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"""Wrapper around Atlas by Nomic."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Iterable, List, Optional
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
logger = logging.getLogger()
class AtlasDB(VectorStore):
"""Wrapper around Atlas: Nomic's neural database and rhizomatic instrument.
To use, you should have the ``nomic`` python package installed.
Example:
.. code-block:: python
from langchain.vectorstores import AtlasDB
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = AtlasDB("my_project", embeddings.embed_query)
"""
_ATLAS_DEFAULT_ID_FIELD = "atlas_id"
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[Callable]): 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 userful during development and testing.
"""
try:
import nomic
from nomic import AtlasProject
except ImportError:
raise ValueError(
"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()
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.uuid1()) 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
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)
def similarity_search(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search with AtlasDB
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
Returns:
List[Document]: List of documents most similar to the query text.
"""
if self._embedding_function is None:
raise NotImplementedError(
"AtlasDB requires an embedding_function for text similarity search!"
)
_embedding = self._embedding_function.embed_documents([query])[0]
embedding = np.array(_embedding).reshape(1, -1)
with self.project.wait_for_project_lock():
neighbors, _ = self.project.projections[0].vector_search(
queries=embedding, k=k
)
datas = self.project.get_data(ids=neighbors[0])
docs = [
Document(page_content=datas[i]["text"], metadata=datas[i])
for i, neighbor in enumerate(neighbors)
]
return docs
@classmethod
def from_texts(
cls,
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 userful 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
@classmethod
def from_documents(
cls,
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 userful 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,
)
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