From 6335cb5b3a7b57a4a3dfa4b8070d7962aed789b1 Mon Sep 17 00:00:00 2001
From: "Aivin V. Solatorio" <avsolatorio@gmail.com>
Date: Tue, 9 May 2023 13:34:11 -0400
Subject: [PATCH] Add support for Qdrant nested filter (#4354)

# Add support for Qdrant nested filter

This extends the filter functionality for the Qdrant vectorstore. The
current filter implementation is limited to a single-level metadata
structure; however, Qdrant supports nested metadata filtering. This
extends the functionality for users to maximize the filter functionality
when using Qdrant as the vectorstore.

Reference: https://qdrant.tech/documentation/filtering/#nested-key

---------

Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com>
---
 langchain/vectorstores/qdrant.py              | 55 ++++++++++++++++---
 .../vectorstores/test_qdrant.py               | 17 +++++-
 2 files changed, 61 insertions(+), 11 deletions(-)

diff --git a/langchain/vectorstores/qdrant.py b/langchain/vectorstores/qdrant.py
index 168da43a..5af38b96 100644
--- a/langchain/vectorstores/qdrant.py
+++ b/langchain/vectorstores/qdrant.py
@@ -5,7 +5,18 @@ import uuid
 import warnings
 from hashlib import md5
 from operator import itemgetter
-from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    Union,
+)
 
 import numpy as np
 
@@ -14,7 +25,11 @@ from langchain.embeddings.base import Embeddings
 from langchain.vectorstores import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
-MetadataFilter = Dict[str, Union[str, int, bool]]
+if TYPE_CHECKING:
+    from qdrant_client.http import models as rest
+
+
+MetadataFilter = Dict[str, Union[str, int, bool, dict, list]]
 
 
 class Qdrant(VectorStore):
@@ -461,18 +476,42 @@ class Qdrant(VectorStore):
             metadata=scored_point.payload.get(metadata_payload_key) or {},
         )
 
-    def _qdrant_filter_from_dict(self, filter: Optional[MetadataFilter]) -> Any:
-        if filter is None or 0 == len(filter):
-            return None
-
+    def _build_condition(self, key: str, value: Any) -> List[rest.FieldCondition]:
         from qdrant_client.http import models as rest
 
-        return rest.Filter(
-            must=[
+        out = []
+
+        if isinstance(value, dict):
+            for _key, value in value.items():
+                out.extend(self._build_condition(f"{key}.{_key}", value))
+        elif isinstance(value, list):
+            for _value in value:
+                if isinstance(_value, dict):
+                    out.extend(self._build_condition(f"{key}[]", _value))
+                else:
+                    out.extend(self._build_condition(f"{key}", _value))
+        else:
+            out.append(
                 rest.FieldCondition(
                     key=f"{self.metadata_payload_key}.{key}",
                     match=rest.MatchValue(value=value),
                 )
+            )
+
+        return out
+
+    def _qdrant_filter_from_dict(
+        self, filter: Optional[MetadataFilter]
+    ) -> Optional[rest.Filter]:
+        from qdrant_client.http import models as rest
+
+        if not filter:
+            return None
+
+        return rest.Filter(
+            must=[
+                condition
                 for key, value in filter.items()
+                for condition in self._build_condition(key, value)
             ]
         )
diff --git a/tests/integration_tests/vectorstores/test_qdrant.py b/tests/integration_tests/vectorstores/test_qdrant.py
index 1f43a0bc..8362951c 100644
--- a/tests/integration_tests/vectorstores/test_qdrant.py
+++ b/tests/integration_tests/vectorstores/test_qdrant.py
@@ -78,15 +78,26 @@ def test_qdrant_with_metadatas(
 def test_qdrant_similarity_search_filters() -> None:
     """Test end to end construction and search."""
     texts = ["foo", "bar", "baz"]
-    metadatas = [{"page": i} for i in range(len(texts))]
+    metadatas = [
+        {"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
+        for i in range(len(texts))
+    ]
     docsearch = Qdrant.from_texts(
         texts,
         FakeEmbeddings(),
         metadatas=metadatas,
         location=":memory:",
     )
-    output = docsearch.similarity_search("foo", k=1, filter={"page": 1})
-    assert output == [Document(page_content="bar", metadata={"page": 1})]
+
+    output = docsearch.similarity_search(
+        "foo", k=1, filter={"page": 1, "metadata": {"page": 2, "pages": [3]}}
+    )
+    assert output == [
+        Document(
+            page_content="bar",
+            metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}},
+        )
+    ]
 
 
 @pytest.mark.parametrize(