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Commit873ca9b

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metadata and generic filters in vector search (#689)
1 parent96dd570 commit873ca9b

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3 files changed

+125
-43
lines changed

3 files changed

+125
-43
lines changed

‎pgml-sdks/python/pgml/examples/question_answering.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -33,10 +33,10 @@
3333

3434
start=time()
3535
query="Who won 20 grammy awards?"
36-
results=collection.vector_search(query,top_k=5,title="Beyoncé")
36+
results=collection.vector_search(query,top_k=5,metadata_filter={"title" :"Beyoncé"})
3737
_end=time()
3838
console.print("\nResults for '%s'"% (query),style="bold")
3939
console.print(results)
4040
console.print("Query time = %0.3f"% (_end-start))
4141

42-
db.archive_collection(collection_name)
42+
#db.archive_collection(collection_name)

‎pgml-sdks/python/pgml/pgml/collection.py‎

Lines changed: 23 additions & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -298,18 +298,23 @@ def upsert_documents(
298298
)
299299
continue
300300

301+
metadata=document
302+
301303
_uuid=""
302304
ifid_keynotinlist(document.keys()):
303305
log.info("id key is not present.. hashing")
304-
source_uuid=hashlib.md5(text.encode("utf-8")).hexdigest()
306+
source_uuid=hashlib.md5(
307+
(text+" "+json.dumps(document)).encode("utf-8")
308+
).hexdigest()
305309
else:
306310
_uuid=document.pop(id_key)
307311
try:
308312
source_uuid=str(uuid.UUID(_uuid))
309313
exceptException:
310-
source_uuid=hashlib.md5(text.encode("utf-8")).hexdigest()
314+
source_uuid=hashlib.md5(str(_uuid).encode("utf-8")).hexdigest()
311315

312-
metadata=document
316+
if_uuid:
317+
document[id_key]=source_uuid
313318

314319
upsert_statement="INSERT INTO {documents_table} (text, source_uuid, metadata) VALUES ({text}, {source_uuid}, {metadata})\
315320
ON CONFLICT (source_uuid)\
@@ -323,9 +328,6 @@ def upsert_documents(
323328

324329
# put the text and id back in document
325330
document[text_key]=text
326-
if_uuid:
327-
document[id_key]=source_uuid
328-
329331
self.pool.putconn(conn)
330332

331333
defregister_text_splitter(
@@ -683,7 +685,8 @@ def vector_search(
683685
top_k:int=5,
684686
model_id:int=1,
685687
splitter_id:int=1,
686-
**kwargs:Any,
688+
metadata_filter:Optional[Dict[str,Any]]= {},
689+
generic_filter:Optional[str]="",
687690
)->List[Dict[str,Any]]:
688691
"""
689692
This function performs a vector search on a database using a query and returns the top matching
@@ -753,13 +756,6 @@ def vector_search(
753756
% (model_id,splitter_id,model_id,splitter_id)
754757
)
755758
return []
756-
757-
ifkwargs:
758-
metadata_filter= [f"documents.metadata->>'{k}' = '{v}'"ifisinstance(v,str)elsef"documents.metadata->>'{k}' ={v}"fork,vinkwargs.items()]
759-
metadata_filter=" AND ".join(metadata_filter)
760-
metadata_filter=f"AND{metadata_filter}"
761-
else:
762-
metadata_filter=""
763759

764760
cte_select_statement="""
765761
WITH query_cte AS (
@@ -775,7 +771,7 @@ def vector_search(
775771
SELECT cte.score, chunks.chunk, documents.metadata
776772
FROM cte
777773
INNER JOIN {chunks_table} chunks ON chunks.id = cte.chunk_id
778-
INNER JOIN {documents_table} documents ON documents.id = chunks.document_id {metadata_filter}
774+
INNER JOIN {documents_table} documents ON documents.id = chunks.document_id
779775
""".format(
780776
model=sql.Literal(model).as_string(conn),
781777
query_text=query,
@@ -784,9 +780,20 @@ def vector_search(
784780
top_k=top_k,
785781
chunks_table=self.chunks_table,
786782
documents_table=self.documents_table,
787-
metadata_filter=metadata_filter,
788783
)
789784

785+
ifmetadata_filter:
786+
cte_select_statement+= (
787+
" AND documents.metadata @> {metadata_filter}".format(
788+
metadata_filter=sql.Literal(json.dumps(metadata_filter)).as_string(
789+
conn
790+
)
791+
)
792+
)
793+
794+
ifgeneric_filter:
795+
cte_select_statement+=" AND "+generic_filter
796+
790797
search_results=run_select_statement(
791798
conn,cte_select_statement,order_by="score",ascending=False
792799
)

‎pgml-sdks/python/pgml/tests/test_collection.py‎

Lines changed: 100 additions & 25 deletions
Original file line numberDiff line numberDiff line change
@@ -4,45 +4,86 @@
44
importhashlib
55
importos
66

7-
classTestCollection(unittest.TestCase):
87

8+
classTestCollection(unittest.TestCase):
99
defsetUp(self)->None:
1010
local_pgml="postgres://postgres@127.0.0.1:5433/pgml_development"
11-
conninfo=os.environ.get("PGML_CONNECTION",local_pgml)
11+
conninfo=os.environ.get("PGML_CONNECTION",local_pgml)
1212
self.db=Database(conninfo)
1313
self.collection_name="test_collection_1"
1414
self.documents= [
1515
{
16-
"id":hashlib.md5(f"abcded-{i}".encode('utf-8')).hexdigest(),
17-
"text":f"Lorem ipsum{i}",
18-
"metadata": {"source":"test_suite"}
16+
"id":hashlib.md5(f"abcded-{i}".encode("utf-8")).hexdigest(),
17+
"text":f"Lorem ipsum{i}",
18+
"source":"test_suite",
1919
}
2020
foriinrange(4,7)
2121
]
2222
self.documents_no_ids= [
2323
{
24-
"text":f"Lorem ipsum{i}",
25-
"metadata": {"source":"test_suite_no_ids"}
24+
"text":f"Lorem ipsum{i}",
25+
"source":"test_suite_no_ids",
2626
}
2727
foriinrange(1,4)
2828
]
29-
29+
30+
self.documents_with_metadata= [
31+
{
32+
"text":f"Lorem ipsum metadata",
33+
"source":f"url{i}",
34+
"url":f"/home{i}",
35+
"user":f"John Doe-{i+1}",
36+
}
37+
foriinrange(8,12)
38+
]
39+
40+
self.documents_with_reviews= [
41+
{
42+
"text":f"product is abc{i}",
43+
"reviews":i*2,
44+
}
45+
foriinrange(20,25)
46+
]
47+
48+
self.documents_with_reviews_metadata= [
49+
{
50+
"text":f"product is abc{i}",
51+
"reviews":i*2,
52+
"source":"amazon",
53+
"user":"John Doe",
54+
}
55+
foriinrange(20,25)
56+
]
57+
58+
self.documents_with_reviews_metadata+= [
59+
{
60+
"text":f"product is abc{i}",
61+
"reviews":i*2,
62+
"source":"ebay",
63+
}
64+
foriinrange(20,25)
65+
]
66+
3067
self.collection=self.db.create_or_get_collection(self.collection_name)
31-
68+
3269
deftest_create_collection(self):
33-
assertisinstance(self.collection,Collection)
34-
70+
assertisinstance(self.collection,Collection)
71+
3572
deftest_documents_upsert(self):
3673
self.collection.upsert_documents(self.documents)
3774
conn=self.db.pool.getconn()
38-
results=run_select_statement(conn,"SELECT id FROM %s"%self.collection.documents_table)
75+
results=run_select_statement(
76+
conn,"SELECT id FROM %s"%self.collection.documents_table
77+
)
3978
self.db.pool.putconn(conn)
4079
assertlen(results)>=len(self.documents)
41-
80+
4281
deftest_documents_upsert_no_ids(self):
4382
self.collection.upsert_documents(self.documents_no_ids)
4483
conn=self.db.pool.getconn()
45-
results=run_select_statement(conn,"SELECT id FROM %s"%self.collection.documents_table)
84+
results=run_select_statement(
85+
conn,"SELECT id FROM %s"%self.collection.documents_table
86+
)
4687
self.db.pool.putconn(conn)
4788
assertlen(results)>=len(self.documents_no_ids)
4889

@@ -52,23 +93,25 @@ def test_default_text_splitter(self):
5293

5394
assertsplitter_id==1
5495
assertsplitters[0]["name"]=="RecursiveCharacterTextSplitter"
55-
96+
5697
deftest_default_embeddings_model(self):
5798
model_id=self.collection.register_model()
5899
models=self.collection.get_models()
59-
100+
60101
assertmodel_id==1
61102
assertmodels[0]["name"]=="intfloat/e5-small"
62-
103+
63104
deftest_generate_chunks(self):
64105
self.collection.upsert_documents(self.documents)
65106
self.collection.upsert_documents(self.documents_no_ids)
66107
splitter_id=self.collection.register_text_splitter()
67108
self.collection.generate_chunks(splitter_id=splitter_id)
68-
splitter_params= {"chunk_size":100,"chunk_overlap":20}
69-
splitter_id=self.collection.register_text_splitter(splitter_params=splitter_params)
109+
splitter_params= {"chunk_size":100,"chunk_overlap":20}
110+
splitter_id=self.collection.register_text_splitter(
111+
splitter_params=splitter_params
112+
)
70113
self.collection.generate_chunks(splitter_id=splitter_id)
71-
114+
72115
deftest_generate_embeddings(self):
73116
self.collection.upsert_documents(self.documents)
74117
self.collection.upsert_documents(self.documents_no_ids)
@@ -84,10 +127,42 @@ def test_vector_search(self):
84127
self.collection.generate_embeddings()
85128
results=self.collection.vector_search("Lorem ipsum 1",top_k=2)
86129
assertresults[0]["score"]==1.0
87-
88-
# def tearDown(self) -> None:
89-
# self.db.archive_collection(self.collection_name)
90130

131+
deftest_vector_search_metadata_filter(self):
132+
self.collection.upsert_documents(self.documents)
133+
self.collection.upsert_documents(self.documents_no_ids)
134+
self.collection.upsert_documents(self.documents_with_metadata)
135+
self.collection.generate_chunks()
136+
self.collection.generate_embeddings()
137+
results=self.collection.vector_search(
138+
"Lorem ipsum metadata",
139+
top_k=2,
140+
metadata_filter={"url":"/home 8","source":"url 8"},
141+
)
142+
assertresults[0]["metadata"]["user"]=="John Doe-9"
143+
144+
deftest_vector_search_generic_filter(self):
145+
self.collection.upsert_documents(self.documents_with_reviews)
146+
self.collection.generate_chunks()
147+
self.collection.generate_embeddings()
148+
results=self.collection.vector_search(
149+
"product is abc 21",
150+
top_k=2,
151+
generic_filter="(documents.metadata->>'reviews')::int < 45",
152+
)
153+
assertresults[0]["metadata"]["reviews"]==42
91154

92-
93-
155+
deftest_vector_search_generic_and_metadata_filter(self):
156+
self.collection.upsert_documents(self.documents_with_reviews_metadata)
157+
self.collection.generate_chunks()
158+
self.collection.generate_embeddings()
159+
results=self.collection.vector_search(
160+
"product is abc 21",
161+
top_k=2,
162+
generic_filter="(documents.metadata->>'reviews')::int < 45",
163+
metadata_filter={"source":"amazon"},
164+
)
165+
assertresults[0]["metadata"]["user"]=="John Doe"
166+
167+
# def tearDown(self) -> None:
168+
# self.db.archive_collection(self.collection_name)

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