mirror of https://github.com/hwchase17/langchain
Harrison/hologres (#6012)
Co-authored-by: Changgeng Zhao <changgeng@nyu.edu> Co-authored-by: Changgeng Zhao <zhaochanggeng.zcg@alibaba-inc.com>pull/6011/head^2
parent
c5bce4a465
commit
e05997c25e
@ -0,0 +1,506 @@
|
||||
"""VectorStore wrapper around a Hologres database."""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import uuid
|
||||
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.embeddings.base import Embeddings
|
||||
from langchain.utils import get_from_dict_or_env
|
||||
from langchain.vectorstores.base import VectorStore
|
||||
|
||||
ADA_TOKEN_COUNT = 1536
|
||||
_LANGCHAIN_DEFAULT_TABLE_NAME = "langchain_pg_embedding"
|
||||
|
||||
|
||||
class HologresWrapper:
|
||||
def __init__(self, connection_string: str, ndims: int, table_name: str) -> None:
|
||||
import psycopg2
|
||||
|
||||
self.table_name = table_name
|
||||
self.conn = psycopg2.connect(connection_string)
|
||||
self.cursor = self.conn.cursor()
|
||||
self.conn.autocommit = False
|
||||
self.ndims = ndims
|
||||
|
||||
def create_vector_extension(self) -> None:
|
||||
self.cursor.execute("create extension if not exists proxima")
|
||||
self.conn.commit()
|
||||
|
||||
def create_table(self, drop_if_exist: bool = True) -> None:
|
||||
if drop_if_exist:
|
||||
self.cursor.execute(f"drop table if exists {self.table_name}")
|
||||
self.conn.commit()
|
||||
|
||||
self.cursor.execute(
|
||||
f"""create table if not exists {self.table_name} (
|
||||
id text,
|
||||
embedding float4[] check(array_ndims(embedding) = 1 and \
|
||||
array_length(embedding, 1) = {self.ndims}),
|
||||
metadata json,
|
||||
document text);"""
|
||||
)
|
||||
self.cursor.execute(
|
||||
f"call set_table_property('{self.table_name}'"
|
||||
+ """, 'proxima_vectors',
|
||||
'{"embedding":{"algorithm":"Graph",
|
||||
"distance_method":"SquaredEuclidean",
|
||||
"build_params":{"min_flush_proxima_row_count" : 1,
|
||||
"min_compaction_proxima_row_count" : 1,
|
||||
"max_total_size_to_merge_mb" : 2000}}}');"""
|
||||
)
|
||||
self.conn.commit()
|
||||
|
||||
def get_by_id(self, id: str) -> List[Tuple]:
|
||||
statement = (
|
||||
f"select id, embedding, metadata, "
|
||||
f"document from {self.table_name} where id = %s;"
|
||||
)
|
||||
self.cursor.execute(
|
||||
statement,
|
||||
(id),
|
||||
)
|
||||
self.conn.commit()
|
||||
return self.cursor.fetchall()
|
||||
|
||||
def insert(
|
||||
self,
|
||||
embedding: List[float],
|
||||
metadata: dict,
|
||||
document: str,
|
||||
id: Optional[str] = None,
|
||||
) -> None:
|
||||
self.cursor.execute(
|
||||
f'insert into "{self.table_name}" '
|
||||
f"values (%s, array{json.dumps(embedding)}::float4[], %s, %s)",
|
||||
(id if id is not None else "null", json.dumps(metadata), document),
|
||||
)
|
||||
self.conn.commit()
|
||||
|
||||
def query_nearest_neighbours(
|
||||
self, embedding: List[float], k: int, filter: Optional[Dict[str, str]] = None
|
||||
) -> List[Tuple[str, str, float]]:
|
||||
params = []
|
||||
filter_clause = ""
|
||||
if filter is not None:
|
||||
conjuncts = []
|
||||
for key, val in filter.items():
|
||||
conjuncts.append("metadata->>%s=%s")
|
||||
params.append(key)
|
||||
params.append(val)
|
||||
filter_clause = "where " + " and ".join(conjuncts)
|
||||
|
||||
sql = (
|
||||
f"select document, metadata::text, "
|
||||
f"pm_approx_squared_euclidean_distance(array{json.dumps(embedding)}"
|
||||
f"::float4[], embedding) as distance from"
|
||||
f" {self.table_name} {filter_clause} order by distance asc limit {k};"
|
||||
)
|
||||
self.cursor.execute(sql, tuple(params))
|
||||
self.conn.commit()
|
||||
return self.cursor.fetchall()
|
||||
|
||||
|
||||
class Hologres(VectorStore):
|
||||
"""
|
||||
VectorStore implementation using Hologres.
|
||||
- `connection_string` is a hologres connection string.
|
||||
- `embedding_function` any embedding function implementing
|
||||
`langchain.embeddings.base.Embeddings` interface.
|
||||
- `ndims` is the number of dimensions of the embedding output.
|
||||
- `table_name` is the name of the table to store embeddings and data.
|
||||
(default: langchain_pg_embedding)
|
||||
- NOTE: The table will be created when initializing the store (if not exists)
|
||||
So, make sure the user has the right permissions to create tables.
|
||||
- `pre_delete_table` if True, will delete the table if it exists.
|
||||
(default: False)
|
||||
- Useful for testing.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
connection_string: str,
|
||||
embedding_function: Embeddings,
|
||||
ndims: int = ADA_TOKEN_COUNT,
|
||||
table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
|
||||
pre_delete_table: bool = False,
|
||||
logger: Optional[logging.Logger] = None,
|
||||
) -> None:
|
||||
self.connection_string = connection_string
|
||||
self.ndims = ndims
|
||||
self.table_name = table_name
|
||||
self.embedding_function = embedding_function
|
||||
self.pre_delete_table = pre_delete_table
|
||||
self.logger = logger or logging.getLogger(__name__)
|
||||
self.__post_init__()
|
||||
|
||||
def __post_init__(
|
||||
self,
|
||||
) -> None:
|
||||
"""
|
||||
Initialize the store.
|
||||
"""
|
||||
self.storage = HologresWrapper(
|
||||
self.connection_string, self.ndims, self.table_name
|
||||
)
|
||||
self.create_vector_extension()
|
||||
self.create_table()
|
||||
|
||||
def create_vector_extension(self) -> None:
|
||||
try:
|
||||
self.storage.create_vector_extension()
|
||||
except Exception as e:
|
||||
self.logger.exception(e)
|
||||
raise e
|
||||
|
||||
def create_table(self) -> None:
|
||||
self.storage.create_table(self.pre_delete_table)
|
||||
|
||||
@classmethod
|
||||
def __from(
|
||||
cls,
|
||||
texts: List[str],
|
||||
embeddings: List[List[float]],
|
||||
embedding_function: Embeddings,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
ids: Optional[List[str]] = None,
|
||||
ndims: int = ADA_TOKEN_COUNT,
|
||||
table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
|
||||
pre_delete_table: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> Hologres:
|
||||
if ids is None:
|
||||
ids = [str(uuid.uuid1()) for _ in texts]
|
||||
|
||||
if not metadatas:
|
||||
metadatas = [{} for _ in texts]
|
||||
|
||||
connection_string = cls.get_connection_string(kwargs)
|
||||
|
||||
store = cls(
|
||||
connection_string=connection_string,
|
||||
embedding_function=embedding_function,
|
||||
ndims=ndims,
|
||||
table_name=table_name,
|
||||
pre_delete_table=pre_delete_table,
|
||||
)
|
||||
|
||||
store.add_embeddings(
|
||||
texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
|
||||
)
|
||||
|
||||
return store
|
||||
|
||||
def add_embeddings(
|
||||
self,
|
||||
texts: Iterable[str],
|
||||
embeddings: List[List[float]],
|
||||
metadatas: List[dict],
|
||||
ids: List[str],
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
"""Add embeddings to the vectorstore.
|
||||
|
||||
Args:
|
||||
texts: Iterable of strings to add to the vectorstore.
|
||||
embeddings: List of list of embedding vectors.
|
||||
metadatas: List of metadatas associated with the texts.
|
||||
kwargs: vectorstore specific parameters
|
||||
"""
|
||||
try:
|
||||
for text, metadata, embedding, id in zip(texts, metadatas, embeddings, ids):
|
||||
self.storage.insert(embedding, metadata, text, id)
|
||||
except Exception as e:
|
||||
self.logger.exception(e)
|
||||
self.storage.conn.commit()
|
||||
|
||||
def add_texts(
|
||||
self,
|
||||
texts: Iterable[str],
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
ids: Optional[List[str]] = None,
|
||||
**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.
|
||||
kwargs: vectorstore specific parameters
|
||||
|
||||
Returns:
|
||||
List of ids from adding the texts into the vectorstore.
|
||||
"""
|
||||
if ids is None:
|
||||
ids = [str(uuid.uuid1()) for _ in texts]
|
||||
|
||||
embeddings = self.embedding_function.embed_documents(list(texts))
|
||||
|
||||
if not metadatas:
|
||||
metadatas = [{} for _ in texts]
|
||||
|
||||
self.add_embeddings(texts, embeddings, metadatas, ids, **kwargs)
|
||||
|
||||
return ids
|
||||
|
||||
def similarity_search(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
filter: Optional[dict] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
"""Run similarity search with Hologres with distance.
|
||||
|
||||
Args:
|
||||
query (str): Query text to search for.
|
||||
k (int): Number of results to return. Defaults to 4.
|
||||
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
|
||||
|
||||
Returns:
|
||||
List of Documents most similar to the query.
|
||||
"""
|
||||
embedding = self.embedding_function.embed_query(text=query)
|
||||
return self.similarity_search_by_vector(
|
||||
embedding=embedding,
|
||||
k=k,
|
||||
filter=filter,
|
||||
)
|
||||
|
||||
def similarity_search_by_vector(
|
||||
self,
|
||||
embedding: List[float],
|
||||
k: int = 4,
|
||||
filter: Optional[dict] = None,
|
||||
**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.
|
||||
|
||||
Returns:
|
||||
List of Documents most similar to the query vector.
|
||||
"""
|
||||
docs_and_scores = self.similarity_search_with_score_by_vector(
|
||||
embedding=embedding, k=k, filter=filter
|
||||
)
|
||||
return [doc for doc, _ in docs_and_scores]
|
||||
|
||||
def similarity_search_with_score(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
filter: Optional[dict] = None,
|
||||
) -> 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.
|
||||
|
||||
Returns:
|
||||
List of Documents most similar to the query and score for each
|
||||
"""
|
||||
embedding = self.embedding_function.embed_query(query)
|
||||
docs = self.similarity_search_with_score_by_vector(
|
||||
embedding=embedding, k=k, filter=filter
|
||||
)
|
||||
return docs
|
||||
|
||||
def similarity_search_with_score_by_vector(
|
||||
self,
|
||||
embedding: List[float],
|
||||
k: int = 4,
|
||||
filter: Optional[dict] = None,
|
||||
) -> List[Tuple[Document, float]]:
|
||||
results: List[Tuple[str, str, float]] = self.storage.query_nearest_neighbours(
|
||||
embedding, k, filter
|
||||
)
|
||||
|
||||
docs = [
|
||||
(
|
||||
Document(
|
||||
page_content=result[0],
|
||||
metadata=json.loads(result[1]),
|
||||
),
|
||||
result[2],
|
||||
)
|
||||
for result in results
|
||||
]
|
||||
return docs
|
||||
|
||||
@classmethod
|
||||
def from_texts(
|
||||
cls: Type[Hologres],
|
||||
texts: List[str],
|
||||
embedding: Embeddings,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
ndims: int = ADA_TOKEN_COUNT,
|
||||
table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
|
||||
ids: Optional[List[str]] = None,
|
||||
pre_delete_table: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> Hologres:
|
||||
"""
|
||||
Return VectorStore initialized from texts and embeddings.
|
||||
Postgres connection string is required
|
||||
"Either pass it as a parameter
|
||||
or set the HOLOGRES_CONNECTION_STRING environment variable.
|
||||
"""
|
||||
embeddings = embedding.embed_documents(list(texts))
|
||||
|
||||
return cls.__from(
|
||||
texts,
|
||||
embeddings,
|
||||
embedding,
|
||||
metadatas=metadatas,
|
||||
ids=ids,
|
||||
ndims=ndims,
|
||||
table_name=table_name,
|
||||
pre_delete_table=pre_delete_table,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_embeddings(
|
||||
cls,
|
||||
text_embeddings: List[Tuple[str, List[float]]],
|
||||
embedding: Embeddings,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
ndims: int = ADA_TOKEN_COUNT,
|
||||
table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
|
||||
ids: Optional[List[str]] = None,
|
||||
pre_delete_table: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> Hologres:
|
||||
"""Construct Hologres wrapper from raw documents and pre-
|
||||
generated embeddings.
|
||||
|
||||
Return VectorStore initialized from documents and embeddings.
|
||||
Postgres connection string is required
|
||||
"Either pass it as a parameter
|
||||
or set the HOLOGRES_CONNECTION_STRING environment variable.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain import Hologres
|
||||
from langchain.embeddings import OpenAIEmbeddings
|
||||
embeddings = OpenAIEmbeddings()
|
||||
text_embeddings = embeddings.embed_documents(texts)
|
||||
text_embedding_pairs = list(zip(texts, text_embeddings))
|
||||
faiss = Hologres.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,
|
||||
embeddings,
|
||||
embedding,
|
||||
metadatas=metadatas,
|
||||
ids=ids,
|
||||
ndims=ndims,
|
||||
table_name=table_name,
|
||||
pre_delete_table=pre_delete_table,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_existing_index(
|
||||
cls: Type[Hologres],
|
||||
embedding: Embeddings,
|
||||
ndims: int = ADA_TOKEN_COUNT,
|
||||
table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
|
||||
pre_delete_table: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> Hologres:
|
||||
"""
|
||||
Get intsance of an existing Hologres store.This method will
|
||||
return the instance of the store without inserting any new
|
||||
embeddings
|
||||
"""
|
||||
|
||||
connection_string = cls.get_connection_string(kwargs)
|
||||
|
||||
store = cls(
|
||||
connection_string=connection_string,
|
||||
ndims=ndims,
|
||||
table_name=table_name,
|
||||
embedding_function=embedding,
|
||||
pre_delete_table=pre_delete_table,
|
||||
)
|
||||
|
||||
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="HOLOGRES_CONNECTION_STRING",
|
||||
)
|
||||
|
||||
if not connection_string:
|
||||
raise ValueError(
|
||||
"Postgres connection string is required"
|
||||
"Either pass it as a parameter"
|
||||
"or set the HOLOGRES_CONNECTION_STRING environment variable."
|
||||
)
|
||||
|
||||
return connection_string
|
||||
|
||||
@classmethod
|
||||
def from_documents(
|
||||
cls: Type[Hologres],
|
||||
documents: List[Document],
|
||||
embedding: Embeddings,
|
||||
ndims: int = ADA_TOKEN_COUNT,
|
||||
table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
|
||||
ids: Optional[List[str]] = None,
|
||||
pre_delete_collection: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> Hologres:
|
||||
"""
|
||||
Return VectorStore initialized from documents and embeddings.
|
||||
Postgres connection string is required
|
||||
"Either pass it as a parameter
|
||||
or set the HOLOGRES_CONNECTION_STRING environment variable.
|
||||
"""
|
||||
|
||||
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,
|
||||
ndims=ndims,
|
||||
table_name=table_name,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def connection_string_from_db_params(
|
||||
cls,
|
||||
host: str,
|
||||
port: int,
|
||||
database: str,
|
||||
user: str,
|
||||
password: str,
|
||||
) -> str:
|
||||
"""Return connection string from database parameters."""
|
||||
return (
|
||||
f"dbname={database} user={user} password={password} host={host} port={port}"
|
||||
)
|
@ -0,0 +1,142 @@
|
||||
"""Test Hologres functionality."""
|
||||
import os
|
||||
from typing import List
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.vectorstores.hologres import Hologres
|
||||
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
|
||||
|
||||
CONNECTION_STRING = Hologres.connection_string_from_db_params(
|
||||
host=os.environ.get("TEST_HOLOGRES_HOST", "localhost"),
|
||||
port=int(os.environ.get("TEST_HOLOGRES_PORT", "80")),
|
||||
database=os.environ.get("TEST_HOLOGRES_DATABASE", "postgres"),
|
||||
user=os.environ.get("TEST_HOLOGRES_USER", "postgres"),
|
||||
password=os.environ.get("TEST_HOLOGRES_PASSWORD", "postgres"),
|
||||
)
|
||||
|
||||
|
||||
ADA_TOKEN_COUNT = 1536
|
||||
|
||||
|
||||
class FakeEmbeddingsWithAdaDimension(FakeEmbeddings):
|
||||
"""Fake embeddings functionality for testing."""
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Return simple embeddings."""
|
||||
return [
|
||||
[float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(i)] for i in range(len(texts))
|
||||
]
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Return simple embeddings."""
|
||||
return [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(0.0)]
|
||||
|
||||
|
||||
def test_hologres() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
docsearch = Hologres.from_texts(
|
||||
texts=texts,
|
||||
table_name="test_table",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_table=True,
|
||||
)
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
def test_hologres_embeddings() -> None:
|
||||
"""Test end to end construction with embeddings and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
text_embeddings = FakeEmbeddingsWithAdaDimension().embed_documents(texts)
|
||||
text_embedding_pairs = list(zip(texts, text_embeddings))
|
||||
docsearch = Hologres.from_embeddings(
|
||||
text_embeddings=text_embedding_pairs,
|
||||
table_name="test_table",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_table=True,
|
||||
)
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
def test_hologres_with_metadatas() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = Hologres.from_texts(
|
||||
texts=texts,
|
||||
table_name="test_table",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_table=True,
|
||||
)
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo", metadata={"page": "0"})]
|
||||
|
||||
|
||||
def test_hologres_with_metadatas_with_scores() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = Hologres.from_texts(
|
||||
texts=texts,
|
||||
table_name="test_table",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_table=True,
|
||||
)
|
||||
output = docsearch.similarity_search_with_score("foo", k=1)
|
||||
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
|
||||
|
||||
|
||||
def test_hologres_with_filter_match() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = Hologres.from_texts(
|
||||
texts=texts,
|
||||
table_name="test_table_filter",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_table=True,
|
||||
)
|
||||
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "0"})
|
||||
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
|
||||
|
||||
|
||||
def test_hologres_with_filter_distant_match() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = Hologres.from_texts(
|
||||
texts=texts,
|
||||
table_name="test_table_filter",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_table=True,
|
||||
)
|
||||
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"})
|
||||
assert output == [(Document(page_content="baz", metadata={"page": "2"}), 4.0)]
|
||||
|
||||
|
||||
def test_hologres_with_filter_no_match() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = Hologres.from_texts(
|
||||
texts=texts,
|
||||
table_name="test_table_filter",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_table=True,
|
||||
)
|
||||
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "5"})
|
||||
assert output == []
|
Loading…
Reference in New Issue