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[Feature] Return score when doing search in vectorDB (#1060)

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Co-authored-by: Deven Patel <deven298@yahoo.com>
This commit is contained in:
Deven Patel
2023-12-29 15:56:12 +05:30
committed by GitHub
parent 19d80914df
commit c0aafd38c9
12 changed files with 72 additions and 28 deletions
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4
embedchain/embedchain.py
View File

@@ -515,7 +515,7 @@ class EmbedChain(JSONSerializable):
where: Optional[Dict] = None,
citations: bool = False,
**kwargs: Dict[str, Any],
) -> Union[Tuple[str, List[Tuple[str, str, str]]], str]:
) -> Union[Tuple[str, List[Tuple[str, Dict]]], str]:
"""
Queries the vector database based on the given input query.
Gets relevant doc based on the query and then passes it to an
@@ -566,7 +566,7 @@ class EmbedChain(JSONSerializable):
where: Optional[Dict[str, str]] = None,
citations: bool = False,
**kwargs: Dict[str, Any],
) -> Union[Tuple[str, List[Tuple[str, str, str]]], str]:
) -> Union[Tuple[str, List[Tuple[str, Dict]]], str]:
"""
Queries the vector database on the given input query.
Gets relevant doc based on the query and then passes it to an

3
embedchain/vectordb/chroma.py
View File

@@ -200,7 +200,7 @@ class ChromaDB(BaseVectorDB):
skip_embedding: bool,
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, str, str]], List[str]]:
) -> Union[List[Tuple[str, Dict]], List[str]]:
"""
Query contents from vector database based on vector similarity
@@ -250,6 +250,7 @@ class ChromaDB(BaseVectorDB):
context = result[0].page_content
if citations:
metadata = result[0].metadata
metadata["score"] = result[1]
contexts.append((context, metadata))
else:
contexts.append(context)

3
embedchain/vectordb/elasticsearch.py
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@@ -164,7 +164,7 @@ class ElasticsearchDB(BaseVectorDB):
skip_embedding: bool,
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, str, str]], List[str]]:
) -> Union[List[Tuple[str, Dict]], List[str]]:
"""
query contents from vector data base based on vector similarity
@@ -210,6 +210,7 @@ class ElasticsearchDB(BaseVectorDB):
context = doc["_source"]["text"]
if citations:
metadata = doc["_source"]["metadata"]
metadata["score"] = doc["_score"]
contexts.append(tuple((context, metadata)))
else:
contexts.append(context)

10
embedchain/vectordb/opensearch.py
View File

@@ -169,7 +169,7 @@ class OpenSearchDB(BaseVectorDB):
skip_embedding: bool,
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, str, str]], List[str]]:
) -> Union[List[Tuple[str, Dict]], List[str]]:
"""
query contents from vector data base based on vector similarity
@@ -202,7 +202,7 @@ class OpenSearchDB(BaseVectorDB):
if "app_id" in where:
app_id = where["app_id"]
pre_filter = {"bool": {"must": [{"term": {"metadata.app_id.keyword": app_id}}]}}
docs = docsearch.similarity_search(
docs = docsearch.similarity_search_with_score(
input_query,
search_type="script_scoring",
space_type="cosinesimil",
@@ -215,10 +215,12 @@ class OpenSearchDB(BaseVectorDB):
)
contexts = []
for doc in docs:
for doc, score in docs:
context = doc.page_content
if citations:
contexts.append(tuple((context, doc.metadata)))
metadata = doc.metadata
metadata["score"] = score
contexts.append(tuple((context, metadata)))
else:
contexts.append(context)
return contexts

3
embedchain/vectordb/pinecone.py
View File

@@ -127,7 +127,7 @@ class PineconeDB(BaseVectorDB):
skip_embedding: bool,
citations: bool = False,
**kwargs: Optional[Dict[str, any]],
) -> Union[List[Tuple[str, str, str]], List[str]]:
) -> Union[List[Tuple[str, Dict]], List[str]]:
"""
query contents from vector database based on vector similarity
:param input_query: list of query string
@@ -154,6 +154,7 @@ class PineconeDB(BaseVectorDB):
metadata = doc["metadata"]
context = metadata["text"]
if citations:
metadata["score"] = doc["score"]
contexts.append(tuple((context, metadata)))
else:
contexts.append(context)

3
embedchain/vectordb/qdrant.py
View File

@@ -170,7 +170,7 @@ class QdrantDB(BaseVectorDB):
skip_embedding: bool,
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, str, str]], List[str]]:
) -> Union[List[Tuple[str, Dict]], List[str]]:
"""
query contents from vector database based on vector similarity
:param input_query: list of query string
@@ -219,6 +219,7 @@ class QdrantDB(BaseVectorDB):
context = result.payload["text"]
if citations:
metadata = result.payload["metadata"]
metadata["score"] = result.score
contexts.append(tuple((context, metadata)))
else:
contexts.append(context)

6
embedchain/vectordb/weaviate.py
View File

@@ -205,7 +205,7 @@ class WeaviateDB(BaseVectorDB):
skip_embedding: bool,
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, str, str]], List[str]]:
) -> Union[List[Tuple[str, Dict]], List[str]]:
"""
query contents from vector database based on vector similarity
:param input_query: list of query string
@@ -255,6 +255,7 @@ class WeaviateDB(BaseVectorDB):
.with_where(weaviate_where_clause)
.with_near_vector({"vector": query_vector})
.with_limit(n_results)
.with_additional(["distance"])
.do()
)
else:
@@ -262,6 +263,7 @@ class WeaviateDB(BaseVectorDB):
self.client.query.get(self.index_name, data_fields)
.with_near_vector({"vector": query_vector})
.with_limit(n_results)
.with_additional(["distance"])
.do()
)
@@ -271,6 +273,8 @@ class WeaviateDB(BaseVectorDB):
context = doc["text"]
if citations:
metadata = doc["metadata"][0]
score = doc["_additional"]["distance"]
metadata["score"] = score
contexts.append((context, metadata))
else:
contexts.append(context)

14
embedchain/vectordb/zilliz.py
View File

@@ -135,7 +135,7 @@ class ZillizVectorDB(BaseVectorDB):
skip_embedding: bool,
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, str, str]], List[str]]:
) -> Union[List[Tuple[str, Dict]], List[str]]:
"""
Query contents from vector data base based on vector similarity
@@ -159,7 +159,7 @@ class ZillizVectorDB(BaseVectorDB):
if not isinstance(where, str):
where = None
output_fields = ["text", "url", "doc_id"]
output_fields = ["*"]
if skip_embedding:
query_vector = input_query
query_result = self.client.search(
@@ -181,12 +181,18 @@ class ZillizVectorDB(BaseVectorDB):
output_fields=output_fields,
**kwargs,
)
query_result = query_result[0]
contexts = []
for query in query_result:
data = query[0]["entity"]
data = query["entity"]
score = query["distance"]
context = data["text"]
if "embeddings" in data:
data.pop("embeddings")
if citations:
data["score"] = score
contexts.append(tuple((context, data)))
else:
contexts.append(context)

2
pyproject.toml
View File

@@ -1,6 +1,6 @@
[tool.poetry]
name = "embedchain"
version = "0.1.44"
version = "0.1.45"
description = "Data platform for LLMs - Load, index, retrieve and sync any unstructured data"
authors = [
"Taranjeet Singh <taranjeet@embedchain.ai>",

18
tests/vectordb/test_chroma_db.py
View File

@@ -342,8 +342,22 @@ def test_chroma_db_collection_query(app_with_settings):
input_query=[0, 0, 0], where={}, n_results=2, skip_embedding=True, citations=True
)
expected_value_with_citations = [
("document", {"url": "url_1", "doc_id": "doc_id_1"}),
("document2", {"url": "url_2", "doc_id": "doc_id_2"}),
(
"document",
{
"url": "url_1",
"doc_id": "doc_id_1",
"score": 0.0,
},
),
(
"document2",
{
"url": "url_2",
"doc_id": "doc_id_2",
"score": 1.0,
},
),
]
assert data_with_citations == expected_value_with_citations

4
tests/vectordb/test_elasticsearch_db.py
View File

@@ -66,8 +66,8 @@ class TestEsDB(unittest.TestCase):
results_with_citations = self.db.query(query, n_results=2, where={}, skip_embedding=False, citations=True)
expected_results_with_citations = [
("This is a document.", {"url": "url_1", "doc_id": "doc_id_1"}),
("This is another document.", {"url": "url_2", "doc_id": "doc_id_2"}),
("This is a document.", {"url": "url_1", "doc_id": "doc_id_1", "score": 0.9}),
("This is another document.", {"url": "url_2", "doc_id": "doc_id_2", "score": 0.8}),
]
self.assertEqual(results_with_citations, expected_results_with_citations)

30
tests/vectordb/test_zilliz_db.py
View File

@@ -123,7 +123,14 @@ class TestZillizDBCollection:
# Mock the MilvusClient search method
with patch.object(zilliz_db.client, "search") as mock_search:
# Mock the search result
mock_search.return_value = [[{"entity": {"text": "result_doc", "url": "url_1", "doc_id": "doc_id_1"}}]]
mock_search.return_value = [
[
{
"distance": 0.5,
"entity": {"text": "result_doc", "url": "url_1", "doc_id": "doc_id_1", "embeddings": [1, 2, 3]},
}
]
]
# Call the query method with skip_embedding=True
query_result = zilliz_db.query(input_query=["query_text"], n_results=1, where={}, skip_embedding=True)
@@ -133,7 +140,7 @@ class TestZillizDBCollection:
collection_name=mock_config.collection_name,
data=["query_text"],
limit=1,
output_fields=["text", "url", "doc_id"],
output_fields=["*"],
)
# Assert that the query result matches the expected result
@@ -147,11 +154,11 @@ class TestZillizDBCollection:
collection_name=mock_config.collection_name,
data=["query_text"],
limit=1,
output_fields=["text", "url", "doc_id"],
output_fields=["*"],
)
assert query_result_with_citations == [
("result_doc", {"text": "result_doc", "url": "url_1", "doc_id": "doc_id_1"})
("result_doc", {"text": "result_doc", "url": "url_1", "doc_id": "doc_id_1", "score": 0.5})
]
@patch("embedchain.vectordb.zilliz.MilvusClient", autospec=True)
@@ -177,7 +184,14 @@ class TestZillizDBCollection:
mock_embedder.embedding_fn.return_value = ["query_vector"]
# Mock the search result
mock_search.return_value = [[{"entity": {"text": "result_doc", "url": "url_1", "doc_id": "doc_id_1"}}]]
mock_search.return_value = [
[
{
"distance": 0.0,
"entity": {"text": "result_doc", "url": "url_1", "doc_id": "doc_id_1", "embeddings": [1, 2, 3]},
}
]
]
# Call the query method with skip_embedding=False
query_result = zilliz_db.query(input_query=["query_text"], n_results=1, where={}, skip_embedding=False)
@@ -187,7 +201,7 @@ class TestZillizDBCollection:
collection_name=mock_config.collection_name,
data=["query_vector"],
limit=1,
output_fields=["text", "url", "doc_id"],
output_fields=["*"],
)
# Assert that the query result matches the expected result
@@ -201,9 +215,9 @@ class TestZillizDBCollection:
collection_name=mock_config.collection_name,
data=["query_vector"],
limit=1,
output_fields=["text", "url", "doc_id"],
output_fields=["*"],
)
assert query_result_with_citations == [
("result_doc", {"text": "result_doc", "url": "url_1", "doc_id": "doc_id_1"})
("result_doc", {"text": "result_doc", "url": "url_1", "doc_id": "doc_id_1", "score": 0.0})
]