diff --git a/README.md b/README.md index bc9a556..4978e9c 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,50 @@ -# nvidia-nat-redis +
+Redis -Redis Agent Memory integrations for -[NVIDIA NeMo Agent Toolkit](https://github.com/NVIDIA/NeMo-Agent-Toolkit). +# Nvidia-NAT-Redis +
-This standalone plugin exposes two NAT surfaces: +**Redis-backed memory for [NVIDIA NeMo Agent Toolkit](https://github.com/NVIDIA/NeMo-Agent-Toolkit)** — production [Redis Agent Memory](https://redis.github.io/agent-memory-server/quick-start/) integrations plus direct Redis plugins (the historical `nat.plugins.redis` stack). -1. `_type: redis_agent_memory_backend` - Redis Agent Memory as a NAT `MemoryEditor` long-term memory backend. -2. `_type: redis_agent_memory_auto_memory` - A native Redis Agent Memory wrapper that uses working memory plus - `memory_prompt` hydration on every turn. +In NAT, long-term recall is usually wired through memory tools such as `get_memory` and `add_memory`, which delegate to a **`MemoryEditor`** implementation. This package adds Redis-backed options in **two families**: **Redis Agent Memory** (AMS over HTTP — richer lifecycle, optional auto-memory workflow) and **direct Redis** (JSON + vector search or plain KV inside `nat.plugins.redis`, no AMS). + +This repo is the standalone home for integrations that used to ship under +[`packages/nvidia_nat_redis`](https://github.com/NVIDIA/NeMo-Agent-Toolkit/tree/develop/packages/nvidia_nat_redis) in the NeMo Agent Toolkit monorepo. + +### Redis Agent Memory (full stack) +1. `_type: redis_agent_memory_backend` — Redis Agent Memory as a NAT `MemoryEditor` long-term memory backend. +2. `_type: redis_agent_memory_auto_memory` — A native Redis Agent Memory wrapper that uses working memory plus +`memory_prompt` hydration on every turn. +AMS runs as a separate service; these surfaces talk to it via the agent-memory client. + +1. `_type: redis_agent_memory_backend` — Redis Agent Memory behind NAT’s standard **`MemoryEditor`** contract (fits tool-driven long-term memory). +2. `_type: redis_agent_memory_auto_memory` — A **`workflow` wrapper** (not just another editor): working memory, `memory_prompt` hydration every turn, turn capture, and promotion — the fullest AMS-shaped integration. + +### Direct Redis (simple in-Redis memory) + +Loaded via the `nat_redis` setuptools entry point (same name as the historical +monorepo package): + +- `_type: redis_memory` — Vector search over JSON documents in Redis (RediSearch); requires a workflow `embedder` and Redis Stack (or Redis with search + JSON support). Implements **`MemoryEditor`**-style semantic memory without AMS. +- `_type: redis` — NAT **object store** on plain Redis key–value storage (not semantic long-term memory). + +| | **Redis Agent Memory** | **Direct Redis** | +| --- | --- | --- | +| **Runs** | AMS service + Redis | Redis only (your NAT process uses the client) | +| **Best for** | Learning-style memory, working memory, AMS filters and APIs | Lightweight Redis-native memory or KV object store | +| **Tradeoff** | Operate AMS; HTTP path | Simpler ops for `redis` / `redis_memory`; vector path needs embedder + search-capable Redis | + +Use **Redis Agent Memory** when you want the Redis Agent Memory feature set. +Use **`redis_memory`** when you only need a lightweight Redis-native `MemoryEditor` +without AMS. + +### Python imports (same as NeMo) + +Direct Redis support is implemented only under **`nat.plugins.redis`** (for example +`from nat.plugins.redis.redis_editor import RedisEditor`), matching NeMo Agent +Toolkit. The setuptools entry point **`nat_redis`** loads +`nat.plugins.redis.register`. Redis Agent Memory code lives under +**`nvidia_nat_redis.redis_agent_memory`**. ## Install @@ -30,9 +65,13 @@ uv sync --group dev --extra test - Use `_type: redis_agent_memory_backend` when your workflow already uses NAT memory tools and you want Redis Agent Memory behind the standard `MemoryEditor` contract. - Use `_type: redis_agent_memory_auto_memory` when you want Redis Agent Memory to own working-memory continuity, prompt hydration, and turn capture on every request. This exposes the richness of Redis Agent Memory in its fullest form. +- Use `_type: redis_memory` when you want the simpler direct-Redis memory from NeMo Agent Toolkit (Redis JSON + vector index, no Redis Agent Memory). You must configure an `embedder` reference and run a Redis deployment that supports the search commands used by the plugin. +- Use `_type: redis` when you need NAT’s Redis-backed **object store** (KV), not vector or AMS-backed semantic memory. ## Integration Modes +These examples cover the two **Redis Agent Memory** integration shapes (`MemoryEditor` backend vs `workflow` wrapper). For **`redis_memory`** / **`redis`**, see [Configuration reference](docs/configuration.md). + ### Direct Long-Term Memory Backend Use `_type: redis_agent_memory_backend` when you want Redis Agent Memory behind NAT's diff --git a/assets/redis-logo.svg b/assets/redis-logo.svg new file mode 100644 index 0000000..1471c56 --- /dev/null +++ b/assets/redis-logo.svg @@ -0,0 +1,66 @@ + + + + + + + + + + +]> + + + + + + + + + + + diff --git a/docs/configuration.md b/docs/configuration.md index b5cddda..81f1803 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -1,9 +1,25 @@ # Configuration -This package ships two Redis Agent Memory surfaces for NAT: +This package ships Redis Agent Memory surfaces and direct Redis plugins (migrated +from the NeMo Agent Toolkit `nvidia_nat_redis` package). -1. A long-term memory backend: `_type: redis_agent_memory_backend` -2. A native automatic wrapper: `_type: redis_agent_memory_auto_memory` +Redis Agent Memory is a production-ready agent memory layer that extracts and stores +relevant information and learns over time. + +Direct Redis memory provides a more standard semantic memory layer for LLM applications. + +**Full Redis Agent Memory auto wrapper** + +1. Long-term memory backend: `_type: redis_agent_memory_backend` +2. Native automatic wrapper: `_type: redis_agent_memory_auto_memory` + +**Direct Redis (entry point `nat_redis` → `nat.plugins.redis.register`)** + +3. In-Redis vector memory: `_type: redis_memory` +4. Object store: `_type: redis` + +For Python code, use **`nat.plugins.redis.*`** — the same module layout as NeMo +Agent Toolkit’s in-tree package. ## Long-Term Memory Backend @@ -116,6 +132,50 @@ Wrapper flow: 2. Create or load Redis Agent Memory working memory for that identity. 3. Call `memory_prompt(...)`, invoke the inner chat function with the hydrated request, and append the finished turn back into working memory. +## Direct Redis memory (`redis_memory`) + +Use `_type: redis_memory` for the lightweight Redis-backed `MemoryEditor` that +stores JSON documents and uses RediSearch vector queries. This matches the +behavior of the standalone +[`nvidia_nat_redis` package in NeMo Agent Toolkit](https://github.com/NVIDIA/NeMo-Agent-Toolkit/tree/develop/packages/nvidia_nat_redis). + +Requirements: + +- Redis with **JSON** and **search** (for example Redis Stack), because the implementation creates a JSON index and runs vector KNN queries. +- A workflow **embedder** instance; `embedder` must reference its name in the workflow configuration. + +```yaml +memory: + redis_ltm: + _type: redis_memory + host: localhost + port: 6379 + db: 0 + key_prefix: nat + embedder: my_openai_embedder +``` + +Supported config fields: + +- `host`, `port`, `db`, `password` (optional secret), `key_prefix` +- `embedder`: `EmbedderRef` to a configured embedder (LangChain wrapper) + +## Direct Redis object store (`redis`) + +Use `_type: redis` for the NAT object store that persists `ObjectStoreItem` +JSON at keys under `nat/object_store/{bucket_name}/...`, with optional TTL. + +```yaml +object_store: + artifacts: + _type: redis + host: localhost + port: 6379 + db: 0 + bucket_name: my_bucket + ttl: 3600 +``` + ## Examples - [Redis Agent Memory native wrapper](../examples/agent_auto_memory/README.md) diff --git a/examples/agent_auto_memory/README.md b/examples/agent_auto_memory/README.md index ea65618..556c611 100644 --- a/examples/agent_auto_memory/README.md +++ b/examples/agent_auto_memory/README.md @@ -40,7 +40,7 @@ The example expects: ## Start Services -Compose starts Redis Stack and Agent Memory Server for local development. Both +Compose starts Redis Stack and Redis Agent Memory containers for local development. Both ports bind to `127.0.0.1`, and AMS auth is disabled. ```bash diff --git a/pyproject.toml b/pyproject.toml index 57c952c..61af626 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,7 +21,9 @@ classifiers = [ ] dependencies = [ "agent-memory-client>=0.14.0,<1.0.0", + "langchain-core>=0.3.0,<2.0.0", "nvidia-nat-core>=1.5.0,<2.0.0", + "redis>=4.3.4,<5.0.0", ] [project.optional-dependencies] @@ -38,6 +40,7 @@ source = "https://github.com/redis-developer/nvidia-nat-redis" [project.entry-points.'nat.components'] redis_agent_memory = "nvidia_nat_redis.redis_agent_memory.register" +nat_redis = "nat.plugins.redis.register" [dependency-groups] dev = [ @@ -47,7 +50,7 @@ dev = [ [tool.setuptools.packages.find] where = ["src"] -include = ["nvidia_nat_redis*"] +include = ["nvidia_nat_redis*", "nat.plugins*"] [tool.setuptools_scm] git_describe_command = "git describe --long --first-parent" diff --git a/src/nat/plugins/redis/__init__.py b/src/nat/plugins/redis/__init__.py new file mode 100644 index 0000000..6b6f609 --- /dev/null +++ b/src/nat/plugins/redis/__init__.py @@ -0,0 +1,16 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Direct Redis memory and object-store NAT components (NeMo ``nat.plugins.redis`` layout).""" diff --git a/src/nat/plugins/redis/memory.py b/src/nat/plugins/redis/memory.py new file mode 100644 index 0000000..b7729dc --- /dev/null +++ b/src/nat/plugins/redis/memory.py @@ -0,0 +1,60 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nat.builder.builder import Builder +from nat.cli.register_workflow import register_memory +from nat.data_models.common import OptionalSecretStr, get_secret_value +from nat.data_models.component_ref import EmbedderRef +from nat.data_models.memory import MemoryBaseConfig +from pydantic import Field + + +class RedisMemoryClientConfig(MemoryBaseConfig, name="redis_memory"): + host: str = Field(default="localhost", description="Redis server host") + db: int = Field(default=0, description="Redis DB") + port: int = Field(default=6379, description="Redis server port") + password: OptionalSecretStr = Field(default=None, description="Password for the Redis server") + key_prefix: str = Field(default="nat", description="Key prefix to use for redis keys") + embedder: EmbedderRef = Field(description=("Instance name of the memory client instance from the workflow " + "configuration object.")) + + +@register_memory(config_type=RedisMemoryClientConfig) +async def redis_memory_client(config: RedisMemoryClientConfig, builder: Builder): + + from nat.builder.framework_enum import LLMFrameworkEnum + + import redis.asyncio as redis + + from .redis_editor import RedisEditor + from .schema import ensure_index_exists + + redis_client = redis.Redis(host=config.host, + port=config.port, + db=config.db, + password=get_secret_value(config.password), + decode_responses=True, + socket_timeout=5.0, + socket_connect_timeout=5.0) + + embedder = await builder.get_embedder(config.embedder, wrapper_type=LLMFrameworkEnum.LANGCHAIN) + + test_embedding = await embedder.aembed_query("test") + embedding_dim = len(test_embedding) + await ensure_index_exists(client=redis_client, key_prefix=config.key_prefix, embedding_dim=embedding_dim) + + memory_editor = RedisEditor(redis_client=redis_client, key_prefix=config.key_prefix, embedder=embedder) + + yield memory_editor diff --git a/src/nat/plugins/redis/object_store.py b/src/nat/plugins/redis/object_store.py new file mode 100644 index 0000000..9285b84 --- /dev/null +++ b/src/nat/plugins/redis/object_store.py @@ -0,0 +1,49 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nat.builder.builder import Builder +from nat.cli.register_workflow import register_object_store +from nat.data_models.common import OptionalSecretStr +from nat.data_models.object_store import ObjectStoreBaseConfig +from pydantic import Field, field_validator + + +class RedisObjectStoreClientConfig(ObjectStoreBaseConfig, name="redis"): + """ + Object store that stores objects in a Redis database with optional TTL. + """ + + host: str = Field(default="localhost", description="The host of the Redis server") + db: int = Field(default=0, description="The Redis logical database number") + port: int = Field(default=6379, description="The port of the Redis server") + bucket_name: str = Field(description="The name of the bucket to use for the object store") + password: OptionalSecretStr = Field(default=None, description="The password for the Redis server") + ttl: int | None = Field(default=None, description="TTL in seconds for objects (None = no expiration)") + + @field_validator("ttl") + @classmethod + def validate_ttl(cls, v: int | None) -> int | None: + if v is not None and v <= 0: + raise ValueError("TTL must be a positive integer greater than 0") + return v + + +@register_object_store(config_type=RedisObjectStoreClientConfig) +async def redis_object_store_client(config: RedisObjectStoreClientConfig, _builder: Builder): + + from .redis_object_store import RedisObjectStore + + async with RedisObjectStore(**config.model_dump(exclude={"type"})) as store: + yield store diff --git a/src/nat/plugins/redis/redis_editor.py b/src/nat/plugins/redis/redis_editor.py new file mode 100644 index 0000000..0ab84c6 --- /dev/null +++ b/src/nat/plugins/redis/redis_editor.py @@ -0,0 +1,254 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import secrets + +import numpy as np +from langchain_core.embeddings import Embeddings +from nat.memory.interfaces import MemoryEditor +from nat.memory.models import MemoryItem + +import redis.asyncio as redis +import redis.exceptions as redis_exceptions +from redis.commands.search.query import Query + +logger = logging.getLogger(__name__) + +INDEX_NAME = "memory_idx" + + +class RedisEditor(MemoryEditor): + """ + Wrapper class that implements NAT interfaces for Redis memory storage. + """ + + def __init__(self, redis_client: redis.Redis, key_prefix: str, embedder: Embeddings): + """ + Initialize Redis client for memory storage. + + Args: + redis_client: (redis.Redis) Redis client + key_prefix: (str) Redis key prefix + embedder: (Embeddings) Embedder for semantic search functionality + """ + + self._client: redis.Redis = redis_client + self._key_prefix: str = key_prefix + self._embedder: Embeddings = embedder + + async def add_items(self, items: list[MemoryItem]) -> None: + """ + Insert Multiple MemoryItems into Redis. + Each MemoryItem is stored with its metadata and tags. + """ + logger.debug("Attempting to add %d items to Redis", len(items)) + + for memory_item in items: + item_meta = memory_item.metadata + conversation = memory_item.conversation + user_id = memory_item.user_id + tags = memory_item.tags + memory_id = secrets.token_hex(4) # e.g. 02ba3fe9 + + # Create a unique key for this memory item + memory_key = f"{self._key_prefix}:memory:{memory_id}" + logger.debug("Generated memory key: %s", memory_key) + + # Prepare memory data + memory_data = { + "conversation": conversation, + "user_id": user_id, + "tags": tags, + "metadata": item_meta, + "memory": memory_item.memory or "" + } + logger.debug("Prepared memory data for key %s", memory_key) + + # If we have memory, compute and store the embedding + if memory_item.memory: + logger.debug("Computing embedding for memory text") + search_vector = await self._embedder.aembed_query(memory_item.memory) + logger.debug("Generated embedding vector of length: %d", len(search_vector)) + memory_data["embedding"] = search_vector + + try: + # Store as JSON in Redis + logger.debug("Attempting to store memory data in Redis for key: %s", memory_key) + await self._client.json().set(memory_key, "$", memory_data) + logger.debug("Successfully stored memory data for key: %s", memory_key) + + # Verify the data was stored + stored_data = await self._client.json().get(memory_key) + logger.debug("Verified data storage for key %s: %s", memory_key, bool(stored_data)) + + except redis_exceptions.ResponseError as e: + logger.error("Failed to store memory item: %s", e) + raise + except redis_exceptions.ConnectionError as e: + logger.error("Redis connection error while storing memory item: %s", e) + raise + + async def search(self, query: str, top_k: int = 5, **kwargs) -> list[MemoryItem]: + """ + Retrieve items relevant to the given query. + + Args: + query (str): The query string to match. + top_k (int): Maximum number of items to return. + kwargs (dict): Keyword arguments to pass to the search method. + + - user_id (str): User ID for filtering results. + - similarity_threshold (float, optional): Maximum similarity score threshold + based on L2 (Euclidean) distance metric. Results with scores above this threshold + are filtered out; if not specified, all top_k results are returned. + Lower scores indicate higher similarity (0.0 = identical). Typical ranges: + 0.0-0.5 (very similar), 0.5-1.0 (moderately similar), >1.0 (loosely related). + + Returns: + list[MemoryItem]: The most relevant MemoryItems for the given query. + """ + logger.debug("Search called with query: %s, top_k: %d, kwargs: %s", query, top_k, kwargs) + + user_id = kwargs.get("user_id", "redis") # TODO: remove this fallback username + similarity_threshold = kwargs.get("similarity_threshold", None) + logger.debug("Using user_id: %s, similarity_threshold: %s", user_id, similarity_threshold) + + # Perform vector search using Redis search + logger.debug("Using embedder for vector search") + try: + logger.debug("Generating embedding for query: '%s'", query) + query_vector = await self._embedder.aembed_query(query) + logger.debug("Generated embedding vector of length: %d", len(query_vector)) + except Exception as e: + logger.error("Failed to generate embedding: %s", e) + raise + + # Create vector search query; escape special characters in user_id + escaped_user_id = user_id.replace("\\", "\\\\").replace("\"", "\\\"") + search_query = (Query(f'(@user_id:"{escaped_user_id}")=>[KNN {top_k} @embedding $vec AS score]').sort_by( + "score").return_fields("conversation", "user_id", "tags", "metadata", "memory", "score").dialect(2)) + logger.debug("Created search query: %s", search_query) + logger.debug("Query string: %s", search_query.query_string()) + + # Convert query vector to bytes + try: + logger.debug("Converting query vector to bytes") + query_vector_bytes = np.array(query_vector, dtype=np.float32).tobytes() + logger.debug("Converted vector to bytes of length: %d", len(query_vector_bytes)) + except Exception as e: + logger.error("Failed to convert vector to bytes: %s", e) + raise + + try: + # Execute search with vector parameters + logger.debug("Executing Redis search with vector parameters") + logger.debug("Search query parameters: vec length=%d", len(query_vector_bytes)) + + # Log the actual query being executed + logger.debug("Full search query: %s", search_query.query_string()) + + # Check if there are any documents in the index + try: + total_docs = await self._client.ft(INDEX_NAME).info() + logger.debug("Total documents in index: %d", total_docs.get('num_docs', 0)) + except Exception as e: + logger.exception("Failed to get index info: %s", e) + + # Execute the search + results = await self._client.ft(INDEX_NAME).search(search_query, query_params={"vec": query_vector_bytes}) + + # Log detailed results information + logger.debug("Search returned %d results", len(results.docs)) + logger.debug("Total results found: %d", results.total) + + # Convert results to MemoryItems + memories = [] + for i, doc in enumerate(results.docs): + try: + logger.debug("Processing result %d/%d", i + 1, len(results.docs)) + + # Extract similarity score + similarity_score = float(getattr(doc, 'score', 0.0)) + logger.debug("Similarity score: %.4f", similarity_score) + + # Apply similarity threshold filtering if specified + if similarity_threshold is not None and similarity_score > similarity_threshold: + logger.debug("Filtering out result %d due to score %.4f > threshold %.4f", + i + 1, + similarity_score, + similarity_threshold) + continue + + # Get the full document data + full_doc = await self._client.json().get(doc.id) + logger.debug("Extracted data for result %d: %s", i + 1, full_doc) + memory_item = self._create_memory_item(dict(full_doc), user_id, similarity_score) + memories.append(memory_item) + logger.debug("Successfully created MemoryItem for result %d", i + 1) + except Exception as e: + logger.error("Failed to process result %d: %s", i + 1, e) + raise + + logger.debug("Successfully processed %d results (filtered from %d)", len(memories), len(results.docs)) + return memories + except redis_exceptions.ResponseError as e: + logger.error("Search failed with ResponseError: %s", e) + raise + except redis_exceptions.ConnectionError as e: + logger.error("Search failed with ConnectionError: %s", e) + raise + except Exception as e: + logger.error("Unexpected error during search: %s", e) + raise + + def _create_memory_item(self, memory_data: dict, user_id: str, similarity_score: float | None = None) -> MemoryItem: + """Helper method to create a MemoryItem from Redis data.""" + # Ensure tags is always a list + tags = memory_data.get("tags", []) + # Not sure why but sometimes the tags are retrieved as a string + if isinstance(tags, str): + tags = [tags] + elif not isinstance(tags, list): + tags = [] + + return MemoryItem(conversation=memory_data.get("conversation", []), + user_id=user_id, + memory=memory_data.get("memory", ""), + tags=tags, + metadata=memory_data.get("metadata", {}), + similarity_score=similarity_score) + + async def remove_items(self, **kwargs): + """ + Remove memory items based on provided criteria. + """ + try: + pattern = f"{self._key_prefix}:memory:*" + batch_size = 500 + batch = [] + async for key in self._client.scan_iter(match=pattern, count=batch_size): + batch.append(key) + if len(batch) >= batch_size: + await self._client.unlink(*batch) + batch.clear() + if batch: + await self._client.unlink(*batch) + except redis_exceptions.ResponseError as e: + logger.error("Failed to remove items: %s", e) + raise + except redis_exceptions.ConnectionError as e: + logger.error("Redis connection error while removing items: %s", e) + raise diff --git a/src/nat/plugins/redis/redis_object_store.py b/src/nat/plugins/redis/redis_object_store.py new file mode 100644 index 0000000..bc12aee --- /dev/null +++ b/src/nat/plugins/redis/redis_object_store.py @@ -0,0 +1,138 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + +from nat.data_models.object_store import KeyAlreadyExistsError, NoSuchKeyError +from nat.object_store.interfaces import ObjectStore +from nat.object_store.models import ObjectStoreItem +from nat.utils.type_utils import override + +import redis.asyncio as redis + +logger = logging.getLogger(__name__) + + +class RedisObjectStore(ObjectStore): + """ + Implementation of ObjectStore that stores objects in Redis with optional TTL. + + Each object is stored as a single binary value at key "nat/object_store/{bucket_name}/{object_key}". + When TTL is configured, keys will automatically expire after the specified duration in seconds. + """ + + def __init__( + self, + *, + bucket_name: str, + host: str, + port: int, + db: int, + password: str | None = None, + ttl: int | None = None, + ): + + super().__init__() + + self._bucket_name = bucket_name + self._host = host + self._port = port + self._db = db + self._password = password + self._ttl = ttl + self._client: redis.Redis | None = None + + async def __aenter__(self) -> "RedisObjectStore": + + if self._client is not None: + raise RuntimeError("Connection already established") + + self._client = redis.Redis( + host=self._host, + port=self._port, + db=self._db, + password=self._password, + socket_timeout=5.0, + socket_connect_timeout=5.0, + ) + + # Ping to ensure connectivity + res = await self._client.ping() + if not res: + raise RuntimeError("Failed to connect to Redis") + + logger.info("Connected Redis client for %s at %s:%s/%s", self._bucket_name, self._host, self._port, self._db) + + return self + + async def __aexit__(self, exc_type, exc_value, traceback) -> None: + + if not self._client: + raise RuntimeError("Connection not established") + + await self._client.close() + self._client = None + + def _make_key(self, key: str) -> str: + return f"nat/object_store/{self._bucket_name}/{key}" + + @override + async def put_object(self, key: str, item: ObjectStoreItem): + + if not self._client: + raise RuntimeError("Connection not established") + + full_key = self._make_key(key) + + item_json = item.model_dump_json() + # Redis SET with NX ensures we do not overwrite existing keys + if not await self._client.set(full_key, item_json, nx=True, ex=self._ttl): + raise KeyAlreadyExistsError(key=key, + additional_message=f"Redis bucket {self._bucket_name} already has key {key}") + + @override + async def upsert_object(self, key: str, item: ObjectStoreItem): + + if not self._client: + raise RuntimeError("Connection not established") + + full_key = self._make_key(key) + item_json = item.model_dump_json() + await self._client.set(full_key, item_json, ex=self._ttl) + + @override + async def get_object(self, key: str) -> ObjectStoreItem: + + if not self._client: + raise RuntimeError("Connection not established") + + full_key = self._make_key(key) + data = await self._client.get(full_key) + if data is None: + raise NoSuchKeyError(key=key, + additional_message=f"Redis bucket {self._bucket_name} does not have key {key}") + return ObjectStoreItem.model_validate_json(data) + + @override + async def delete_object(self, key: str): + + if not self._client: + raise RuntimeError("Connection not established") + + full_key = self._make_key(key) + deleted = await self._client.delete(full_key) + if deleted == 0: + raise NoSuchKeyError(key=key, + additional_message=f"Redis bucket {self._bucket_name} does not have key {key}") diff --git a/src/nat/plugins/redis/register.py b/src/nat/plugins/redis/register.py new file mode 100644 index 0000000..d5121c8 --- /dev/null +++ b/src/nat/plugins/redis/register.py @@ -0,0 +1,23 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# flake8: noqa +# isort:skip_file + +# Import any providers which need to be automatically registered here. +# The ``nat.components`` entry point ``nat_redis`` targets this module. + +from . import memory +from . import object_store diff --git a/src/nat/plugins/redis/schema.py b/src/nat/plugins/redis/schema.py new file mode 100644 index 0000000..d150e9e --- /dev/null +++ b/src/nat/plugins/redis/schema.py @@ -0,0 +1,133 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + +import redis.asyncio as redis +import redis.exceptions as redis_exceptions +from redis.commands.search.field import TagField, TextField, VectorField +from redis.commands.search.indexDefinition import IndexDefinition, IndexType + +logger = logging.getLogger(__name__) + +INDEX_NAME = "memory_idx" +DEFAULT_DIM = 384 # Default embedding dimension + + +def create_schema(embedding_dim: int = DEFAULT_DIM): + """ + Create the Redis search schema for redis_memory. + + Args: + embedding_dim (int): Dimension of the embedding vectors + + Returns: + tuple: Schema definition for Redis search + """ + logger.info("Creating schema with embedding dimension: %d", embedding_dim) + + embedding_field = VectorField("$.embedding", + "HNSW", + { + "TYPE": "FLOAT32", + "DIM": embedding_dim, + "DISTANCE_METRIC": "L2", + "INITIAL_CAP": 100, + "M": 16, + "EF_CONSTRUCTION": 200, + "EF_RUNTIME": 10 + }, + as_name="embedding") + logger.info("Created embedding field with dimension %d", embedding_dim) + + schema = ( + # Redis search can't directly index complex objects (e.g. conversation and metadata) in return_fields + # They need to be retrieved via json().get() for full object access + TextField("$.user_id", as_name="user_id"), + TagField("$.tags[*]", as_name="tags"), + TextField("$.memory", as_name="memory"), + embedding_field) + + # Log the schema details + logger.info("Schema fields:") + for field in schema: + logger.info(" - %s: %s", field.name, type(field).__name__) + + return schema + + +async def ensure_index_exists(client: redis.Redis, key_prefix: str, embedding_dim: int | None) -> None: + """ + Ensure the Redis search index exists, creating it if necessary. + + Args: + client (redis.Redis): Redis client instance + key_prefix (str): Prefix for keys to be indexed + embedding_dim (Optional[int]): Dimension of embedding vectors. If None, uses default. + """ + try: + # Check if index exists + logger.info("Checking if index '%s' exists...", INDEX_NAME) + info = await client.ft(INDEX_NAME).info() + logger.info("Redis search index '%s' exists.", INDEX_NAME) + + # Verify the schema + schema = info.get('attributes', []) + + return + except redis_exceptions.ResponseError as ex: + error_msg = str(ex) + if "no such index" not in error_msg.lower() and "Index needs recreation" not in error_msg: + logger.error("Unexpected Redis error: %s", error_msg) + raise + + # Index doesn't exist or needs recreation + logger.info("Creating Redis search index '%s' with prefix '%s'", INDEX_NAME, key_prefix) + + # Drop any existing index + try: + logger.info("Attempting to drop existing index '%s' if it exists", INDEX_NAME) + await client.ft(INDEX_NAME).dropindex() + logger.info("Successfully dropped existing index '%s'", INDEX_NAME) + except redis_exceptions.ResponseError as e: + if "no such index" not in str(e).lower(): + logger.warning("Error while dropping index: %s", str(e)) + + # Create new schema and index + schema = create_schema(embedding_dim or DEFAULT_DIM) + logger.info("Created schema with embedding dimension: %d", embedding_dim or DEFAULT_DIM) + + try: + # Create the index + logger.info("Creating new index '%s' with schema", INDEX_NAME) + await client.ft(INDEX_NAME).create_index(schema, + definition=IndexDefinition(prefix=[f"{key_prefix}:"], + index_type=IndexType.JSON)) + + # Verify index was created + info = await client.ft(INDEX_NAME).info() + logger.info("Successfully created Redis search index '%s'", INDEX_NAME) + logger.debug("Redis search index info: %s", info) + + # Verify the schema + schema = info.get('attributes', []) + logger.debug("New index schema: %s", schema) + + except redis_exceptions.ResponseError as e: + logger.error("Failed to create index: %s", str(e)) + raise + except redis_exceptions.ConnectionError as e: + logger.error("Redis connection error while creating index: %s", str(e)) + raise diff --git a/src/nvidia_nat_redis/__init__.py b/src/nvidia_nat_redis/__init__.py index e5daacf..122e43d 100644 --- a/src/nvidia_nat_redis/__init__.py +++ b/src/nvidia_nat_redis/__init__.py @@ -1,7 +1,7 @@ # SPDX-FileCopyrightText: Copyright (c) 2026, Redis # SPDX-License-Identifier: Apache-2.0 -"""NVIDIA NeMo Agent Toolkit plugin package for Redis Agent Memory.""" +"""NVIDIA NeMo Agent Toolkit integrations for Redis Agent Memory and direct Redis plugins.""" from importlib.metadata import PackageNotFoundError, version diff --git a/tests/conftest.py b/tests/conftest.py index 3ddf6c0..e9e4a1a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -11,7 +11,7 @@ def pytest_addoption(parser: pytest.Parser) -> None: "--run-integration", action="store_true", default=False, - help="Run integration tests that start real Redis and Agent Memory Server containers.", + help="Run integration tests that start real Redis and Redis Agent Memory containers.", ) parser.addoption( "--run-api-tests", @@ -24,7 +24,7 @@ def pytest_addoption(parser: pytest.Parser) -> None: def pytest_configure(config: pytest.Config) -> None: config.addinivalue_line( "markers", - "integration: mark a test as requiring Docker-backed Redis and Agent Memory Server services", + "integration: mark a test as requiring Docker-backed Redis and Redis Agent Memory services", ) config.addinivalue_line( "markers", diff --git a/tests/integration/test_redis_object_store.py b/tests/integration/test_redis_object_store.py new file mode 100644 index 0000000..3f2f7fe --- /dev/null +++ b/tests/integration/test_redis_object_store.py @@ -0,0 +1,39 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026, Redis +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +from urllib.parse import urlparse + +import pytest +from nat.data_models.object_store import NoSuchKeyError +from nat.object_store.models import ObjectStoreItem + +from nat.plugins.redis.redis_object_store import RedisObjectStore + + +@pytest.mark.integration +async def test_redis_object_store_roundtrip(ams_stack: dict[str, str], unique_suffix: str) -> None: + parsed = urlparse(ams_stack["redis_url"]) + host = parsed.hostname or "127.0.0.1" + port = parsed.port or 6379 + + key = f"redis_os_{unique_suffix}" + item = ObjectStoreItem(data=b"hello", content_type="application/octet-stream", metadata={"k": "v"}) + + store = RedisObjectStore( + bucket_name=f"test_bucket_{unique_suffix}", + host=host, + port=port, + db=0, + ) + async with store: + await store.put_object(key, item) + loaded = await store.get_object(key) + assert loaded.data == item.data + assert loaded.content_type == item.content_type + assert loaded.metadata == item.metadata + + await store.delete_object(key) + with pytest.raises(NoSuchKeyError): + await store.get_object(key) diff --git a/tests/test_redis_editor.py b/tests/test_redis_editor.py new file mode 100644 index 0000000..40803cd --- /dev/null +++ b/tests/test_redis_editor.py @@ -0,0 +1,196 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from unittest.mock import AsyncMock, MagicMock + +import pytest +from langchain_core.embeddings import Embeddings +from nat.memory.models import MemoryItem +from nat.utils.type_utils import override + +from nat.plugins.redis.redis_editor import RedisEditor + + +class TestEmbeddings(Embeddings): + + @override + def embed_query(self, text: str) -> list[float]: + if not text or len(text) == 0: + raise ValueError("No query passed to embedding model") + return [0.0, 1.0, 2.0, 3.0, 4.0, 5.0] + + @override + def embed_documents(self, texts: list[str]) -> list[list[float]]: + res: list[list[float]] = [] + counter = 0 + for text in texts: + embedding = [e + counter for e in self.embed_query(text)] + res.append(embedding) + counter += len(embedding) + return res + + +@pytest.fixture(name="mock_redis_client") +def mock_redis_client_fixture() -> AsyncMock: + """Fixture to provide a mocked AsyncMemoryClient.""" + mock_client = AsyncMock() + + # Create a mock for the JSON commands + mock_json = AsyncMock() + mock_json.set = AsyncMock() + mock_json.get = AsyncMock() + + # Set up the json() method to return our mock + mock_client.json = MagicMock(return_value=mock_json) + + return mock_client + + +@pytest.fixture(name="redis_editor") +def redis_editor_fixture(mock_redis_client: AsyncMock): + """Fixture to provide an instance of RedisEditor with a mocked client.""" + + editor = RedisEditor( + redis_client=mock_redis_client, + key_prefix="pytest", + embedder=TestEmbeddings(), + ) + return editor + + +@pytest.fixture(name="sample_memory_item") +def sample_memory_item_fixture(): + """Fixture to provide a sample MemoryItem.""" + + conversation = [ + { + "role": "user", + "content": "Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts.", + }, + { + "role": "assistant", + "content": "Hello Alex! I've noted that you're a vegetarian and have a nut allergy.", + }, + ] + + return MemoryItem(conversation=conversation, + user_id="user123", + memory="Sample memory", + metadata={"key1": "value1"}, + tags=["tag1", "tag2"]) + + +async def test_add_items_success(redis_editor: RedisEditor, + mock_redis_client: AsyncMock, + sample_memory_item: MemoryItem): + """Test adding multiple MemoryItem objects successfully.""" + items = [sample_memory_item] + await redis_editor.add_items(items) + + # Verify json().set was called once + mock_redis_client.json().set.assert_called_once() + + # Get the actual call arguments + call_args = mock_redis_client.json().set.call_args[0] + + # First argument should be the memory key (which starts with the prefix) + assert call_args[0].startswith("pytest:memory:") + + # Second argument should be "$" + assert call_args[1] == "$" + + # Third argument should be the memory data + memory_data = call_args[2] + assert memory_data["conversation"] == sample_memory_item.conversation + assert memory_data["user_id"] == sample_memory_item.user_id + assert memory_data["tags"] == sample_memory_item.tags + assert memory_data["metadata"] == sample_memory_item.metadata + assert memory_data["memory"] == sample_memory_item.memory + + +async def test_add_items_empty_list(redis_editor: RedisEditor, mock_redis_client: AsyncMock): + """Test adding an empty list of MemoryItem objects.""" + await redis_editor.add_items([]) + + mock_redis_client.json().set.assert_not_called() + + +@pytest.mark.asyncio +async def test_search_success(redis_editor: RedisEditor, mock_redis_client: AsyncMock): + """Test searching with a valid query and user ID.""" + # Create a mock document with the required attributes + mock_doc = MagicMock() + mock_doc.conversation = [{"role": "system", "content": "Hello"}, {"role": "system", "content": "Hi"}] + mock_doc.user_id = "user123" + mock_doc.tags = ["tag1", "tag2"] + mock_doc.metadata = {"key1": "value1"} + mock_doc.memory = "Sample memory" + mock_doc.score = 0.95 + + # Create a mock results object with a docs attribute + mock_results = MagicMock() + mock_results.docs = [mock_doc] + + # Create a mock for the ft method that returns an object with the search method + mock_ft_index = MagicMock() + mock_ft_index.search = AsyncMock(return_value=mock_results) + + # Set up the client mock to return the ft mock + mock_redis_client.ft = MagicMock(return_value=mock_ft_index) + + # Mock Redis JSON get to return document data + mock_redis_client.json().get.return_value = { + "conversation": mock_doc.conversation, + "user_id": mock_doc.user_id, + "tags": mock_doc.tags, + "metadata": mock_doc.metadata, + "memory": mock_doc.memory + } + + result = await redis_editor.search(query="test query", user_id="user123", top_k=1) + + assert len(result) == 1 + assert result[0].conversation == [{"role": "system", "content": "Hello"}, {"role": "system", "content": "Hi"}] + assert result[0].memory == "Sample memory" + assert result[0].tags == ["tag1", "tag2"] + assert result[0].metadata == {"key1": "value1"} + assert result[0].similarity_score == 0.95 # Verify similarity score is captured + + +@pytest.mark.asyncio +async def test_search_with_similarity_threshold_filters_results(redis_editor: RedisEditor, + mock_redis_client: AsyncMock): + """Test that similarity_threshold parameter filters out results above the threshold.""" + # Create mock documents with varying similarity scores (0.2, 0.6, 0.4) + mock_docs = [MagicMock(id=f"pytest:memory:doc{i}", score=score) for i, score in enumerate([0.2, 0.6, 0.4], 1)] + + mock_results = MagicMock() + mock_results.docs = mock_docs + + mock_ft_index = MagicMock() + mock_ft_index.search = AsyncMock(return_value=mock_results) + mock_redis_client.ft = MagicMock(return_value=mock_ft_index) + + # Mock JSON get - called for doc1 and doc3 only (doc2 filtered before fetch) + mock_redis_client.json().get.side_effect = [{ + "conversation": [], "user_id": "user123", "tags": [], "metadata": {}, "memory": f"Memory {i}" + } for i in [1, 2]] + + # Search with threshold=0.5 filters out doc2 (score 0.6) + result = await redis_editor.search(query="test query", user_id="user123", top_k=3, similarity_threshold=0.5) + + assert len(result) == 2 + assert result[0].similarity_score == 0.2 + assert result[1].similarity_score == 0.4 diff --git a/uv.lock b/uv.lock index eb89976..84e8d41 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 1 +revision = 3 requires-python = ">=3.11, <3.14" resolution-markers = [ "python_full_version >= '3.13'", @@ -16,9 +16,9 @@ dependencies = [ { name = "pydantic" }, { name = "python-ulid" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/63/c0/ccabb355a5566886936e8d81b6184475e22030821411251297b99848c6e0/agent_memory_client-0.14.0.tar.gz", hash = "sha256:7178a38682ec61538002adec34140c7751387b82fbab8c214edf766b6d913d85", size = 211719 } +sdist = { url = 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