Get by ids

libs/redis/langchain_redis/vectorstores.py

@@ -2,7 +2,8 @@
 
 from __future__ import annotations
 
-from typing import Any, Iterable, List, Optional, Tuple, Union, cast
+from typing import Any, Iterable, List, Optional, Tuple, Union, cast,\
+    Sequence, Dict
 
 import numpy as np
 from langchain_core.documents import Document
@@ -739,6 +740,47 @@ def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[boo
             return self._index.drop_keys(keys) == len(ids)
         else:
             return False
+
+    def get_by_ids(self, ids: Sequence[str], /) -> List[Document]:
+        """Get documents by their IDs.
+
+        The returned documents are expected to have the ID field set to the ID of the
+        document in the vector store.
+
+        Fewer documents may be returned than requested if some IDs are not found or
+        if there are duplicated IDs.
+
+        Users should not assume that the order of the returned documents matches
+        the order of the input IDs. Instead, users should rely on the ID field of the
+        returned documents.
+
+        This method should **NOT** raise exceptions if no documents are found for
+        some IDs.
+
+        Args:
+            ids: List of ids to retrieve.
+
+        Returns:
+            List of Documents.
+        """
+
+        result_docs = []
+        for key in ids:
+            doc_dict = self.index.client.hgetall(
+                name = key
+            )
+
+            if doc_dict is None or len(doc_dict)==0:
+                continue
+
+            doc_dict.pop(self.config.embedding_field.encode(),None)
+            doc_dict: Dict = convert_bytes(doc_dict)
+            doc = Document(doc_dict.pop(self.config.content_field),
+                           metadata=doc_dict)
+            result_docs.append(doc)
+
+        return result_docs
+
 
     def similarity_search_by_vector(
         self,

libs/redis/tests/integration_tests/test_vectorstores_hash.py

@@ -490,6 +490,39 @@ def test_similarity_search(redis_url: str) -> None:
     vector_store.index.delete(drop=True)
 
 
+def test_get_by_ids(redis_url: str) -> None:
+    """Test end to end construction and search."""
+    # Create embeddings
+    embeddings = OpenAIEmbeddings()
+
+    # Create a unique index name for testing
+    index_name = f"test_index_{str(ULID())}"
+
+    texts = ["foo", "bar", "baz"]
+
+    keys = ["a", "b", "c"]
+
+    # Create the RedisVectorStore
+    vector_store = RedisVectorStore.from_texts(
+        texts,
+        embeddings,
+        index_name=index_name,
+        key_prefix="tst11",
+        redis_url=redis_url,
+    )
+
+    ids = [f"tst11:{k}" for k in ["a","c"]]
+
+    docs = vector_store.get_by_ids(ids)
+
+    result_texts = [doc.page_content for doc in docs]
+
+    assert all(txt in result_texts for txt in texts)
+
+    # Clean up
+    vector_store.index.delete(drop=True)
+
+
 def test_similarity_search_with_scores(redis_url: str) -> None:
     """Test end to end construction and search."""
     # Create embeddings

libs/redis/tests/integration_tests/test_vectorstores_json.py

@@ -501,6 +501,40 @@ def test_similarity_search(redis_url: str) -> None:
     vector_store.index.delete(drop=True)
 
 
+def test_get_by_ids(redis_url: str) -> None:
+    """Test end to end construction and search."""
+    # Create embeddings
+    embeddings = OpenAIEmbeddings()
+
+    # Create a unique index name for testing
+    index_name = f"test_index_{str(ULID())}"
+
+    texts = ["foo", "bar", "baz"]
+
+    keys = ["a", "b", "c"]
+
+    # Create the RedisVectorStore
+    vector_store = RedisVectorStore.from_texts(
+        texts,
+        embeddings,
+        index_name=index_name,
+        key_prefix="tst11",
+        redis_url=redis_url,
+        storage_type="json",
+    )
+
+    ids = [f"tst11:{k}" for k in ["a","c"]]
+
+    docs = vector_store.get_by_ids(ids)
+
+    result_texts = [doc.page_content for doc in docs]
+
+    assert all(txt in result_texts for txt in texts)
+
+    # Clean up
+    vector_store.index.delete(drop=True)
+
+
 def test_similarity_search_with_scores(redis_url: str) -> None:
     """Test end to end construction and search."""
     # Create embeddings

libs/redis/tests/unit_tests/test_vectorstores.py

@@ -42,6 +42,9 @@ def __init__(self) -> None:
 
     def get(self, client: Any, doc_ids: List[str]) -> List[Dict[str, Any]]:
         return [self.data.get(doc_id, {}) for doc_id in doc_ids]
+
+    def hgetall(self, name: str) -> Dict[str, Any]:
+        return self.data.get(name,{})
 
 
 class MockSearchIndex:
@@ -63,8 +66,9 @@ def __init__(
         self.schema = MockSchema(
             schema["fields"] if schema and "fields" in schema else default_schema
         )
-        self.client = client or Mock()
+
         self._storage = MockStorage()
+        self.client = self._storage
 
     def create(self, overwrite: bool = False) -> None:
         pass
@@ -148,6 +152,16 @@ def test_add_texts(self, vector_store: RedisVectorStore) -> None:
         assert len(keys) == 2
         assert all(key.startswith("key_") for key in keys)
 
+    def test_get_by_ids(self, vector_store: RedisVectorStore) -> None:
+        texts = ["Hello, world!", "Test document"]
+        metadatas = [{"source": "greeting"}, {"source": "test"}]
+        keys = vector_store.add_texts(texts, metadatas)
+        docs = vector_store.get_by_ids(keys)
+        assert len(keys) == len(docs)
+        assert all(isinstance(doc, Document) for doc in docs)
+        assert all(isinstance(doc.page_content, str) for doc in docs)
+        assert all(isinstance(doc.metadata, dict) for doc in docs)
+
     def test_similarity_search(self, vector_store: RedisVectorStore) -> None:
         vector_store.add_texts(["Hello, world!", "Test document"])
         results = vector_store.similarity_search("Hello", k=1)