mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
309 lines
10 KiB
Python
309 lines
10 KiB
Python
from __future__ import annotations
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import os
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import uuid
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import warnings
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from typing import Any, Iterable, List, Optional
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.vectorstores import VectorStore
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def import_lancedb() -> Any:
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"""Import lancedb package."""
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try:
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import lancedb
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except ImportError as e:
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raise ImportError(
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"Could not import pinecone lancedb package. "
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"Please install it with `pip install lancedb`."
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) from e
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return lancedb
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class LanceDB(VectorStore):
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"""`LanceDB` vector store.
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To use, you should have ``lancedb`` python package installed.
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You can install it with ``pip install lancedb``.
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Args:
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connection: LanceDB connection to use. If not provided, a new connection
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will be created.
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embedding: Embedding to use for the vectorstore.
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vector_key: Key to use for the vector in the database. Defaults to ``vector``.
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id_key: Key to use for the id in the database. Defaults to ``id``.
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text_key: Key to use for the text in the database. Defaults to ``text``.
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table_name: Name of the table to use. Defaults to ``vectorstore``.
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api_key: API key to use for LanceDB cloud database.
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region: Region to use for LanceDB cloud database.
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mode: Mode to use for adding data to the table. Defaults to ``overwrite``.
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Example:
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.. code-block:: python
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vectorstore = LanceDB(uri='/lancedb', embedding_function)
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vectorstore.add_texts(['text1', 'text2'])
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result = vectorstore.similarity_search('text1')
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"""
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def __init__(
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self,
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connection: Optional[Any] = None,
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embedding: Optional[Embeddings] = None,
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uri: Optional[str] = "/tmp/lancedb",
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vector_key: Optional[str] = "vector",
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id_key: Optional[str] = "id",
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text_key: Optional[str] = "text",
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table_name: Optional[str] = "vectorstore",
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api_key: Optional[str] = None,
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region: Optional[str] = None,
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mode: Optional[str] = "overwrite",
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):
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"""Initialize with Lance DB vectorstore"""
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lancedb = import_lancedb()
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self._embedding = embedding
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self._vector_key = vector_key
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self._id_key = id_key
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self._text_key = text_key
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self._table_name = table_name
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self.api_key = api_key or os.getenv("LANCE_API_KEY") if api_key != "" else None
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self.region = region
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self.mode = mode
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if isinstance(uri, str) and self.api_key is None:
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if uri.startswith("db://"):
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raise ValueError("API key is required for LanceDB cloud.")
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if self._embedding is None:
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raise ValueError("embedding object should be provided")
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if isinstance(connection, lancedb.db.LanceDBConnection):
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self._connection = connection
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elif isinstance(connection, (str, lancedb.db.LanceTable)):
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raise ValueError(
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"`connection` has to be a lancedb.db.LanceDBConnection object.\
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`lancedb.db.LanceTable` is deprecated."
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)
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else:
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if self.api_key is None:
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self._connection = lancedb.connect(uri)
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else:
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if isinstance(uri, str):
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if uri.startswith("db://"):
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self._connection = lancedb.connect(
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uri, api_key=self.api_key, region=self.region
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)
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else:
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self._connection = lancedb.connect(uri)
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warnings.warn(
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"api key provided with local uri.\
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The data will be stored locally"
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)
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@property
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def embeddings(self) -> Optional[Embeddings]:
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return self._embedding
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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ids: Optional[List[str]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Turn texts into embedding and add it to the database
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Args:
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texts: Iterable of strings to add to the vectorstore.
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metadatas: Optional list of metadatas associated with the texts.
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ids: Optional list of ids to associate w ith the texts.
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Returns:
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List of ids of the added texts.
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"""
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# Embed texts and create documents
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docs = []
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ids = ids or [str(uuid.uuid4()) for _ in texts]
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embeddings = self._embedding.embed_documents(list(texts)) # type: ignore
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for idx, text in enumerate(texts):
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embedding = embeddings[idx]
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metadata = metadatas[idx] if metadatas else {"id": ids[idx]}
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docs.append(
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{
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self._vector_key: embedding,
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self._id_key: ids[idx],
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self._text_key: text,
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"metadata": metadata,
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}
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)
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if self._table_name in self._connection.table_names():
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tbl = self._connection.open_table(self._table_name)
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if self.api_key is None:
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tbl.add(docs, mode=self.mode)
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else:
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tbl.add(docs)
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else:
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self._connection.create_table(self._table_name, data=docs)
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return ids
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def get_table(
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self, name: Optional[str] = None, set_default: Optional[bool] = False
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) -> Any:
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"""
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Fetches a table object from the database.
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Args:
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name (str, optional): The name of the table to fetch. Defaults to None
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and fetches current table object.
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set_default (bool, optional): Sets fetched table as the default table.
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Defaults to False.
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Returns:
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Any: The fetched table object.
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Raises:
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ValueError: If the specified table is not found in the database.
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"""
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if name is not None:
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try:
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if set_default:
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self._table_name = name
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return self._connection.open_table(name)
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except Exception:
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raise ValueError(f"Table {name} not found in the database")
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else:
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return self._connection.open_table(self._table_name)
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def create_index(
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self,
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col_name: Optional[str] = None,
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vector_col: Optional[str] = None,
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num_partitions: Optional[int] = 256,
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num_sub_vectors: Optional[int] = 96,
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index_cache_size: Optional[int] = None,
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metric: Optional[str] = "L2",
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) -> None:
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"""
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Create a scalar(for non-vector cols) or a vector index on a table.
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Make sure your vector column has enough data before creating an index on it.
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Args:
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vector_col: Provide if you want to create index on a vector column.
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col_name: Provide if you want to create index on a non-vector column.
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metric: Provide the metric to use for vector index. Defaults to 'L2'
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choice of metrics: 'L2', 'dot', 'cosine'
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Returns:
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None
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"""
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tbl = self.get_table()
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if vector_col:
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tbl.create_index(
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metric=metric,
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vector_column_name=vector_col,
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num_partitions=num_partitions,
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num_sub_vectors=num_sub_vectors,
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index_cache_size=index_cache_size,
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)
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elif col_name:
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tbl.create_scalar_index(col_name)
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else:
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raise ValueError("Provide either vector_col or col_name")
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def similarity_search(
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self, query: str, k: int = 4, name: Optional[str] = None, **kwargs: Any
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) -> List[Document]:
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"""Return documents most similar to the query
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Args:
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query: String to query the vectorstore with.
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k: Number of documents to return.
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Returns:
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List of documents most similar to the query.
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"""
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embedding = self._embedding.embed_query(query) # type: ignore
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tbl = self.get_table(name)
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docs = (
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tbl.search(embedding, vector_column_name=self._vector_key)
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.limit(k)
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.to_arrow()
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)
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columns = docs.schema.names
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return [
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Document(
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page_content=docs[self._text_key][idx].as_py(),
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metadata={
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col: docs[col][idx].as_py()
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for col in columns
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if col != self._text_key
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},
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)
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for idx in range(len(docs))
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]
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@classmethod
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def from_texts(
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cls,
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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connection: Any = None,
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vector_key: Optional[str] = "vector",
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id_key: Optional[str] = "id",
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text_key: Optional[str] = "text",
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**kwargs: Any,
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) -> LanceDB:
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instance = LanceDB(
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connection=connection,
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embedding=embedding,
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vector_key=vector_key,
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id_key=id_key,
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text_key=text_key,
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)
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instance.add_texts(texts, metadatas=metadatas, **kwargs)
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return instance
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def delete(
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self,
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ids: Optional[List[str]] = None,
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delete_all: Optional[bool] = None,
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filter: Optional[str] = None,
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drop_columns: Optional[List[str]] = None,
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name: Optional[str] = None,
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**kwargs: Any,
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) -> None:
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"""
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Allows deleting rows by filtering, by ids or drop columns from the table.
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Args:
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filter: Provide a string SQL expression - "{col} {operation} {value}".
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ids: Provide list of ids to delete from the table.
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drop_columns: Provide list of columns to drop from the table.
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delete_all: If True, delete all rows from the table.
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"""
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tbl = self.get_table(name)
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if filter:
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tbl.delete(filter)
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elif ids:
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tbl.delete("id in ('{}')".format(",".join(ids)))
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elif drop_columns:
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if self.api_key is not None:
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raise NotImplementedError(
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"Column operations currently not supported in LanceDB Cloud."
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)
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else:
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tbl.drop_columns(drop_columns)
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elif delete_all:
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tbl.delete("true")
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else:
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raise ValueError("Provide either filter, ids, drop_columns or delete_all")
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