py new file mode 100644 index 0000000000000..4ad407f71e19a --- /dev/null +++ b/libs/partners/elasticsearch/tests/unit_tests/test_vectorstores.py @@ -0,0 +1,34 @@ +"""Test Elasticsearch functionality.""" + +import pytest + +from langchain_elasticsearch.vectorstores import ( + ApproxRetrievalStrategy, + ElasticsearchStore, +) + +from ..fake_embeddings import FakeEmbeddings + + +@pytest.mark.requires("elasticsearch") +def test_elasticsearch_hybrid_scores_guard() -> None: + """Ensure an error is raised when search with score in hybrid mode + because in this case Elasticsearch does not return any score. + """ + from elasticsearch import Elasticsearch + + query_string = "foo" + embeddings = FakeEmbeddings() + + store = ElasticsearchStore( + index_name="dummy_index", + es_connection=Elasticsearch(hosts=["http://dummy-host:9200"]), + embedding=embeddings, + strategy=ApproxRetrievalStrategy(hybrid=True), + ) + with pytest.raises(ValueError): + store.similarity_search_with_score(query_string) + + embedded_query = embeddings.embed_query(query_string) + with pytest.raises(ValueError): + store.similarity_search_by_vector_with_relevance_scores(embedded_query)