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fix: update insert statement #29

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merged 9 commits into from
Feb 15, 2024
Merged

fix: update insert statement #29

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d1c6b0b
test
averikitsch Feb 15, 2024
8aa1379
fix: update insert statement
averikitsch Feb 15, 2024
524e379
lint
averikitsch Feb 15, 2024
4e07de7
Merge branch 'main' into akitsch-bug
averikitsch Feb 15, 2024
ed83474
fix
averikitsch Feb 15, 2024
8edc4e2
lint
averikitsch Feb 15, 2024
45e688e
Update cloudsql_vectorstore.py
averikitsch Feb 15, 2024
5b14dd8
Merge branch 'main' into akitsch-bug
averikitsch Feb 15, 2024
95ec32b
lint
averikitsch Feb 15, 2024
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lint
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@averikitsch
averikitsch committed Feb 15, 2024
commit 524e3798adcd2c5ea7440bd5ab21500810047c75
52 changes: 13 additions & 39 deletions src/langchain_google_cloud_sql_pg/cloudsql_vectorstore.py
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Original file line number Diff line number Diff line change
Expand Up @@ -124,27 +124,21 @@ async def create(
if id_column not in columns:
raise ValueError(f"Id column, {id_column}, does not exist.")
if content_column not in columns:
raise ValueError(
f"Content column, {content_column}, does not exist."
)
raise ValueError(f"Content column, {content_column}, does not exist.")
content_type = columns[content_column]
if content_type != "text" and "char" not in content_type:
raise ValueError(
f"Content column, {content_column}, is type, {content_type}. It must be a type of character string."
)
if embedding_column not in columns:
raise ValueError(
f"Embedding column, {embedding_column}, does not exist."
)
raise ValueError(f"Embedding column, {embedding_column}, does not exist.")
if columns[embedding_column] != "USER-DEFINED":
raise ValueError(
f"Embedding column, {embedding_column}, is not type Vector."
)

metadata_json_column = (
None
if metadata_json_column not in columns
else metadata_json_column
None if metadata_json_column not in columns else metadata_json_column
)

# If using metadata_columns check to make sure column exists
Expand Down Expand Up @@ -233,9 +227,7 @@ async def _aadd_embeddings(
if not metadatas:
metadatas = [{} for _ in texts]
# Insert embeddings
for id, content, embedding, metadata in zip(
ids, texts, embeddings, metadatas
):
for id, content, embedding, metadata in zip(ids, texts, embeddings, metadatas):
metadata_col_names = (
", " + ", ".join(self.metadata_columns)
if len(self.metadata_columns) > 0
Expand All @@ -257,9 +249,7 @@ async def _aadd_embeddings(

# Add JSON column and/or close statement
insert_stmt += (
f", {self.metadata_json_column})"
if self.metadata_json_column
else ")"
f", {self.metadata_json_column})" if self.metadata_json_column else ")"
)
if self.metadata_json_column:
values_stmt += ", :extra)"
Expand Down Expand Up @@ -293,9 +283,7 @@ async def aadd_documents(
) -> List[str]:
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
ids = await self.aadd_texts(
texts, metadatas=metadatas, ids=ids, **kwargs
)
ids = await self.aadd_texts(texts, metadatas=metadatas, ids=ids, **kwargs)
return ids

def add_texts(
Expand All @@ -305,19 +293,15 @@ def add_texts(
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
return self.engine.run_as_sync(
self.aadd_texts(texts, metadatas, ids, **kwargs)
)
return self.engine.run_as_sync(self.aadd_texts(texts, metadatas, ids, **kwargs))

def add_documents(
self,
documents: List[Document],
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
return self.engine.run_as_sync(
self.aadd_documents(documents, ids, **kwargs)
)
return self.engine.run_as_sync(self.aadd_documents(documents, ids, **kwargs))

async def adelete(
self,
Expand Down Expand Up @@ -628,9 +612,7 @@ async def amax_marginal_relevance_search_with_score_by_vector(
k = k if k else self.k
fetch_k = fetch_k if fetch_k else self.fetch_k
lambda_mult = lambda_mult if lambda_mult else self.lambda_mult
embedding_list = [
json.loads(row[self.embedding_column]) for row in results
]
embedding_list = [json.loads(row[self.embedding_column]) for row in results]
mmr_selected = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
embedding_list,
Expand All @@ -657,9 +639,7 @@ async def amax_marginal_relevance_search_with_score_by_vector(
)
)

return [
r for i, r in enumerate(documents_with_scores) if i in mmr_selected
]
return [r for i, r in enumerate(documents_with_scores) if i in mmr_selected]

def similarity_search_with_score(
self,
Expand All @@ -668,9 +648,7 @@ def similarity_search_with_score(
filter: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
coro = self.asimilarity_search_with_score(
query, k, filter=filter, **kwargs
)
coro = self.asimilarity_search_with_score(query, k, filter=filter, **kwargs)
return self.engine.run_as_sync(coro)

def similarity_search_by_vector(
Expand All @@ -680,9 +658,7 @@ def similarity_search_by_vector(
filter: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
coro = self.asimilarity_search_by_vector(
embedding, k, filter=filter, **kwargs
)
coro = self.asimilarity_search_by_vector(embedding, k, filter=filter, **kwargs)
return self.engine.run_as_sync(coro)

def similarity_search_with_score_by_vector(
Expand Down Expand Up @@ -764,9 +740,7 @@ async def aapply_vector_index(
await self.adrop_vector_index()
return

filter = (
f"WHERE ({index.partial_indexes})" if index.partial_indexes else ""
)
filter = f"WHERE ({index.partial_indexes})" if index.partial_indexes else ""
params = "WITH " + index.index_options()
function = index.distance_strategy.index_function
name = name or index.name
Expand Down
13 changes: 3 additions & 10 deletions tests/test_cloudsql_vectorstore.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,11 +20,7 @@
from langchain_community.embeddings import DeterministicFakeEmbedding
from langchain_core.documents import Document

from langchain_google_cloud_sql_pg import (
CloudSQLVectorStore,
Column,
PostgreSQLEngine,
)
from langchain_google_cloud_sql_pg import CloudSQLVectorStore, Column, PostgreSQLEngine

DEFAULT_TABLE = "test_table" + str(uuid.uuid4()).replace("-", "_")
DEFAULT_TABLE_SYNC = "test_table_sync" + str(uuid.uuid4()).replace("-", "_")
Expand All @@ -34,12 +30,9 @@
embeddings_service = DeterministicFakeEmbedding(size=VECTOR_SIZE)

texts = ["foo", "bar", "baz"]
metadatas = [
{"page": str(i), "source": "google.com"} for i in range(len(texts))
]
metadatas = [{"page": str(i), "source": "google.com"} for i in range(len(texts))]
docs = [
Document(page_content=texts[i], metadata=metadatas[i])
for i in range(len(texts))
Document(page_content=texts[i], metadata=metadatas[i]) for i in range(len(texts))
]

embeddings = [embeddings_service.embed_query("foo") for i in range(len(texts))]
Expand Down