id: fff86dca-918c-421d-9124-333e8ac3bf8a
metadata: {
"scope": "company",
"title": "data_test.txt",
"filename": "data_test.txt",
"document_id": "31927",
"product_ids": [],
"start_index": 0,
"source_document_id": "31927"
}
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum in justo sodales, accumsan orci quis, malesuada ligula. Ut dapibus lectus a eros efficitur, at convallis … (+510 chars)
all_results = vector_store.similarity_search_with_score(
query=query,
k=k,
filter=filter,
)
2026-05-14 15:16:54.281 | [14/May/2026 13:16:54] ERROR [4699.ai.rag:504] Similarity search with distance filter failed: (psycopg.errors.UndefinedColumn) column "product_ids" does not exist
2026-05-14 15:16:54.281 | LINE 2: FROM "public"."rag_company_11" WHERE product_ids = A...
2026-05-14 15:16:54.281 | ^
2026-05-14 15:16:54.281 | [SQL: SELECT "langchain_id", "content", "embedding", "langchain_metadata", cosine_distance("embedding", %(query_embedding)s) as distance
2026-05-14 15:16:54.281 | FROM "public"."rag_company_11" WHERE product_ids = ANY(%(product_ids_in_bc347a3b)s) ORDER BY "embedding" <=> %(query_embedding)s LIMIT %(dense_limit)s;
2026-05-14 15:16:54.281 | ]
2026-05-14 15:16:54.281 | [parameters: {'query_embedding': '[-0.004596710205078125, 0.01456451416015625, 0.0518798828125, 0.012725830078125, 0.0634765625, 0.03546142578125, -0.046844482421875, -0.0014133453369 ... (30701 characters truncated) ... 34942626953125, 0.002788543701171875, -0.0008635520935058594, -0.0179290771484375, -0.034637451171875, 0.002643585205078125, -0.00012230873107910156]', 'product_ids_in_bc347a3b': [34], 'dense_limit': 50}]
2026-05-14 15:16:54.281 | (Background on this error at: https://sqlalche.me/e/20/f405)
2026-05-14 15:16:54.281 | Traceback (most recent call last):
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/engine/base.py", line 1967, in _exec_single_context
2026-05-14 15:16:54.281 | self.dialect.do_execute(
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/engine/default.py", line 952, in do_execute
2026-05-14 15:16:54.281 | cursor.execute(statement, parameters)
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/dialects/postgresql/psycopg.py", line 673, in execute
2026-05-14 15:16:54.281 | result = self.await_(self._cursor.execute(query, params, **kw))
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/util/_concurrency_py3k.py", line 132, in await_only
2026-05-14 15:16:54.281 | return current.parent.switch(awaitable) # type: ignore[no-any-return,attr-defined] # noqa: E501
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/util/_concurrency_py3k.py", line 196, in greenlet_spawn
2026-05-14 15:16:54.281 | value = await result
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/psycopg/cursor_async.py", line 117, in execute
2026-05-14 15:16:54.281 | raise ex.with_traceback(None)
2026-05-14 15:16:54.281 | psycopg.errors.UndefinedColumn: column "product_ids" does not exist
2026-05-14 15:16:54.281 | LINE 2: FROM "public"."rag_company_11" WHERE product_ids = A...
2026-05-14 15:16:54.281 | ^
2026-05-14 15:16:54.281 |
2026-05-14 15:16:54.281 | The above exception was the direct cause of the following exception:
2026-05-14 15:16:54.281 |
2026-05-14 15:16:54.281 | Traceback (most recent call last):
2026-05-14 15:16:54.281 | File "/opt/src/ai/rag.py", line 491, in similarity_search_with_distance_filter
2026-05-14 15:16:54.281 | all_results = vector_store.similarity_search_with_score(
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/langchain_postgres/v2/vectorstores.py", line 714, in similarity_search_with_score
2026-05-14 15:16:54.281 | return self._engine._run_as_sync(
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/langchain_postgres/v2/engine.py", line 131, in _run_as_sync
2026-05-14 15:16:54.281 | return asyncio.run_coroutine_threadsafe(coro, self._loop).result() # type: ignore[arg-type]
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 456, in result
2026-05-14 15:16:54.281 | return self.__get_result()
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
2026-05-14 15:16:54.281 | raise self._exception
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/langchain_postgres/v2/async_vectorstore.py", line 738, in asimilarity_search_with_score
2026-05-14 15:16:54.281 | docs = await self.asimilarity_search_with_score_by_vector(
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/langchain_postgres/v2/async_vectorstore.py", line 765, in asimilarity_search_with_score_by_vector
2026-05-14 15:16:54.281 | results = await self.__query_collection(
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/langchain_postgres/v2/async_vectorstore.py", line 636, in __query_collection
2026-05-14 15:16:54.281 | result = await conn.execute(text(dense_query_stmt), param_dict)
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/ext/asyncio/engine.py", line 659, in execute
2026-05-14 15:16:54.281 | result = await greenlet_spawn(
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/util/_concurrency_py3k.py", line 201, in greenlet_spawn
2026-05-14 15:16:54.281 | result = context.throw(*sys.exc_info())
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/engine/base.py", line 1419, in execute
2026-05-14 15:16:54.281 | return meth(
2026-05-14 15:16:54.281 | ^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py", line 527, in _execute_on_connection
2026-05-14 15:16:54.281 | return connection._execute_clauseelement(
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/engine/base.py", line 1641, in _execute_clauseelement
2026-05-14 15:16:54.281 | ret = self._execute_context(
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/engine/base.py", line 1846, in _execute_context
2026-05-14 15:16:54.281 | return self._exec_single_context(
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/engine/base.py", line 1986, in _exec_single_context
2026-05-14 15:16:54.281 | self._handle_dbapi_exception(
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/engine/base.py", line 2363, in _handle_dbapi_exception
2026-05-14 15:16:54.281 | raise sqlalchemy_exception.with_traceback(exc_info[2]) from e
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/engine/base.py", line 1967, in _exec_single_context
2026-05-14 15:16:54.281 | self.dialect.do_execute(
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/engine/default.py", line 952, in do_execute
2026-05-14 15:16:54.281 | cursor.execute(statement, parameters)
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/dialects/postgresql/psycopg.py", line 673, in execute
2026-05-14 15:16:54.281 | result = self.await_(self._cursor.execute(query, params, **kw))
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/util/_concurrency_py3k.py", line 132, in await_only
2026-05-14 15:16:54.281 | return current.parent.switch(awaitable) # type: ignore[no-any-return,attr-defined] # noqa: E501
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/sqlalchemy/util/_concurrency_py3k.py", line 196, in greenlet_spawn
2026-05-14 15:16:54.281 | value = await result
2026-05-14 15:16:54.281 | ^^^^^^^^^^^^
2026-05-14 15:16:54.281 | File "/opt/.venv/lib/python3.12/site-packages/psycopg/cursor_async.py", line 117, in execute
2026-05-14 15:16:54.281 | raise ex.with_traceback(None)
2026-05-14 15:16:54.281 | sqlalchemy.exc.ProgrammingError: (psycopg.errors.UndefinedColumn) column "product_ids" does not exist
2026-05-14 15:16:54.281 | LINE 2: FROM "public"."rag_company_11" WHERE product_ids = A...
2026-05-14 15:16:54.281 | ^
2026-05-14 15:16:54.281 | [SQL: SELECT "langchain_id", "content", "embedding", "langchain_metadata", cosine_distance("embedding", %(query_embedding)s) as distance
2026-05-14 15:16:54.281 | FROM "public"."rag_company_11" WHERE product_ids = ANY(%(product_ids_in_bc347a3b)s) ORDER BY "embedding" <=> %(query_embedding)s LIMIT %(dense_limit)s;
2026-05-14 15:16:54.281 | ]
2026-05-14 15:16:54.281 | [parameters: {'query_embedding': '[-0.004596710205078125, 0.01456451416015625, 0.0518798828125, 0.012725830078125, 0.0634765625, 0.03546142578125, -0.046844482421875, -0.0014133453369 ... (30701 characters truncated) ... 34942626953125, 0.002788543701171875, -0.0008635520935058594, -0.0179290771484375, -0.034637451171875, 0.002643585205078125, -0.00012230873107910156]', 'product_ids_in_bc347a3b': [34], 'dense_limit': 50}]
2026-05-14 15:16:54.281 | (Background on this error at: https://sqlalche.me/e/20/f405)
Hello,
I've a PGVectorStore where i've instered Documents in this content:
Now, i'm performing a query of this type
where the actual content is this
Performing similarity search with query Qual è l'architettura di conservazione dei dati e dove si trovano fisicamente i server? , filter={'product_ids': {'$in': [34]}} and k=50i get this as an error
Where do i make the mistake?