Support linear memory in RedisMemory

python/packages/autogen-ext/src/autogen_ext/memory/redis/_redis_memory.py

@@ -11,8 +11,9 @@
 
 try:
     from redis import Redis
-    from redisvl.extensions.message_history import SemanticMessageHistory
+    from redisvl.extensions.message_history import MessageHistory, SemanticMessageHistory
     from redisvl.utils.utils import deserialize, serialize
+    from redisvl.utils.vectorize import HFTextVectorizer
 except ImportError as e:
     raise ImportError("To use Redis Memory RedisVL must be installed. Run `pip install autogen-ext[redisvl]`") from e
 
@@ -29,24 +30,25 @@ class RedisMemoryConfig(BaseModel):
     redis_url: str = Field(default="redis://localhost:6379", description="url of the Redis instance")
     index_name: str = Field(default="chat_history", description="Name of the Redis collection")
     prefix: str = Field(default="memory", description="prefix of the Redis collection")
+    sequential: bool = Field(
+        default=False, description="ignore semantic similarity and simply return memories in sequential order"
+    )
     distance_metric: Literal["cosine", "ip", "l2"] = "cosine"
     algorithm: Literal["flat", "hnsw"] = "flat"
     top_k: int = Field(default=10, description="Number of results to return in queries")
     datatype: Literal["uint8", "int8", "float16", "float32", "float64", "bfloat16"] = "float32"
     distance_threshold: float = Field(default=0.7, description="Minimum similarity score threshold")
-    model_name: str | None = Field(
-        default="sentence-transformers/all-mpnet-base-v2", description="Embedding model name"
-    )
+    model_name: str = Field(default="sentence-transformers/all-mpnet-base-v2", description="Embedding model name")
 
 
 class RedisMemory(Memory, Component[RedisMemoryConfig]):
     """
     Store and retrieve memory using vector similarity search powered by RedisVL.
 
     `RedisMemory` provides a vector-based memory implementation that uses RedisVL for storing and
-    retrieving content based on semantic similarity. It enhances agents with the ability to recall
-    contextually relevant information during conversations by leveraging vector embeddings to find
-    similar content.
+    retrieving content based on semantic similarity or sequential order. It enhances agents with the
+    ability to recall relevant information during conversations by leveraging vector embeddings to
+    find similar content.
 
         This implementation requires the RedisVL extra to be installed. Install with:
 
@@ -175,7 +177,19 @@ def __init__(self, config: RedisMemoryConfig | None = None) -> None:
         self.config = config or RedisMemoryConfig()
         client = Redis.from_url(url=self.config.redis_url)  # type: ignore[reportUknownMemberType]
 
-        self.message_history = SemanticMessageHistory(name=self.config.index_name, redis_client=client)
+        if self.config.sequential:
+            self.message_history = MessageHistory(
+                name=self.config.index_name, prefix=self.config.prefix, redis_client=client
+            )
+        else:
+            vectorizer = HFTextVectorizer(model=self.config.model_name, dtype=self.config.datatype)
+            self.message_history = SemanticMessageHistory(
+                name=self.config.index_name,
+                prefix=self.config.prefix,
+                vectorizer=vectorizer,
+                distance_threshold=self.config.distance_threshold,
+                redis_client=client,
+            )
 
     async def update_context(
         self,
@@ -203,7 +217,7 @@ async def update_context(
         else:
             last_message = ""
 
-        query_results = await self.query(last_message)
+        query_results = await self.query(last_message, sequential=self.config.sequential)
 
         stringified_messages = "\n\n".join([str(m.content) for m in query_results.results])
 
@@ -216,10 +230,10 @@ async def add(self, content: MemoryContent, cancellation_token: CancellationToke
 
         .. note::
 
-            To perform semantic search over stored memories RedisMemory creates a vector embedding
-            from the content field of a MemoryContent object. This content is assumed to be text,
-            JSON, or Markdown, and is passed to the vector embedding model specified in
-            RedisMemoryConfig.
+            If RedisMemoryConfig is not set to 'sequential', to perform semantic search over stored
+            memories RedisMemory creates a vector embedding from the content field of a
+            MemoryContent object. This content is assumed to be text, JSON, or Markdown, and is
+            passed to the vector embedding model specified in RedisMemoryConfig.
 
         Args:
             content (MemoryContent): The memory content to store within Redis.
@@ -241,7 +255,7 @@ async def add(self, content: MemoryContent, cancellation_token: CancellationToke
         metadata = {"mime_type": mime_type}
         metadata.update(content.metadata if content.metadata else {})
         self.message_history.add_message(
-            {"role": "user", "content": memory_content, "tool_call_id": serialize(metadata)}  # type: ignore[reportArgumentType]
+            {"role": "user", "content": memory_content, "metadata": serialize(metadata)}  # type: ignore[reportArgumentType]
         )
 
     async def query(
@@ -258,6 +272,7 @@ async def query(
             top_k (int): The maximum number of relevant memories to include. Defaults to 10.
             distance_threshold (float): The maximum distance in vector space to consider a memory
             semantically similar when performining cosine similarity search. Defaults to 0.7.
+            sequential (bool): Ignore semantic similarity and return the top_k most recent memories.
 
         Args:
             query (str | MemoryContent): query to perform vector similarity search with. If a
@@ -270,34 +285,46 @@ async def query(
         Returns:
             memoryQueryResult: Object containing memories relevant to the provided query.
         """
-        # get the query string, or raise an error for unsupported MemoryContent types
-        if isinstance(query, str):
-            prompt = query
-        elif isinstance(query, MemoryContent):
-            if query.mime_type in (MemoryMimeType.TEXT, MemoryMimeType.MARKDOWN):
-                prompt = str(query.content)
-            elif query.mime_type == MemoryMimeType.JSON:
-                prompt = serialize(query.content)
-            else:
-                raise NotImplementedError(
-                    f"Error: {query.mime_type} is not supported. Only MemoryMimeType.TEXT, MemoryMimeType.JSON, MemoryMimeType.MARKDOWN are currently supported."
-                )
-        else:
-            raise TypeError("'query' must be either a string or MemoryContent")
-
         top_k = kwargs.pop("top_k", self.config.top_k)
         distance_threshold = kwargs.pop("distance_threshold", self.config.distance_threshold)
 
-        results = self.message_history.get_relevant(
-            prompt=prompt,  # type: ignore[reportArgumentType]
-            top_k=top_k,
-            distance_threshold=distance_threshold,
-            raw=False,
-        )
+        # if sequential memory is requested skip prompt creation
+        sequential = bool(kwargs.pop("sequential", self.config.sequential))
+        if self.config.sequential and not sequential:
+            raise ValueError(
+                "Non-sequential queries cannot be run with an underlying sequential RedisMemory. Set sequential=False in RedisMemoryConfig to enable semantic memory querying."
+            )
+        elif sequential or self.config.sequential:
+            results = self.message_history.get_recent(
+                top_k=top_k,
+                raw=False,
+            )
+        else:
+            # get the query string, or raise an error for unsupported MemoryContent types
+            if isinstance(query, str):
+                prompt = query
+            elif isinstance(query, MemoryContent):
+                if query.mime_type in (MemoryMimeType.TEXT, MemoryMimeType.MARKDOWN):
+                    prompt = str(query.content)
+                elif query.mime_type == MemoryMimeType.JSON:
+                    prompt = serialize(query.content)
+                else:
+                    raise NotImplementedError(
+                        f"Error: {query.mime_type} is not supported. Only MemoryMimeType.TEXT, MemoryMimeType.JSON, MemoryMimeType.MARKDOWN are currently supported."
+                    )
+            else:
+                raise TypeError("'query' must be either a string or MemoryContent")
+
+            results = self.message_history.get_relevant(  # type: ignore
+                prompt=prompt,  # type: ignore[reportArgumentType]
+                top_k=top_k,
+                distance_threshold=distance_threshold,
+                raw=False,
+            )
 
         memories: List[MemoryContent] = []
-        for result in results:
-            metadata = deserialize(result["tool_call_id"])  # type: ignore[reportArgumentType]
+        for result in results:  # type: ignore[reportUnkownVariableType]
+            metadata = deserialize(result["metadata"])  # type: ignore[reportArgumentType]
             mime_type = MemoryMimeType(metadata.pop("mime_type"))
             if mime_type in (MemoryMimeType.TEXT, MemoryMimeType.MARKDOWN):
                 memory_content = result["content"]  # type: ignore[reportArgumentType]

python/packages/autogen-ext/tests/memory/test_redis_memory.py

@@ -36,7 +36,7 @@ async def test_redis_memory_query_with_mock() -> None:
         memory = RedisMemory(config=config)
 
         mock_history.get_relevant.return_value = [
-            {"content": "test content", "tool_call_id": '{"foo": "bar", "mime_type": "text/plain"}'}
+            {"content": "test content", "metadata": '{"foo": "bar", "mime_type": "text/plain"}'}
         ]
         result = await memory.query("test")
         assert len(result.results) == 1
@@ -86,13 +86,26 @@ def semantic_config() -> RedisMemoryConfig:
     return RedisMemoryConfig(top_k=5, distance_threshold=0.5, model_name="sentence-transformers/all-mpnet-base-v2")
 
 
+@pytest.fixture
+def sequential_config() -> RedisMemoryConfig:
+    """Create base configuration using semantic memory."""
+    return RedisMemoryConfig(top_k=5, sequential=True)
+
+
 @pytest_asyncio.fixture  # type: ignore[reportUntypedFunctionDecorator]
 async def semantic_memory(semantic_config: RedisMemoryConfig) -> AsyncGenerator[RedisMemory]:
     memory = RedisMemory(semantic_config)
     yield memory
     await memory.close()
 
 
+@pytest_asyncio.fixture  # type: ignore[reportUntypedFunctionDecorator]
+async def sequential_memory(sequential_config: RedisMemoryConfig) -> AsyncGenerator[RedisMemory]:
+    memory = RedisMemory(sequential_config)
+    yield memory
+    await memory.close()
+
+
 ## UNIT TESTS ##
 def test_memory_config() -> None:
     default_config = RedisMemoryConfig()
@@ -104,6 +117,7 @@ def test_memory_config() -> None:
     assert default_config.top_k == 10
     assert default_config.distance_threshold == 0.7
     assert default_config.model_name == "sentence-transformers/all-mpnet-base-v2"
+    assert not default_config.sequential
 
     # test we can specify each of these values
     url = "rediss://localhost:7010"
@@ -144,14 +158,36 @@ def test_memory_config() -> None:
 
 @pytest.mark.asyncio
 @pytest.mark.skipif(not redis_available(), reason="Redis instance not available locally")
-async def test_create_semantic_memory() -> None:
-    config = RedisMemoryConfig(index_name="semantic_agent")
+@pytest.mark.parametrize("sequential", [True, False])
+async def test_create_memory(sequential: bool) -> None:
+    config = RedisMemoryConfig(index_name="semantic_agent", sequential=sequential)
     memory = RedisMemory(config=config)
 
     assert memory.message_history is not None
     await memory.close()
 
 
+@pytest.mark.asyncio
+@pytest.mark.skipif(not redis_available(), reason="Redis instance not available locally")
+async def test_specify_vectorizer() -> None:
+    config = RedisMemoryConfig(index_name="semantic_agent", model_name="redis/langcache-embed-v1")
+    memory = RedisMemory(config=config)
+    assert memory.message_history._vectorizer.dims == 768  # type: ignore[reportPrivateUsage]
+    await memory.close()
+
+    config = RedisMemoryConfig(
+        index_name="semantic_agent", model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
+    )
+    memory = RedisMemory(config=config)
+    assert memory.message_history._vectorizer.dims == 384  # type: ignore[reportPrivateUsage]
+    await memory.close()
+
+    # throw an error if a non-existant model name is passed
+    config = RedisMemoryConfig(index_name="semantic_agent", model_name="not-a-real-model")
+    with pytest.raises(OSError):
+        memory = RedisMemory(config=config)
+
+
 @pytest.mark.asyncio
 @pytest.mark.skipif(not redis_available(), reason="Redis instance not available locally")
 async def test_update_context(semantic_memory: RedisMemory) -> None:
@@ -223,7 +259,7 @@ async def test_update_context(semantic_memory: RedisMemory) -> None:
 
 @pytest.mark.asyncio
 @pytest.mark.skipif(not redis_available(), reason="Redis instance not available locally")
-async def test_add_and_query(semantic_memory: RedisMemory) -> None:
+async def test_add_and_query_with_string(semantic_memory: RedisMemory) -> None:
     content_1 = MemoryContent(
         content="I enjoy fruits like apples, oranges, and bananas.", mime_type=MemoryMimeType.TEXT, metadata={}
     )
@@ -251,6 +287,38 @@ async def test_add_and_query(semantic_memory: RedisMemory) -> None:
     assert memories.results[1].metadata == {"description": "additional info"}
 
 
+@pytest.mark.asyncio
+@pytest.mark.skipif(not redis_available(), reason="Redis instance not available locally")
+async def test_add_and_query_with_memory_content(semantic_memory: RedisMemory) -> None:
+    content_1 = MemoryContent(
+        content="I enjoy fruits like apples, oranges, and bananas.", mime_type=MemoryMimeType.TEXT, metadata={}
+    )
+    await semantic_memory.add(content_1)
+
+    # find matches with a similar query
+    memories = await semantic_memory.query(MemoryContent(content="Fruits that I like.", mime_type=MemoryMimeType.TEXT))
+    assert len(memories.results) == 1
+
+    # don't return anything for dissimilar queries
+    no_memories = await semantic_memory.query(
+        MemoryContent(content="The king of England", mime_type=MemoryMimeType.TEXT)
+    )
+    assert len(no_memories.results) == 0
+
+    # match multiple relevant memories
+    content_2 = MemoryContent(
+        content="I also like mangos and pineapples.",
+        mime_type=MemoryMimeType.TEXT,
+        metadata={"description": "additional info"},
+    )
+    await semantic_memory.add(content_2)
+
+    memories = await semantic_memory.query(MemoryContent(content="Fruits that I like.", mime_type=MemoryMimeType.TEXT))
+    assert len(memories.results) == 2
+    assert memories.results[0].metadata == {}
+    assert memories.results[1].metadata == {"description": "additional info"}
+
+
 @pytest.mark.asyncio
 @pytest.mark.skipif(not redis_available(), reason="Redis instance not available locally")
 async def test_clear(semantic_memory: RedisMemory) -> None:
@@ -283,9 +351,16 @@ async def test_close(semantic_config: RedisMemoryConfig) -> None:
 ## INTEGRATION TESTS ##
 @pytest.mark.asyncio
 @pytest.mark.skipif(not redis_available(), reason="Redis instance not available locally")
-async def test_basic_workflow(semantic_config: RedisMemoryConfig) -> None:
+@pytest.mark.parametrize("config_type", ["sequential", "semantic"])
+async def test_basic_workflow(config_type: str) -> None:
     """Test basic memory operations with semantic memory."""
-    memory = RedisMemory(config=semantic_config)
+    if config_type == "sequential":
+        config = RedisMemoryConfig(top_k=5, sequential=True)
+    else:
+        config = RedisMemoryConfig(
+            top_k=5, distance_threshold=0.5, model_name="sentence-transformers/all-mpnet-base-v2"
+        )
+    memory = RedisMemory(config=config)
     await memory.clear()
 
     await memory.add(
@@ -318,6 +393,11 @@ async def test_text_memory_type(semantic_memory: RedisMemory) -> None:
     assert len(results.results) > 0
     assert any("Simple text content" in str(r.content) for r in results.results)
 
+    # Query for text content with a MemoryContent object
+    results = await semantic_memory.query(MemoryContent(content="simple text content", mime_type=MemoryMimeType.TEXT))
+    assert len(results.results) > 0
+    assert any("Simple text content" in str(r.content) for r in results.results)
+
 
 @pytest.mark.asyncio
 @pytest.mark.skipif(not redis_available(), reason="Redis instance not available locally")
@@ -419,3 +499,57 @@ async def test_query_arguments(semantic_memory: RedisMemory) -> None:
     # limit search to only close matches
     results = await semantic_memory.query("my favorite fruit are what?", distance_threshold=0.2)
     assert len(results.results) == 1
+
+    # get memories based on recency instead of relevance
+    results = await semantic_memory.query("fast sports cars", sequential=True)
+    assert len(results.results) == 3
+
+    # setting 'sequential' to False results in default behaviour
+    results = await semantic_memory.query("my favorite fruit are what?", sequential=False)
+    assert len(results.results) == 3
+
+
+@pytest.mark.asyncio
+@pytest.mark.skipif(not redis_available(), reason="Redis instance not available locally")
+async def test_sequential_memory_workflow(sequential_memory: RedisMemory) -> None:
+    await sequential_memory.clear()
+
+    await sequential_memory.add(MemoryContent(content="my favorite fruit are apples", mime_type=MemoryMimeType.TEXT))
+    await sequential_memory.add(
+        MemoryContent(
+            content="I read the encyclopedia britanica and my favorite section was on the Napoleonic Wars.",
+            mime_type=MemoryMimeType.TEXT,
+        )
+    )
+    await sequential_memory.add(
+        MemoryContent(content="Sharks have no idea that camels exist.", mime_type=MemoryMimeType.TEXT)
+    )
+    await sequential_memory.add(
+        MemoryContent(
+            content="Python is a popular programming language used for machine learning and AI applications.",
+            mime_type=MemoryMimeType.TEXT,
+        )
+    )
+    await sequential_memory.add(
+        MemoryContent(content="Fifth random and unrelated sentence", mime_type=MemoryMimeType.TEXT)
+    )
+
+    # default search returns last 5 memories
+    results = await sequential_memory.query("what fruits do I like?")
+    assert len(results.results) == 5
+
+    # limit search to 2 results
+    results = await sequential_memory.query("what fruits do I like?", top_k=2)
+    assert len(results.results) == 2
+
+    # sequential memory does not consider semantic similarity
+    results = await sequential_memory.query("How do I make peanut butter sandwiches?")
+    assert len(results.results) == 5
+
+    # seting 'sequential' to True in query method is redundant
+    results = await sequential_memory.query("fast sports cars", sequential=True)
+    assert len(results.results) == 5
+
+    # setting 'sequential' to False with a Sequential memory object raises an error
+    with pytest.raises(ValueError):
+        _ = await sequential_memory.query("my favorite fruit are what?", sequential=False)