from __future__ import annotations
import contextlib
import enum
import logging
import uuid
from typing import (
Any,
Callable,
Dict,
Generator,
Iterable,
List,
Optional,
Tuple,
Type,
Union,
)
import numpy as np
import sqlalchemy
from sqlalchemy import delete, func
from sqlalchemy.dialects.postgresql import JSON, UUID
from sqlalchemy.exc import ProgrammingError
from sqlalchemy.orm import Session
from sqlalchemy.sql import quoted_name
from langchain_community.vectorstores.utils import maximal_marginal_relevance
try:
from sqlalchemy.orm import declarative_base
except ImportError:
from sqlalchemy.ext.declarative import declarative_base
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.utils import get_from_dict_or_env
from langchain_core.vectorstores import VectorStore
ADA_TOKEN_COUNT = 1536
_LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
def _results_to_docs(docs_and_scores: Any) -> List[Document]:
"""Return docs from docs and scores."""
return [doc for doc, _ in docs_and_scores]
[docs]
class BaseEmbeddingStore:
"""Base class for the Lantern embedding store."""
[docs]
def get_embedding_store(
distance_strategy: DistanceStrategy, collection_name: str
) -> Any:
"""Get the embedding store class."""
embedding_type = None
if distance_strategy == DistanceStrategy.HAMMING:
embedding_type = sqlalchemy.INTEGER # type: ignore
else:
embedding_type = sqlalchemy.REAL # type: ignore
DynamicBase = declarative_base(class_registry=dict()) # type: Any
class EmbeddingStore(DynamicBase, BaseEmbeddingStore):
__tablename__ = collection_name
uuid = sqlalchemy.Column(
UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
)
__table_args__ = {"extend_existing": True}
document = sqlalchemy.Column(sqlalchemy.String, nullable=True)
cmetadata = sqlalchemy.Column(JSON, nullable=True)
# custom_id : any user defined id
custom_id = sqlalchemy.Column(sqlalchemy.String, nullable=True)
embedding = sqlalchemy.Column(sqlalchemy.ARRAY(embedding_type)) # type: ignore
return EmbeddingStore
[docs]
class QueryResult:
"""Result from a query."""
EmbeddingStore: BaseEmbeddingStore
distance: float
[docs]
class DistanceStrategy(str, enum.Enum):
"""Enumerator of the Distance strategies."""
EUCLIDEAN = "l2sq"
COSINE = "cosine"
HAMMING = "hamming"
DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.COSINE
[docs]
class Lantern(VectorStore):
"""`Postgres` with the `lantern` extension as a vector store.
lantern uses sequential scan by default. but you can create a HNSW index
using the create_hnsw_index method.
- `connection_string` is a postgres connection string.
- `embedding_function` any embedding function implementing
`langchain.embeddings.base.Embeddings` interface.
- `collection_name` is the name of the collection to use. (default: langchain)
- NOTE: This is the name of the table in which embedding data will be stored
The table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
- `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN)
- `EUCLIDEAN` is the euclidean distance.
- `COSINE` is the cosine distance.
- `HAMMING` is the hamming distance.
- `pre_delete_collection` if True, will delete the collection if it exists.
(default: False)
- Useful for testing.
"""
[docs]
def __init__(
self,
connection_string: str,
embedding_function: Embeddings,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
collection_metadata: Optional[dict] = None,
pre_delete_collection: bool = False,
logger: Optional[logging.Logger] = None,
relevance_score_fn: Optional[Callable[[float], float]] = None,
) -> None:
self.connection_string = connection_string
self.embedding_function = embedding_function
self.collection_name = collection_name
self.collection_metadata = collection_metadata
self._distance_strategy = distance_strategy
self.pre_delete_collection = pre_delete_collection
self.logger = logger or logging.getLogger(__name__)
self.override_relevance_score_fn = relevance_score_fn
self.EmbeddingStore = get_embedding_store(
self.distance_strategy, collection_name
)
self.__post_init__()
def __post_init__(
self,
) -> None:
self._conn = self.connect()
self.create_hnsw_extension()
self.create_collection()
@property
def distance_strategy(self) -> DistanceStrategy:
if isinstance(self._distance_strategy, DistanceStrategy):
return self._distance_strategy
if self._distance_strategy == DistanceStrategy.EUCLIDEAN.value:
return DistanceStrategy.EUCLIDEAN
elif self._distance_strategy == DistanceStrategy.COSINE.value:
return DistanceStrategy.COSINE
elif self._distance_strategy == DistanceStrategy.HAMMING.value:
return DistanceStrategy.HAMMING
else:
raise ValueError(
f"Got unexpected value for distance: {self._distance_strategy}. "
f"Should be one of {', '.join([ds.value for ds in DistanceStrategy])}."
)
@property
def embeddings(self) -> Embeddings:
return self.embedding_function
[docs]
@classmethod
def connection_string_from_db_params(
cls,
driver: str,
host: str,
port: int,
database: str,
user: str,
password: str,
) -> str:
"""Return connection string from database parameters."""
return f"postgresql+{driver}://{user}:{password}@{host}:{port}/{database}"
[docs]
def connect(self) -> sqlalchemy.engine.Connection:
engine = sqlalchemy.create_engine(self.connection_string)
conn = engine.connect()
return conn
@property
def distance_function(self) -> Any:
if self.distance_strategy == DistanceStrategy.EUCLIDEAN:
return "l2sq_dist"
elif self.distance_strategy == DistanceStrategy.COSINE:
return "cos_dist"
elif self.distance_strategy == DistanceStrategy.HAMMING:
return "hamming_dist"
[docs]
def create_hnsw_extension(self) -> None:
try:
with Session(self._conn) as session:
statement = sqlalchemy.text("CREATE EXTENSION IF NOT EXISTS lantern")
session.execute(statement)
session.commit()
except Exception as e:
self.logger.exception(e)
[docs]
def create_tables_if_not_exists(self) -> None:
try:
self.create_collection()
except ProgrammingError:
pass
[docs]
def drop_table(self) -> None:
try:
self.EmbeddingStore.__table__.drop(self._conn.engine)
except ProgrammingError:
pass
[docs]
def drop_tables(self) -> None:
self.drop_table()
def _hamming_relevance_score_fn(self, distance: float) -> float:
return distance
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
"""
if self.override_relevance_score_fn is not None:
return self.override_relevance_score_fn
# Default strategy is to rely on distance strategy provided
# in vectorstore constructor
if self.distance_strategy == DistanceStrategy.COSINE:
return self._cosine_relevance_score_fn
elif self.distance_strategy == DistanceStrategy.EUCLIDEAN:
return self._euclidean_relevance_score_fn
elif self.distance_strategy == DistanceStrategy.HAMMING:
return self._hamming_relevance_score_fn
else:
raise ValueError(
"No supported normalization function"
f" for distance_strategy of {self._distance_strategy}."
"Consider providing relevance_score_fn to Lantern constructor."
)
def _get_op_class(self) -> str:
if self.distance_strategy == DistanceStrategy.COSINE:
return "dist_cos_ops"
elif self.distance_strategy == DistanceStrategy.EUCLIDEAN:
return "dist_l2sq_ops"
elif self.distance_strategy == DistanceStrategy.HAMMING:
return "dist_hamming_ops"
else:
raise ValueError(
"No supported operator class"
f" for distance_strategy of {self._distance_strategy}."
)
def _get_operator(self) -> str:
if self.distance_strategy == DistanceStrategy.COSINE:
return "<=>"
elif self.distance_strategy == DistanceStrategy.EUCLIDEAN:
return "<->"
elif self.distance_strategy == DistanceStrategy.HAMMING:
return "<+>"
else:
raise ValueError(
"No supported operator"
f" for distance_strategy of {self._distance_strategy}."
)
def _typed_arg_for_distance(
self, embedding: List[Union[float, int]]
) -> List[Union[float, int]]:
if self.distance_strategy == DistanceStrategy.HAMMING:
return list(map(lambda x: int(x), embedding))
return embedding
@property
def _index_name(self) -> str:
return f"langchain_{self.collection_name}_idx"
[docs]
def create_hnsw_index(
self,
dims: int = ADA_TOKEN_COUNT,
m: int = 16,
ef_construction: int = 64,
ef_search: int = 64,
**_kwargs: Any,
) -> None:
"""Create HNSW index on collection.
Optional Keyword Args for HNSW Index:
engine: "nmslib", "faiss", "lucene"; default: "nmslib"
ef: Size of the dynamic list used during k-NN searches. Higher values
lead to more accurate but slower searches; default: 64
ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate graph but slower indexing speed;
default: 64
m: Number of bidirectional links created for each new element. Large impact
on memory consumption. Between 2 and 100; default: 16
dims: Dimensions of the vectors in collection. default: 1536
"""
create_index_query = sqlalchemy.text(
"CREATE INDEX IF NOT EXISTS {} "
"ON {} USING hnsw (embedding {}) "
"WITH ("
"dim = :dim, "
"m = :m, "
"ef_construction = :ef_construction, "
"ef = :ef"
");".format(
quoted_name(self._index_name, True),
quoted_name(self.collection_name, True),
self._get_op_class(),
)
)
with Session(self._conn) as session:
# Create the HNSW index
session.execute(
create_index_query,
{
"dim": dims,
"m": m,
"ef_construction": ef_construction,
"ef": ef_search,
},
)
session.commit()
self.logger.info("HNSW extension and index created successfully.")
[docs]
def drop_index(self) -> None:
with Session(self._conn) as session:
# Drop the HNSW index
session.execute(
sqlalchemy.text(
"DROP INDEX IF EXISTS {}".format(
quoted_name(self._index_name, True)
)
)
)
session.commit()
[docs]
def create_collection(self) -> None:
if self.pre_delete_collection:
self.delete_collection()
self.drop_table()
with self._conn.begin():
try:
self.EmbeddingStore.__table__.create(self._conn.engine)
except ProgrammingError as e:
# Duplicate table
if e.code == "f405":
pass
else:
raise e
[docs]
def delete_collection(self) -> None:
self.logger.debug("Trying to delete collection")
self.drop_table()
@contextlib.contextmanager
def _make_session(self) -> Generator[Session, None, None]:
"""Create a context manager for the session, bind to _conn string."""
yield Session(self._conn)
[docs]
def delete(
self,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> None:
"""Delete vectors by ids or uuids.
Args:
ids: List of ids to delete.
"""
with Session(self._conn) as session:
if ids is not None:
self.logger.debug(
"Trying to delete vectors by ids (represented by the model "
"using the custom ids field)"
)
stmt = delete(self.EmbeddingStore).where(
self.EmbeddingStore.custom_id.in_(ids)
)
session.execute(stmt)
session.commit()
@classmethod
def _initialize_from_embeddings(
cls,
texts: List[str],
embeddings: List[List[float]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> Lantern:
"""
Order of elements for lists `ids`, `embeddings`, `texts`, `metadatas`
should match, so each row will be associated with correct values.
Postgres connection string is required
"Either pass it as `connection_string` parameter
or set the LANTERN_CONNECTION_STRING environment variable.
- `texts` texts to insert into collection.
- `embeddings` an Embeddings to insert into collection
- `embedding` is :class:`Embeddings` that will be used for
embedding the text sent. If none is sent, then the
multilingual Tensorflow Universal Sentence Encoder will be used.
- `metadatas` row metadata to insert into collection.
- `ids` row ids to insert into collection.
- `collection_name` is the name of the collection to use. (default: langchain)
- NOTE: This is the name of the table in which embedding data will be stored
The table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
- `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN)
- `EUCLIDEAN` is the euclidean distance.
- `COSINE` is the cosine distance.
- `HAMMING` is the hamming distance.
- `pre_delete_collection` if True, will delete the collection if it exists.
(default: False)
- Useful for testing.
"""
if ids is None:
ids = [str(uuid.uuid4()) for _ in texts]
if not metadatas:
metadatas = [{} for _ in texts]
connection_string = cls.__get_connection_string(kwargs)
store = cls(
connection_string=connection_string,
collection_name=collection_name,
embedding_function=embedding,
pre_delete_collection=pre_delete_collection,
distance_strategy=distance_strategy,
)
store.add_embeddings(
texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
)
store.create_hnsw_index(**kwargs)
return store
[docs]
def add_embeddings(
self,
texts: List[str],
embeddings: List[List[float]],
metadatas: List[dict],
ids: List[str],
**kwargs: Any,
) -> None:
with Session(self._conn) as session:
for text, metadata, embedding, id in zip(texts, metadatas, embeddings, ids):
embedding_store = self.EmbeddingStore(
embedding=embedding,
document=text,
cmetadata=metadata,
custom_id=id,
)
session.add(embedding_store)
session.commit()
[docs]
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
if ids is None:
ids = [str(uuid.uuid4()) for _ in texts]
embeddings = self.embedding_function.embed_documents(list(texts))
if not metadatas:
metadatas = [{} for _ in texts]
with Session(self._conn) as session:
for text, metadata, embedding, id in zip(texts, metadatas, embeddings, ids):
embedding_store = self.EmbeddingStore(
embedding=embedding,
document=text,
cmetadata=metadata,
custom_id=id,
)
session.add(embedding_store)
session.commit()
return ids
def _results_to_docs_and_scores(self, results: Any) -> List[Tuple[Document, float]]:
"""Return docs and scores from results."""
docs = [
(
Document(
page_content=result.EmbeddingStore.document,
metadata=result.EmbeddingStore.cmetadata,
),
result.distance if self.embedding_function is not None else None,
)
for result in results
]
return docs
[docs]
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
embedding = self.embedding_function.embed_query(text=query)
return self.similarity_search_by_vector(
embedding=embedding,
k=k,
filter=filter,
)
[docs]
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
) -> List[Tuple[Document, float]]:
embedding = self.embedding_function.embed_query(query)
docs = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, filter=filter
)
return docs
[docs]
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[dict] = None,
) -> List[Tuple[Document, float]]:
results = self.__query_collection(embedding=embedding, k=k, filter=filter)
return self._results_to_docs_and_scores(results)
def __query_collection(
self,
embedding: List[float],
k: int = 4,
filter: Optional[dict] = None,
) -> List[Any]:
with Session(self._conn) as session:
set_enable_seqscan_stmt = sqlalchemy.text("SET enable_seqscan = off")
set_init_k = sqlalchemy.text("SET hnsw.init_k = :k")
session.execute(set_enable_seqscan_stmt)
session.execute(set_init_k, {"k": k})
filter_by = None
if filter is not None:
filter_clauses = []
for key, value in filter.items():
IN = "in"
if isinstance(value, dict) and IN in map(str.lower, value):
value_case_insensitive = {
k.lower(): v for k, v in value.items()
}
filter_by_metadata = self.EmbeddingStore.cmetadata[
key
].astext.in_(value_case_insensitive[IN])
filter_clauses.append(filter_by_metadata)
else:
filter_by_metadata = self.EmbeddingStore.cmetadata[
key
].astext == str(value)
filter_clauses.append(filter_by_metadata)
filter_by = sqlalchemy.and_(*filter_clauses)
embedding = self._typed_arg_for_distance(embedding)
query = session.query(
self.EmbeddingStore,
getattr(func, self.distance_function)(
self.EmbeddingStore.embedding, embedding
).label("distance"),
) # Specify the columns you need here, e.g., EmbeddingStore.embedding
if filter_by is not None:
query = query.filter(filter_by)
results: List[QueryResult] = (
query.order_by(
self.EmbeddingStore.embedding.op(self._get_operator())(embedding)
) # Using PostgreSQL specific operator with the correct column name
.limit(k)
.all()
)
return results
[docs]
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, filter=filter
)
return _results_to_docs(docs_and_scores)
[docs]
@classmethod
def from_texts(
cls: Type[Lantern],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> Lantern:
"""
Initialize Lantern vectorstore from list of texts.
The embeddings will be generated using `embedding` class provided.
Order of elements for lists `ids`, `texts`, `metadatas` should match,
so each row will be associated with correct values.
Postgres connection string is required
"Either pass it as `connection_string` parameter
or set the LANTERN_CONNECTION_STRING environment variable.
- `connection_string` is fully populated connection string for postgres database
- `texts` texts to insert into collection.
- `embedding` is :class:`Embeddings` that will be used for
embedding the text sent. If none is sent, then the
multilingual Tensorflow Universal Sentence Encoder will be used.
- `metadatas` row metadata to insert into collection.
- `collection_name` is the name of the collection to use. (default: langchain)
- NOTE: This is the name of the table in which embedding data will be stored
The table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
- `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN)
- `EUCLIDEAN` is the euclidean distance.
- `COSINE` is the cosine distance.
- `HAMMING` is the hamming distance.
- `ids` row ids to insert into collection.
- `pre_delete_collection` if True, will delete the collection if it exists.
(default: False)
- Useful for testing.
"""
embeddings = embedding.embed_documents(list(texts))
return cls._initialize_from_embeddings(
texts,
embeddings,
embedding,
metadatas=metadatas,
ids=ids,
collection_name=collection_name,
pre_delete_collection=pre_delete_collection,
distance_strategy=distance_strategy,
**kwargs,
)
[docs]
@classmethod
def from_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
**kwargs: Any,
) -> Lantern:
"""Construct Lantern wrapper from raw documents and pre-
generated embeddings.
Postgres connection string is required
"Either pass it as `connection_string` parameter
or set the LANTERN_CONNECTION_STRING environment variable.
Order of elements for lists `ids`, `text_embeddings`, `metadatas` should match,
so each row will be associated with correct values.
- `connection_string` is fully populated connection string for postgres database
- `text_embeddings` is array with tuples (text, embedding)
to insert into collection.
- `embedding` is :class:`Embeddings` that will be used for
embedding the text sent. If none is sent, then the
multilingual Tensorflow Universal Sentence Encoder will be used.
- `metadatas` row metadata to insert into collection.
- `collection_name` is the name of the collection to use. (default: langchain)
- NOTE: This is the name of the table in which embedding data will be stored
The table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
- `ids` row ids to insert into collection.
- `pre_delete_collection` if True, will delete the collection if it exists.
(default: False)
- Useful for testing.
- `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN)
- `EUCLIDEAN` is the euclidean distance.
- `COSINE` is the cosine distance.
- `HAMMING` is the hamming distance.
"""
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls._initialize_from_embeddings(
texts,
embeddings,
embedding,
metadatas=metadatas,
ids=ids,
collection_name=collection_name,
pre_delete_collection=pre_delete_collection,
distance_strategy=distance_strategy,
**kwargs,
)
[docs]
@classmethod
def from_existing_index(
cls: Type[Lantern],
embedding: Embeddings,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
pre_delete_collection: bool = False,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
**kwargs: Any,
) -> Lantern:
"""
Get instance of an existing Lantern store.This method will
return the instance of the store without inserting any new
embeddings
Postgres connection string is required
"Either pass it as `connection_string` parameter
or set the LANTERN_CONNECTION_STRING environment variable.
- `connection_string` is a postgres connection string.
- `embedding` is :class:`Embeddings` that will be used for
embedding the text sent. If none is sent, then the
multilingual Tensorflow Universal Sentence Encoder will be used.
- `collection_name` is the name of the collection to use. (default: langchain)
- NOTE: This is the name of the table in which embedding data will be stored
The table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
- `ids` row ids to insert into collection.
- `pre_delete_collection` if True, will delete the collection if it exists.
(default: False)
- Useful for testing.
- `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN)
- `EUCLIDEAN` is the euclidean distance.
- `COSINE` is the cosine distance.
- `HAMMING` is the hamming distance.
"""
connection_string = cls.__get_connection_string(kwargs)
store = cls(
connection_string=connection_string,
collection_name=collection_name,
embedding_function=embedding,
pre_delete_collection=pre_delete_collection,
distance_strategy=distance_strategy,
)
return store
@classmethod
def __get_connection_string(cls, kwargs: Dict[str, Any]) -> str:
connection_string: str = get_from_dict_or_env(
data=kwargs,
key="connection_string",
env_key="LANTERN_CONNECTION_STRING",
)
if not connection_string:
raise ValueError(
"Postgres connection string is required"
"Either pass it as `connection_string` parameter"
"or set the LANTERN_CONNECTION_STRING variable."
)
return connection_string
[docs]
@classmethod
def from_documents(
cls: Type[Lantern],
documents: List[Document],
embedding: Embeddings,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> Lantern:
"""
Initialize a vector store with a set of documents.
Postgres connection string is required
"Either pass it as `connection_string` parameter
or set the LANTERN_CONNECTION_STRING environment variable.
- `connection_string` is a postgres connection string.
- `documents` is list of :class:`Document` to initialize the vector store with
- `embedding` is :class:`Embeddings` that will be used for
embedding the text sent. If none is sent, then the
multilingual Tensorflow Universal Sentence Encoder will be used.
- `collection_name` is the name of the collection to use. (default: langchain)
- NOTE: This is the name of the table in which embedding data will be stored
The table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
- `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN)
- `EUCLIDEAN` is the euclidean distance.
- `COSINE` is the cosine distance.
- `HAMMING` is the hamming distance.
- `ids` row ids to insert into collection.
- `pre_delete_collection` if True, will delete the collection if it exists.
(default: False)
- Useful for testing.
"""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
connection_string = cls.__get_connection_string(kwargs)
kwargs["connection_string"] = connection_string
return cls.from_texts(
texts=texts,
pre_delete_collection=pre_delete_collection,
embedding=embedding,
metadatas=metadatas,
ids=ids,
collection_name=collection_name,
distance_strategy=distance_strategy,
**kwargs,
)
[docs]
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, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs selected using the maximal marginal relevance with score
to embedding vector.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult (float): 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.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of Documents selected by maximal marginal
relevance to the query and score for each.
"""
results = self.__query_collection(embedding=embedding, k=fetch_k, filter=filter)
embedding_list = [result.EmbeddingStore.embedding for result in results]
mmr_selected = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
embedding_list,
k=k,
lambda_mult=lambda_mult,
)
candidates = self._results_to_docs_and_scores(results)
return [r for i, r in enumerate(candidates) if i in mmr_selected]
[docs]
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = 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 (str): Text to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult (float): 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.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Document]: List of Documents selected by maximal marginal relevance.
"""
embedding = self.embedding_function.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
**kwargs,
)
[docs]
def max_marginal_relevance_search_with_score(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs selected using the maximal marginal relevance with score.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query (str): Text to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult (float): 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.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of Documents selected by maximal marginal
relevance to the query and score for each.
"""
embedding = self.embedding_function.embed_query(query)
docs = self.max_marginal_relevance_search_with_score_by_vector(
embedding=embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
**kwargs,
)
return docs
[docs]
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, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance
to embedding vector.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding (str): Text to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult (float): 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.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Document]: 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 _results_to_docs(docs_and_scores)
