Source code for langchain_community.vectorstores.atlas

from __future__ import annotations

import logging
import uuid
from typing import Any, Iterable, List, Optional, Type

import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore

logger = logging.getLogger(__name__)


[docs]class AtlasDB(VectorStore): """`Atlas` vector store. Atlas is the `Nomic's` neural database and `rhizomatic` instrument. To use, you should have the ``nomic`` python package installed. Example: .. code-block:: python from langchain_community.vectorstores import AtlasDB from langchain_community.embeddings.openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() vectorstore = AtlasDB("my_project", embeddings.embed_query) """ _ATLAS_DEFAULT_ID_FIELD = "atlas_id"
[docs] def __init__( self, name: str, embedding_function: Optional[Embeddings] = None, api_key: Optional[str] = None, description: str = "A description for your project", is_public: bool = True, reset_project_if_exists: bool = False, ) -> None: """ Initialize the Atlas Client Args: name (str): The name of your project. If the project already exists, it will be loaded. embedding_function (Optional[Embeddings]): An optional function used for embedding your data. If None, data will be embedded with Nomic's embed model. api_key (str): Your nomic API key description (str): A description for your project. is_public (bool): Whether your project is publicly accessible. True by default. reset_project_if_exists (bool): Whether to reset this project if it already exists. Default False. Generally useful during development and testing. """ try: import nomic from nomic import AtlasProject except ImportError: raise ImportError( "Could not import nomic python package. " "Please install it with `pip install nomic`." ) if api_key is None: raise ValueError("No API key provided. Sign up at atlas.nomic.ai!") nomic.login(api_key) self._embedding_function = embedding_function modality = "text" if self._embedding_function is not None: modality = "embedding" # Check if the project exists, create it if not self.project = AtlasProject( name=name, description=description, modality=modality, is_public=is_public, reset_project_if_exists=reset_project_if_exists, unique_id_field=AtlasDB._ATLAS_DEFAULT_ID_FIELD, ) self.project._latest_project_state()
@property def embeddings(self) -> Optional[Embeddings]: return self._embedding_function
[docs] def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, refresh: bool = True, **kwargs: Any, ) -> List[str]: """Run more texts through the embeddings and add to the vectorstore. Args: texts (Iterable[str]): Texts to add to the vectorstore. metadatas (Optional[List[dict]], optional): Optional list of metadatas. ids (Optional[List[str]]): An optional list of ids. refresh(bool): Whether or not to refresh indices with the updated data. Default True. Returns: List[str]: List of IDs of the added texts. """ if ( metadatas is not None and len(metadatas) > 0 and "text" in metadatas[0].keys() ): raise ValueError("Cannot accept key text in metadata!") texts = list(texts) if ids is None: ids = [str(uuid.uuid4()) for _ in texts] # Embedding upload case if self._embedding_function is not None: _embeddings = self._embedding_function.embed_documents(texts) embeddings = np.stack(_embeddings) if metadatas is None: data = [ {AtlasDB._ATLAS_DEFAULT_ID_FIELD: ids[i], "text": texts[i]} for i, _ in enumerate(texts) ] else: for i in range(len(metadatas)): metadatas[i][AtlasDB._ATLAS_DEFAULT_ID_FIELD] = ids[i] metadatas[i]["text"] = texts[i] data = metadatas self.project._validate_map_data_inputs( [], id_field=AtlasDB._ATLAS_DEFAULT_ID_FIELD, data=data ) with self.project.wait_for_project_lock(): self.project.add_embeddings(embeddings=embeddings, data=data) # Text upload case else: if metadatas is None: data = [ {"text": text, AtlasDB._ATLAS_DEFAULT_ID_FIELD: ids[i]} for i, text in enumerate(texts) ] else: for i, text in enumerate(texts): metadatas[i]["text"] = texts metadatas[i][AtlasDB._ATLAS_DEFAULT_ID_FIELD] = ids[i] data = metadatas self.project._validate_map_data_inputs( [], id_field=AtlasDB._ATLAS_DEFAULT_ID_FIELD, data=data ) with self.project.wait_for_project_lock(): self.project.add_text(data) if refresh: if len(self.project.indices) > 0: with self.project.wait_for_project_lock(): self.project.rebuild_maps() return ids
[docs] def create_index(self, **kwargs: Any) -> Any: """Creates an index in your project. See https://docs.nomic.ai/atlas_api.html#nomic.project.AtlasProject.create_index for full detail. """ with self.project.wait_for_project_lock(): return self.project.create_index(**kwargs)
[docs] @classmethod def from_texts( cls: Type[AtlasDB], texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, name: Optional[str] = None, api_key: Optional[str] = None, description: str = "A description for your project", is_public: bool = True, reset_project_if_exists: bool = False, index_kwargs: Optional[dict] = None, **kwargs: Any, ) -> AtlasDB: """Create an AtlasDB vectorstore from a raw documents. Args: texts (List[str]): The list of texts to ingest. name (str): Name of the project to create. api_key (str): Your nomic API key, embedding (Optional[Embeddings]): Embedding function. Defaults to None. metadatas (Optional[List[dict]]): List of metadatas. Defaults to None. ids (Optional[List[str]]): Optional list of document IDs. If None, ids will be auto created description (str): A description for your project. is_public (bool): Whether your project is publicly accessible. True by default. reset_project_if_exists (bool): Whether to reset this project if it already exists. Default False. Generally useful during development and testing. index_kwargs (Optional[dict]): Dict of kwargs for index creation. See https://docs.nomic.ai/atlas_api.html Returns: AtlasDB: Nomic's neural database and finest rhizomatic instrument """ if name is None or api_key is None: raise ValueError("`name` and `api_key` cannot be None.") # Inject relevant kwargs all_index_kwargs = {"name": name + "_index", "indexed_field": "text"} if index_kwargs is not None: for k, v in index_kwargs.items(): all_index_kwargs[k] = v # Build project atlasDB = cls( name, embedding_function=embedding, api_key=api_key, description="A description for your project", is_public=is_public, reset_project_if_exists=reset_project_if_exists, ) with atlasDB.project.wait_for_project_lock(): atlasDB.add_texts(texts=texts, metadatas=metadatas, ids=ids) atlasDB.create_index(**all_index_kwargs) return atlasDB
[docs] @classmethod def from_documents( cls: Type[AtlasDB], documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, name: Optional[str] = None, api_key: Optional[str] = None, persist_directory: Optional[str] = None, description: str = "A description for your project", is_public: bool = True, reset_project_if_exists: bool = False, index_kwargs: Optional[dict] = None, **kwargs: Any, ) -> AtlasDB: """Create an AtlasDB vectorstore from a list of documents. Args: name (str): Name of the collection to create. api_key (str): Your nomic API key, documents (List[Document]): List of documents to add to the vectorstore. embedding (Optional[Embeddings]): Embedding function. Defaults to None. ids (Optional[List[str]]): Optional list of document IDs. If None, ids will be auto created description (str): A description for your project. is_public (bool): Whether your project is publicly accessible. True by default. reset_project_if_exists (bool): Whether to reset this project if it already exists. Default False. Generally useful during development and testing. index_kwargs (Optional[dict]): Dict of kwargs for index creation. See https://docs.nomic.ai/atlas_api.html Returns: AtlasDB: Nomic's neural database and finest rhizomatic instrument """ if name is None or api_key is None: raise ValueError("`name` and `api_key` cannot be None.") texts = [doc.page_content for doc in documents] metadatas = [doc.metadata for doc in documents] return cls.from_texts( name=name, api_key=api_key, texts=texts, embedding=embedding, metadatas=metadatas, ids=ids, description=description, is_public=is_public, reset_project_if_exists=reset_project_if_exists, index_kwargs=index_kwargs, )