mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
b2fd41331e
Addded missed docstrings. Fixed inconsistency in docstrings. **Note** CC @efriis There were PR errors on `langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py` But, I didn't touch this file in this PR! Can it be some cache problems? I fixed this error.
801 lines
28 KiB
Python
801 lines
28 KiB
Python
"""Wrapper around TileDB vector database."""
|
|
from __future__ import annotations
|
|
|
|
import pickle
|
|
import random
|
|
import sys
|
|
from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple
|
|
|
|
import numpy as np
|
|
from langchain_core.documents import Document
|
|
from langchain_core.embeddings import Embeddings
|
|
from langchain_core.vectorstores import VectorStore
|
|
|
|
from langchain_community.vectorstores.utils import maximal_marginal_relevance
|
|
|
|
INDEX_METRICS = frozenset(["euclidean"])
|
|
DEFAULT_METRIC = "euclidean"
|
|
DOCUMENTS_ARRAY_NAME = "documents"
|
|
VECTOR_INDEX_NAME = "vectors"
|
|
MAX_UINT64 = np.iinfo(np.dtype("uint64")).max
|
|
MAX_FLOAT_32 = np.finfo(np.dtype("float32")).max
|
|
MAX_FLOAT = sys.float_info.max
|
|
|
|
|
|
def dependable_tiledb_import() -> Any:
|
|
"""Import tiledb-vector-search if available, otherwise raise error."""
|
|
try:
|
|
import tiledb as tiledb
|
|
import tiledb.vector_search as tiledb_vs
|
|
except ImportError:
|
|
raise ValueError(
|
|
"Could not import tiledb-vector-search python package. "
|
|
"Please install it with `conda install -c tiledb tiledb-vector-search` "
|
|
"or `pip install tiledb-vector-search`"
|
|
)
|
|
return tiledb_vs, tiledb
|
|
|
|
|
|
def get_vector_index_uri_from_group(group: Any) -> str:
|
|
"""Get the URI of the vector index."""
|
|
return group[VECTOR_INDEX_NAME].uri
|
|
|
|
|
|
def get_documents_array_uri_from_group(group: Any) -> str:
|
|
"""Get the URI of the documents array from group.
|
|
|
|
Args:
|
|
group: TileDB group object.
|
|
|
|
Returns:
|
|
URI of the documents array.
|
|
"""
|
|
return group[DOCUMENTS_ARRAY_NAME].uri
|
|
|
|
|
|
def get_vector_index_uri(uri: str) -> str:
|
|
"""Get the URI of the vector index."""
|
|
return f"{uri}/{VECTOR_INDEX_NAME}"
|
|
|
|
|
|
def get_documents_array_uri(uri: str) -> str:
|
|
"""Get the URI of the documents array."""
|
|
return f"{uri}/{DOCUMENTS_ARRAY_NAME}"
|
|
|
|
|
|
class TileDB(VectorStore):
|
|
"""TileDB vector store.
|
|
|
|
To use, you should have the ``tiledb-vector-search`` python package installed.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community import TileDB
|
|
embeddings = OpenAIEmbeddings()
|
|
db = TileDB(embeddings, index_uri, metric)
|
|
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
embedding: Embeddings,
|
|
index_uri: str,
|
|
metric: str,
|
|
*,
|
|
vector_index_uri: str = "",
|
|
docs_array_uri: str = "",
|
|
config: Optional[Mapping[str, Any]] = None,
|
|
timestamp: Any = None,
|
|
**kwargs: Any,
|
|
):
|
|
"""Initialize with necessary components."""
|
|
self.embedding = embedding
|
|
self.embedding_function = embedding.embed_query
|
|
self.index_uri = index_uri
|
|
self.metric = metric
|
|
self.config = config
|
|
|
|
tiledb_vs, tiledb = dependable_tiledb_import()
|
|
with tiledb.scope_ctx(ctx_or_config=config):
|
|
index_group = tiledb.Group(self.index_uri, "r")
|
|
self.vector_index_uri = (
|
|
vector_index_uri
|
|
if vector_index_uri != ""
|
|
else get_vector_index_uri_from_group(index_group)
|
|
)
|
|
self.docs_array_uri = (
|
|
docs_array_uri
|
|
if docs_array_uri != ""
|
|
else get_documents_array_uri_from_group(index_group)
|
|
)
|
|
index_group.close()
|
|
group = tiledb.Group(self.vector_index_uri, "r")
|
|
self.index_type = group.meta.get("index_type")
|
|
group.close()
|
|
self.timestamp = timestamp
|
|
if self.index_type == "FLAT":
|
|
self.vector_index = tiledb_vs.flat_index.FlatIndex(
|
|
uri=self.vector_index_uri,
|
|
config=self.config,
|
|
timestamp=self.timestamp,
|
|
**kwargs,
|
|
)
|
|
elif self.index_type == "IVF_FLAT":
|
|
self.vector_index = tiledb_vs.ivf_flat_index.IVFFlatIndex(
|
|
uri=self.vector_index_uri,
|
|
config=self.config,
|
|
timestamp=self.timestamp,
|
|
**kwargs,
|
|
)
|
|
|
|
@property
|
|
def embeddings(self) -> Optional[Embeddings]:
|
|
return self.embedding
|
|
|
|
def process_index_results(
|
|
self,
|
|
ids: List[int],
|
|
scores: List[float],
|
|
*,
|
|
k: int = 4,
|
|
filter: Optional[Dict[str, Any]] = None,
|
|
score_threshold: float = MAX_FLOAT,
|
|
) -> List[Tuple[Document, float]]:
|
|
"""Turns TileDB results into a list of documents and scores.
|
|
|
|
Args:
|
|
ids: List of indices of the documents in the index.
|
|
scores: List of distances of the documents in the index.
|
|
k: Number of Documents to return. Defaults to 4.
|
|
filter (Optional[Dict[str, Any]]): Filter by metadata. Defaults to None.
|
|
score_threshold: Optional, a floating point value to filter the
|
|
resulting set of retrieved docs
|
|
Returns:
|
|
List of Documents and scores.
|
|
"""
|
|
tiledb_vs, tiledb = dependable_tiledb_import()
|
|
docs = []
|
|
docs_array = tiledb.open(
|
|
self.docs_array_uri, "r", timestamp=self.timestamp, config=self.config
|
|
)
|
|
for idx, score in zip(ids, scores):
|
|
if idx == 0 and score == 0:
|
|
continue
|
|
if idx == MAX_UINT64 and score == MAX_FLOAT_32:
|
|
continue
|
|
doc = docs_array[idx]
|
|
if doc is None or len(doc["text"]) == 0:
|
|
raise ValueError(f"Could not find document for id {idx}, got {doc}")
|
|
pickled_metadata = doc.get("metadata")
|
|
result_doc = Document(page_content=str(doc["text"][0]))
|
|
if pickled_metadata is not None:
|
|
metadata = pickle.loads(
|
|
np.array(pickled_metadata.tolist()).astype(np.uint8).tobytes()
|
|
)
|
|
result_doc.metadata = metadata
|
|
if filter is not None:
|
|
filter = {
|
|
key: [value] if not isinstance(value, list) else value
|
|
for key, value in filter.items()
|
|
}
|
|
if all(
|
|
result_doc.metadata.get(key) in value
|
|
for key, value in filter.items()
|
|
):
|
|
docs.append((result_doc, score))
|
|
else:
|
|
docs.append((result_doc, score))
|
|
docs_array.close()
|
|
docs = [(doc, score) for doc, score in docs if score <= score_threshold]
|
|
return docs[:k]
|
|
|
|
def similarity_search_with_score_by_vector(
|
|
self,
|
|
embedding: List[float],
|
|
*,
|
|
k: int = 4,
|
|
filter: Optional[Dict[str, Any]] = None,
|
|
fetch_k: int = 20,
|
|
**kwargs: Any,
|
|
) -> List[Tuple[Document, float]]:
|
|
"""Return docs most similar to query.
|
|
|
|
Args:
|
|
embedding: Embedding vector to look up documents similar to.
|
|
k: Number of Documents to return. Defaults to 4.
|
|
filter (Optional[Dict[str, Any]]): Filter by metadata. Defaults to None.
|
|
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
|
|
Defaults to 20.
|
|
**kwargs: kwargs to be passed to similarity search. Can include:
|
|
nprobe: Optional, number of partitions to check if using IVF_FLAT index
|
|
score_threshold: Optional, a floating point value to filter the
|
|
resulting set of retrieved docs
|
|
|
|
Returns:
|
|
List of documents most similar to the query text and distance
|
|
in float for each. Lower score represents more similarity.
|
|
"""
|
|
if "score_threshold" in kwargs:
|
|
score_threshold = kwargs.pop("score_threshold")
|
|
else:
|
|
score_threshold = MAX_FLOAT
|
|
d, i = self.vector_index.query(
|
|
np.array([np.array(embedding).astype(np.float32)]).astype(np.float32),
|
|
k=k if filter is None else fetch_k,
|
|
**kwargs,
|
|
)
|
|
return self.process_index_results(
|
|
ids=i[0], scores=d[0], filter=filter, k=k, score_threshold=score_threshold
|
|
)
|
|
|
|
def similarity_search_with_score(
|
|
self,
|
|
query: str,
|
|
*,
|
|
k: int = 4,
|
|
filter: Optional[Dict[str, Any]] = None,
|
|
fetch_k: int = 20,
|
|
**kwargs: Any,
|
|
) -> List[Tuple[Document, float]]:
|
|
"""Return docs most similar to query.
|
|
|
|
Args:
|
|
query: Text to look up documents similar to.
|
|
k: Number of Documents to return. Defaults to 4.
|
|
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
|
|
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
|
|
Defaults to 20.
|
|
|
|
Returns:
|
|
List of documents most similar to the query text with
|
|
Distance as float. Lower score represents more similarity.
|
|
"""
|
|
embedding = self.embedding_function(query)
|
|
docs = self.similarity_search_with_score_by_vector(
|
|
embedding,
|
|
k=k,
|
|
filter=filter,
|
|
fetch_k=fetch_k,
|
|
**kwargs,
|
|
)
|
|
return docs
|
|
|
|
def similarity_search_by_vector(
|
|
self,
|
|
embedding: List[float],
|
|
k: int = 4,
|
|
filter: Optional[Dict[str, Any]] = None,
|
|
fetch_k: int = 20,
|
|
**kwargs: Any,
|
|
) -> List[Document]:
|
|
"""Return docs most similar to embedding vector.
|
|
|
|
Args:
|
|
embedding: Embedding to look up documents similar to.
|
|
k: Number of Documents to return. Defaults to 4.
|
|
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
|
|
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
|
|
Defaults to 20.
|
|
|
|
Returns:
|
|
List of Documents most similar to the embedding.
|
|
"""
|
|
docs_and_scores = self.similarity_search_with_score_by_vector(
|
|
embedding,
|
|
k=k,
|
|
filter=filter,
|
|
fetch_k=fetch_k,
|
|
**kwargs,
|
|
)
|
|
return [doc for doc, _ in docs_and_scores]
|
|
|
|
def similarity_search(
|
|
self,
|
|
query: str,
|
|
k: int = 4,
|
|
filter: Optional[Dict[str, Any]] = None,
|
|
fetch_k: int = 20,
|
|
**kwargs: Any,
|
|
) -> List[Document]:
|
|
"""Return docs most similar to query.
|
|
|
|
Args:
|
|
query: Text to look up documents similar to.
|
|
k: Number of Documents to return. Defaults to 4.
|
|
filter: (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
|
|
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
|
|
Defaults to 20.
|
|
|
|
Returns:
|
|
List of Documents most similar to the query.
|
|
"""
|
|
docs_and_scores = self.similarity_search_with_score(
|
|
query, k=k, filter=filter, fetch_k=fetch_k, **kwargs
|
|
)
|
|
return [doc for doc, _ in docs_and_scores]
|
|
|
|
def max_marginal_relevance_search_with_score_by_vector(
|
|
self,
|
|
embedding: List[float],
|
|
*,
|
|
k: int = 4,
|
|
fetch_k: int = 20,
|
|
lambda_mult: float = 0.5,
|
|
filter: Optional[Dict[str, Any]] = None,
|
|
**kwargs: Any,
|
|
) -> List[Tuple[Document, float]]:
|
|
"""Return docs and their similarity scores selected using the maximal marginal
|
|
relevance.
|
|
|
|
Maximal marginal relevance optimizes for similarity to query AND diversity
|
|
among selected documents.
|
|
|
|
Args:
|
|
embedding: Embedding to look up documents similar to.
|
|
k: Number of Documents to return. Defaults to 4.
|
|
fetch_k: Number of Documents to fetch before filtering to
|
|
pass to MMR algorithm.
|
|
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.
|
|
Returns:
|
|
List of Documents and similarity scores selected by maximal marginal
|
|
relevance and score for each.
|
|
"""
|
|
if "score_threshold" in kwargs:
|
|
score_threshold = kwargs.pop("score_threshold")
|
|
else:
|
|
score_threshold = MAX_FLOAT
|
|
scores, indices = self.vector_index.query(
|
|
np.array([np.array(embedding).astype(np.float32)]).astype(np.float32),
|
|
k=fetch_k if filter is None else fetch_k * 2,
|
|
**kwargs,
|
|
)
|
|
results = self.process_index_results(
|
|
ids=indices[0],
|
|
scores=scores[0],
|
|
filter=filter,
|
|
k=fetch_k if filter is None else fetch_k * 2,
|
|
score_threshold=score_threshold,
|
|
)
|
|
embeddings = [
|
|
self.embedding.embed_documents([doc.page_content])[0] for doc, _ in results
|
|
]
|
|
mmr_selected = maximal_marginal_relevance(
|
|
np.array([embedding], dtype=np.float32),
|
|
embeddings,
|
|
k=k,
|
|
lambda_mult=lambda_mult,
|
|
)
|
|
docs_and_scores = []
|
|
for i in mmr_selected:
|
|
docs_and_scores.append(results[i])
|
|
return docs_and_scores
|
|
|
|
def max_marginal_relevance_search_by_vector(
|
|
self,
|
|
embedding: List[float],
|
|
k: int = 4,
|
|
fetch_k: int = 20,
|
|
lambda_mult: float = 0.5,
|
|
filter: Optional[Dict[str, Any]] = None,
|
|
**kwargs: Any,
|
|
) -> List[Document]:
|
|
"""Return docs selected using the maximal marginal relevance.
|
|
|
|
Maximal marginal relevance optimizes for similarity to query AND diversity
|
|
among selected documents.
|
|
|
|
Args:
|
|
embedding: Embedding to look up documents similar to.
|
|
k: Number of Documents to return. Defaults to 4.
|
|
fetch_k: Number of Documents to fetch before filtering to
|
|
pass to MMR algorithm.
|
|
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.
|
|
Returns:
|
|
List of Documents selected by maximal marginal relevance.
|
|
"""
|
|
docs_and_scores = self.max_marginal_relevance_search_with_score_by_vector(
|
|
embedding,
|
|
k=k,
|
|
fetch_k=fetch_k,
|
|
lambda_mult=lambda_mult,
|
|
filter=filter,
|
|
**kwargs,
|
|
)
|
|
return [doc for doc, _ in docs_and_scores]
|
|
|
|
def max_marginal_relevance_search(
|
|
self,
|
|
query: str,
|
|
k: int = 4,
|
|
fetch_k: int = 20,
|
|
lambda_mult: float = 0.5,
|
|
filter: Optional[Dict[str, Any]] = None,
|
|
**kwargs: Any,
|
|
) -> List[Document]:
|
|
"""Return docs selected using the maximal marginal relevance.
|
|
|
|
Maximal marginal relevance optimizes for similarity to query AND diversity
|
|
among selected documents.
|
|
|
|
Args:
|
|
query: Text to look up documents similar to.
|
|
k: Number of Documents to return. Defaults to 4.
|
|
fetch_k: Number of Documents to fetch before filtering (if needed) to
|
|
pass to MMR algorithm.
|
|
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.
|
|
Returns:
|
|
List of Documents selected by maximal marginal relevance.
|
|
"""
|
|
embedding = self.embedding_function(query)
|
|
docs = self.max_marginal_relevance_search_by_vector(
|
|
embedding,
|
|
k=k,
|
|
fetch_k=fetch_k,
|
|
lambda_mult=lambda_mult,
|
|
filter=filter,
|
|
**kwargs,
|
|
)
|
|
return docs
|
|
|
|
@classmethod
|
|
def create(
|
|
cls,
|
|
index_uri: str,
|
|
index_type: str,
|
|
dimensions: int,
|
|
vector_type: np.dtype,
|
|
*,
|
|
metadatas: bool = True,
|
|
config: Optional[Mapping[str, Any]] = None,
|
|
) -> None:
|
|
tiledb_vs, tiledb = dependable_tiledb_import()
|
|
with tiledb.scope_ctx(ctx_or_config=config):
|
|
try:
|
|
tiledb.group_create(index_uri)
|
|
except tiledb.TileDBError as err:
|
|
raise err
|
|
group = tiledb.Group(index_uri, "w")
|
|
vector_index_uri = get_vector_index_uri(group.uri)
|
|
docs_uri = get_documents_array_uri(group.uri)
|
|
if index_type == "FLAT":
|
|
tiledb_vs.flat_index.create(
|
|
uri=vector_index_uri,
|
|
dimensions=dimensions,
|
|
vector_type=vector_type,
|
|
config=config,
|
|
)
|
|
elif index_type == "IVF_FLAT":
|
|
tiledb_vs.ivf_flat_index.create(
|
|
uri=vector_index_uri,
|
|
dimensions=dimensions,
|
|
vector_type=vector_type,
|
|
config=config,
|
|
)
|
|
group.add(vector_index_uri, name=VECTOR_INDEX_NAME)
|
|
|
|
# Create TileDB array to store Documents
|
|
# TODO add a Document store API to tiledb-vector-search to allow storing
|
|
# different types of objects and metadata in a more generic way.
|
|
dim = tiledb.Dim(
|
|
name="id",
|
|
domain=(0, MAX_UINT64 - 1),
|
|
dtype=np.dtype(np.uint64),
|
|
)
|
|
dom = tiledb.Domain(dim)
|
|
|
|
text_attr = tiledb.Attr(name="text", dtype=np.dtype("U1"), var=True)
|
|
attrs = [text_attr]
|
|
if metadatas:
|
|
metadata_attr = tiledb.Attr(name="metadata", dtype=np.uint8, var=True)
|
|
attrs.append(metadata_attr)
|
|
schema = tiledb.ArraySchema(
|
|
domain=dom,
|
|
sparse=True,
|
|
allows_duplicates=False,
|
|
attrs=attrs,
|
|
)
|
|
tiledb.Array.create(docs_uri, schema)
|
|
group.add(docs_uri, name=DOCUMENTS_ARRAY_NAME)
|
|
group.close()
|
|
|
|
@classmethod
|
|
def __from(
|
|
cls,
|
|
texts: List[str],
|
|
embeddings: List[List[float]],
|
|
embedding: Embeddings,
|
|
index_uri: str,
|
|
*,
|
|
metadatas: Optional[List[dict]] = None,
|
|
ids: Optional[List[str]] = None,
|
|
metric: str = DEFAULT_METRIC,
|
|
index_type: str = "FLAT",
|
|
config: Optional[Mapping[str, Any]] = None,
|
|
index_timestamp: int = 0,
|
|
**kwargs: Any,
|
|
) -> TileDB:
|
|
if metric not in INDEX_METRICS:
|
|
raise ValueError(
|
|
(
|
|
f"Unsupported distance metric: {metric}. "
|
|
f"Expected one of {list(INDEX_METRICS)}"
|
|
)
|
|
)
|
|
tiledb_vs, tiledb = dependable_tiledb_import()
|
|
input_vectors = np.array(embeddings).astype(np.float32)
|
|
cls.create(
|
|
index_uri=index_uri,
|
|
index_type=index_type,
|
|
dimensions=input_vectors.shape[1],
|
|
vector_type=input_vectors.dtype,
|
|
metadatas=metadatas is not None,
|
|
config=config,
|
|
)
|
|
with tiledb.scope_ctx(ctx_or_config=config):
|
|
if not embeddings:
|
|
raise ValueError("embeddings must be provided to build a TileDB index")
|
|
|
|
vector_index_uri = get_vector_index_uri(index_uri)
|
|
docs_uri = get_documents_array_uri(index_uri)
|
|
if ids is None:
|
|
ids = [str(random.randint(0, MAX_UINT64 - 1)) for _ in texts]
|
|
external_ids = np.array(ids).astype(np.uint64)
|
|
|
|
tiledb_vs.ingestion.ingest(
|
|
index_type=index_type,
|
|
index_uri=vector_index_uri,
|
|
input_vectors=input_vectors,
|
|
external_ids=external_ids,
|
|
index_timestamp=index_timestamp if index_timestamp != 0 else None,
|
|
config=config,
|
|
**kwargs,
|
|
)
|
|
with tiledb.open(docs_uri, "w") as A:
|
|
if external_ids is None:
|
|
external_ids = np.zeros(len(texts), dtype=np.uint64)
|
|
for i in range(len(texts)):
|
|
external_ids[i] = i
|
|
data = {}
|
|
data["text"] = np.array(texts)
|
|
if metadatas is not None:
|
|
metadata_attr = np.empty([len(metadatas)], dtype=object)
|
|
i = 0
|
|
for metadata in metadatas:
|
|
metadata_attr[i] = np.frombuffer(
|
|
pickle.dumps(metadata), dtype=np.uint8
|
|
)
|
|
i += 1
|
|
data["metadata"] = metadata_attr
|
|
|
|
A[external_ids] = data
|
|
return cls(
|
|
embedding=embedding,
|
|
index_uri=index_uri,
|
|
metric=metric,
|
|
config=config,
|
|
**kwargs,
|
|
)
|
|
|
|
def delete(
|
|
self, ids: Optional[List[str]] = None, timestamp: int = 0, **kwargs: Any
|
|
) -> Optional[bool]:
|
|
"""Delete by vector ID or other criteria.
|
|
|
|
Args:
|
|
ids: List of ids to delete.
|
|
timestamp: Optional timestamp to delete with.
|
|
**kwargs: Other keyword arguments that subclasses might use.
|
|
|
|
Returns:
|
|
Optional[bool]: True if deletion is successful,
|
|
False otherwise, None if not implemented.
|
|
"""
|
|
|
|
external_ids = np.array(ids).astype(np.uint64)
|
|
self.vector_index.delete_batch(
|
|
external_ids=external_ids, timestamp=timestamp if timestamp != 0 else None
|
|
)
|
|
return True
|
|
|
|
def add_texts(
|
|
self,
|
|
texts: Iterable[str],
|
|
metadatas: Optional[List[dict]] = None,
|
|
ids: Optional[List[str]] = None,
|
|
timestamp: int = 0,
|
|
**kwargs: Any,
|
|
) -> List[str]:
|
|
"""Run more texts through the embeddings and add to the vectorstore.
|
|
|
|
Args:
|
|
texts: Iterable of strings to add to the vectorstore.
|
|
metadatas: Optional list of metadatas associated with the texts.
|
|
ids: Optional ids of each text object.
|
|
timestamp: Optional timestamp to write new texts with.
|
|
kwargs: vectorstore specific parameters
|
|
|
|
Returns:
|
|
List of ids from adding the texts into the vectorstore.
|
|
"""
|
|
tiledb_vs, tiledb = dependable_tiledb_import()
|
|
embeddings = self.embedding.embed_documents(list(texts))
|
|
if ids is None:
|
|
ids = [str(random.randint(0, MAX_UINT64 - 1)) for _ in texts]
|
|
|
|
external_ids = np.array(ids).astype(np.uint64)
|
|
vectors = np.empty((len(embeddings)), dtype="O")
|
|
for i in range(len(embeddings)):
|
|
vectors[i] = np.array(embeddings[i], dtype=np.float32)
|
|
self.vector_index.update_batch(
|
|
vectors=vectors,
|
|
external_ids=external_ids,
|
|
timestamp=timestamp if timestamp != 0 else None,
|
|
)
|
|
|
|
docs = {}
|
|
docs["text"] = np.array(texts)
|
|
if metadatas is not None:
|
|
metadata_attr = np.empty([len(metadatas)], dtype=object)
|
|
i = 0
|
|
for metadata in metadatas:
|
|
metadata_attr[i] = np.frombuffer(pickle.dumps(metadata), dtype=np.uint8)
|
|
i += 1
|
|
docs["metadata"] = metadata_attr
|
|
|
|
docs_array = tiledb.open(
|
|
self.docs_array_uri,
|
|
"w",
|
|
timestamp=timestamp if timestamp != 0 else None,
|
|
config=self.config,
|
|
)
|
|
docs_array[external_ids] = docs
|
|
docs_array.close()
|
|
return ids
|
|
|
|
@classmethod
|
|
def from_texts(
|
|
cls,
|
|
texts: List[str],
|
|
embedding: Embeddings,
|
|
metadatas: Optional[List[dict]] = None,
|
|
ids: Optional[List[str]] = None,
|
|
metric: str = DEFAULT_METRIC,
|
|
index_uri: str = "/tmp/tiledb_array",
|
|
index_type: str = "FLAT",
|
|
config: Optional[Mapping[str, Any]] = None,
|
|
index_timestamp: int = 0,
|
|
**kwargs: Any,
|
|
) -> TileDB:
|
|
"""Construct a TileDB index from raw documents.
|
|
|
|
Args:
|
|
texts: List of documents to index.
|
|
embedding: Embedding function to use.
|
|
metadatas: List of metadata dictionaries to associate with documents.
|
|
ids: Optional ids of each text object.
|
|
metric: Metric to use for indexing. Defaults to "euclidean".
|
|
index_uri: The URI to write the TileDB arrays
|
|
index_type: Optional, Vector index type ("FLAT", IVF_FLAT")
|
|
config: Optional, TileDB config
|
|
index_timestamp: Optional, timestamp to write new texts with.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community import TileDB
|
|
from langchain_community.embeddings import OpenAIEmbeddings
|
|
embeddings = OpenAIEmbeddings()
|
|
index = TileDB.from_texts(texts, embeddings)
|
|
"""
|
|
embeddings = []
|
|
embeddings = embedding.embed_documents(texts)
|
|
return cls.__from(
|
|
texts=texts,
|
|
embeddings=embeddings,
|
|
embedding=embedding,
|
|
metadatas=metadatas,
|
|
ids=ids,
|
|
metric=metric,
|
|
index_uri=index_uri,
|
|
index_type=index_type,
|
|
config=config,
|
|
index_timestamp=index_timestamp,
|
|
**kwargs,
|
|
)
|
|
|
|
@classmethod
|
|
def from_embeddings(
|
|
cls,
|
|
text_embeddings: List[Tuple[str, List[float]]],
|
|
embedding: Embeddings,
|
|
index_uri: str,
|
|
*,
|
|
metadatas: Optional[List[dict]] = None,
|
|
ids: Optional[List[str]] = None,
|
|
metric: str = DEFAULT_METRIC,
|
|
index_type: str = "FLAT",
|
|
config: Optional[Mapping[str, Any]] = None,
|
|
index_timestamp: int = 0,
|
|
**kwargs: Any,
|
|
) -> TileDB:
|
|
"""Construct TileDB index from embeddings.
|
|
|
|
Args:
|
|
text_embeddings: List of tuples of (text, embedding)
|
|
embedding: Embedding function to use.
|
|
index_uri: The URI to write the TileDB arrays
|
|
metadatas: List of metadata dictionaries to associate with documents.
|
|
metric: Optional, Metric to use for indexing. Defaults to "euclidean".
|
|
index_type: Optional, Vector index type ("FLAT", IVF_FLAT")
|
|
config: Optional, TileDB config
|
|
index_timestamp: Optional, timestamp to write new texts with.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community import TileDB
|
|
from langchain_community.embeddings import OpenAIEmbeddings
|
|
embeddings = OpenAIEmbeddings()
|
|
text_embeddings = embeddings.embed_documents(texts)
|
|
text_embedding_pairs = list(zip(texts, text_embeddings))
|
|
db = TileDB.from_embeddings(text_embedding_pairs, embeddings)
|
|
"""
|
|
texts = [t[0] for t in text_embeddings]
|
|
embeddings = [t[1] for t in text_embeddings]
|
|
|
|
return cls.__from(
|
|
texts=texts,
|
|
embeddings=embeddings,
|
|
embedding=embedding,
|
|
metadatas=metadatas,
|
|
ids=ids,
|
|
metric=metric,
|
|
index_uri=index_uri,
|
|
index_type=index_type,
|
|
config=config,
|
|
index_timestamp=index_timestamp,
|
|
**kwargs,
|
|
)
|
|
|
|
@classmethod
|
|
def load(
|
|
cls,
|
|
index_uri: str,
|
|
embedding: Embeddings,
|
|
*,
|
|
metric: str = DEFAULT_METRIC,
|
|
config: Optional[Mapping[str, Any]] = None,
|
|
timestamp: Any = None,
|
|
**kwargs: Any,
|
|
) -> TileDB:
|
|
"""Load a TileDB index from a URI.
|
|
|
|
Args:
|
|
index_uri: The URI of the TileDB vector index.
|
|
embedding: Embeddings to use when generating queries.
|
|
metric: Optional, Metric to use for indexing. Defaults to "euclidean".
|
|
config: Optional, TileDB config
|
|
timestamp: Optional, timestamp to use for opening the arrays.
|
|
"""
|
|
return cls(
|
|
embedding=embedding,
|
|
index_uri=index_uri,
|
|
metric=metric,
|
|
config=config,
|
|
timestamp=timestamp,
|
|
**kwargs,
|
|
)
|
|
|
|
def consolidate_updates(self, **kwargs: Any) -> None:
|
|
self.vector_index = self.vector_index.consolidate_updates(**kwargs)
|