diff --git a/libs/partners/milvus/.gitignore b/libs/partners/milvus/.gitignore deleted file mode 100644 index bee8a64b79..0000000000 --- a/libs/partners/milvus/.gitignore +++ /dev/null @@ -1 +0,0 @@ -__pycache__ diff --git a/libs/partners/milvus/LICENSE b/libs/partners/milvus/LICENSE deleted file mode 100644 index 426b650903..0000000000 --- a/libs/partners/milvus/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2023 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/milvus/Makefile b/libs/partners/milvus/Makefile deleted file mode 100644 index 9234f584f0..0000000000 --- a/libs/partners/milvus/Makefile +++ /dev/null @@ -1,59 +0,0 @@ -.PHONY: all format lint test tests integration_tests docker_tests help extended_tests - -# Default target executed when no arguments are given to make. -all: help - -# Define a variable for the test file path. -TEST_FILE ?= tests/unit_tests/ -integration_test integration_tests: TEST_FILE=tests/integration_tests/ - -test tests integration_test integration_tests: - poetry run pytest $(TEST_FILE) - -test_watch: - poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) - - -###################### -# LINTING AND FORMATTING -###################### - -# Define a variable for Python and notebook files. -PYTHON_FILES=. -MYPY_CACHE=.mypy_cache -lint format: PYTHON_FILES=. -lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/milvus --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') -lint_package: PYTHON_FILES=langchain_milvus -lint_tests: PYTHON_FILES=tests -lint_tests: MYPY_CACHE=.mypy_cache_test - -lint lint_diff lint_package lint_tests: - [ "$(PYTHON_FILES)" = "" ] || poetry run ruff $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff - [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) - -format format_diff: - [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --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_milvus -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/milvus/README.md b/libs/partners/milvus/README.md index 26c0ee4fef..5d7b8dccac 100644 --- a/libs/partners/milvus/README.md +++ b/libs/partners/milvus/README.md @@ -1,42 +1,3 @@ -# langchain-milvus +This package has moved! -This is a library integration with [Milvus](https://milvus.io/) and [Zilliz Cloud](https://zilliz.com/cloud). - -## Installation - -```bash -pip install -U langchain-milvus -``` - -## Milvus vector database - -See a [usage example](https://python.langchain.com/docs/integrations/vectorstores/milvus/) - -```python -from langchain_milvus import Milvus -``` - -## Milvus hybrid search - -See a [usage example](https://python.langchain.com/docs/integrations/retrievers/milvus_hybrid_search/). - -```python -from langchain_milvus import MilvusCollectionHybridSearchRetriever -``` - - -## Zilliz Cloud vector database - -See a [usage example](https://python.langchain.com/docs/integrations/vectorstores/zilliz/). - -```python -from langchain_milvus import Zilliz -``` - -## Zilliz Cloud Pipeline Retriever - -See a [usage example](https://python.langchain.com/docs/integrations/retrievers/zilliz_cloud_pipeline/). - -```python -from langchain_milvus import ZillizCloudPipelineRetriever -``` \ No newline at end of file +https://github.com/langchain-ai/langchain-milvus/tree/main/libs/milvus diff --git a/libs/partners/milvus/langchain_milvus/__init__.py b/libs/partners/milvus/langchain_milvus/__init__.py deleted file mode 100644 index b19bc1d7e6..0000000000 --- a/libs/partners/milvus/langchain_milvus/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from langchain_milvus.retrievers import ( - MilvusCollectionHybridSearchRetriever, - ZillizCloudPipelineRetriever, -) -from langchain_milvus.vectorstores import Milvus, Zilliz - -__all__ = [ - "Milvus", - "Zilliz", - "ZillizCloudPipelineRetriever", - "MilvusCollectionHybridSearchRetriever", -] diff --git a/libs/partners/milvus/langchain_milvus/py.typed b/libs/partners/milvus/langchain_milvus/py.typed deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/libs/partners/milvus/langchain_milvus/retrievers/__init__.py b/libs/partners/milvus/langchain_milvus/retrievers/__init__.py deleted file mode 100644 index 1edac3cb5a..0000000000 --- a/libs/partners/milvus/langchain_milvus/retrievers/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from langchain_milvus.retrievers.milvus_hybrid_search import ( - MilvusCollectionHybridSearchRetriever, -) -from langchain_milvus.retrievers.zilliz_cloud_pipeline_retriever import ( - ZillizCloudPipelineRetriever, -) - -__all__ = ["ZillizCloudPipelineRetriever", "MilvusCollectionHybridSearchRetriever"] diff --git a/libs/partners/milvus/langchain_milvus/retrievers/milvus_hybrid_search.py b/libs/partners/milvus/langchain_milvus/retrievers/milvus_hybrid_search.py deleted file mode 100644 index b6e309c32e..0000000000 --- a/libs/partners/milvus/langchain_milvus/retrievers/milvus_hybrid_search.py +++ /dev/null @@ -1,161 +0,0 @@ -from typing import Any, Dict, List, Optional, Union - -from langchain_core.callbacks import CallbackManagerForRetrieverRun -from langchain_core.documents import Document -from langchain_core.embeddings import Embeddings -from langchain_core.retrievers import BaseRetriever -from pymilvus import AnnSearchRequest, Collection -from pymilvus.client.abstract import BaseRanker, SearchResult # type: ignore - -from langchain_milvus.utils.sparse import BaseSparseEmbedding - - -class MilvusCollectionHybridSearchRetriever(BaseRetriever): - """Hybrid search retriever - that uses Milvus Collection to retrieve documents based on multiple fields. - - For more information, please refer to: - https://milvus.io/docs/release_notes.md#Multi-Embedding---Hybrid-Search - """ - - collection: Collection - """Milvus Collection object.""" - rerank: BaseRanker - """Milvus ranker object. Such as WeightedRanker or RRFRanker.""" - anns_fields: List[str] - """The names of vector fields that are used for ANNS search.""" - field_embeddings: List[Union[Embeddings, BaseSparseEmbedding]] - """The embedding functions of each vector fields, - which can be either Embeddings or BaseSparseEmbedding.""" - field_search_params: Optional[List[Dict]] = None - """The search parameters of each vector fields. - If not specified, the default search parameters will be used.""" - field_limits: Optional[List[int]] = None - """Limit number of results for each ANNS field. - If not specified, the default top_k will be used.""" - field_exprs: Optional[List[Optional[str]]] = None - """The boolean expression for filtering the search results.""" - top_k: int = 4 - """Final top-K number of documents to retrieve.""" - text_field: str = "text" - """The text field name, - which will be used as the `page_content` of a `Document` object.""" - output_fields: Optional[List[str]] = None - """Final output fields of the documents. - If not specified, all fields except the vector fields will be used as output fields, - which will be the `metadata` of a `Document` object.""" - - def __init__(self, **kwargs: Any): - super().__init__(**kwargs) - - # If some parameters are not specified, set default values - if self.field_search_params is None: - default_search_params = { - "metric_type": "L2", - "params": {"nprobe": 10}, - } - self.field_search_params = [default_search_params] * len(self.anns_fields) - if self.field_limits is None: - self.field_limits = [self.top_k] * len(self.anns_fields) - if self.field_exprs is None: - self.field_exprs = [None] * len(self.anns_fields) - - # Check the fields - self._validate_fields_num() - self.output_fields = self._get_output_fields() - self._validate_fields_name() - - # Load collection - self.collection.load() - - def _validate_fields_num(self) -> None: - assert ( - len(self.anns_fields) >= 2 - ), "At least two fields are required for hybrid search." - lengths = [len(self.anns_fields)] - if self.field_limits is not None: - lengths.append(len(self.field_limits)) - if self.field_exprs is not None: - lengths.append(len(self.field_exprs)) - - if not all(length == lengths[0] for length in lengths): - raise ValueError("All field-related lists must have the same length.") - - if len(self.field_search_params) != len(self.anns_fields): # type: ignore[arg-type] - raise ValueError( - "field_search_params must have the same length as anns_fields." - ) - - def _validate_fields_name(self) -> None: - collection_fields = [x.name for x in self.collection.schema.fields] - for field in self.anns_fields: - assert ( - field in collection_fields - ), f"{field} is not a valid field in the collection." - assert ( - self.text_field in collection_fields - ), f"{self.text_field} is not a valid field in the collection." - for field in self.output_fields: # type: ignore[union-attr] - assert ( - field in collection_fields - ), f"{field} is not a valid field in the collection." - - def _get_output_fields(self) -> List[str]: - if self.output_fields: - return self.output_fields - output_fields = [x.name for x in self.collection.schema.fields] - for field in self.anns_fields: - if field in output_fields: - output_fields.remove(field) - if self.text_field not in output_fields: - output_fields.append(self.text_field) - return output_fields - - def _build_ann_search_requests(self, query: str) -> List[AnnSearchRequest]: - search_requests = [] - for ann_field, embedding, param, limit, expr in zip( - self.anns_fields, - self.field_embeddings, - self.field_search_params, # type: ignore[arg-type] - self.field_limits, # type: ignore[arg-type] - self.field_exprs, # type: ignore[arg-type] - ): - request = AnnSearchRequest( - data=[embedding.embed_query(query)], - anns_field=ann_field, - param=param, - limit=limit, - expr=expr, - ) - search_requests.append(request) - return search_requests - - def _parse_document(self, data: dict) -> Document: - return Document( - page_content=data.pop(self.text_field), - metadata=data, - ) - - def _process_search_result( - self, search_results: List[SearchResult] - ) -> List[Document]: - documents = [] - for result in search_results[0]: - data = {x: result.entity.get(x) for x in self.output_fields} # type: ignore[union-attr] - doc = self._parse_document(data) - documents.append(doc) - return documents - - def _get_relevant_documents( - self, - query: str, - *, - run_manager: CallbackManagerForRetrieverRun, - **kwargs: Any, - ) -> List[Document]: - requests = self._build_ann_search_requests(query) - search_result = self.collection.hybrid_search( - requests, self.rerank, limit=self.top_k, output_fields=self.output_fields - ) - documents = self._process_search_result(search_result) - return documents diff --git a/libs/partners/milvus/langchain_milvus/retrievers/zilliz_cloud_pipeline_retriever.py b/libs/partners/milvus/langchain_milvus/retrievers/zilliz_cloud_pipeline_retriever.py deleted file mode 100644 index 88c6a55cac..0000000000 --- a/libs/partners/milvus/langchain_milvus/retrievers/zilliz_cloud_pipeline_retriever.py +++ /dev/null @@ -1,215 +0,0 @@ -from typing import Any, Dict, List, Optional - -import requests -from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun -from langchain_core.documents import Document -from langchain_core.retrievers import BaseRetriever - - -class ZillizCloudPipelineRetriever(BaseRetriever): - """`Zilliz Cloud Pipeline` retriever. - - Parameters: - pipeline_ids: A dictionary of pipeline ids. - Valid keys: "ingestion", "search", "deletion". - token: Zilliz Cloud's token. Defaults to "". - cloud_region: The region of Zilliz Cloud's cluster. - Defaults to 'gcp-us-west1'. - """ - - pipeline_ids: Dict - token: str = "" - cloud_region: str = "gcp-us-west1" - - def _get_relevant_documents( - self, - query: str, - top_k: int = 10, - offset: int = 0, - output_fields: List = [], - filter: str = "", - *, - run_manager: CallbackManagerForRetrieverRun, - ) -> List[Document]: - """ - Get documents relevant to a query. - - Args: - query: String to find relevant documents for - top_k: The number of results. Defaults to 10. - offset: The number of records to skip in the search result. - Defaults to 0. - output_fields: The extra fields to present in output. - filter: The Milvus expression to filter search results. - Defaults to "". - run_manager: The callbacks handler to use. - - Returns: - List of relevant documents - """ - if "search" in self.pipeline_ids: - search_pipe_id = self.pipeline_ids.get("search") - else: - raise Exception( - "A search pipeline id must be provided in pipeline_ids to " - "get relevant documents." - ) - domain = ( - f"https://controller.api.{self.cloud_region}.zillizcloud.com/v1/pipelines" - ) - headers = { - "Authorization": f"Bearer {self.token}", - "Accept": "application/json", - "Content-Type": "application/json", - } - url = f"{domain}/{search_pipe_id}/run" - - params = { - "data": {"query_text": query}, - "params": { - "limit": top_k, - "offset": offset, - "outputFields": output_fields, - "filter": filter, - }, - } - - response = requests.post(url, headers=headers, json=params) - if response.status_code != 200: - raise RuntimeError(response.text) - response_dict = response.json() - if response_dict["code"] != 200: - raise RuntimeError(response_dict) - response_data = response_dict["data"] - search_results = response_data["result"] - return [ - Document( - page_content=result.pop("text") - if "text" in result - else result.pop("chunk_text"), - metadata=result, - ) - for result in search_results - ] - - def add_texts( - self, texts: List[str], metadata: Optional[Dict[str, Any]] = None - ) -> Dict: - """ - Add documents to store. - Only supported by a text ingestion pipeline in Zilliz Cloud. - - Args: - texts: A list of text strings. - metadata: A key-value dictionary of metadata will - be inserted as preserved fields required by ingestion pipeline. - Defaults to None. - """ - if "ingestion" in self.pipeline_ids: - ingeset_pipe_id = self.pipeline_ids.get("ingestion") - else: - raise Exception( - "An ingestion pipeline id must be provided in pipeline_ids to" - " add documents." - ) - domain = ( - f"https://controller.api.{self.cloud_region}.zillizcloud.com/v1/pipelines" - ) - headers = { - "Authorization": f"Bearer {self.token}", - "Accept": "application/json", - "Content-Type": "application/json", - } - url = f"{domain}/{ingeset_pipe_id}/run" - - metadata = {} if metadata is None else metadata - params = {"data": {"text_list": texts}} - params["data"].update(metadata) - - response = requests.post(url, headers=headers, json=params) - if response.status_code != 200: - raise Exception(response.text) - response_dict = response.json() - if response_dict["code"] != 200: - raise Exception(response_dict) - response_data = response_dict["data"] - return response_data - - def add_doc_url( - self, doc_url: str, metadata: Optional[Dict[str, Any]] = None - ) -> Dict: - """ - Add a document from url. - Only supported by a document ingestion pipeline in Zilliz Cloud. - - Args: - doc_url: A document url. - metadata: A key-value dictionary of metadata will - be inserted as preserved fields required by ingestion pipeline. - Defaults to None. - """ - if "ingestion" in self.pipeline_ids: - ingest_pipe_id = self.pipeline_ids.get("ingestion") - else: - raise Exception( - "An ingestion pipeline id must be provided in pipeline_ids to " - "add documents." - ) - domain = ( - f"https://controller.api.{self.cloud_region}.zillizcloud.com/v1/pipelines" - ) - headers = { - "Authorization": f"Bearer {self.token}", - "Accept": "application/json", - "Content-Type": "application/json", - } - url = f"{domain}/{ingest_pipe_id}/run" - - params = {"data": {"doc_url": doc_url}} - metadata = {} if metadata is None else metadata - params["data"].update(metadata) - - response = requests.post(url, headers=headers, json=params) - if response.status_code != 200: - raise Exception(response.text) - response_dict = response.json() - if response_dict["code"] != 200: - raise Exception(response_dict) - response_data = response_dict["data"] - return response_data - - def delete(self, key: str, value: Any) -> Dict: - """ - Delete documents. Only supported by a deletion pipeline in Zilliz Cloud. - - Args: - key: input name to run the deletion pipeline - value: input value to run deletion pipeline - """ - if "deletion" in self.pipeline_ids: - deletion_pipe_id = self.pipeline_ids.get("deletion") - else: - raise Exception( - "A deletion pipeline id must be provided in pipeline_ids to " - "add documents." - ) - domain = ( - f"https://controller.api.{self.cloud_region}.zillizcloud.com/v1/pipelines" - ) - headers = { - "Authorization": f"Bearer {self.token}", - "Accept": "application/json", - "Content-Type": "application/json", - } - url = f"{domain}/{deletion_pipe_id}/run" - - params = {"data": {key: value}} - - response = requests.post(url, headers=headers, json=params) - if response.status_code != 200: - raise Exception(response.text) - response_dict = response.json() - if response_dict["code"] != 200: - raise Exception(response_dict) - response_data = response_dict["data"] - return response_data diff --git a/libs/partners/milvus/langchain_milvus/utils/__init__.py b/libs/partners/milvus/langchain_milvus/utils/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/libs/partners/milvus/langchain_milvus/utils/sparse.py b/libs/partners/milvus/langchain_milvus/utils/sparse.py deleted file mode 100644 index 47c19567ce..0000000000 --- a/libs/partners/milvus/langchain_milvus/utils/sparse.py +++ /dev/null @@ -1,55 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Dict, List - -from scipy.sparse import csr_array # type: ignore - - -class BaseSparseEmbedding(ABC): - """Interface for Sparse embedding models. - - You can inherit from it and implement your custom sparse embedding model. - """ - - @abstractmethod - def embed_query(self, query: str) -> Dict[int, float]: - """Embed query text.""" - - @abstractmethod - def embed_documents(self, texts: List[str]) -> List[Dict[int, float]]: - """Embed search docs.""" - - -class BM25SparseEmbedding(BaseSparseEmbedding): - """Sparse embedding model based on BM25. - - This class uses the BM25 model in Milvus model to implement sparse vector embedding. - This model requires pymilvus[model] to be installed. - `pip install pymilvus[model]` - For more information please refer to: - https://milvus.io/docs/embed-with-bm25.md - """ - - def __init__(self, corpus: List[str], language: str = "en"): - from pymilvus.model.sparse import BM25EmbeddingFunction # type: ignore - from pymilvus.model.sparse.bm25.tokenizers import ( # type: ignore - build_default_analyzer, - ) - - self.analyzer = build_default_analyzer(language=language) - self.bm25_ef = BM25EmbeddingFunction(self.analyzer, num_workers=1) - self.bm25_ef.fit(corpus) - - def embed_query(self, text: str) -> Dict[int, float]: - return self._sparse_to_dict(self.bm25_ef.encode_queries([text])) - - def embed_documents(self, texts: List[str]) -> List[Dict[int, float]]: - sparse_arrays = self.bm25_ef.encode_documents(texts) - return [self._sparse_to_dict(sparse_array) for sparse_array in sparse_arrays] - - def _sparse_to_dict(self, sparse_array: csr_array) -> Dict[int, float]: - row_indices, col_indices = sparse_array.nonzero() - non_zero_values = sparse_array.data - result_dict = {} - for col_index, value in zip(col_indices, non_zero_values): - result_dict[col_index] = value - return result_dict diff --git a/libs/partners/milvus/langchain_milvus/vectorstores/__init__.py b/libs/partners/milvus/langchain_milvus/vectorstores/__init__.py deleted file mode 100644 index 5c6f304db9..0000000000 --- a/libs/partners/milvus/langchain_milvus/vectorstores/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from langchain_milvus.vectorstores.milvus import Milvus -from langchain_milvus.vectorstores.zilliz import Zilliz - -__all__ = [ - "Milvus", - "Zilliz", -] diff --git a/libs/partners/milvus/langchain_milvus/vectorstores/milvus.py b/libs/partners/milvus/langchain_milvus/vectorstores/milvus.py deleted file mode 100644 index 279f963036..0000000000 --- a/libs/partners/milvus/langchain_milvus/vectorstores/milvus.py +++ /dev/null @@ -1,1351 +0,0 @@ -from __future__ import annotations - -import logging -from typing import Any, Dict, Iterable, List, Optional, Tuple, Union -from uuid import uuid4 - -import numpy as np -from langchain_core.documents import Document -from langchain_core.embeddings import Embeddings -from langchain_core.vectorstores import VectorStore - -from langchain_milvus.utils.sparse import BaseSparseEmbedding - -logger = logging.getLogger(__name__) - -DEFAULT_MILVUS_CONNECTION = { - "uri": "http://localhost:19530", -} - -Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray] - - -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 - - X = np.array(X, dtype=np.float32) - Y = np.array(Y, dtype=np.float32) - Z = 1 - np.array(simd.cdist(X, Y, metric="cosine")) - return Z - except ImportError: - logger.debug( - "Unable to import simsimd, defaulting to NumPy implementation. If you want " - "to use simsimd please install with `pip install simsimd`." - ) - 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 - - -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. - - Args: - query_embedding: The query embedding. - embedding_list: The list of embeddings. - lambda_mult: The lambda multiplier. Defaults to 0.5. - k: The number of results to return. Defaults to 4. - - Returns: - List[int]: The list of indices. - """ - 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 - - -class Milvus(VectorStore): - """Milvus vector store integration. - - Setup: - Install ``langchain_milvus`` package: - - .. code-block:: bash - - pip install -qU langchain_milvus - - Key init args — indexing params: - collection_name: str - Name of the collection. - collection_description: str - Description of the collection. - embedding_function: Union[Embeddings, BaseSparseEmbedding] - Embedding function to use. - - Key init args — client params: - connection_args: Optional[dict] - Connection arguments. - - Instantiate: - .. code-block:: python - - from langchain_milvus import Milvus - from langchain_openai import OpenAIEmbeddings - - URI = "./milvus_example.db" - - vector_store = Milvus( - embedding_function=OpenAIEmbeddings(), - connection_args={"uri": URI}, - ) - - Add Documents: - .. code-block:: python - - from langchain_core.documents import Document - - document_1 = Document(page_content="foo", metadata={"baz": "bar"}) - document_2 = Document(page_content="thud", metadata={"baz": "baz"}) - document_3 = Document(page_content="i will be deleted :(", metadata={"baz": "qux"}) - - documents = [document_1, document_2, document_3] - ids = ["1", "2", "3"] - vector_store.add_documents(documents=documents, ids=ids) - - Delete Documents: - .. code-block:: python - - vector_store.delete(ids=["3"]) - - Search: - .. code-block:: python - - results = vector_store.similarity_search(query="thud",k=1) - for doc in results: - print(f"* {doc.page_content} [{doc.metadata}]") - - .. code-block:: python - - * thud [{'baz': 'baz', 'pk': '2'}] - - Search with filter: - .. code-block:: python - - results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"}) - for doc in results: - print(f"* {doc.page_content} [{doc.metadata}]") - - .. code-block:: python - - * thud [{'baz': 'baz', 'pk': '2'}] - - Search with score: - .. code-block:: python - - results = vector_store.similarity_search_with_score(query="qux",k=1) - for doc, score in results: - print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") - - .. code-block:: python - - * [SIM=0.335463] foo [{'baz': 'bar', 'pk': '1'}] - - Async: - .. code-block:: python - - # add documents - # await vector_store.aadd_documents(documents=documents, ids=ids) - - # delete documents - # await vector_store.adelete(ids=["3"]) - - # search - # results = vector_store.asimilarity_search(query="thud",k=1) - - # search with score - results = await vector_store.asimilarity_search_with_score(query="qux",k=1) - for doc,score in results: - print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") - - .. code-block:: python - - * [SIM=0.335463] foo [{'baz': 'bar', 'pk': '1'}] - - Use as Retriever: - .. code-block:: python - - retriever = vector_store.as_retriever( - search_type="mmr", - search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5}, - ) - retriever.invoke("thud") - - .. code-block:: python - - [Document(metadata={'baz': 'baz', 'pk': '2'}, page_content='thud')] - - """ # noqa: E501 - - def __init__( - self, - embedding_function: Union[Embeddings, BaseSparseEmbedding], # type: ignore - collection_name: str = "LangChainCollection", - collection_description: str = "", - collection_properties: Optional[dict[str, Any]] = None, - connection_args: Optional[dict[str, Any]] = None, - consistency_level: str = "Session", - index_params: Optional[dict] = None, - search_params: Optional[dict] = None, - drop_old: Optional[bool] = False, - auto_id: bool = False, - *, - primary_field: str = "pk", - text_field: str = "text", - vector_field: str = "vector", - enable_dynamic_field: bool = False, - metadata_field: Optional[str] = None, - partition_key_field: Optional[str] = None, - partition_names: Optional[list] = None, - replica_number: int = 1, - timeout: Optional[float] = None, - num_shards: Optional[int] = None, - metadata_schema: Optional[dict[str, Any]] = None, - ): - """Initialize the Milvus vector store.""" - try: - from pymilvus import Collection, utility - except ImportError: - raise ValueError( - "Could not import pymilvus python package. " - "Please install it with `pip install pymilvus`." - ) - - # Default search params when one is not provided. - self.default_search_params = { - "IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}}, - "IVF_SQ8": {"metric_type": "L2", "params": {"nprobe": 10}}, - "IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}}, - "HNSW": {"metric_type": "L2", "params": {"ef": 10}}, - "RHNSW_FLAT": {"metric_type": "L2", "params": {"ef": 10}}, - "RHNSW_SQ": {"metric_type": "L2", "params": {"ef": 10}}, - "RHNSW_PQ": {"metric_type": "L2", "params": {"ef": 10}}, - "IVF_HNSW": {"metric_type": "L2", "params": {"nprobe": 10, "ef": 10}}, - "ANNOY": {"metric_type": "L2", "params": {"search_k": 10}}, - "SCANN": {"metric_type": "L2", "params": {"search_k": 10}}, - "AUTOINDEX": {"metric_type": "L2", "params": {}}, - "GPU_CAGRA": { - "metric_type": "L2", - "params": { - "itopk_size": 128, - "search_width": 4, - "min_iterations": 0, - "max_iterations": 0, - "team_size": 0, - }, - }, - "GPU_IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}}, - "GPU_IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}}, - "SPARSE_INVERTED_INDEX": { - "metric_type": "IP", - "params": {"drop_ratio_build": 0.2}, - }, - "SPARSE_WAND": {"metric_type": "IP", "params": {"drop_ratio_build": 0.2}}, - } - - self.embedding_func = embedding_function - self.collection_name = collection_name - self.collection_description = collection_description - self.collection_properties = collection_properties - self.index_params = index_params - self.search_params = search_params - self.consistency_level = consistency_level - self.auto_id = auto_id - - # In order for a collection to be compatible, pk needs to be varchar - self._primary_field = primary_field - # In order for compatibility, the text field will need to be called "text" - self._text_field = text_field - # In order for compatibility, the vector field needs to be called "vector" - self._vector_field = vector_field - if metadata_field: - logger.warning( - "DeprecationWarning: `metadata_field` is about to be deprecated, " - "please set `enable_dynamic_field`=True instead." - ) - if enable_dynamic_field and metadata_field: - metadata_field = None - logger.warning( - "When `enable_dynamic_field` is True, `metadata_field` is ignored." - ) - self.enable_dynamic_field = enable_dynamic_field - self._metadata_field = metadata_field - self._partition_key_field = partition_key_field - self.fields: list[str] = [] - self.partition_names = partition_names - self.replica_number = replica_number - self.timeout = timeout - self.num_shards = num_shards - self.metadata_schema = metadata_schema - - # Create the connection to the server - if connection_args is None: - connection_args = DEFAULT_MILVUS_CONNECTION - self.alias = self._create_connection_alias(connection_args) - self.col: Optional[Collection] = None - - # Grab the existing collection if it exists - if utility.has_collection(self.collection_name, using=self.alias): - self.col = Collection( - self.collection_name, - using=self.alias, - ) - if self.collection_properties is not None: - self.col.set_properties(self.collection_properties) - # If need to drop old, drop it - if drop_old and isinstance(self.col, Collection): - self.col.drop() - self.col = None - - # Initialize the vector store - self._init( - partition_names=partition_names, - replica_number=replica_number, - timeout=timeout, - ) - - @property - def embeddings(self) -> Union[Embeddings, BaseSparseEmbedding]: # type: ignore - return self.embedding_func - - def _create_connection_alias(self, connection_args: dict) -> str: - """Create the connection to the Milvus server.""" - from pymilvus import MilvusException, connections - - # Grab the connection arguments that are used for checking existing connection - host: str = connection_args.get("host", None) - port: Union[str, int] = connection_args.get("port", None) - address: str = connection_args.get("address", None) - uri: str = connection_args.get("uri", None) - user = connection_args.get("user", None) - db_name = connection_args.get("db_name", "default") - - # Order of use is host/port, uri, address - if host is not None and port is not None: - given_address = str(host) + ":" + str(port) - elif uri is not None: - if uri.startswith("https://"): - given_address = uri.split("https://")[1] - elif uri.startswith("http://"): - given_address = uri.split("http://")[1] - else: - given_address = uri # Milvus lite - elif address is not None: - given_address = address - else: - given_address = None - logger.debug("Missing standard address type for reuse attempt") - - # User defaults to empty string when getting connection info - if user is not None: - tmp_user = user - else: - tmp_user = "" - - # If a valid address was given, then check if a connection exists - if given_address is not None: - for con in connections.list_connections(): - addr = connections.get_connection_addr(con[0]) - if ( - con[1] - and ("address" in addr) - and (addr["address"] == given_address) - and ("user" in addr) - and (addr["user"] == tmp_user) - and (addr.get("db_name", "default") == db_name) - ): - logger.debug("Using previous connection: %s", con[0]) - return con[0] - - # Generate a new connection if one doesn't exist - alias = uuid4().hex - try: - connections.connect(alias=alias, **connection_args) - logger.debug("Created new connection using: %s", alias) - return alias - except MilvusException as e: - logger.error("Failed to create new connection using: %s", alias) - raise e - - @property - def _is_sparse_embedding(self) -> bool: - return isinstance(self.embedding_func, BaseSparseEmbedding) - - def _init( - self, - embeddings: Optional[list] = None, - metadatas: Optional[list[dict]] = None, - partition_names: Optional[list] = None, - replica_number: int = 1, - timeout: Optional[float] = None, - ) -> None: - if embeddings is not None: - self._create_collection(embeddings, metadatas) - self._extract_fields() - self._create_index() - self._create_search_params() - self._load( - partition_names=partition_names, - replica_number=replica_number, - timeout=timeout, - ) - - def _create_collection( - self, embeddings: list, metadatas: Optional[list[dict]] = None - ) -> None: - from pymilvus import ( - Collection, - CollectionSchema, - DataType, - FieldSchema, - MilvusException, - ) - from pymilvus.orm.types import infer_dtype_bydata # type: ignore - - # Determine embedding dim - dim = len(embeddings[0]) - fields = [] - # If enable_dynamic_field, we don't need to create fields, and just pass it. - # In the future, when metadata_field is deprecated, - # This logical structure will be simplified like this: - # ``` - # if not self.enable_dynamic_field and metadatas: - # for key, value in metadatas[0].items(): - # ... - # ``` - if self.enable_dynamic_field: - # If both dynamic fields and partition key field are enabled - if self._partition_key_field is not None: - # create the partition field - fields.append( - FieldSchema( - self._partition_key_field, DataType.VARCHAR, max_length=65_535 - ) - ) - elif self._metadata_field is not None: - fields.append(FieldSchema(self._metadata_field, DataType.JSON)) - else: - # Determine metadata schema - if metadatas: - # Create FieldSchema for each entry in metadata. - for key, value in metadatas[0].items(): - if key in [ - self._vector_field, - self._primary_field, - self._text_field, - ]: - logger.error( - ( - "Failure to create collection, " - "metadata key: %s is reserved." - ), - key, - ) - raise ValueError(f"Metadata key {key} is reserved.") - # Infer the corresponding datatype of the metadata - if ( - self.metadata_schema - and key in self.metadata_schema # type: ignore - and "dtype" in self.metadata_schema[key] # type: ignore - ): - kwargs = self.metadata_schema[key].get("kwargs", {}) # type: ignore - fields.append( - FieldSchema( - name=key, - dtype=self.metadata_schema[key]["dtype"], # type: ignore - **kwargs, - ) - ) - else: - dtype = infer_dtype_bydata(value) - # Datatype isn't compatible - if dtype == DataType.UNKNOWN or dtype == DataType.NONE: - logger.error( - ( - "Failure to create collection, " - "unrecognized dtype for key: %s" - ), - key, - ) - raise ValueError(f"Unrecognized datatype for {key}.") - # Datatype is a string/varchar equivalent - elif dtype == DataType.VARCHAR: - fields.append( - FieldSchema(key, DataType.VARCHAR, max_length=65_535) - ) - # infer_dtype_bydata currently can't recognize array type, - # so this line can not be accessed. - # This line may need to be modified in the future when - # infer_dtype_bydata can recognize array type. - # https://github.com/milvus-io/pymilvus/issues/2165 - elif dtype == DataType.ARRAY: - kwargs = self.metadata_schema[key]["kwargs"] # type: ignore - fields.append( - FieldSchema(name=key, dtype=DataType.ARRAY, **kwargs) - ) - else: - fields.append(FieldSchema(key, dtype)) - - # Create the text field - fields.append( - FieldSchema(self._text_field, DataType.VARCHAR, max_length=65_535) - ) - # Create the primary key field - if self.auto_id: - fields.append( - FieldSchema( - self._primary_field, DataType.INT64, is_primary=True, auto_id=True - ) - ) - else: - fields.append( - FieldSchema( - self._primary_field, - DataType.VARCHAR, - is_primary=True, - auto_id=False, - max_length=65_535, - ) - ) - # Create the vector field, supports binary or float vectors - if self._is_sparse_embedding: - fields.append(FieldSchema(self._vector_field, DataType.SPARSE_FLOAT_VECTOR)) - else: - fields.append( - FieldSchema( - self._vector_field, infer_dtype_bydata(embeddings[0]), dim=dim - ) - ) - - # Create the schema for the collection - schema = CollectionSchema( - fields, - description=self.collection_description, - partition_key_field=self._partition_key_field, - enable_dynamic_field=self.enable_dynamic_field, - ) - - # Create the collection - try: - if self.num_shards is not None: - # Issue with defaults: - # https://github.com/milvus-io/pymilvus/blob/59bf5e811ad56e20946559317fed855330758d9c/pymilvus/client/prepare.py#L82-L85 - self.col = Collection( - name=self.collection_name, - schema=schema, - consistency_level=self.consistency_level, - using=self.alias, - num_shards=self.num_shards, - ) - else: - self.col = Collection( - name=self.collection_name, - schema=schema, - consistency_level=self.consistency_level, - using=self.alias, - ) - # Set the collection properties if they exist - if self.collection_properties is not None: - self.col.set_properties(self.collection_properties) - except MilvusException as e: - logger.error( - "Failed to create collection: %s error: %s", self.collection_name, e - ) - raise e - - def _extract_fields(self) -> None: - """Grab the existing fields from the Collection""" - from pymilvus import Collection - - if isinstance(self.col, Collection): - schema = self.col.schema - for x in schema.fields: - self.fields.append(x.name) - - def _get_index(self) -> Optional[dict[str, Any]]: - """Return the vector index information if it exists""" - from pymilvus import Collection - - if isinstance(self.col, Collection): - for x in self.col.indexes: - if x.field_name == self._vector_field: - return x.to_dict() - return None - - def _create_index(self) -> None: - """Create a index on the collection""" - from pymilvus import Collection, MilvusException - - if isinstance(self.col, Collection) and self._get_index() is None: - try: - # If no index params, use a default HNSW based one - if self.index_params is None: - if self._is_sparse_embedding: - self.index_params = { - "metric_type": "IP", - "index_type": "SPARSE_INVERTED_INDEX", - "params": {"drop_ratio_build": 0.2}, - } - else: - self.index_params = { - "metric_type": "L2", - "index_type": "HNSW", - "params": {"M": 8, "efConstruction": 64}, - } - - try: - self.col.create_index( - self._vector_field, - index_params=self.index_params, - using=self.alias, - ) - - # If default did not work, most likely on Zilliz Cloud - except MilvusException: - # Use AUTOINDEX based index - self.index_params = { - "metric_type": "L2", - "index_type": "AUTOINDEX", - "params": {}, - } - self.col.create_index( - self._vector_field, - index_params=self.index_params, - using=self.alias, - ) - logger.debug( - "Successfully created an index on collection: %s", - self.collection_name, - ) - - except MilvusException as e: - logger.error( - "Failed to create an index on collection: %s", self.collection_name - ) - raise e - - def _create_search_params(self) -> None: - """Generate search params based on the current index type""" - from pymilvus import Collection - - if isinstance(self.col, Collection) and self.search_params is None: - index = self._get_index() - if index is not None: - index_type: str = index["index_param"]["index_type"] - metric_type: str = index["index_param"]["metric_type"] - self.search_params = self.default_search_params[index_type] - self.search_params["metric_type"] = metric_type - - def _load( - self, - partition_names: Optional[list] = None, - replica_number: int = 1, - timeout: Optional[float] = None, - ) -> None: - """Load the collection if available.""" - from pymilvus import Collection, utility - from pymilvus.client.types import LoadState # type: ignore - - timeout = self.timeout or timeout - if ( - isinstance(self.col, Collection) - and self._get_index() is not None - and utility.load_state(self.collection_name, using=self.alias) - == LoadState.NotLoad - ): - self.col.load( - partition_names=partition_names, - replica_number=replica_number, - timeout=timeout, - ) - - def add_texts( - self, - texts: Iterable[str], - metadatas: Optional[List[dict]] = None, - timeout: Optional[float] = None, - batch_size: int = 1000, - *, - ids: Optional[List[str]] = None, - **kwargs: Any, - ) -> List[str]: - """Insert text data into Milvus. - - Inserting data when the collection has not be made yet will result - in creating a new Collection. The data of the first entity decides - the schema of the new collection, the dim is extracted from the first - embedding and the columns are decided by the first metadata dict. - Metadata keys will need to be present for all inserted values. At - the moment there is no None equivalent in Milvus. - - Args: - texts (Iterable[str]): The texts to embed, it is assumed - that they all fit in memory. - metadatas (Optional[List[dict]]): Metadata dicts attached to each of - the texts. Defaults to None. - should be less than 65535 bytes. Required and work when auto_id is False. - timeout (Optional[float]): Timeout for each batch insert. Defaults - to None. - batch_size (int, optional): Batch size to use for insertion. - Defaults to 1000. - ids (Optional[List[str]]): List of text ids. The length of each item - - Raises: - MilvusException: Failure to add texts - - Returns: - List[str]: The resulting keys for each inserted element. - """ - from pymilvus import Collection, MilvusException - - texts = list(texts) - if not self.auto_id: - assert isinstance(ids, list), ( - "A list of valid ids are required when auto_id is False. " - "You can set `auto_id` to True in this Milvus instance to generate " - "ids automatically, or specify string-type ids for each text." - ) - assert len(set(ids)) == len( - texts - ), "Different lengths of texts and unique ids are provided." - assert all(isinstance(x, str) for x in ids), "All ids should be strings." - assert all( - len(x.encode()) <= 65_535 for x in ids - ), "Each id should be a string less than 65535 bytes." - - else: - if ids is not None: - logger.warning( - "The ids parameter is ignored when auto_id is True. " - "The ids will be generated automatically." - ) - - try: - embeddings: list = self.embedding_func.embed_documents(texts) - except NotImplementedError: - embeddings = [self.embedding_func.embed_query(x) for x in texts] - - if len(embeddings) == 0: - logger.debug("Nothing to insert, skipping.") - return [] - - # If the collection hasn't been initialized yet, perform all steps to do so - if not isinstance(self.col, Collection): - kwargs = {"embeddings": embeddings, "metadatas": metadatas} - if self.partition_names: - kwargs["partition_names"] = self.partition_names - if self.replica_number: - kwargs["replica_number"] = self.replica_number - if self.timeout: - kwargs["timeout"] = self.timeout - self._init(**kwargs) - - insert_list: list[dict] = [] - - assert len(texts) == len( - embeddings - ), "Mismatched lengths of texts and embeddings." - if metadatas is not None: - assert len(texts) == len( - metadatas - ), "Mismatched lengths of texts and metadatas." - - for i, text, embedding in zip(range(len(texts)), texts, embeddings): - entity_dict = {} - metadata = metadatas[i] if metadatas else {} - if not self.auto_id: - entity_dict[self._primary_field] = ids[i] # type: ignore[index] - - entity_dict[self._text_field] = text - entity_dict[self._vector_field] = embedding - - if self._metadata_field and not self.enable_dynamic_field: - entity_dict[self._metadata_field] = metadata - else: - for key, value in metadata.items(): - # if not enable_dynamic_field, skip fields not in the collection. - if not self.enable_dynamic_field and key not in self.fields: - continue - # If enable_dynamic_field, all fields are allowed. - entity_dict[key] = value - - insert_list.append(entity_dict) - - # Total insert count - total_count = len(insert_list) - - pks: list[str] = [] - - assert isinstance(self.col, Collection) - for i in range(0, total_count, batch_size): - # Grab end index - end = min(i + batch_size, total_count) - batch_insert_list = insert_list[i:end] - # Insert into the collection. - try: - res: Collection - timeout = self.timeout or timeout - res = self.col.insert(batch_insert_list, timeout=timeout, **kwargs) - pks.extend(res.primary_keys) - except MilvusException as e: - logger.error( - "Failed to insert batch starting at entity: %s/%s", i, total_count - ) - raise e - return pks - - def _collection_search( - self, - embedding: List[float] | Dict[int, float], - k: int = 4, - param: Optional[dict] = None, - expr: Optional[str] = None, - timeout: Optional[float] = None, - **kwargs: Any, - ) -> "pymilvus.client.abstract.SearchResult | None": # type: ignore[name-defined] # noqa: F821 - """Perform a search on an embedding and return milvus search results. - - For more information about the search parameters, take a look at the pymilvus - documentation found here: - https://milvus.io/api-reference/pymilvus/v2.4.x/ORM/Collection/search.md - - Args: - embedding (List[float] | Dict[int, float]): The embedding vector being - searched. - k (int, optional): The amount of results to return. Defaults to 4. - param (dict): The search params for the specified index. - Defaults to None. - expr (str, optional): Filtering expression. Defaults to None. - timeout (float, optional): How long to wait before timeout error. - Defaults to None. - kwargs: Collection.search() keyword arguments. - - Returns: - pymilvus.client.abstract.SearchResult: Milvus search result. - """ - if self.col is None: - logger.debug("No existing collection to search.") - return None - - if param is None: - param = self.search_params - - # Determine result metadata fields with PK. - if self.enable_dynamic_field: - output_fields = ["*"] - else: - output_fields = self.fields[:] - output_fields.remove(self._vector_field) - timeout = self.timeout or timeout - # Perform the search. - res = self.col.search( - data=[embedding], - anns_field=self._vector_field, - param=param, - limit=k, - expr=expr, - output_fields=output_fields, - timeout=timeout, - **kwargs, - ) - return res - - def similarity_search( - self, - query: str, - k: int = 4, - param: Optional[dict] = None, - expr: Optional[str] = None, - timeout: Optional[float] = None, - **kwargs: Any, - ) -> List[Document]: - """Perform a similarity search against the query string. - - Args: - query (str): The text to search. - k (int, optional): How many results to return. Defaults to 4. - param (dict, optional): The search params for the index type. - Defaults to None. - expr (str, optional): Filtering expression. Defaults to None. - timeout (int, optional): How long to wait before timeout error. - Defaults to None. - kwargs: Collection.search() keyword arguments. - - Returns: - List[Document]: Document results for search. - """ - if self.col is None: - logger.debug("No existing collection to search.") - return [] - timeout = self.timeout or timeout - res = self.similarity_search_with_score( - query=query, k=k, param=param, expr=expr, timeout=timeout, **kwargs - ) - return [doc for doc, _ in res] - - def similarity_search_by_vector( - self, - embedding: List[float], - k: int = 4, - param: Optional[dict] = None, - expr: Optional[str] = None, - timeout: Optional[float] = None, - **kwargs: Any, - ) -> List[Document]: - """Perform a similarity search against the query string. - - Args: - embedding (List[float]): The embedding vector to search. - k (int, optional): How many results to return. Defaults to 4. - param (dict, optional): The search params for the index type. - Defaults to None. - expr (str, optional): Filtering expression. Defaults to None. - timeout (int, optional): How long to wait before timeout error. - Defaults to None. - kwargs: Collection.search() keyword arguments. - - Returns: - List[Document]: Document results for search. - """ - if self.col is None: - logger.debug("No existing collection to search.") - return [] - timeout = self.timeout or timeout - res = self.similarity_search_with_score_by_vector( - embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs - ) - return [doc for doc, _ in res] - - def similarity_search_with_score( - self, - query: str, - k: int = 4, - param: Optional[dict] = None, - expr: Optional[str] = None, - timeout: Optional[float] = None, - **kwargs: Any, - ) -> List[Tuple[Document, float]]: - """Perform a search on a query string and return results with score. - - For more information about the search parameters, take a look at the pymilvus - documentation found here: - https://milvus.io/api-reference/pymilvus/v2.4.x/ORM/Collection/search.md - - Args: - query (str): The text being searched. - k (int, optional): The amount of results to return. Defaults to 4. - param (dict): The search params for the specified index. - Defaults to None. - expr (str, optional): Filtering expression. Defaults to None. - timeout (float, optional): How long to wait before timeout error. - Defaults to None. - kwargs: Collection.search() keyword arguments. - - Returns: - List[float], List[Tuple[Document, any, any]]: - """ - if self.col is None: - logger.debug("No existing collection to search.") - return [] - - # Embed the query text. - embedding = self.embedding_func.embed_query(query) - timeout = self.timeout or timeout - res = self.similarity_search_with_score_by_vector( - embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs - ) - return res - - def similarity_search_with_score_by_vector( - self, - embedding: List[float] | Dict[int, float], - k: int = 4, - param: Optional[dict] = None, - expr: Optional[str] = None, - timeout: Optional[float] = None, - **kwargs: Any, - ) -> List[Tuple[Document, float]]: - """Perform a search on an embedding and return results with score. - - For more information about the search parameters, take a look at the pymilvus - documentation found here: - https://milvus.io/api-reference/pymilvus/v2.4.x/ORM/Collection/search.md - - Args: - embedding (List[float] | Dict[int, float]): The embedding vector being - searched. - k (int, optional): The amount of results to return. Defaults to 4. - param (dict): The search params for the specified index. - Defaults to None. - expr (str, optional): Filtering expression. Defaults to None. - timeout (float, optional): How long to wait before timeout error. - Defaults to None. - kwargs: Collection.search() keyword arguments. - - Returns: - List[Tuple[Document, float]]: Result doc and score. - """ - col_search_res = self._collection_search( - embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs - ) - if col_search_res is None: - return [] - ret = [] - for result in col_search_res[0]: - data = {x: result.entity.get(x) for x in result.entity.fields} - doc = self._parse_document(data) - pair = (doc, result.score) - ret.append(pair) - - return ret - - def max_marginal_relevance_search( - self, - query: str, - k: int = 4, - fetch_k: int = 20, - lambda_mult: float = 0.5, - param: Optional[dict] = None, - expr: Optional[str] = None, - timeout: Optional[float] = None, - **kwargs: Any, - ) -> List[Document]: - """Perform a search and return results that are reordered by MMR. - - Args: - query (str): The text being searched. - k (int, optional): How many results to give. Defaults to 4. - fetch_k (int, optional): Total results to select k from. - Defaults to 20. - lambda_mult: Number between 0 and 1 that determines the degree - of diversity among the results with 0 corresponding - to maximum diversity and 1 to minimum diversity. - Defaults to 0.5 - param (dict, optional): The search params for the specified index. - Defaults to None. - expr (str, optional): Filtering expression. Defaults to None. - timeout (float, optional): How long to wait before timeout error. - Defaults to None. - kwargs: Collection.search() keyword arguments. - - - Returns: - List[Document]: Document results for search. - """ - if self.col is None: - logger.debug("No existing collection to search.") - return [] - - embedding = self.embedding_func.embed_query(query) - timeout = self.timeout or timeout - return self.max_marginal_relevance_search_by_vector( - embedding=embedding, - k=k, - fetch_k=fetch_k, - lambda_mult=lambda_mult, - param=param, - expr=expr, - timeout=timeout, - **kwargs, - ) - - def max_marginal_relevance_search_by_vector( - self, - embedding: list[float] | dict[int, float], - k: int = 4, - fetch_k: int = 20, - lambda_mult: float = 0.5, - param: Optional[dict] = None, - expr: Optional[str] = None, - timeout: Optional[float] = None, - **kwargs: Any, - ) -> List[Document]: - """Perform a search and return results that are reordered by MMR. - - Args: - embedding (list[float] | dict[int, float]): The embedding vector being - searched. - k (int, optional): How many results to give. Defaults to 4. - fetch_k (int, optional): Total results to select k from. - Defaults to 20. - lambda_mult: Number between 0 and 1 that determines the degree - of diversity among the results with 0 corresponding - to maximum diversity and 1 to minimum diversity. - Defaults to 0.5 - param (dict, optional): The search params for the specified index. - Defaults to None. - expr (str, optional): Filtering expression. Defaults to None. - timeout (float, optional): How long to wait before timeout error. - Defaults to None. - kwargs: Collection.search() keyword arguments. - - Returns: - List[Document]: Document results for search. - """ - col_search_res = self._collection_search( - embedding=embedding, - k=fetch_k, - param=param, - expr=expr, - timeout=timeout, - **kwargs, - ) - if col_search_res is None: - return [] - ids = [] - documents = [] - scores = [] - for result in col_search_res[0]: - data = {x: result.entity.get(x) for x in result.entity.fields} - doc = self._parse_document(data) - documents.append(doc) - scores.append(result.score) - ids.append(result.id) - - vectors = self.col.query( # type: ignore[union-attr] - expr=f"{self._primary_field} in {ids}", - output_fields=[self._primary_field, self._vector_field], - timeout=timeout, - ) - # Reorganize the results from query to match search order. - vectors = {x[self._primary_field]: x[self._vector_field] for x in vectors} - - ordered_result_embeddings = [vectors[x] for x in ids] - - # Get the new order of results. - new_ordering = maximal_marginal_relevance( - np.array(embedding), ordered_result_embeddings, k=k, lambda_mult=lambda_mult - ) - - # Reorder the values and return. - ret = [] - for x in new_ordering: - # Function can return -1 index - if x == -1: - break - else: - ret.append(documents[x]) - return ret - - def delete( # type: ignore[no-untyped-def] - self, ids: Optional[List[str]] = None, expr: Optional[str] = None, **kwargs: str - ): - """Delete by vector ID or boolean expression. - Refer to [Milvus documentation](https://milvus.io/docs/delete_data.md) - for notes and examples of expressions. - - Args: - ids: List of ids to delete. - expr: Boolean expression that specifies the entities to delete. - kwargs: Other parameters in Milvus delete api. - """ - if isinstance(ids, list) and len(ids) > 0: - if expr is not None: - logger.warning( - "Both ids and expr are provided. " "Ignore expr and delete by ids." - ) - expr = f"{self._primary_field} in {ids}" - else: - assert isinstance( - expr, str - ), "Either ids list or expr string must be provided." - return self.col.delete(expr=expr, **kwargs) # type: ignore[union-attr] - - @classmethod - def from_texts( - cls, - texts: List[str], - embedding: Union[Embeddings, BaseSparseEmbedding], # type: ignore - metadatas: Optional[List[dict]] = None, - collection_name: str = "LangChainCollection", - connection_args: dict[str, Any] = DEFAULT_MILVUS_CONNECTION, - consistency_level: str = "Session", - index_params: Optional[dict] = None, - search_params: Optional[dict] = None, - drop_old: bool = False, - *, - ids: Optional[List[str]] = None, - **kwargs: Any, - ) -> Milvus: - """Create a Milvus collection, indexes it with HNSW, and insert data. - - Args: - texts (List[str]): Text data. - embedding (Union[Embeddings, BaseSparseEmbedding]): Embedding function. - metadatas (Optional[List[dict]]): Metadata for each text if it exists. - Defaults to None. - collection_name (str, optional): Collection name to use. Defaults to - "LangChainCollection". - connection_args (dict[str, Any], optional): Connection args to use. Defaults - to DEFAULT_MILVUS_CONNECTION. - consistency_level (str, optional): Which consistency level to use. Defaults - to "Session". - index_params (Optional[dict], optional): Which index_params to use. Defaults - to None. - search_params (Optional[dict], optional): Which search params to use. - Defaults to None. - drop_old (Optional[bool], optional): Whether to drop the collection with - that name if it exists. Defaults to False. - ids (Optional[List[str]]): List of text ids. Defaults to None. - - Returns: - Milvus: Milvus Vector Store - """ - if isinstance(ids, list) and len(ids) > 0: - auto_id = False - else: - auto_id = True - - vector_db = cls( - embedding_function=embedding, - collection_name=collection_name, - connection_args=connection_args, - consistency_level=consistency_level, - index_params=index_params, - search_params=search_params, - drop_old=drop_old, - auto_id=auto_id, - **kwargs, - ) - vector_db.add_texts(texts=texts, metadatas=metadatas, ids=ids) - return vector_db - - def _parse_document(self, data: dict) -> Document: - if self._vector_field in data: - data.pop(self._vector_field) - return Document( - page_content=data.pop(self._text_field), - metadata=data.pop(self._metadata_field) if self._metadata_field else data, - ) - - def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]: - """Run more documents through the embeddings and add to the vectorstore. - - Args: - documents: Documents to add to the vectorstore. - - Returns: - List of IDs of the added texts. - """ - # TODO: Handle the case where the user doesn't provide ids on the Collection - texts = [doc.page_content for doc in documents] - metadatas = [doc.metadata for doc in documents] - return self.add_texts(texts, metadatas, **kwargs) - - async def aadd_documents( - self, documents: List[Document], **kwargs: Any - ) -> List[str]: - """Run more documents through the embeddings and add to the vectorstore. - - Args: - documents: Documents to add to the vectorstore. - - Returns: - List of IDs of the added texts. - """ - texts = [doc.page_content for doc in documents] - metadatas = [doc.metadata for doc in documents] - return await self.aadd_texts(texts, metadatas, **kwargs) - - def get_pks(self, expr: str, **kwargs: Any) -> List[int] | None: - """Get primary keys with expression - - Args: - expr: Expression - E.g: "id in [1, 2]", or "title LIKE 'Abc%'" - - Returns: - List[int]: List of IDs (Primary Keys) - """ - - from pymilvus import MilvusException - - if self.col is None: - logger.debug("No existing collection to get pk.") - return None - - try: - query_result = self.col.query( - expr=expr, output_fields=[self._primary_field] - ) - except MilvusException as exc: - logger.error("Failed to get ids: %s error: %s", self.collection_name, exc) - raise exc - pks = [item.get(self._primary_field) for item in query_result] - return pks - - def upsert( # type: ignore - self, - ids: Optional[List[str]] = None, - documents: List[Document] | None = None, - **kwargs: Any, - ) -> List[str] | None: - """Update/Insert documents to the vectorstore. - - Args: - ids: IDs to update - Let's call get_pks to get ids with expression \n - documents (List[Document]): Documents to add to the vectorstore. - - Returns: - List[str]: IDs of the added texts. - """ - - from pymilvus import MilvusException - - if documents is None or len(documents) == 0: - logger.debug("No documents to upsert.") - return None - - if ids is not None and len(ids): - try: - self.delete(ids=ids) - except MilvusException: - pass - try: - return self.add_documents(documents=documents, **kwargs) - except MilvusException as exc: - logger.error( - "Failed to upsert entities: %s error: %s", self.collection_name, exc - ) - raise exc diff --git a/libs/partners/milvus/langchain_milvus/vectorstores/zilliz.py b/libs/partners/milvus/langchain_milvus/vectorstores/zilliz.py deleted file mode 100644 index 976651dbde..0000000000 --- a/libs/partners/milvus/langchain_milvus/vectorstores/zilliz.py +++ /dev/null @@ -1,197 +0,0 @@ -from __future__ import annotations - -import logging -from typing import Any, Dict, List, Optional, Union - -from langchain_core.embeddings import Embeddings - -from langchain_milvus.utils.sparse import BaseSparseEmbedding -from langchain_milvus.vectorstores.milvus import Milvus - -logger = logging.getLogger(__name__) - - -class Zilliz(Milvus): - """`Zilliz` vector store. - - You need to have `pymilvus` installed and a - running Zilliz database. - - See the following documentation for how to run a Zilliz instance: - https://docs.zilliz.com/docs/create-cluster - - - IF USING L2/IP metric IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA. - - Args: - embedding_function (Embeddings): Function used to embed the text. - collection_name (str): Which Zilliz collection to use. Defaults to - "LangChainCollection". - connection_args (Optional[dict[str, any]]): The connection args used for - this class comes in the form of a dict. - consistency_level (str): The consistency level to use for a collection. - Defaults to "Session". - index_params (Optional[dict]): Which index params to use. Defaults to - HNSW/AUTOINDEX depending on service. - search_params (Optional[dict]): Which search params to use. Defaults to - default of index. - drop_old (Optional[bool]): Whether to drop the current collection. Defaults - to False. - auto_id (bool): Whether to enable auto id for primary key. Defaults to False. - If False, you needs to provide text ids (string less than 65535 bytes). - If True, Milvus will generate unique integers as primary keys. - - The connection args used for this class comes in the form of a dict, - here are a few of the options: - address (str): The actual address of Zilliz - instance. Example address: "localhost:19530" - uri (str): The uri of Zilliz instance. Example uri: - "https://in03-ba4234asae.api.gcp-us-west1.zillizcloud.com", - host (str): The host of Zilliz instance. Default at "localhost", - PyMilvus will fill in the default host if only port is provided. - port (str/int): The port of Zilliz instance. Default at 19530, PyMilvus - will fill in the default port if only host is provided. - user (str): Use which user to connect to Zilliz instance. If user and - password are provided, we will add related header in every RPC call. - password (str): Required when user is provided. The password - corresponding to the user. - token (str): API key, for serverless clusters which can be used as - replacements for user and password. - secure (bool): Default is false. If set to true, tls will be enabled. - client_key_path (str): If use tls two-way authentication, need to - write the client.key path. - client_pem_path (str): If use tls two-way authentication, need to - write the client.pem path. - ca_pem_path (str): If use tls two-way authentication, need to write - the ca.pem path. - server_pem_path (str): If use tls one-way authentication, need to - write the server.pem path. - server_name (str): If use tls, need to write the common name. - - Example: - .. code-block:: python - - from langchain_community.vectorstores import Zilliz - from langchain_community.embeddings import OpenAIEmbeddings - - embedding = OpenAIEmbeddings() - # Connect to a Zilliz instance - milvus_store = Milvus( - embedding_function = embedding, - collection_name = "LangChainCollection", - connection_args = { - "uri": "https://in03-ba4234asae.api.gcp-us-west1.zillizcloud.com", - "user": "temp", - "password": "temp", - "token": "temp", # API key as replacements for user and password - "secure": True - } - drop_old: True, - ) - - Raises: - ValueError: If the pymilvus python package is not installed. - """ - - def _create_index(self) -> None: - """Create a index on the collection""" - from pymilvus import Collection, MilvusException - - if isinstance(self.col, Collection) and self._get_index() is None: - try: - # If no index params, use a default AutoIndex based one - if self.index_params is None: - self.index_params = { - "metric_type": "L2", - "index_type": "AUTOINDEX", - "params": {}, - } - - try: - self.col.create_index( - self._vector_field, - index_params=self.index_params, - using=self.alias, - ) - - # If default did not work, most likely Milvus self-hosted - except MilvusException: - # Use HNSW based index - self.index_params = { - "metric_type": "L2", - "index_type": "HNSW", - "params": {"M": 8, "efConstruction": 64}, - } - self.col.create_index( - self._vector_field, - index_params=self.index_params, - using=self.alias, - ) - logger.debug( - "Successfully created an index on collection: %s", - self.collection_name, - ) - - except MilvusException as e: - logger.error( - "Failed to create an index on collection: %s", self.collection_name - ) - raise e - - @classmethod - def from_texts( - cls, - texts: List[str], - embedding: Union[Embeddings, BaseSparseEmbedding], - metadatas: Optional[List[dict]] = None, - collection_name: str = "LangChainCollection", - connection_args: Optional[Dict[str, Any]] = None, - consistency_level: str = "Session", - index_params: Optional[dict] = None, - search_params: Optional[dict] = None, - drop_old: bool = False, - *, - ids: Optional[List[str]] = None, - auto_id: bool = False, - **kwargs: Any, - ) -> Zilliz: - """Create a Zilliz collection, indexes it with HNSW, and insert data. - - Args: - texts (List[str]): Text data. - embedding (Embeddings): Embedding function. - metadatas (Optional[List[dict]]): Metadata for each text if it exists. - Defaults to None. - collection_name (str, optional): Collection name to use. Defaults to - "LangChainCollection". - connection_args (dict[str, Any], optional): Connection args to use. Defaults - to DEFAULT_MILVUS_CONNECTION. - consistency_level (str, optional): Which consistency level to use. Defaults - to "Session". - index_params (Optional[dict], optional): Which index_params to use. - Defaults to None. - search_params (Optional[dict], optional): Which search params to use. - Defaults to None. - drop_old (Optional[bool], optional): Whether to drop the collection with - that name if it exists. Defaults to False. - ids (Optional[List[str]]): List of text ids. - auto_id (bool): Whether to enable auto id for primary key. Defaults to - False. If False, you needs to provide text ids (string less than 65535 - bytes). 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-[metadata] -lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "21f5bd85bbf28a2ff76b016dd527d8358ecf0a57905d5c8e54c7c66ac68a018e" diff --git a/libs/partners/milvus/pyproject.toml b/libs/partners/milvus/pyproject.toml deleted file mode 100644 index bfa8fcbea5..0000000000 --- a/libs/partners/milvus/pyproject.toml +++ /dev/null @@ -1,123 +0,0 @@ -[build-system] -requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" - -[tool.poetry] -name = "langchain-milvus" -version = "0.1.5" -description = "An integration package connecting Milvus and LangChain" -authors = [] -readme = "README.md" -repository = "https://github.com/langchain-ai/langchain" -license = "MIT" - -[tool.ruff] -select = ["E", "F", "I", "T201"] - -[tool.mypy] -disallow_untyped_defs = "True" -[[tool.mypy.overrides]] -module = ["pymilvus"] -ignore_missing_imports = "True" - -[tool.poetry.urls] -"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/milvus" -"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-milvus%3D%3D0%22&expanded=true" - -[tool.poetry.dependencies] -python = ">=3.8.1,<4.0" -pymilvus = "^2.4.3" - -[[tool.poetry.dependencies.langchain-core]] -version = ">=0.2.38,<0.4" -python = ">=3.9" - -[[tool.poetry.dependencies.langchain-core]] -version = ">=0.2.38,<0.3" -python = "<3.9" - -[[tool.poetry.dependencies.scipy]] -version = "^1.7" -python = "<3.12" - -[[tool.poetry.dependencies.scipy]] -version = "^1.9" -python = ">=3.12" - -[tool.coverage.run] -omit = ["tests/*"] - -[tool.pytest.ini_options] -addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" -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" - -[tool.poetry.group.test] -optional = true - -[tool.poetry.group.codespell] -optional = true - -[tool.poetry.group.test_integration] -optional = true - -[tool.poetry.group.lint] -optional = true - -[tool.poetry.group.dev] -optional = true - -[tool.poetry.group.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" -milvus_model = "^0.2.0" - -[[tool.poetry.group.test.dependencies.langchain-core]] -path = "../../core" -develop = true -python = ">=3.9" - -[[tool.poetry.group.test.dependencies.langchain-core]] -version = ">=0.2.38,<0.3" -python = "<3.9" - -[tool.poetry.group.codespell.dependencies] -codespell = "^2.2.0" - -[tool.poetry.group.test_integration.dependencies] -milvus_model = "^0.2.0" - -[tool.poetry.group.lint.dependencies] -ruff = "^0.1.5" - -[tool.poetry.group.typing.dependencies] -mypy = "^0.991" -types-requests = "^2" -simsimd = "^5.0.0" - -[[tool.poetry.group.typing.dependencies.langchain-core]] -path = "../../core" -develop = true -python = ">=3.9" - -[[tool.poetry.group.typing.dependencies.langchain-core]] -version = ">=0.2.38,<0.3" -python = "<3.9" - -[tool.poetry.group.dev.dependencies] -[[tool.poetry.group.dev.dependencies.langchain-core]] -path = "../../core" -develop = true -python = ">=3.9" - -[[tool.poetry.group.dev.dependencies.langchain-core]] -version = ">=0.2.38,<0.3" -python = "<3.9" diff --git a/libs/partners/milvus/scripts/check_imports.py b/libs/partners/milvus/scripts/check_imports.py deleted file mode 100644 index 58a460c149..0000000000 --- a/libs/partners/milvus/scripts/check_imports.py +++ /dev/null @@ -1,17 +0,0 @@ -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_failure = True - print(file) # noqa: T201 - traceback.print_exc() - print() # noqa: T201 - - sys.exit(1 if has_failure else 0) diff --git a/libs/partners/milvus/scripts/lint_imports.sh b/libs/partners/milvus/scripts/lint_imports.sh deleted file mode 100755 index 695613c7ba..0000000000 --- a/libs/partners/milvus/scripts/lint_imports.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/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/milvus/tests/__init__.py b/libs/partners/milvus/tests/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/libs/partners/milvus/tests/integration_tests/__init__.py b/libs/partners/milvus/tests/integration_tests/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/libs/partners/milvus/tests/integration_tests/retrievers/__init__.py b/libs/partners/milvus/tests/integration_tests/retrievers/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/libs/partners/milvus/tests/integration_tests/test_compile.py b/libs/partners/milvus/tests/integration_tests/test_compile.py deleted file mode 100644 index 33ecccdfa0..0000000000 --- a/libs/partners/milvus/tests/integration_tests/test_compile.py +++ /dev/null @@ -1,7 +0,0 @@ -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/milvus/tests/integration_tests/utils.py b/libs/partners/milvus/tests/integration_tests/utils.py deleted file mode 100644 index f3ef87d2f2..0000000000 --- a/libs/partners/milvus/tests/integration_tests/utils.py +++ /dev/null @@ -1,40 +0,0 @@ -from typing import List - -from langchain_core.documents import Document -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) - - -def assert_docs_equal_without_pk( - docs1: List[Document], docs2: List[Document], pk_field: str = "pk" -) -> None: - """Assert two lists of Documents are equal, ignoring the primary key field.""" - assert len(docs1) == len(docs2) - for doc1, doc2 in zip(docs1, docs2): - assert doc1.page_content == doc2.page_content - doc1.metadata.pop(pk_field, None) - doc2.metadata.pop(pk_field, None) - assert doc1.metadata == doc2.metadata diff --git a/libs/partners/milvus/tests/integration_tests/vectorstores/__init__.py b/libs/partners/milvus/tests/integration_tests/vectorstores/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/libs/partners/milvus/tests/integration_tests/vectorstores/test_milvus.py b/libs/partners/milvus/tests/integration_tests/vectorstores/test_milvus.py deleted file mode 100644 index fffb0bb85d..0000000000 --- a/libs/partners/milvus/tests/integration_tests/vectorstores/test_milvus.py +++ /dev/null @@ -1,430 +0,0 @@ -"""Test Milvus functionality.""" - -import tempfile -from typing import Any, List, Optional - -import pytest -from langchain_core.documents import Document - -from langchain_milvus.utils.sparse import BM25SparseEmbedding -from langchain_milvus.vectorstores import Milvus -from tests.integration_tests.utils import ( - FakeEmbeddings, - assert_docs_equal_without_pk, - fake_texts, -) - - -# -# To run this test properly, please start a Milvus server with the following command: -# -# ```shell -# wget https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh -# bash standalone_embed.sh start -# ``` -# -# Here is the reference: -# https://milvus.io/docs/install_standalone-docker.md -# -@pytest.fixture -def temp_milvus_db() -> Any: - with tempfile.NamedTemporaryFile(suffix=".db") as temp_file: - yield temp_file.name - - -def _milvus_from_texts( - metadatas: Optional[List[dict]] = None, - ids: Optional[List[str]] = None, - drop: bool = True, - db_path: str = "./milvus_demo.db", - **kwargs: Any, -) -> Milvus: - return Milvus.from_texts( - fake_texts, - FakeEmbeddings(), - metadatas=metadatas, - ids=ids, - # connection_args={"uri": "http://127.0.0.1:19530"}, - connection_args={"uri": db_path}, - drop_old=drop, - consistency_level="Strong", - **kwargs, - ) - - -def _get_pks(expr: str, docsearch: Milvus) -> List[Any]: - return docsearch.get_pks(expr) # type: ignore[return-value] - - -def test_milvus(temp_milvus_db: Any) -> None: - """Test end to end construction and search.""" - docsearch = _milvus_from_texts(db_path=temp_milvus_db) - output = docsearch.similarity_search("foo", k=1) - assert_docs_equal_without_pk(output, [Document(page_content="foo")]) - - -def test_milvus_vector_search(temp_milvus_db: Any) -> None: - """Test end to end construction and search by vector.""" - docsearch = _milvus_from_texts(db_path=temp_milvus_db) - output = docsearch.similarity_search_by_vector( - FakeEmbeddings().embed_query("foo"), k=1 - ) - assert_docs_equal_without_pk(output, [Document(page_content="foo")]) - - -def test_milvus_with_metadata(temp_milvus_db: Any) -> None: - """Test with metadata""" - docsearch = _milvus_from_texts( - metadatas=[{"label": "test"}] * len(fake_texts), db_path=temp_milvus_db - ) - output = docsearch.similarity_search("foo", k=1) - assert_docs_equal_without_pk( - output, [Document(page_content="foo", metadata={"label": "test"})] - ) - - -def test_milvus_with_id(temp_milvus_db: Any) -> None: - """Test with ids""" - ids = ["id_" + str(i) for i in range(len(fake_texts))] - docsearch = _milvus_from_texts(ids=ids, db_path=temp_milvus_db) - output = docsearch.similarity_search("foo", k=1) - assert_docs_equal_without_pk(output, [Document(page_content="foo")]) - - output = docsearch.delete(ids=ids) - assert output.delete_count == len(fake_texts) # type: ignore[attr-defined] - - try: - ids = ["dup_id" for _ in fake_texts] - _milvus_from_texts(ids=ids, db_path=temp_milvus_db) - except Exception as e: - assert isinstance(e, AssertionError) - - -def test_milvus_with_score(temp_milvus_db: Any) -> None: - """Test end to end construction and search with scores and IDs.""" - texts = ["foo", "bar", "baz"] - metadatas = [{"page": i} for i in range(len(texts))] - docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) - output = docsearch.similarity_search_with_score("foo", k=3) - docs = [o[0] for o in output] - scores = [o[1] for o in output] - assert_docs_equal_without_pk( - docs, - [ - Document(page_content="foo", metadata={"page": 0}), - Document(page_content="bar", metadata={"page": 1}), - Document(page_content="baz", metadata={"page": 2}), - ], - ) - assert scores[0] < scores[1] < scores[2] - - -def test_milvus_max_marginal_relevance_search(temp_milvus_db: Any) -> None: - """Test end to end construction and MRR search.""" - texts = ["foo", "bar", "baz"] - metadatas = [{"page": i} for i in range(len(texts))] - docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) - output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3) - assert_docs_equal_without_pk( - output, - [ - Document(page_content="foo", metadata={"page": 0}), - Document(page_content="bar", metadata={"page": 1}), - ], - ) - - -def test_milvus_max_marginal_relevance_search_with_dynamic_field( - temp_milvus_db: Any, -) -> None: - """Test end to end construction and MRR search with enabling dynamic field.""" - texts = ["foo", "bar", "baz"] - metadatas = [{"page": i} for i in range(len(texts))] - docsearch = _milvus_from_texts( - metadatas=metadatas, enable_dynamic_field=True, db_path=temp_milvus_db - ) - output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3) - assert_docs_equal_without_pk( - output, - [ - Document(page_content="foo", metadata={"page": 0}), - Document(page_content="bar", metadata={"page": 1}), - ], - ) - - -def test_milvus_add_extra(temp_milvus_db: Any) -> None: - """Test end to end construction and MRR search.""" - texts = ["foo", "bar", "baz"] - metadatas = [{"page": i} for i in range(len(texts))] - docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) - - docsearch.add_texts(texts, metadatas) - - output = docsearch.similarity_search("foo", k=10) - assert len(output) == 6 - - -def test_milvus_no_drop(temp_milvus_db: Any) -> None: - """Test construction without dropping old data.""" - texts = ["foo", "bar", "baz"] - metadatas = [{"page": i} for i in range(len(texts))] - docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) - del docsearch - - docsearch = _milvus_from_texts( - metadatas=metadatas, drop=False, db_path=temp_milvus_db - ) - - output = docsearch.similarity_search("foo", k=10) - assert len(output) == 6 - - -def test_milvus_get_pks(temp_milvus_db: Any) -> None: - """Test end to end construction and get pks with expr""" - texts = ["foo", "bar", "baz"] - metadatas = [{"id": i} for i in range(len(texts))] - docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) - expr = "id in [1,2]" - output = _get_pks(expr, docsearch) - assert len(output) == 2 - - -def test_milvus_delete_entities(temp_milvus_db: Any) -> None: - """Test end to end construction and delete entities""" - texts = ["foo", "bar", "baz"] - metadatas = [{"id": i} for i in range(len(texts))] - docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) - expr = "id in [1,2]" - pks = _get_pks(expr, docsearch) - result = docsearch.delete(pks) - assert result.delete_count == 2 # type: ignore[attr-defined] - - -def test_milvus_upsert_entities(temp_milvus_db: Any) -> None: - """Test end to end construction and upsert entities""" - texts = ["foo", "bar", "baz"] - metadatas = [{"id": i} for i in range(len(texts))] - docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) - expr = "id in [1,2]" - pks = _get_pks(expr, docsearch) - documents = [ - Document(page_content="test_1", metadata={"id": 1}), - Document(page_content="test_2", metadata={"id": 3}), - ] - ids = docsearch.upsert(pks, documents) - assert len(ids) == 2 # type: ignore[arg-type] - - -def test_milvus_enable_dynamic_field(temp_milvus_db: Any) -> None: - """Test end to end construction and enable dynamic field""" - texts = ["foo", "bar", "baz"] - metadatas = [{"id": i} for i in range(len(texts))] - docsearch = _milvus_from_texts( - metadatas=metadatas, enable_dynamic_field=True, db_path=temp_milvus_db - ) - output = docsearch.similarity_search("foo", k=10) - assert len(output) == 3 - - # When enable dynamic field, any new field data will be added to the collection. - new_metadatas = [{"id_new": i} for i in range(len(texts))] - docsearch.add_texts(texts, new_metadatas) - - output = docsearch.similarity_search("foo", k=10) - assert len(output) == 6 - - assert set(docsearch.fields) == { - docsearch._primary_field, - docsearch._text_field, - docsearch._vector_field, - } - - -def test_milvus_disable_dynamic_field(temp_milvus_db: Any) -> None: - """Test end to end construction and disable dynamic field""" - texts = ["foo", "bar", "baz"] - metadatas = [{"id": i} for i in range(len(texts))] - docsearch = _milvus_from_texts( - metadatas=metadatas, enable_dynamic_field=False, db_path=temp_milvus_db - ) - output = docsearch.similarity_search("foo", k=10) - assert len(output) == 3 - # ["pk", "text", "vector", "id"] - assert set(docsearch.fields) == { - docsearch._primary_field, - docsearch._text_field, - docsearch._vector_field, - "id", - } - - # Try to add new fields "id_new", but since dynamic field is disabled, - # all fields in the collection is specified as ["pk", "text", "vector", "id"], - # new field information "id_new" will not be added. - new_metadatas = [{"id": i, "id_new": i} for i in range(len(texts))] - docsearch.add_texts(texts, new_metadatas) - output = docsearch.similarity_search("foo", k=10) - assert len(output) == 6 - for doc in output: - assert set(doc.metadata.keys()) == {"id", "pk"} # `id_new` is not added. - - # When disable dynamic field, - # missing data of the created fields "id", will raise an exception. - with pytest.raises(Exception): - new_metadatas = [{"id_new": i} for i in range(len(texts))] - docsearch.add_texts(texts, new_metadatas) - - -def test_milvus_metadata_field(temp_milvus_db: Any) -> None: - """Test end to end construction and use metadata field""" - texts = ["foo", "bar", "baz"] - metadatas = [{"id": i} for i in range(len(texts))] - docsearch = _milvus_from_texts( - metadatas=metadatas, metadata_field="metadata", db_path=temp_milvus_db - ) - output = docsearch.similarity_search("foo", k=10) - assert len(output) == 3 - - new_metadatas = [{"id_new": i} for i in range(len(texts))] - docsearch.add_texts(texts, new_metadatas) - - output = docsearch.similarity_search("foo", k=10) - assert len(output) == 6 - - assert set(docsearch.fields) == { - docsearch._primary_field, - docsearch._text_field, - docsearch._vector_field, - docsearch._metadata_field, - } - - -def test_milvus_enable_dynamic_field_with_partition_key(temp_milvus_db: Any) -> None: - """ - Test end to end construction and enable dynamic field - with partition_key_field - """ - texts = ["foo", "bar", "baz"] - metadatas = [{"id": i, "namespace": f"name_{i}"} for i in range(len(texts))] - - docsearch = _milvus_from_texts( - metadatas=metadatas, - enable_dynamic_field=True, - partition_key_field="namespace", - db_path=temp_milvus_db, - ) - - # filter on a single namespace - output = docsearch.similarity_search("foo", k=10, expr="namespace == 'name_2'") - assert len(output) == 1 - - # without namespace filter - output = docsearch.similarity_search("foo", k=10) - assert len(output) == 3 - - assert set(docsearch.fields) == { - docsearch._primary_field, - docsearch._text_field, - docsearch._vector_field, - docsearch._partition_key_field, - } - - -def test_milvus_sparse_embeddings() -> None: - texts = [ - "In 'The Clockwork Kingdom' by Augusta Wynter, a brilliant inventor discovers " - "a hidden world of clockwork machines and ancient magic, where a rebellion is " - "brewing against the tyrannical ruler of the land.", - "In 'The Phantom Pilgrim' by Rowan Welles, a charismatic smuggler is hired by " - "a mysterious organization to transport a valuable artifact across a war-torn " - "continent, but soon finds themselves pursued by assassins and rival factions.", - "In 'The Dreamwalker's Journey' by Lyra Snow, a young dreamwalker discovers " - "she has the ability to enter people's dreams, but soon finds herself trapped " - "in a surreal world of nightmares and illusions, where the boundaries between " - "reality and fantasy blur.", - ] - try: - sparse_embedding_func = BM25SparseEmbedding(corpus=texts) - except LookupError: - import nltk # type: ignore[import] - - nltk.download("punkt_tab") - sparse_embedding_func = BM25SparseEmbedding(corpus=texts) - - with tempfile.NamedTemporaryFile(suffix=".db") as temp_db: - docsearch = Milvus.from_texts( - embedding=sparse_embedding_func, - texts=texts, - connection_args={"uri": temp_db.name}, - drop_old=True, - ) - - output = docsearch.similarity_search("Pilgrim", k=1) - assert "Pilgrim" in output[0].page_content - - -def test_milvus_array_field(temp_milvus_db: Any) -> None: - """Manually specify metadata schema, including an array_field. - For more information about array data type and filtering, please refer to - https://milvus.io/docs/array_data_type.md - """ - from pymilvus import DataType - - texts = ["foo", "bar", "baz"] - metadatas = [{"id": i, "array_field": [i, i + 1, i + 2]} for i in range(len(texts))] - - # Manually specify metadata schema, including an array_field. - # If some fields are not specified, Milvus will automatically infer their schemas. - docsearch = _milvus_from_texts( - metadatas=metadatas, - metadata_schema={ - "array_field": { - "dtype": DataType.ARRAY, - "kwargs": {"element_type": DataType.INT64, "max_capacity": 50}, - }, - # "id": { - # "dtype": DataType.INT64, - # } - }, - db_path=temp_milvus_db, - ) - output = docsearch.similarity_search("foo", k=10, expr="array_field[0] < 2") - assert len(output) == 2 - output = docsearch.similarity_search( - "foo", k=10, expr="ARRAY_CONTAINS(array_field, 3)" - ) - assert len(output) == 2 - - # If we use enable_dynamic_field, - # there is no need to manually specify metadata schema. - docsearch = _milvus_from_texts( - enable_dynamic_field=True, - metadatas=metadatas, - db_path=temp_milvus_db, - ) - output = docsearch.similarity_search("foo", k=10, expr="array_field[0] < 2") - assert len(output) == 2 - output = docsearch.similarity_search( - "foo", k=10, expr="ARRAY_CONTAINS(array_field, 3)" - ) - assert len(output) == 2 - - -# if __name__ == "__main__": -# test_milvus() -# test_milvus_vector_search() -# test_milvus_with_metadata() -# test_milvus_with_id() -# test_milvus_with_score() -# test_milvus_max_marginal_relevance_search() -# test_milvus_max_marginal_relevance_search_with_dynamic_field() -# test_milvus_add_extra() -# test_milvus_no_drop() -# test_milvus_get_pks() -# test_milvus_delete_entities() -# test_milvus_upsert_entities() -# test_milvus_enable_dynamic_field() -# test_milvus_disable_dynamic_field() -# test_milvus_metadata_field() -# test_milvus_enable_dynamic_field_with_partition_key() -# test_milvus_sparse_embeddings() -# test_milvus_array_field() diff --git a/libs/partners/milvus/tests/unit_tests/__init__.py b/libs/partners/milvus/tests/unit_tests/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/libs/partners/milvus/tests/unit_tests/retrievers/__init__.py b/libs/partners/milvus/tests/unit_tests/retrievers/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/libs/partners/milvus/tests/unit_tests/test_imports.py b/libs/partners/milvus/tests/unit_tests/test_imports.py deleted file mode 100644 index 8be170e36f..0000000000 --- a/libs/partners/milvus/tests/unit_tests/test_imports.py +++ /dev/null @@ -1,12 +0,0 @@ -from langchain_milvus import __all__ - -EXPECTED_ALL = [ - "Milvus", - "MilvusCollectionHybridSearchRetriever", - "Zilliz", - "ZillizCloudPipelineRetriever", -] - - -def test_all_imports() -> None: - assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/milvus/tests/unit_tests/vectorstores/__init__.py b/libs/partners/milvus/tests/unit_tests/vectorstores/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/libs/partners/milvus/tests/unit_tests/vectorstores/test_milvus.py b/libs/partners/milvus/tests/unit_tests/vectorstores/test_milvus.py deleted file mode 100644 index 1ef2314e79..0000000000 --- a/libs/partners/milvus/tests/unit_tests/vectorstores/test_milvus.py +++ /dev/null @@ -1,17 +0,0 @@ -import os -from tempfile import TemporaryDirectory -from unittest.mock import Mock - -from langchain_milvus.vectorstores import Milvus - - -def test_initialization() -> None: - """Test integration milvus initialization.""" - embedding = Mock() - with TemporaryDirectory() as tmp_dir: - Milvus( - embedding_function=embedding, - connection_args={ - "uri": os.path.join(tmp_dir, "milvus.db"), - }, - )