postgresml
diff --git a/‎pgml-sdks/python/pgml/examples/question_answering.py
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+1-1Lines changed: 1 addition & 1 deletion b/‎pgml-sdks/python/pgml/examples/question_answering.py
Copy file name to clipboardExpand all lines: pgml-sdks/python/pgml/examples/question_answering.py
+1-1Lines changed: 1 addition & 1 deletion
diff --git a/‎pgml-sdks/python/pgml/pgml/collection.py
Copy file name to clipboardExpand all lines: pgml-sdks/python/pgml/pgml/collection.py
+13-1Lines changed: 13 additions & 1 deletion b/‎pgml-sdks/python/pgml/pgml/collection.py
Copy file name to clipboardExpand all lines: pgml-sdks/python/pgml/pgml/collection.py
+13-1Lines changed: 13 additions & 1 deletion
@@ -33,7 +33,7 @@
 
 start = time()
 query = "Who won 20 grammy awards?"
-results = collection.vector_search(query, top_k=5)
+results = collection.vector_search(query, top_k=5, title="Beyoncé")
 _end = time()
 console.print("\nResults for '%s'" % (query), style="bold")
 console.print(results)
 
@@ -683,6 +683,7 @@ def vector_search(
         top_k: int = 5,
         model_id: int = 1,
         splitter_id: int = 1,
+        **kwargs: Any, 
     ) -> List[Dict[str, Any]]:
         """
         This function performs a vector search on a database using a query and returns the top matching
@@ -702,6 +703,9 @@ def vector_search(
         splitter used to split the documents into chunks. It is used to retrieve the embeddings table
         associated with the specified splitter, defaults to 1
         :type splitter_id: int (optional)
+        :param kwargs: Additional filtering parameters to be used in the search query. These parameters
+        are from the metadata of the documents and can be used to filter the search results based on
+        metadata values.
         :return: a list of dictionaries containing search results for a given query. Each dictionary
         contains the following keys: "score", "text", and "metadata". The "score" key contains a float
         value representing the similarity score between the query and the search result. The "text" key
@@ -749,6 +753,13 @@ def vector_search(
                 % (model_id, splitter_id, model_id, splitter_id)
             )
             return []
+        
+        if kwargs:
+            metadata_filter = [f"documents.metadata->>'{k}' = '{v}'" if isinstance(v, str) else f"documents.metadata->>'{k}' = {v}" for k, v in kwargs.items()]
+            metadata_filter = " AND ".join(metadata_filter)
+            metadata_filter = f"AND {metadata_filter}"
+        else:
+            metadata_filter = ""
 
         cte_select_statement = """
         WITH query_cte AS (
@@ -764,7 +775,7 @@ def vector_search(
         SELECT cte.score, chunks.chunk, documents.metadata
         FROM cte
         INNER JOIN {chunks_table} chunks ON chunks.id = cte.chunk_id
-        INNER JOIN {documents_table} documents ON documents.id = chunks.document_id;
+        INNER JOIN {documents_table} documents ON documents.id = chunks.document_id {metadata_filter}
         """.format(
             model=sql.Literal(model).as_string(conn),
             query_text=query,
@@ -773,6 +784,7 @@ def vector_search(
             top_k=top_k,
             chunks_table=self.chunks_table,
             documents_table=self.documents_table,
+            metadata_filter=metadata_filter,
         )
 
         search_results = run_select_statement(