Spaces:
Runtime error
Runtime error
File size: 15,028 Bytes
129cd69 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 | from __future__ import annotations
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
if TYPE_CHECKING:
import bagel
import bagel.config
from bagel.api.types import ID, OneOrMany, Where, WhereDocument
from langchain_core.embeddings import Embeddings
from langchain_core.utils import xor_args
from langchain_core.vectorstores import VectorStore
from langchain.docstore.document import Document
DEFAULT_K = 5
def _results_to_docs(results: Any) -> List[Document]:
return [doc for doc, _ in _results_to_docs_and_scores(results)]
def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
return [
(Document(page_content=result[0], metadata=result[1] or {}), result[2])
for result in zip(
results["documents"][0],
results["metadatas"][0],
results["distances"][0],
)
]
class Bagel(VectorStore):
"""``BagelDB.ai`` vector store.
To use, you should have the ``betabageldb`` python package installed.
Example:
.. code-block:: python
from langchain.vectorstores import Bagel
vectorstore = Bagel(cluster_name="langchain_store")
"""
_LANGCHAIN_DEFAULT_CLUSTER_NAME = "langchain"
def __init__(
self,
cluster_name: str = _LANGCHAIN_DEFAULT_CLUSTER_NAME,
client_settings: Optional[bagel.config.Settings] = None,
embedding_function: Optional[Embeddings] = None,
cluster_metadata: Optional[Dict] = None,
client: Optional[bagel.Client] = None,
relevance_score_fn: Optional[Callable[[float], float]] = None,
) -> None:
"""Initialize with bagel client"""
try:
import bagel
import bagel.config
except ImportError:
raise ImportError("Please install bagel `pip install betabageldb`.")
if client is not None:
self._client_settings = client_settings
self._client = client
else:
if client_settings:
_client_settings = client_settings
else:
_client_settings = bagel.config.Settings(
bagel_api_impl="rest",
bagel_server_host="api.bageldb.ai",
)
self._client_settings = _client_settings
self._client = bagel.Client(_client_settings)
self._cluster = self._client.get_or_create_cluster(
name=cluster_name,
metadata=cluster_metadata,
)
self.override_relevance_score_fn = relevance_score_fn
self._embedding_function = embedding_function
@property
def embeddings(self) -> Optional[Embeddings]:
return self._embedding_function
@xor_args(("query_texts", "query_embeddings"))
def __query_cluster(
self,
query_texts: Optional[List[str]] = None,
query_embeddings: Optional[List[List[float]]] = None,
n_results: int = 4,
where: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Query the BagelDB cluster based on the provided parameters."""
try:
import bagel # noqa: F401
except ImportError:
raise ImportError("Please install bagel `pip install betabageldb`.")
return self._cluster.find(
query_texts=query_texts,
query_embeddings=query_embeddings,
n_results=n_results,
where=where,
**kwargs,
)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
embeddings: Optional[List[List[float]]] = None,
**kwargs: Any,
) -> List[str]:
"""
Add texts along with their corresponding embeddings and optional
metadata to the BagelDB cluster.
Args:
texts (Iterable[str]): Texts to be added.
embeddings (Optional[List[float]]): List of embeddingvectors
metadatas (Optional[List[dict]]): Optional list of metadatas.
ids (Optional[List[str]]): List of unique ID for the texts.
Returns:
List[str]: List of unique ID representing the added texts.
"""
# creating unique ids if None
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
texts = list(texts)
if self._embedding_function and embeddings is None and texts:
embeddings = self._embedding_function.embed_documents(texts)
if metadatas:
length_diff = len(texts) - len(metadatas)
if length_diff:
metadatas = metadatas + [{}] * length_diff
empty_ids = []
non_empty_ids = []
for idx, metadata in enumerate(metadatas):
if metadata:
non_empty_ids.append(idx)
else:
empty_ids.append(idx)
if non_empty_ids:
metadatas = [metadatas[idx] for idx in non_empty_ids]
texts_with_metadatas = [texts[idx] for idx in non_empty_ids]
embeddings_with_metadatas = (
[embeddings[idx] for idx in non_empty_ids] if embeddings else None
)
ids_with_metadata = [ids[idx] for idx in non_empty_ids]
self._cluster.upsert(
embeddings=embeddings_with_metadatas,
metadatas=metadatas,
documents=texts_with_metadatas,
ids=ids_with_metadata,
)
if empty_ids:
texts_without_metadatas = [texts[j] for j in empty_ids]
embeddings_without_metadatas = (
[embeddings[j] for j in empty_ids] if embeddings else None
)
ids_without_metadatas = [ids[j] for j in empty_ids]
self._cluster.upsert(
embeddings=embeddings_without_metadatas,
documents=texts_without_metadatas,
ids=ids_without_metadatas,
)
else:
metadatas = [{}] * len(texts)
self._cluster.upsert(
embeddings=embeddings,
documents=texts,
metadatas=metadatas,
ids=ids,
)
return ids
def similarity_search(
self,
query: str,
k: int = DEFAULT_K,
where: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""
Run a similarity search with BagelDB.
Args:
query (str): The query text to search for similar documents/texts.
k (int): The number of results to return.
where (Optional[Dict[str, str]]): Metadata filters to narrow down.
Returns:
List[Document]: List of documents objects representing
the documents most similar to the query text.
"""
docs_and_scores = self.similarity_search_with_score(query, k, where=where)
return [doc for doc, _ in docs_and_scores]
def similarity_search_with_score(
self,
query: str,
k: int = DEFAULT_K,
where: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Run a similarity search with BagelDB and return documents with their
corresponding similarity scores.
Args:
query (str): The query text to search for similar documents.
k (int): The number of results to return.
where (Optional[Dict[str, str]]): Filter using metadata.
Returns:
List[Tuple[Document, float]]: List of tuples, each containing a
Document object representing a similar document and its
corresponding similarity score.
"""
results = self.__query_cluster(query_texts=[query], n_results=k, where=where)
return _results_to_docs_and_scores(results)
@classmethod
def from_texts(
cls: Type[Bagel],
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
cluster_name: str = _LANGCHAIN_DEFAULT_CLUSTER_NAME,
client_settings: Optional[bagel.config.Settings] = None,
cluster_metadata: Optional[Dict] = None,
client: Optional[bagel.Client] = None,
text_embeddings: Optional[List[List[float]]] = None,
**kwargs: Any,
) -> Bagel:
"""
Create and initialize a Bagel instance from list of texts.
Args:
texts (List[str]): List of text content to be added.
cluster_name (str): The name of the BagelDB cluster.
client_settings (Optional[bagel.config.Settings]): Client settings.
cluster_metadata (Optional[Dict]): Metadata of the cluster.
embeddings (Optional[Embeddings]): List of embedding.
metadatas (Optional[List[dict]]): List of metadata.
ids (Optional[List[str]]): List of unique ID. Defaults to None.
client (Optional[bagel.Client]): Bagel client instance.
Returns:
Bagel: Bagel vectorstore.
"""
bagel_cluster = cls(
cluster_name=cluster_name,
embedding_function=embedding,
client_settings=client_settings,
client=client,
cluster_metadata=cluster_metadata,
**kwargs,
)
_ = bagel_cluster.add_texts(
texts=texts, embeddings=text_embeddings, metadatas=metadatas, ids=ids
)
return bagel_cluster
def delete_cluster(self) -> None:
"""Delete the cluster."""
self._client.delete_cluster(self._cluster.name)
def similarity_search_by_vector_with_relevance_scores(
self,
query_embeddings: List[float],
k: int = DEFAULT_K,
where: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Return docs most similar to embedding vector and similarity score.
"""
results = self.__query_cluster(
query_embeddings=query_embeddings, n_results=k, where=where
)
return _results_to_docs_and_scores(results)
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = DEFAULT_K,
where: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector."""
results = self.__query_cluster(
query_embeddings=embedding, n_results=k, where=where
)
return _results_to_docs(results)
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
Select and return the appropriate relevance score function based
on the distance metric used in the BagelDB cluster.
"""
if self.override_relevance_score_fn:
return self.override_relevance_score_fn
distance = "l2"
distance_key = "hnsw:space"
metadata = self._cluster.metadata
if metadata and distance_key in metadata:
distance = metadata[distance_key]
if distance == "cosine":
return self._cosine_relevance_score_fn
elif distance == "l2":
return self._euclidean_relevance_score_fn
elif distance == "ip":
return self._max_inner_product_relevance_score_fn
else:
raise ValueError(
"No supported normalization function for distance"
f" metric of type: {distance}. Consider providing"
" relevance_score_fn to Bagel constructor."
)
@classmethod
def from_documents(
cls: Type[Bagel],
documents: List[Document],
embedding: Optional[Embeddings] = None,
ids: Optional[List[str]] = None,
cluster_name: str = _LANGCHAIN_DEFAULT_CLUSTER_NAME,
client_settings: Optional[bagel.config.Settings] = None,
client: Optional[bagel.Client] = None,
cluster_metadata: Optional[Dict] = None,
**kwargs: Any,
) -> Bagel:
"""
Create a Bagel vectorstore from a list of documents.
Args:
documents (List[Document]): List of Document objects to add to the
Bagel vectorstore.
embedding (Optional[List[float]]): List of embedding.
ids (Optional[List[str]]): List of IDs. Defaults to None.
cluster_name (str): The name of the BagelDB cluster.
client_settings (Optional[bagel.config.Settings]): Client settings.
client (Optional[bagel.Client]): Bagel client instance.
cluster_metadata (Optional[Dict]): Metadata associated with the
Bagel cluster. Defaults to None.
Returns:
Bagel: Bagel vectorstore.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return cls.from_texts(
texts=texts,
embedding=embedding,
metadatas=metadatas,
ids=ids,
cluster_name=cluster_name,
client_settings=client_settings,
client=client,
cluster_metadata=cluster_metadata,
**kwargs,
)
def update_document(self, document_id: str, document: Document) -> None:
"""Update a document in the cluster.
Args:
document_id (str): ID of the document to update.
document (Document): Document to update.
"""
text = document.page_content
metadata = document.metadata
self._cluster.update(
ids=[document_id],
documents=[text],
metadatas=[metadata],
)
def get(
self,
ids: Optional[OneOrMany[ID]] = None,
where: Optional[Where] = None,
limit: Optional[int] = None,
offset: Optional[int] = None,
where_document: Optional[WhereDocument] = None,
include: Optional[List[str]] = None,
) -> Dict[str, Any]:
"""Gets the collection."""
kwargs = {
"ids": ids,
"where": where,
"limit": limit,
"offset": offset,
"where_document": where_document,
}
if include is not None:
kwargs["include"] = include
return self._cluster.get(**kwargs)
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
"""
Delete by IDs.
Args:
ids: List of ids to delete.
"""
self._cluster.delete(ids=ids)
|