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This commit is contained in:
Aayush Kataria 2024-11-01 05:54:51 -07:00
parent 1ce7bbf855
commit 9c3bdcbb6c
2 changed files with 13 additions and 8 deletions
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2
libs/community/langchain_community/cache.py
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@ -2106,7 +2106,7 @@ class AzureCosmosDBSemanticCache(BaseCache):
ef_construction: int = 64,
ef_search: int = 40,
score_threshold: Optional[float] = None,
application_name: str = "LangChain-CDBNoSQL-SemanticCache-Python",
application_name: str = "LangChain-CDBMongoVCore-SemanticCache-Python",
):
"""
Args:

19
libs/community/langchain_community/vectorstores/azure_cosmos_db_no_sql.py
View File

@ -15,7 +15,7 @@ if TYPE_CHECKING:
from azure.cosmos import ContainerProxy, CosmosClient
from azure.identity import ClientSecretCredential
USER_AGENT = ("LlamaIndex-CDBNoSql-VectorStore-Python",)
USER_AGENT = ("LangChain-CDBNoSql-VectorStore-Python",)
class AzureCosmosDBNoSqlVectorSearch(VectorStore):
@ -121,6 +121,10 @@ class AzureCosmosDBNoSqlVectorSearch(VectorStore):
vector_embedding_policy=self._vector_embedding_policy,
)
self._embedding_key = self._vector_embedding_policy["vectorEmbeddings"][0][
"path"
][1:]
def add_texts(
self,
texts: Iterable[str],
@ -164,7 +168,7 @@ class AzureCosmosDBNoSqlVectorSearch(VectorStore):
{
"id": str(uuid.uuid4()),
text_key: t,
"embedding": embedding,
self._embedding_key: embedding,
"metadata": m,
}
for t, m, embedding in zip(texts, metadatas, embeddings)
@ -313,21 +317,22 @@ class AzureCosmosDBNoSqlVectorSearch(VectorStore):
query += "TOP @limit "
query += (
"c.id, c.text, c.metadata, c.embedding, "
"VectorDistance(c.embedding, @embeddings) AS SimilarityScore FROM c"
"c.id, c[@embeddingKey], c.text, c.metadata, "
"VectorDistance(c[@embeddingKey], @embeddings) AS SimilarityScore FROM c"
)
# Add where_clause if specified
if pre_filter is not None and pre_filter.get("where_clause") is not None:
query += " {}".format(pre_filter["where_clause"])
query += " ORDER BY VectorDistance(c.embedding, @embeddings)"
query += " ORDER BY VectorDistance(c[@embeddingKey], @embeddings)"
# Add limit_offset_clause if specified
if pre_filter is not None and pre_filter.get("limit_offset_clause") is not None:
query += " {}".format(pre_filter["limit_offset_clause"])
parameters = [
{"name": "@limit", "value": k},
{"name": "@embeddingKey", "value": self._embedding_key},
{"name": "@embeddings", "value": embeddings},
]
@ -346,7 +351,7 @@ class AzureCosmosDBNoSqlVectorSearch(VectorStore):
metadata["id"] = item["id"]
score = item["SimilarityScore"]
if with_embedding:
metadata["embedding"] = item["embedding"]
metadata[self._embedding_key] = item[self._embedding_key]
docs_and_scores.append(
(Document(page_content=text, metadata=metadata), score)
)
@ -410,7 +415,7 @@ class AzureCosmosDBNoSqlVectorSearch(VectorStore):
# Re-ranks the docs using MMR
mmr_doc_indexes = maximal_marginal_relevance(
np.array(embedding),
[doc.metadata["embedding"] for doc, _ in docs],
[doc.metadata[self._embedding_key] for doc, _ in docs],
k=k,
lambda_mult=lambda_mult,
)