diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..d3d6c11 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,9 @@ +{ + "python-envs.pythonProjects": [ + { + "path": "", + "envManager": "ms-python.python:venv", + "packageManager": "ms-python.python:pip" + } + ] +} \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/.vscode/launch.json b/libs/langgraph-elasticsearch/.vscode/launch.json new file mode 100644 index 0000000..a75c141 --- /dev/null +++ b/libs/langgraph-elasticsearch/.vscode/launch.json @@ -0,0 +1,14 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python Debugger: Python File", + "type": "debugpy", + "request": "launch", + "program": "${file}" + } + ] +} \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/.vscode/settings.json b/libs/langgraph-elasticsearch/.vscode/settings.json new file mode 100644 index 0000000..9b38853 --- /dev/null +++ b/libs/langgraph-elasticsearch/.vscode/settings.json @@ -0,0 +1,7 @@ +{ + "python.testing.pytestArgs": [ + "tests" + ], + "python.testing.unittestEnabled": false, + "python.testing.pytestEnabled": true +} \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/README.md b/libs/langgraph-elasticsearch/README.md new file mode 100644 index 0000000..5aeef78 --- /dev/null +++ b/libs/langgraph-elasticsearch/README.md @@ -0,0 +1,67 @@ +# Langgraph Elasticsearch + +Langgraph Elasticsearch is a library designed to integrate Langchain with Elasticsearch, providing powerful search capabilities for your applications. + +## Table of Contents +- [Installation](#installation) +- [Usage](#usage) +- [Contributing](#contributing) +- [License](#license) + +## Installation + +To install Langgraph Elasticsearch, you can use pip: + +```bash +pip install langgraph-elasticsearch +``` + +Alternatively, you can clone the repository and install it manually: + +```bash +git clone https://github.com/yourusername/langgraph-elasticsearch.git +cd langgraph-elasticsearch +pip install . +``` + +## Usage + +Here is an example of how to use Langgraph Elasticsearch: + +```python + +from langgraph_elasticsearch import ElasticsearchMemoryStore + +# Initialize the Elasticsearch client +es_client = ElasticsearchMemoryStore( + es_url="http://localhost:9200", + es_user="elastic", + es_password="your_password" +) + +# Index a document +doc = { + "title": "Example Document", + "content": "This is an example document for Langgraph Elasticsearch." +} +es_client.index_document(index="documents", id=1, document=doc) + +# Search for a document +query = { + "query": { + "match": { + "content": "example" + } + } +} +results = es_client.search(index="documents", body=query) +print(results) +``` + +## Contributing + +We welcome contributions to Langgraph Elasticsearch! If you would like to contribute, please fork the repository and submit a pull request. For major changes, please open an issue first to discuss what you would like to change. + +## License + +This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/docker-compose.yml b/libs/langgraph-elasticsearch/docker-compose.yml new file mode 100644 index 0000000..e4ea563 --- /dev/null +++ b/libs/langgraph-elasticsearch/docker-compose.yml @@ -0,0 +1,29 @@ +version: "3.8" + +volumes: + esdata: + driver: local + +networks: + default: + name: elastic + +services: + elasticsearch: + image: docker.elastic.co/elasticsearch/elasticsearch:8.9.2 + volumes: + - esdata:/usr/share/elasticsearch/data + ports: + - 9200:9200 + environment: + - node.name=es01 + - discovery.type=single-node + - ELASTIC_PASSWORD=elastic + - xpack.security.enabled=true + - xpack.security.http.ssl.enabled=false + - xpack.security.transport.ssl.enabled=false + healthcheck: + test: ["CMD-SHELL", "curl -s -X GET http://localhost:9200 | grep 'You Know, for Search'"] + interval: 10s + timeout: 5s + retries: 10 diff --git a/libs/langgraph-elasticsearch/langgraph/checkpoint/elasticsearch/__init__.py b/libs/langgraph-elasticsearch/langgraph/checkpoint/elasticsearch/__init__.py new file mode 100644 index 0000000..7486a9d --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/checkpoint/elasticsearch/__init__.py @@ -0,0 +1,127 @@ +import asyncio +from typing import Any, Dict, Generic, Optional, Sequence, Tuple +from elasticsearch import Elasticsearch +from langchain_elasticsearch.client import create_elasticsearch_client +from langgraph.checkpoint.elasticsearch.base import BaseElasticsearchSaver +from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods +from langchain_core.runnables import RunnableConfig +from langgraph.checkpoint.base import CheckpointTuple, get_checkpoint_id +from langgraph.checkpoint.base import ( + WRITES_IDX_MAP, + BaseCheckpointSaver, + ChannelVersions, + Checkpoint, + CheckpointMetadata, + get_checkpoint_id, + CheckpointTuple, + PendingWrite +) + +from langgraph.checkpoint.elasticsearch.configurable import Configurable, get_configurable +from langgraph.util import syncify + +class ElasticsearchSaver(BaseElasticsearchSaver[Elasticsearch]): + def __init__(self, + es_connection: Elasticsearch | None = None, + es_url: str | None = None, + es_cloud_id: str | None = None, + es_user: str | None = None, + es_api_key: str | None = None, + es_password: str | None = None, + es_params: Dict[str, Any] | None = None, + *, + serde: Optional[SerializerProtocol] = None,): + if not es_connection: + es_connection = create_elasticsearch_client( + url=es_url, + cloud_id=es_cloud_id, + api_key=es_api_key, + username=es_user, + password=es_password, + params=es_params, + ) + if not es_connection: + raise ValueError("No Elasticsearch connection provided.") + + try: + es_connection.info() + except Exception as e: + raise ValueError(f"Failed to connect to Elasticsearch: {e}") + + super().__init__(es_connection=es_connection, serde=serde) + + def _search_checkpoint(self, configurable: Configurable) -> Dict[str, Any]: + result_checkpoint = self.es_connection.search( + index=self.index_checkpoints, + body=self._build_query_checkpoint(configurable), + size=1, + ) + + return self._extract_hits(result_checkpoint) + + def _search_writes(self, configurable: Configurable) -> Dict[str, Any]: + result_writes = self.es_connection.search( + index=self.index_writes, + body=self._build_query_writes(configurable), + ) + + return self._extract_hits(result_writes) + + def _search_parent(self, configurable: Configurable, parent_checkpoint_id) -> Dict[str, Any]: + if not parent_checkpoint_id: + return [] + + result_parent = self.es_connection.search( + index=self.index_writes, + body=self._build_query_parent(configurable, parent_checkpoint_id), + ) + + return self._extract_hits(result_parent) + + def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]: + loop = asyncio.get_event_loop() + return loop.run_until_complete(super().aget_tuple(config)) + + async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]: + raise NotImplementedError("Not supported in ElasticsearchSaver, use ElasticsearchSaver.get_tuple instead.") + + def put( + self, + config: RunnableConfig, + checkpoint: Checkpoint, + metadata: CheckpointMetadata, + new_versions: ChannelVersions, + ) -> RunnableConfig: + return syncify(self.aput, config, checkpoint, metadata, new_versions) + + async def aput( + self, + config: RunnableConfig, + checkpoint: Checkpoint, + metadata: CheckpointMetadata, + new_versions: ChannelVersions, + ) -> RunnableConfig: + raise NotImplementedError("Not supported in ElasticsearchSaver, use ElasticsearchSaver.put instead.") + + + + + def put_writes( + self, + config: RunnableConfig, + writes: Sequence[Tuple[str, Any]], + task_id: str, + task_path: str = "", + ) -> None: + """Store intermediate writes linked to a checkpoint. + + Args: + config (RunnableConfig): Configuration of the related checkpoint. + writes (List[Tuple[str, Any]]): List of writes to store. + task_id (str): Identifier for the task creating the writes. + task_path (str): Path of the task creating the writes. + + Raises: + NotImplementedError: Implement this method in your custom checkpoint saver. + """ + raise NotImplementedError \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/langgraph/checkpoint/elasticsearch/base.py b/libs/langgraph-elasticsearch/langgraph/checkpoint/elasticsearch/base.py new file mode 100644 index 0000000..486f344 --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/checkpoint/elasticsearch/base.py @@ -0,0 +1,344 @@ +from abc import abstractmethod +import asyncio +from typing import Any, AsyncIterator, Dict, Generic, Iterator, List, Optional, Sequence, Tuple, TypeVar, Union +from elasticsearch import AsyncElasticsearch, Elasticsearch +from langchain_core.runnables import ConfigurableFieldSpec, RunnableConfig +from langgraph.checkpoint.base import ( + WRITES_IDX_MAP, + BaseCheckpointSaver, + ChannelVersions, + Checkpoint, + CheckpointMetadata, + get_checkpoint_id, + CheckpointTuple, + PendingWrite +) +from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods +from langgraph.checkpoint.serde.types import ( + ERROR, + INTERRUPT, + RESUME, + SCHEDULED, + ChannelProtocol, + SendProtocol, +) + +from langgraph.checkpoint.elasticsearch.configurable import Configurable, get_configurable +T = TypeVar("T", bound=Union[Elasticsearch, AsyncElasticsearch]) + +class BaseElasticsearchSaver(BaseCheckpointSaver[str], Generic[T]): + es_connection: T + index_checkpoints: str = "langgraph-checkpoints" + index_writes: str = "langgraph-writes" + index_blobs: str = "langgraph-blobs" + + def __init__( + self, + es_connection: T, + *, + serde: Optional[SerializerProtocol] = None, + ) -> None: + super().__init__(serde=serde) + self.es_connection = es_connection + + def _extract_hits(result: Dict[str, Any]) -> list[Dict[str, Any]]: + hits = result.get("hits", {}).get("hits", []) + if not hits and (source := result.get("_source")): + hits = [{"_source": source}] + return hits + + def _build_clauses(configurable: Configurable) -> list[Dict[str, Dict[str, str]]]: + clauses = [ + {"term": {"thread_id": configurable.thread_id}}, + {"term": {"checkpoint_ns": configurable.checkpoint_ns}}, + ] + if configurable.checkpoint_id: + clauses.append({"term": {"checkpoint_id": configurable.checkpoint_id}}) + return clauses + + def _build_query_checkpoint(self, configurable: Configurable) -> list[Dict[str, Dict[str, str]]]: + return { + "query": {"bool": {"must": self._build_clauses(configurable)}}, + "sort": {"checkpoint_id": {"order": "desc"}}, + } + + def _build_query_writes(self, configurable: Configurable) -> list[Dict[str, Dict[str, str]]]: + return { + "query": {"bool": {"must": self._build_clauses(configurable)}}, + "sort": [ + {"task_id": {"order": "asc"}}, + {"idx": {"order": "asc"}}, + ], + } + + def _build_query_parent(self, configurable: Configurable, parent_checkpoint_id: str) -> list[Dict[str, Dict[str, str]]]: + return {"query": { + "bool": { + "must": [ + {"term": {"thread_id": configurable.thread_id}}, + {"term": {"checkpoint_ns": configurable.checkpoint_ns}}, + {"term": {"parent_checkpoint_id": parent_checkpoint_id}}, + ], + "filter": [ + {"term": {"task_type": "TASKS"}} # Filtro adicional baseado na lógica original + ] + } + }, + "sort": [ + {"field_3": {"order": "asc"}}, # Substitua por w[3] + {"field_0": {"order": "asc"}}, # Substitua por w[0] + {"field_4": {"order": "asc"}}, # Substitua por w[4] + ], + "size": 100 # Ajuste o tamanho conforme necessário + } + + def _body_upsert(self, + configurable: Configurable, + checkpoint: Checkpoint, + metadata: CheckpointMetadata, ) -> Dict[str, Any]: + + return { + "doc": { + "checkpoint": self.serde.dumps_typed(checkpoint), + "metadata": self.serde.dumps_typed(metadata), + }, + "upsert": { + "thread_id": configurable.thread_id, + "checkpoint_ns": configurable.checkpoint_ns, + "checkpoint_id": checkpoint["id"], + "checkpoint": self.serde.dumps_typed(checkpoint), + "metadata": self.serde.dumps_typed(metadata), + "parent_checkpoint_id": configurable.checkpoint_id, + } + } + + def _load_checkpoint(self, saved, sends) -> Checkpoint: + ( + type, + checkpoint, + ) = saved + return { + **self.serde.loads_typed((type, checkpoint)), + "pending_sends": [self.serde.loads_typed(s[2]) for s in sends], + } + + def _load_pending_writes(self, writes) -> List[PendingWrite]: + return [ + (id, c, self.serde.loads_typed(v)) for id, c, v, _ in writes + ] + + def _load_metadata(self, saved) -> CheckpointMetadata: + (metadata) = saved + return self.serde.loads_typed(metadata) + + def _load_parent_config(self, configurable: Configurable, parent_checkpoint_id) -> RunnableConfig: + return { + "configurable": { + "thread_id": configurable.thread_id, + "checkpoint_ns": configurable.checkpoint_ns, + "checkpoint_id": parent_checkpoint_id, + } + } if parent_checkpoint_id else None, + + @abstractmethod + def _search_checkpoint(self, configurable: Configurable) -> Dict[str, Any]: + raise NotImplementedError + + @abstractmethod + def _search_writes(self, configurable: Configurable) -> Dict[str, Any]: + raise NotImplementedError + + @abstractmethod + def _search_parent(self, configurable: Configurable, parent_checkpoint_id) -> Dict[str, Any]: + raise NotImplementedError + + async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]: + configurable = get_configurable(config) + + if saved := await self._search_checkpoint(configurable): + checkpoint_id, parent_checkpoint_id = saved + + configurable = get_configurable({ + "configurable": { + "thread_id": configurable.thread_id, + "checkpoint_ns": configurable.checkpoint_ns, + "checkpoint_id": checkpoint_id, + } + }) if not get_checkpoint_id(config) else get_configurable(config) + + sends = await self._search_parent(configurable, parent_checkpoint_id) + + writes = await self._search_writes(configurable) + + config["configurable"] = configurable.to_dict() + + return CheckpointTuple( + config=config, + checkpoint=self._load_checkpoint(saved, sends), + metadata=self._load_metadata(saved), + pending_writes=self._load_pending_writes(writes), + parent_config=self._load_parent_config(configurable, parent_checkpoint_id), + ) + async def aput( + self, + config: RunnableConfig, + checkpoint: Checkpoint, + metadata: CheckpointMetadata, + new_versions: ChannelVersions, + ) -> RunnableConfig: + configurable = get_configurable(config) + + await self.es_connection.update( + index=self.index_checkpoints, + id=f"{configurable.thread_id}-{configurable.checkpoint_ns}-{checkpoint['id']}", + body=self._body_upsert(configurable, checkpoint, metadata), + ) + + config["configurable"]["checkpoint_id"] = checkpoint["id"] + + return config + + + + + + + + + + + + + + def list( + self, + config: Optional[RunnableConfig], + *, + filter: Optional[Dict[str, Any]] = None, + before: Optional[RunnableConfig] = None, + limit: Optional[int] = None, + ) -> Iterator[CheckpointTuple]: + """List checkpoints that match the given criteria. + + Args: + config (Optional[RunnableConfig]): Base configuration for filtering checkpoints. + filter (Optional[Dict[str, Any]]): Additional filtering criteria. + before (Optional[RunnableConfig]): List checkpoints created before this configuration. + limit (Optional[int]): Maximum number of checkpoints to return. + + Returns: + Iterator[CheckpointTuple]: Iterator of matching checkpoint tuples. + + Raises: + NotImplementedError: Implement this method in your custom checkpoint saver. + """ + raise NotImplementedError + + + + # async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]: + # """Asynchronously fetch a checkpoint tuple using the given configuration. + + # Args: + # config (RunnableConfig): Configuration specifying which checkpoint to retrieve. + + # Returns: + # Optional[CheckpointTuple]: The requested checkpoint tuple, or None if not found. + + # Raises: + # NotImplementedError: Implement this method in your custom checkpoint saver. + # """ + # raise NotImplementedError + + # async def alist( + # self, + # config: Optional[RunnableConfig], + # *, + # filter: Optional[Dict[str, Any]] = None, + # before: Optional[RunnableConfig] = None, + # limit: Optional[int] = None, + # ) -> AsyncIterator[CheckpointTuple]: + # """Asynchronously list checkpoints that match the given criteria. + + # Args: + # config (Optional[RunnableConfig]): Base configuration for filtering checkpoints. + # filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata. + # before (Optional[RunnableConfig]): List checkpoints created before this configuration. + # limit (Optional[int]): Maximum number of checkpoints to return. + + # Returns: + # AsyncIterator[CheckpointTuple]: Async iterator of matching checkpoint tuples. + + # Raises: + # NotImplementedError: Implement this method in your custom checkpoint saver. + # """ + # raise NotImplementedError + # yield + + # async def aput( + # self, + # config: RunnableConfig, + # checkpoint: Checkpoint, + # metadata: CheckpointMetadata, + # new_versions: ChannelVersions, + # ) -> RunnableConfig: + # """Asynchronously store a checkpoint with its configuration and metadata. + + # Args: + # config (RunnableConfig): Configuration for the checkpoint. + # checkpoint (Checkpoint): The checkpoint to store. + # metadata (CheckpointMetadata): Additional metadata for the checkpoint. + # new_versions (ChannelVersions): New channel versions as of this write. + + # Returns: + # RunnableConfig: Updated configuration after storing the checkpoint. + + # Raises: + # NotImplementedError: Implement this method in your custom checkpoint saver. + # """ + # raise NotImplementedError + + # async def aput_writes( + # self, + # config: RunnableConfig, + # writes: Sequence[Tuple[str, Any]], + # task_id: str, + # task_path: str = "", + # ) -> None: + # """Asynchronously store intermediate writes linked to a checkpoint. + + # Args: + # config (RunnableConfig): Configuration of the related checkpoint. + # writes (List[Tuple[str, Any]]): List of writes to store. + # task_id (str): Identifier for the task creating the writes. + # task_path (str): Path of the task creating the writes. + + # Raises: + # NotImplementedError: Implement this method in your custom checkpoint saver. + # """ + # raise NotImplementedError + + def get_next_version(self, current: Optional[str], channel: ChannelProtocol) -> str: + """Generate the next version ID for a channel. + + Default is to use integer versions, incrementing by 1. If you override, you can use str/int/float versions, + as long as they are monotonically increasing. + + Args: + current (Optional[V]): The current version identifier (int, float, or str). + channel (BaseChannel): The channel being versioned. + + Returns: + V: The next version identifier, which must be increasing. + """ + if isinstance(current, str): + raise NotImplementedError + elif current is None: + return 1 + else: + return current + 1 + + + + + diff --git a/libs/langgraph-elasticsearch/langgraph/checkpoint/elasticsearch/configurable.py b/libs/langgraph-elasticsearch/langgraph/checkpoint/elasticsearch/configurable.py new file mode 100644 index 0000000..e745bbc --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/checkpoint/elasticsearch/configurable.py @@ -0,0 +1,25 @@ +from typing import Any, Dict, Optional +from langchain_core.runnables import RunnableConfig +from langgraph.checkpoint.base import get_checkpoint_id + + +class Configurable: + thread_id: Optional[str] + checkpoint_ns: Optional[str] + checkpoint_id: Optional[str] + + def to_dict(self) -> Dict[str, Any]: + return { + "thread_id": self.thread_id, + "checkpoint_ns": self.checkpoint_ns, + "checkpoint_id": self.checkpoint_id, + } + + +def get_configurable(config: Dict[str, Any]) -> Configurable: + configurable = config.get("configurable", {}) + return Configurable( + thread_id=configurable.get("thread_id"), + checkpoint_ns=configurable.get("checkpoint_ns"), + checkpoint_id=get_checkpoint_id(config), + ) diff --git a/libs/langgraph-elasticsearch/langgraph/checkpoint/elasticsearch/py.typed b/libs/langgraph-elasticsearch/langgraph/checkpoint/elasticsearch/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/libs/langgraph-elasticsearch/langgraph/memorized_value.py b/libs/langgraph-elasticsearch/langgraph/memorized_value.py new file mode 100644 index 0000000..511a05a --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/memorized_value.py @@ -0,0 +1,124 @@ +import collections.abc +from contextlib import asynccontextmanager, contextmanager +from typing import ( + Any, + AsyncIterator, + Iterator, + List, + Optional, + Sequence, + Type, +) + +from typing_extensions import NotRequired, Required, Self + +from langgraph.constants import CONF +from langgraph.errors import InvalidUpdateError +from langgraph.managed.base import ( + ChannelKeyPlaceholder, + ChannelTypePlaceholder, + ConfiguredManagedValue, + WritableManagedValue, +) +from langgraph.store.base import PutOp +from langgraph.types import LoopProtocol + +V = dict[str, Any] + + +Value = dict[str, V] +Update = dict[str, Optional[V]] + + +# Adapted from typing_extensions +def _strip_extras(t): # type: ignore[no-untyped-def] + """Strips Annotated, Required and NotRequired from a given type.""" + if hasattr(t, "__origin__"): + return _strip_extras(t.__origin__) + if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired): + return _strip_extras(t.__args__[0]) + + return t + + +class MemorizedValue(WritableManagedValue[Value, Update]): + @staticmethod + def on(scopes: List[str]) -> ConfiguredManagedValue: + return ConfiguredManagedValue( + MemorizedValue, + { + "scopes": scopes, + "key": ChannelKeyPlaceholder, + "typ": ChannelTypePlaceholder, + }, + ) + + @classmethod + @contextmanager + def enter(cls, loop: LoopProtocol, **kwargs: Any) -> Iterator[Self]: + with super().enter(loop, **kwargs) as value: + if loop.store is not None: + saved = loop.store.search(value.ns) + value.value = {it.key: it.value for it in saved} + yield value + + @classmethod + @asynccontextmanager + async def aenter(cls, loop: LoopProtocol, **kwargs: Any) -> AsyncIterator[Self]: + async with super().aenter(loop, **kwargs) as value: + if loop.store is not None: + saved = await loop.store.asearch(value.ns) + value.value = {it.key: it.value for it in saved} + yield value + + def __init__( + self, loop: LoopProtocol, *, typ: Type[Any], scopes: List[str], key: str + ) -> None: + super().__init__(loop) + if typ := _strip_extras(typ): + if typ not in ( + dict, + collections.abc.Mapping, + collections.abc.MutableMapping, + ): + raise ValueError("SharedValue must be a dict") + self.scopes = scopes + self.value: Value = {} + + try: + if self.loop.store: + self.ns = ("memories",) + tuple( + self.loop.config[CONF][scope] for scope in scopes + ) + except KeyError as e: + raise ValueError(f"Scope {e.args[0]} for shared state key not in config.configurable") + + def __call__(self) -> Value: + return self.value + + def _process_update(self, values: Sequence[Update]) -> list[PutOp]: + writes: list[PutOp] = [] + for vv in values: + for k, v in vv.items(): + if v is None: + if k in self.value: + del self.value[k] + writes.append(PutOp(self.ns, k, None)) + elif not isinstance(v, dict): + raise InvalidUpdateError("Received a non-dict value") + else: + self.value[k] = v + writes.append(PutOp(self.ns, k, v)) + return writes + + def update(self, values: Sequence[Update]) -> None: + if self.loop.store is None: + self._process_update(values) + else: + return self.loop.store.batch(self._process_update(values)) + + async def aupdate(self, writes: Sequence[Update]) -> None: + if self.loop.store is None: + self._process_update(writes) + else: + return await self.loop.store.abatch(self._process_update(writes)) diff --git a/libs/langgraph-elasticsearch/langgraph/py.typed b/libs/langgraph-elasticsearch/langgraph/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/__init__.py b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/__init__.py new file mode 100644 index 0000000..1eee92b --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/__init__.py @@ -0,0 +1,8 @@ +from langgraph.store.elasticsearch.base import ElasticsearchMemoryStore, ElasticsearchIndexConfig +from langgraph.store.elasticsearch.aio import AsyncElasticsearchMemoryStore + +__all__ = [ + "ElasticsearchMemoryStore", + "AsyncElasticsearchMemoryStore", + "ElasticsearchIndexConfig", +] \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/aio.py b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/aio.py new file mode 100644 index 0000000..a162676 --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/aio.py @@ -0,0 +1,36 @@ +import logging +from typing import Any, Awaitable, Dict, Iterable +from elasticsearch import AsyncElasticsearch +from langgraph.store.base import Result, Op +from langgraph.store.base.batch import AsyncBatchedBaseStore +from langgraph.store.elasticsearch.base import BaseElasticsearchMemoryStore, ElasticsearchIndexConfig +from langchain_elasticsearch.client import create_async_elasticsearch_client + +logger = logging.getLogger(__name__) + +class AsyncElasticsearchMemoryStore(BaseElasticsearchMemoryStore[AsyncElasticsearch], AsyncBatchedBaseStore): + def __init__(self, + es_connection: AsyncElasticsearch | None = None, + es_url: str | None = None, + es_cloud_id: str | None = None, + es_user: str | None = None, + es_api_key: str | None = None, + es_password: str | None = None, + es_params: Dict[str, Any] | None = None, + index_config: ElasticsearchIndexConfig | None = None, + ): + if not es_connection: + es_connection = create_async_elasticsearch_client( + url=es_url, + cloud_id=es_cloud_id, + api_key=es_api_key, + username=es_user, + password=es_password, + params=es_params, + ) + + super(BaseElasticsearchMemoryStore[AsyncElasticsearch], self).__init__(es_connection=es_connection, + index_config=index_config) + + async def abatch(self, ops: Iterable[Op]) -> Awaitable[list[Result]]: + return await self.aprocess(ops) \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/base.py b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/base.py new file mode 100644 index 0000000..cb106d6 --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/base.py @@ -0,0 +1,239 @@ +import asyncio +import logging +from typing import ( + Any, + Dict, + Generic, + Iterable, + Tuple, + TypeVar, + Union +) +from elasticsearch import Elasticsearch, AsyncElasticsearch +from langchain_core.runnables import RunnableParallel +from langchain_core.runnables.base import RunnableLike +from langchain_core.runnables.config import run_in_executor +from langchain_elasticsearch.client import create_elasticsearch_client +from langchain_elasticsearch.vectorstores import ElasticsearchStore as ElasticsearchVectorStore +from langgraph.store.base import ( + BaseStore, + Result, + Op, + PutOp, + GetOp, + SearchOp, + ListNamespacesOp, + ensure_embeddings, + tokenize_path, +) +from langgraph.store.elasticsearch.config import ElasticsearchIndexConfig +from langgraph.store.elasticsearch.queries import ( + QueryGet, + QueryListNamespaces, + ElasticQueryPut, + VectorQueryPut, + ElasticQuerySearch, + VectorQuerySearch, +) + +T = TypeVar("T", bound=Union[Elasticsearch, AsyncElasticsearch]) + +logger = logging.getLogger(__name__) + +class BaseElasticsearchMemoryStore(Generic[T]): + """ + Base class for Elasticsearch memory store operations. + + Attributes: + operation_handlers (Dict[Op, RunnableLike]): Handlers for different operations. + """ + + def __init__(self, + es_connection: T | None = None, + index_config: ElasticsearchIndexConfig | None = None,): + """ + Initialize the Elasticsearch memory store. + + Args: + es_connection (T | None): Elasticsearch connection instance. + index_config (ElasticsearchIndexConfig | None): Configuration for Elasticsearch index. + """ + if not es_connection: + raise ValueError("No Elasticsearch connection provided") + + index_config, es_connection, vector_store = self._ensure_index_config(es_connection, index_config) + self.operation_handlers = self._initialize_operation_handlers(index_config, es_connection, vector_store) + + def _initialize_operation_handlers(self, + index_config: ElasticsearchIndexConfig, + es_connection: + T, vector_store: ElasticsearchVectorStore | None) -> Dict[Op, RunnableLike]: + """ + Initialize handlers for different operations. + + Args: + index_config (ElasticsearchIndexConfig): Configuration for Elasticsearch index. + es_connection (T): Elasticsearch connection instance. + vector_store (ElasticsearchVectorStore | None): Vector store instance. + + Returns: + Dict[Op, RunnableLike]: Handlers for different operations. + """ + return { + PutOp: ( + RunnableParallel({ + "elastic": ElasticQueryPut(index_config, es_connection), + "vector": VectorQueryPut(index_config, vector_store) + }) if vector_store + else ElasticQueryPut(index_config, es_connection) + ), + GetOp: QueryGet(index_config, es_connection), + SearchOp: ( + RunnableParallel({ + "elastic": ElasticQuerySearch(index_config, es_connection), + "vector": VectorQuerySearch(index_config, vector_store) + }) if vector_store + else ElasticQuerySearch(index_config, es_connection) + ), + ListNamespacesOp: QueryListNamespaces(index_config, es_connection), + } + + def _ensure_index_config(self, + es_connection: T, + index_config: ElasticsearchIndexConfig | None) -> Tuple[ElasticsearchIndexConfig | None, + T, + ElasticsearchVectorStore | None]: + """ + Ensure the index configuration is set up correctly. + + Args: + es_connection (T): Elasticsearch connection instance. + index_config (ElasticsearchIndexConfig | None): Configuration for Elasticsearch index. + + Returns: + Tuple[ElasticsearchIndexConfig | None, T, ElasticsearchVectorStore | None]: Configured index, connection, and vector store. + """ + config: ElasticsearchIndexConfig | None = index_config.copy() if index_config else ElasticsearchIndexConfig() + vector_store: ElasticsearchVectorStore | None = None + + embedd = ensure_embeddings(config.get("embed")) if config.get("embed") else None + + if embedd: + vector_store = ElasticsearchVectorStore( + es_connection=es_connection, + index_name=config.get("vector_index_name"), + embedding=embedd, + strategy=config.get("strategy"), + distance_strategy=config.get("distance_strategy"), + ) + config["__tokenized_fields"] = [ + (p, tokenize_path(p)) if p != "$" else (p, p) + for p in (config.get("fields") or ["$"]) + ] + + return config, es_connection, vector_store + + def process(self, ops: Iterable[Op]) -> list[Result]: + """ + Process a list of operations and return the results. + + Args: + ops (Iterable[Op]): A list of operations to be processed. + + Returns: + list[Result]: A list of results from the processed operations. + """ + results = [] + for op_type, op_instance in self.operation_handlers.items(): + filtered_ops = [op for op in ops if isinstance(op, op_type)] + if filtered_ops: + processed = op_instance.invoke(filtered_ops) + if isinstance(processed, dict): + flattened_output = list(processed.values()) + results.extend(flattened_output) + elif processed: + results.extend(processed) + return results + + async def aprocess(self, ops: Iterable[Op]) -> list[Result]: + """ + Asynchronously process a list of operations and return the results. + + Args: + ops (Iterable[Op]): A list of operations to be processed. + + Returns: + list[Result]: A list of results from the processed operations. + """ + tasks = [ + op_instance.ainvoke([op for op in ops if isinstance(op, op_type)]) + for op_type, op_instance in self.operation_handlers.items() + if any(isinstance(op, op_type) for op in ops) + ] + results = await asyncio.gather(*tasks) + return [item for sublist in results for item in sublist] + +class ElasticsearchMemoryStore(BaseStore, BaseElasticsearchMemoryStore[Elasticsearch]): + """ + Elasticsearch memory store for synchronous operations. + """ + + def __init__(self, + es_connection: Elasticsearch | None = None, + es_url: str | None = None, + es_cloud_id: str | None = None, + es_user: str | None = None, + es_api_key: str | None = None, + es_password: str | None = None, + es_params: Dict[str, Any] | None = None, + index_config: ElasticsearchIndexConfig | None = None, + ): + """ + Initialize the Elasticsearch memory store. + + Args: + es_connection (Elasticsearch | None): Elasticsearch connection instance. + es_url (str | None): URL for Elasticsearch. + es_cloud_id (str | None): Cloud ID for Elasticsearch. + es_user (str | None): Username for Elasticsearch. + es_api_key (str | None): API key for Elasticsearch. + es_password (str | None): Password for Elasticsearch. + es_params (Dict[str, Any] | None): Additional parameters for Elasticsearch. + index_config (ElasticsearchIndexConfig | None): Configuration for Elasticsearch index. + """ + if not es_connection: + es_connection = create_elasticsearch_client( + url=es_url, + cloud_id=es_cloud_id, + api_key=es_api_key, + username=es_user, + password=es_password, + params=es_params, + ) + super().__init__(es_connection=es_connection, + index_config=index_config) + + def batch(self, ops: Iterable[Op]) -> list[Result]: + """ + Process a batch of operations synchronously. + + Args: + ops (Iterable[Op]): A list of operations to be processed. + + Returns: + list[Result]: A list of results from the processed operations. + """ + return self.process(ops) + + async def abatch(self, ops: Iterable[Op]) -> list[Result]: + """ + Process a batch of operations asynchronously. + + Args: + ops (Iterable[Op]): A list of operations to be processed. + + Returns: + list[Result]: A list of results from the processed operations. + """ + logging.warning("For asynchronous operations, please use the appropriate class: AsyncElasticsearchStore.") + return await run_in_executor(self.aprocess, ops) diff --git a/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/config.py b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/config.py new file mode 100644 index 0000000..ed3b59d --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/config.py @@ -0,0 +1,15 @@ +from typing import Optional +from langgraph.store.base import IndexConfig +from langchain_elasticsearch._utilities import DistanceStrategy +from elasticsearch.helpers.vectorstore import ( + BM25Strategy, + RetrievalStrategy, +) + +DEFAULT_INDEX_NAME = "laggraph" + +class ElasticsearchIndexConfig(IndexConfig): + store_index_name: str = f"{DEFAULT_INDEX_NAME}-store" + vector_index_name: str = f"{DEFAULT_INDEX_NAME}-vectorstore" + strategy: RetrievalStrategy | None = BM25Strategy() + distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE, \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/py.typed b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/__init__.py b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/__init__.py new file mode 100644 index 0000000..2fa1ea4 --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/__init__.py @@ -0,0 +1,15 @@ +from langgraph.store.elasticsearch.queries.base import Query +from langgraph.store.elasticsearch.queries.get import QueryGet +from langgraph.store.elasticsearch.queries.put import VectorQueryPut, ElasticQueryPut +from langgraph.store.elasticsearch.queries.search import ElasticQuerySearch, VectorQuerySearch +from langgraph.store.elasticsearch.queries.listnamespace import QueryListNamespaces + +__all__ = [ + "Query", + "QueryGet", + "VectorQueryPut", + "ElasticQueryPut", + "ElasticQuerySearch", + "VectorQuerySearch", + "QueryListNamespaces", +] \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/base.py b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/base.py new file mode 100644 index 0000000..41ad41f --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/base.py @@ -0,0 +1,412 @@ +import asyncio +from abc import abstractmethod +from datetime import datetime, timezone +import logging +from typing import ( + Any, + Awaitable, + Callable, + Dict, + Iterable, + List, + Optional, + Tuple, + TypeVar, + Union +) +from elasticsearch import Elasticsearch, AsyncElasticsearch +from langchain_core.structured_query import ( + Comparison, + Operation, + Operator, + StructuredQuery +) +from langchain_core.runnables import Runnable, RunnableConfig +from langchain_elasticsearch.vectorstores import ElasticsearchStore +from langgraph.translator import ElasticsearchTranslator, ExtendedStructuredQuery +from langgraph.store.base import Result, Op +from langgraph.store.elasticsearch.config import ElasticsearchIndexConfig + +OpInput = TypeVar("OpInput", bound=Op) +OpResult = TypeVar("OpResult", bound=Result) + +DELIMITER_NAMESPACE = "/" + +logger = logging.getLogger(__name__) + +def text_to_namespace(namespace: Union[str, List[str]]) -> Tuple[str, ...]: + """Convert a text namespace to a tuple of strings. + + Args: + namespace (Union[str, List[str]]): The namespace as a string or list of strings. + + Returns: + Tuple[str, ...]: The namespace as a tuple of strings. + """ + if isinstance(namespace, list): + return tuple(namespace) + return tuple(namespace.split(DELIMITER_NAMESPACE)) + # if isinstance(namespace, str): + # return tuple(namespace.split(DELIMITER_NAMESPACE)) + # elif isinstance(namespace, (list, tuple)): + # # Flatten para garantir que não haja tuples aninhados + # flat_namespace = [] + # for item in namespace: + # if isinstance(item, (list, tuple)): + # flat_namespace.extend(item) + # else: + # flat_namespace.append(item) + # return tuple(flat_namespace) + raise TypeError("Namespace deve ser uma string, lista ou tuple.") + +def namespace_to_text(namespace: Tuple[str, ...], handle_wildcards: bool = False) -> str: + """Convert a tuple of strings to a text namespace. + + Args: + namespace (Tuple[str, ...]): The namespace as a tuple of strings. + handle_wildcards (bool, optional): Whether to handle wildcards. Defaults to False. + + Returns: + str: The namespace as a string. + """ + if handle_wildcards: + namespace = tuple("%" if val == "*" else val for val in namespace) + return DELIMITER_NAMESPACE.join(namespace) + +class Query(Runnable[OpInput, OpResult]): + """Base class for queries. + + Attributes: + index_config (ElasticsearchIndexConfig): The index configuration. + """ + index_config: ElasticsearchIndexConfig + + def __init__(self, index_config: ElasticsearchIndexConfig): + """Initialize the Query with the given index configuration. + + Args: + index_config (ElasticsearchIndexConfig): The index configuration. + """ + self.index_config = index_config + + def _create_item( + self, + namespace: Tuple[str, ...], + key: str, + value: Any, + created_at: Optional[datetime] = None, + updated_at: Optional[datetime] = None, + **kwargs: Any, + ) -> OpResult: + """Create an item with the given parameters. + + Args: + namespace (Tuple[str, ...]): The namespace of the item. + key (str): The key of the item. + value (Any): The value of the item. + created_at (Optional[datetime], optional): The creation time of the item. Defaults to None. + updated_at (Optional[datetime], optional): The update time of the item. Defaults to None. + **kwargs (Any): Additional keyword arguments. + + Returns: + OpResult: The created item. + """ + result = self.OutputType( + namespace=namespace, + key=key, + value=value, + created_at=created_at or datetime.now(tz=timezone.utc), + updated_at=updated_at or datetime.now(tz=timezone.utc), + **kwargs, + ) + return result + + def _execute_operations( + self, + ops: Iterable[OpInput], + sync_func: Callable[[OpInput], List[OpResult]], + ) -> List[OpResult]: + """Execute operations synchronously. + + Args: + ops (Iterable[OpInput]): The operations to execute. + sync_func (Callable[[OpInput], List[OpResult]]): The synchronous function to execute. + + Returns: + List[OpResult]: The list of results. + """ + results: list[OpResult] = [result for op in ops for result in ( sync_func(op) or [])] + return results + + async def _aexecute_operations( + self, + ops: Iterable[OpInput], + async_func: Callable[[OpInput], Awaitable[List[OpResult]]], + ) -> List[OpResult]: + """Execute operations asynchronously. + + Args: + ops (Iterable[OpInput]): The operations to execute. + async_func (Callable[[OpInput], Awaitable[List[OpResult]]]): The asynchronous function to execute. + + Returns: + List[OpResult]: The list of results. + """ + results = await asyncio.gather(*(async_func(op) for op in ops)) + return [item for sublist in results for item in sublist] + + @abstractmethod + def invoke( + self, input: OpInput, config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> OpResult: + """Invoke the query synchronously. + + Args: + input (OpInput): The input operation. + config (Optional[RunnableConfig], optional): The runnable configuration. Defaults to None. + **kwargs (Any): Additional keyword arguments. + + Returns: + OpResult: The result of the query. + """ + ... + + @abstractmethod + async def ainvoke( + self, input: OpInput, config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> OpResult: + """Invoke the query asynchronously. + + Args: + input (OpInput): The input operation. + config (Optional[RunnableConfig], optional): The runnable configuration. Defaults to None. + **kwargs (Any): Additional keyword arguments. + + Returns: + OpResult: The result of the query. + """ + ... + +class ElasticQuery(Query[OpInput, OpResult]): + """Class for Elasticsearch queries. + + Attributes: + es_connection (Optional[Union[Elasticsearch, AsyncElasticsearch]]): The Elasticsearch connection. + index_config (ElasticsearchIndexConfig): The index configuration. + store_index_name (str): The name of the store index. + translator (ElasticsearchTranslator): The Elasticsearch translator. + """ + es_connection: Optional[Union[Elasticsearch, AsyncElasticsearch]] + index_config: ElasticsearchIndexConfig + store_index_name: str + translator: ElasticsearchTranslator + + def __init__( + self, + index_config: ElasticsearchIndexConfig, + es_connection: Optional[Union[Elasticsearch, AsyncElasticsearch]] = None, + ): + """Initialize the ElasticQuery with the given index configuration and Elasticsearch connection. + + Args: + index_config (ElasticsearchIndexConfig): The index configuration. + es_connection (Optional[Union[Elasticsearch, AsyncElasticsearch]], optional): The Elasticsearch connection. Defaults to None. + """ + self.index_config = index_config + self.es_connection = es_connection + self.store_index_name = index_config.get("store_index_name") + self.translator = ElasticsearchTranslator() + + self.es_connection.indices.create( + index=self.store_index_name, + body=self.es_schema, + ignore=400, + ) + + def es_to_items(self, response: Dict[str, Any]) -> List[OpResult]: + """Convert Elasticsearch response to a list of items. + + Args: + response (Dict[str, Any]): The Elasticsearch response. + + Returns: + List[OpResult]: The list of items. + """ + print('es_to_items') + print(response) + if not response: + return [] + + hits = response.get("hits", None).get("hits", None) + if not hits and (source := response.get("_source")): + hits = [{"_source": source}] + print('HITS') + print(hits) + if not hits or hits is []: + print('HITS IS NONE') + return [{}] + print('HITS IS NOT NONE') + now = datetime.now(tz=timezone.utc).isoformat() + return [ + self._create_item( + namespace=text_to_namespace(source.get("namespace")), + key=source.get("key"), + value=source.get("value"), + created_at=datetime.fromisoformat(source.get("created_at", now)), + updated_at=datetime.fromisoformat(source.get("updated_at", now)), + # score=1.0, + ) for hit in hits if (source := hit.get("_source")) + ] + + def es_to_tuple(self, response: Dict[str, Any]) -> Tuple[str, ...]: + """Convert Elasticsearch response to a tuple. + + Args: + response (Dict[str, Any]): The Elasticsearch response. + + Returns: + Tuple[str, ...]: The tuple of namespaces. + """ + if not response or not (hits := response.get("hits", {}).get("hits", [])): + return () + + return tuple(text_to_namespace(hit["_source"]["namespace"]) for hit in hits) + + def es_body_upsert(self, op: OpInput) -> Dict[str, Any]: + """Create the body for an upsert operation. + + Args: + op (OpInput): The input operation. + + Returns: + Dict[str, Any]: The body for the upsert operation. + """ + updated_at = datetime.now(tz=timezone.utc) + return { + "doc": { + "value": op.value, + 'updated_at': updated_at + }, + "upsert": self.es_body(op) + } + + def es_body(self, op: OpInput) -> Dict[str, Any]: + """Create the body for an Elasticsearch document. + + Args: + op (OpInput): The input operation. + + Returns: + Dict[str, Any]: The body for the Elasticsearch document. + """ + created_at = datetime.now(tz=timezone.utc) + return { + "namespace": namespace_to_text(op.namespace), + "key": op.key, + "value": op.value, + 'created_at': created_at, + 'updated_at': created_at + } + + + @property + def es_schema(self) -> Dict[str, Any]: + """Return the Elasticsearch index mapping. + + Returns: + Dict[str, Any]: The Elasticsearch index mapping. + """ + return { + "mappings": { + "properties": { + "namespace": {"type": "keyword"}, + "key": {"type": "keyword"}, + "value": { + "type": "object", + "enabled": True, + }, + "created_at": {"type": "date"}, + "updated_at": {"type": "date"}, + } + } + } + + def build_query(self, + namespace: Optional[Tuple[str, ...]], + filters: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]], + property: Optional[str] = None, + **kwargs: Any) -> Optional[Dict[str, Any]]: + """Build the filter for an Elasticsearch query. + + Args: + filters (Optional[Union[Dict[str, Any], List[Dict[str, Any]]]]): The filters for the query. + property (Optional[str], optional): The property to filter on. Defaults to None. + **kwargs (Any): Additional keyword arguments. + + Returns: + Optional[Dict[str, Any]]: The built query. + """ + namespace_filter = Comparison( + comparator=self.translator.str_to_comparator("$contain"), + attribute="namespace", + value=f"{namespace_to_text(namespace)}*" + ) if namespace else None + + if isinstance(filters, dict): + filters = [filters] + + comparisons = [] + comparisons.append(namespace_filter) + + for filter_item in (filters or []): + for key, value in (filter_item or {}).items(): + if isinstance(value, str): + value = {"$eq": value} + if isinstance(value, dict): + for comparator_str, comp_value in value.items(): + comparator = self.translator.str_to_comparator(comparator_str) + comparison = Comparison( + comparator=comparator, + attribute=key if not property else f"{property}.{key}", + value=comp_value + ) + comparisons.append(comparison) + struc_query = ExtendedStructuredQuery(query="", filter=Operation(Operator.AND, comparisons), **kwargs) + _, query = self.translator.visit_structured_query(struc_query) + return query + +class VectorQuery(Query[OpInput, OpResult]): + """Class for vector store queries. + + Attributes: + vector_store (Optional[ElasticsearchStore]): The vector store. + index_config (ElasticsearchIndexConfig): The index configuration. + vector_index_name (str): The name of the vector index. + """ + vector_store: Optional[ElasticsearchStore] + index_config: ElasticsearchIndexConfig + vector_index_name: str + + def __init__( + self, + index_config: ElasticsearchIndexConfig, + vector_store: Optional[ElasticsearchStore] = None + ): + """Initialize the VectorQuery with the given index configuration and vector store. + + Args: + index_config (ElasticsearchIndexConfig): The index configuration. + vector_store (Optional[ElasticsearchStore], optional): The vector store. Defaults to None. + """ + self.index_config = index_config + self.vector_store = vector_store + self.vector_index_name = index_config.get("vector_index_name") + + def before_execution(self) -> bool: + """Execute before the query is executed. + + Returns: + bool: Whether the execution should continue. + """ + return self.vector_store.client.indices.exists(index=self.vector_index_name) + diff --git a/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/get.py b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/get.py new file mode 100644 index 0000000..3522fb5 --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/get.py @@ -0,0 +1,73 @@ +import logging +from typing import Any, Iterable, Optional +from langchain_core.runnables.config import run_in_executor +from langchain_core.runnables.config import RunnableConfig +from langgraph.store.base import GetOp, Item +from langgraph.store.elasticsearch.queries.base import ElasticQuery, namespace_to_text + +logger = logging.getLogger(__name__) + +class QueryGet(ElasticQuery[GetOp, Item]): + """ + Query class for handling 'Get' operations in Elasticsearch. + """ + + def get_by_id(self, op: GetOp) -> list[Item]: + """ + Retrieve an item by its ID from Elasticsearch. + + Args: + op (GetOp): The operation containing the namespace and key. + + Returns: + list[Item]: The retrieved items. + """ + response = self.es_connection.get( + index=self.store_index_name, + id=namespace_to_text(op.namespace + (op.key,)) + ) + return self.es_to_items(response) + + async def aget_by_id(self, op: GetOp) -> list[Item]: + """ + Asynchronously retrieve an item by its ID from Elasticsearch. + + Args: + op (GetOp): The operation containing the namespace and key. + + Returns: + list[Item]: The retrieved items. + """ + return await run_in_executor(None, self.get_by_id, op) + + def invoke( + self, input: Iterable[GetOp], config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> list[Item]: + """ + Invoke the get operation synchronously for a batch of inputs. + + Args: + input (Iterable[GetOp]): The batch of get operations. + config (Optional[RunnableConfig]): Optional configuration for the operation. + **kwargs (Any): Additional keyword arguments. + + Returns: + list[Item]: The retrieved items. + """ + return self._execute_operations(input, sync_func=self.get_by_id) + + async def ainvoke( + self, input: Iterable[GetOp], config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> list[Item]: + """ + Invoke the get operation asynchronously for a batch of inputs. + + Args: + input (Iterable[GetOp]): The batch of get operations. + config (Optional[RunnableConfig]): Optional configuration for the operation. + **kwargs (Any): Additional keyword arguments. + + Returns: + list[Item]: The retrieved items. + """ + return await self._aexecute_operations(input, sync_func=self.aget_by_id) \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/listnamespace.py b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/listnamespace.py new file mode 100644 index 0000000..bfdbb05 --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/listnamespace.py @@ -0,0 +1,65 @@ +import logging +from typing import ( + Any, + Iterable, + Optional, +) +from langchain_core.runnables.config import RunnableConfig +from langgraph.store.base import ( + ListNamespacesOp, + Item, + Result, +) +from langgraph.store.elasticsearch.queries.base import ElasticQuery, namespace_to_text + +logger = logging.getLogger(__name__) + +class QueryListNamespaces(ElasticQuery[ListNamespacesOp, list[Result]]): + def _query(self, op: ListNamespacesOp) -> dict: + """Build the Elasticsearch query for listing namespaces.""" + return self.build_query( + filters=[ + { + "namespace": { + "$contain": f"{namespace_to_text(match.path)}*" + if match.match_type == "prefix" + else f"*{namespace_to_text(match.path)}" + } + } + for match in op.match_conditions + ], + aggs={ + "group_by_namespace": { + "terms": { + "field": "namespace.keyword" + } + } + }, + _source=["namespace"] + ) + + def execute_list_namespaces(self, op: ListNamespacesOp) -> list[Result]: + query_conditions = self._query(op) + documents = self.es_connection.search( + index=self.store_index_name, + body=query_conditions, + ) + return self.es_to_tuple(documents) + + async def aexecute_list_namespaces(self, op: ListNamespacesOp) -> list[Result]: + query_conditions = self._query(op) + documents = await self.es_connection.search( + index=self.store_index_name, + body=query_conditions, + ) + return self.es_to_tuple(documents) + + def invoke( + self, input: Iterable[ListNamespacesOp], config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> list[Result]: + return self._execute_operations(input, sync_func=self.execute_list_namespaces) + + async def ainvoke( + self, input: Iterable[ListNamespacesOp], config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> list[Result]: + return await self._execute_operations(input, sync_func=self.aexecute_list_namespaces) \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/put.py b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/put.py new file mode 100644 index 0000000..117d3de --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/put.py @@ -0,0 +1,80 @@ +from datetime import datetime, timezone +import logging +from typing import Any, Iterable, Optional +from langchain_core.documents import Document +from langchain_core.runnables.config import RunnableConfig +from langchain_core.runnables.config import run_in_executor +from langgraph.store.base import PutOp, tokenize_path, get_text_at_path +from langgraph.store.elasticsearch.queries.base import ElasticQuery, VectorQuery, namespace_to_text + +logger = logging.getLogger(__name__) + +class ElasticQueryPut(ElasticQuery[Iterable[PutOp], None]): + def put_texts(self, op: PutOp) -> None: + doc_id = namespace_to_text(op.namespace + (op.key,)) + if not op.value: + self.es_connection.delete( + index=self.store_index_name, + id=doc_id, + ignore=[404] + ) + return + + self.es_connection.update( + index=self.store_index_name, + id=doc_id, + body=self.es_body_upsert(op), + ) + + async def aput_texts(self, op: PutOp) -> None: + await run_in_executor(None, self.put_texts, op) + + def invoke( + self, input: Iterable[PutOp], config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> None: + self._execute_operations(input, sync_func=self.put_texts) + + async def ainvoke( + self, input: Iterable[PutOp], config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> None: + await self._aexecute_operations(input, sync_func=self.aput_texts) + +class VectorQueryPut(VectorQuery[Iterable[PutOp], None]): + def put_texts(self, op: PutOp) -> None: + if not op.value: + self.vector_store.delete(ids=[namespace_to_text(op.namespace + (op.key,))]) + return [] + + paths = self.index_config["__tokenized_fields"] if op.index is None else [(ix, tokenize_path(ix)) for ix in op.index] + created_at = datetime.now(tz=timezone.utc) + documents = [ + Document( + id=namespace_to_text(op.namespace + (op.key, path)), + page_content=text, + metadata={ + "namespace": namespace_to_text(op.namespace), + "key": op.key, + "path": path, + "created_at": created_at, + "updated_at": created_at, + }, + ) + for path, tokenized_path in paths + for text in get_text_at_path(op.value, tokenized_path) + ] + self.vector_store.add_documents( + documents=documents, + create_index_if_not_exists=True,) + + async def aput_texts(self, op: PutOp) -> None: + await run_in_executor(None, self.put_texts, op) + + def invoke( + self, input: Iterable[PutOp], config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> None: + self._execute_operations(input, sync_func=self.put_texts) + + async def ainvoke( + self, input: Iterable[PutOp], config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> None: + await self._aexecute_operations(input, sync_func=self.aput_texts) diff --git a/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/search.py b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/search.py new file mode 100644 index 0000000..445df89 --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/store/elasticsearch/queries/search.py @@ -0,0 +1,186 @@ +import logging +from typing import Any, Dict, Iterable, List, Optional +from langchain_core.runnables import run_in_executor, RunnableConfig +from langchain_core.documents import Document +from langgraph.store.base import SearchOp, SearchItem +from langgraph.store.elasticsearch.queries.base import ElasticQuery, VectorQuery, text_to_namespace, namespace_to_text +from langgraph.util import validate_before_execution + +logger = logging.getLogger(__name__) + +class ElasticQuerySearch(ElasticQuery[SearchOp, SearchItem]): + """Class for executing search operations using Elasticsearch.""" + + def execute_search(self, op: SearchOp) -> List[SearchItem]: + """Execute a synchronous search operation. + + Args: + op (SearchOp): The search operation to execute. + + Returns: + List[SearchItem]: The search results. + """ + args = { + "from": op.offset, + "sort": [{"updated_at": {"order": "desc"}}], + } + print('cheguei aqui') + print(self.store_index_name) + print(op.namespace_prefix) + print(op.filter) + print(op.limit) + print(args) + + response = self.es_connection.search( + index=self.store_index_name, + body=self.build_query(op.namespace_prefix, op.filter, limit=op.limit, property="value", **args) + ) + return self.es_to_items(response) + + async def aexecute_search(self, op: SearchOp) -> list[SearchItem]: + """Execute an asynchronous search operation. + + Args: + op (SearchOp): The search operation to execute. + + Returns: + list[SearchItem]: The search results. + """ + return await run_in_executor(None, self.execute_search, op) + + def invoke( + self, input: Iterable[SearchOp], config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> list[SearchItem]: + """Invoke the search operation synchronously. + + Args: + input (Iterable[SearchOp]): The search operations to execute. + config (Optional[RunnableConfig], optional): Configuration for the runnable. Defaults to None. + + Returns: + list[SearchItem]: The search results. + """ + return self._execute_operations(input, sync_func=self.execute_search) + + async def ainvoke( + self, input: Iterable[SearchOp], config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> list[SearchItem]: + """Invoke the search operation asynchronously. + + Args: + input (Iterable[SearchOp]): The search operations to execute. + config (Optional[RunnableConfig], optional): Configuration for the runnable. Defaults to None. + + Returns: + list[SearchItem]: The search results. + """ + return await self._execute_operations(input, sync_func=self.aexecute_search) +class VectorQuerySearch(VectorQuery[SearchOp, SearchItem]): + """Class for executing vector search operations.""" + + def _document_to_search_item(self, documents: list[tuple[Document, float]]) -> list[SearchItem]: + """Convert documents to search items. + + Args: + documents (list[tuple[Document, float]]): The documents to convert. + + Returns: + list[SearchItem]: The converted search items. + """ + return [ + self._create_item( + namespace=text_to_namespace(doc.metadata["namespace"]), + key=doc.id, + value=doc.page_content, + created_at=doc.metadata["created_at"], + updated_at=doc.metadata["updated_at"], + # created_at=doc.model_extra["created_at"], + # updated_at=doc.model_extra["updated_at"], + score=score, + ) + for doc, score in documents + ] + + def _query(self, op: SearchOp) -> Dict[str, Any]: + """Generate the query dictionary for the search operation. + + Args: + op (SearchOp): The search operation. + + Returns: + dict: The query dictionary. + """ + query = { + "query": op.query, + "k": op.limit, + } + if op.namespace_prefix: + query.update({ + "from": op.offset, + "filter": { + "prefix": { + "metadata.namespace.keyword": namespace_to_text(op.namespace_prefix) + } + } + }) + return query + + @validate_before_execution + def execute_search(self, op: SearchOp) -> list[SearchItem]: + """Execute a synchronous vector search operation. + + Args: + op (SearchOp): The search operation to execute. + + Returns: + list[SearchItem]: The search results. + """ + if not self.vector_store or not op.query: + return op + query = self._query(op) + documents = self.vector_store.similarity_search_with_relevance_scores(**query) + return self._document_to_search_item(documents) + + @validate_before_execution + async def aexecute_search(self, op: SearchOp) -> list[SearchItem]: + """Execute an asynchronous vector search operation. + + Args: + op (SearchOp): The search operation to execute. + + Returns: + list[SearchItem]: The search results. + """ + if not self.vector_store or not op.query: + return op + query = self._query(op) + documents = await self.vector_store.asimilarity_search_with_relevance_scores(**query) + return self._document_to_search_item(documents) + + def invoke( + self, input: Iterable[SearchOp], config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> list[SearchItem]: + """Invoke the vector search operation synchronously. + + Args: + input (Iterable[SearchOp]): The search operations to execute. + config (Optional[RunnableConfig], optional): Configuration for the runnable. Defaults to None. + + Returns: + list[SearchItem]: The search results. + """ + return self._execute_operations(input, sync_func=self.execute_search) + + async def ainvoke( + self, input: Iterable[SearchOp], config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> list[SearchItem]: + """Invoke the vector search operation asynchronously. + + Args: + input (Iterable[SearchOp]): The search operations to execute. + config (Optional[RunnableConfig], optional): Configuration for the runnable. Defaults to None. + + Returns: + list[SearchItem]: The search results. + """ + return await self._execute_operations(input, sync_func=self.aexecute_search) \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/langgraph/translator.py b/libs/langgraph-elasticsearch/langgraph/translator.py new file mode 100644 index 0000000..9a29193 --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/translator.py @@ -0,0 +1,206 @@ +from typing import Any, Dict, Optional, Tuple, Union + +from langchain_core.structured_query import ( + Comparator, + Comparison, + Operation, + Operator, + StructuredQuery, + Visitor, + FilterDirective, +) +from pydantic import Extra + + +class ElasticsearchTranslator(Visitor): + """ + Translates a structured query language into Elasticsearch's query DSL. + """ + + # Subset of allowed comparators and operators + allowed_comparators = [ + Comparator.EQ, + Comparator.GT, + Comparator.GTE, + Comparator.LT, + Comparator.LTE, + Comparator.CONTAIN, + Comparator.LIKE, + Comparator.IN, + Comparator.NIN, + ] + allowed_operators = [Operator.AND, Operator.OR, Operator.NOT] + + # Mapping of comparators and operators to Elasticsearch syntax + _operator_map = { + Operator.OR: "should", + Operator.NOT: "must_not", + Operator.AND: "must", + } + _comparator_map = { + Comparator.EQ: "term", + Comparator.GT: "gt", + Comparator.GTE: "gte", + Comparator.LT: "lt", + Comparator.LTE: "lte", + Comparator.CONTAIN: "wildcard", + Comparator.LIKE: "match", + Comparator.IN: "terms", + Comparator.NIN: "must_not_terms", # Custom handling for NOT IN + } + + def _format_func(self, func: Union[Operator, Comparator]) -> str: + """ + Maps an operator or comparator to its Elasticsearch equivalent. + + Args: + func (Union[Operator, Comparator]): The operator or comparator. + + Returns: + str: The Elasticsearch equivalent. + """ + self._validate_func(func) + if isinstance(func, Operator): + return self._operator_map[func] + return self._comparator_map[func] + + def str_to_comparator(self, comparator: str) -> Comparator: + """ + Converts a string representation of a comparator to its enum. + + Args: + comparator (str): The string representation of the comparator. + + Returns: + Comparator: The corresponding `Comparator` enum. + """ + comparator_map = { + "$eq": Comparator.EQ, + "$ne": Comparator.NE, + "$gt": Comparator.GT, + "$gte": Comparator.GTE, + "$lt": Comparator.LT, + "$lte": Comparator.LTE, + "$contain": Comparator.CONTAIN, + "$like": Comparator.LIKE, + "$in": Comparator.IN, + "$nin": Comparator.NIN, + } + return comparator_map.get(comparator.lower(), Comparator.EQ) + + def visit_operation(self, operation: Operation) -> Dict: + """ + Translates logical operations (AND, OR, NOT) into Elasticsearch bool queries. + + Args: + operation (Operation): The logical operation. + + Returns: + Dict: The translated Elasticsearch query. + """ + args = [arg.accept(self) for arg in operation.arguments] + + return {"bool": {self._format_func(operation.operator): args}} + + def visit_comparison(self, comparison: Comparison) -> Dict: + """ + Translates comparisons (EQ, GT, CONTAIN, etc.) into Elasticsearch filters. + + Args: + comparison (Comparison): The comparison expression. + + Returns: + Dict: The translated Elasticsearch query. + """ + if comparison.comparator in [Comparator.GT, Comparator.GTE, Comparator.LT, Comparator.LTE]: + value = comparison.value.get("date") if isinstance(comparison.value, dict) and "date" in comparison.value else comparison.value + return {"range": {comparison.attribute: {self._format_func(comparison.comparator): value}}} + + if comparison.comparator == Comparator.CONTAIN: + return { + self._format_func(comparison.comparator): { + comparison.attribute: f"*{comparison.value}*" + if not comparison.value.startswith("*") and not comparison.value.endswith("*") + else comparison.value + } + } + + if comparison.comparator == Comparator.LIKE: + return { + self._format_func(comparison.comparator): { + comparison.attribute: {"query": comparison.value, "fuzziness": "AUTO"} + } + } + + if comparison.comparator == Comparator.IN: + return { + self._format_func(comparison.comparator): { + comparison.attribute: comparison.value + } + } + + if comparison.comparator == Comparator.NIN: + return { + "bool": { + self._format_func(Comparator.NIN): [ + {"terms": {comparison.attribute: comparison.value}} + ] + } + } + + value = comparison.value.get("date") if isinstance(comparison.value, dict) and "date" in comparison.value else comparison.value + return {self._format_func(comparison.comparator): {comparison.attribute: value}} + + def visit_structured_query( + self, structured_query: 'ExtendedStructuredQuery' + ) -> Tuple[str, dict]: + """ + Translates a structured query into Elasticsearch's query format. + + Args: + structured_query (StructuredQuery): The structured query to translate. + + Returns: + Tuple[str, dict]: A tuple containing the query string and the translated filters. + """ + + elastic_query = {"query": structured_query.filter.accept(self)} if structured_query.filter else {} + + if structured_query.limit is not None: + elastic_query["size"] = structured_query.limit + + if hasattr(structured_query, "extra_attributes"): + elastic_query.update(structured_query.extra_attributes) + + return structured_query.query, elastic_query + + +class ExtendedStructuredQuery(StructuredQuery): + extra_attributes: Dict[str, Any] = {} + + def __init__(self, + query: str, + filter: Optional[FilterDirective], + limit: Optional[int] = None, + **kwargs): + super().__init__( + query=query, filter=filter, limit=limit, **kwargs + ) + + self.extra_attributes = kwargs + + + + def __getattr__(self, name: str): + if name in self.extra_attributes: + return self.extra_attributes[name] + raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'") + + def dict(self, *args, **kwargs): + base_dict = super().model_dump(*args, **kwargs) + return {**base_dict, **self.extra_attributes} + + +# class ExtendedStructuredQuery(StructuredQuery): +# class Config: +# extra = "allow" diff --git a/libs/langgraph-elasticsearch/langgraph/util.py b/libs/langgraph-elasticsearch/langgraph/util.py new file mode 100644 index 0000000..c974aaf --- /dev/null +++ b/libs/langgraph-elasticsearch/langgraph/util.py @@ -0,0 +1,26 @@ +from functools import wraps +from typing import Callable, TypeVar +import asyncio + +T = TypeVar("T") + +def syncify(func: Callable[..., T], *args, **kwargs) -> T: + try: + loop = asyncio.get_running_loop() + future = asyncio.run_coroutine_threadsafe(func(*args, **kwargs), loop) + return future.result() + except RuntimeError: + loop = asyncio.get_event_loop() + return loop.run_until_complete(func(*args, **kwargs)) + +def validate_before_execution(func): + """Decorator para validar before_execution antes de executar uma função.""" + @wraps(func) + def wrapper(self, *args, **kwargs): + if hasattr(self, "before_execution") and callable(getattr(self, "before_execution")): + if not self.before_execution(): + return [] + else: + raise AttributeError("The method 'before_execution' is not defined or callable.") + return func(self, *args, **kwargs) + return wrapper \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/poetry.lock b/libs/langgraph-elasticsearch/poetry.lock new file mode 100644 index 0000000..407b720 --- /dev/null +++ b/libs/langgraph-elasticsearch/poetry.lock @@ -0,0 +1,3792 @@ +# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. + +[[package]] +name = "acres" +version = "0.3.0" +description = "Access resources on your terms" +optional = false +python-versions = ">=3.9" +groups = ["main"] +files = [ + {file = "acres-0.3.0-py3-none-any.whl", hash = "sha256:9a3138f27c5237edbdf1ea97bf20f48beb4ebfb71de8150a0bc2448012b4e280"}, + {file = "acres-0.3.0.tar.gz", hash = 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+lock-version = "2.1" +python-versions = ">=3.12,<4.0" +content-hash = "7ba8f93ee25cced22b959fbb028d074fa5912160d62808d0b6f1ef2fd43095f7" diff --git a/libs/langgraph-elasticsearch/poetry.toml b/libs/langgraph-elasticsearch/poetry.toml new file mode 100644 index 0000000..ab1033b --- /dev/null +++ b/libs/langgraph-elasticsearch/poetry.toml @@ -0,0 +1,2 @@ +[virtualenvs] +in-project = true diff --git a/libs/langgraph-elasticsearch/pyproject.toml b/libs/langgraph-elasticsearch/pyproject.toml new file mode 100644 index 0000000..02efd1c --- /dev/null +++ b/libs/langgraph-elasticsearch/pyproject.toml @@ -0,0 +1,63 @@ +[tool.poetry] +name = "langgraph-elasticsearch" +version = "0.1.0" +description = "Library with a Elasticsearch implementation of LangGraph checkpoint saver." +authors = [] +readme = "README.md" +license = "MIT" +packages = [{ include = "langgraph" }] + +[tool.poetry.dependencies] +python = ">=3.12,<4.0" +langchain = "^0.3.20" +langchain-elasticsearch = "^0.3.2" +langchain-openai = "^0.3.8" +langgraph = ">=0.2.76" +langgraph-checkpoint = "^2.0.19" +langchain-community = "^0.3.19" +pytest-mock = "^3.14.0" +pytest-asyncio = "^0.25.3" +pydantic-settings = "^2.8.1" +fitz = "^0.0.1.dev2" + +[tool.poetry.group.dev.dependencies] +pytest = "^8.3.5" +pytest-asyncio = "^0.25.3" +testcontainers = "^4.9.2" +pytest-cov = "^6.0.0" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.coverage.run] +omit = ["tests/*"] + +[tool.pytest-watcher] +now = true +delay = 0.1 +patterns = ["*.py"] + +[tool.poetry.scripts] +start = "langgraph.examples:main" + + +# [tool.poetry.group.test.dependencies.tests] +# path = "tests" +# develop = true +[tool.pytest.ini_options] + +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +addopts = "--strict-markers --strict-config --durations=5 -vv" +asyncio_mode = "auto" + +[tool.vivoai] +title = "VivoAI API" +root_path = "/api" +docs_url = "/docs" +redoc_url = "/redoc" \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/pytest.ini b/libs/langgraph-elasticsearch/pytest.ini new file mode 100644 index 0000000..62a5fc7 --- /dev/null +++ b/libs/langgraph-elasticsearch/pytest.ini @@ -0,0 +1,9 @@ +[pytest] +testpaths = tests +markers = + smoke: mark test as smoke test + regression: mark test as regression test +log_cli = true +log_cli_level = INFO +log_file_level = INFO +log_file = pytest.log diff --git a/libs/langgraph-elasticsearch/tests/cases/async/conftest.py b/libs/langgraph-elasticsearch/tests/cases/async/conftest.py new file mode 100644 index 0000000..216ed29 --- /dev/null +++ b/libs/langgraph-elasticsearch/tests/cases/async/conftest.py @@ -0,0 +1,50 @@ +from typing import Generator +from unittest import mock +from unittest.mock import AsyncMock, MagicMock, Mock +from elasticsearch import AsyncElasticsearch, Elasticsearch +from elasticsearch.client import IndicesClient as AsyncIndicesClient, IndicesClient +import pytest + +from langgraph.store.elasticsearch.aio import AsyncElasticsearchMemoryStore +from langgraph.store.elasticsearch.base import ElasticsearchMemoryStore +from langgraph.store.elasticsearch.config import ElasticsearchIndexConfig +from elastic_transport import ConnectionError + +@pytest.fixture +def factory_es_client(request): + if request.node.get_closest_marker("fail"): + return request.getfixturevalue("async_es_client_fail") + return request.getfixturevalue("async_es_client") + +@pytest.fixture +def async_es_client() -> Generator[MagicMock, None, None]: + client_mock = MagicMock(spec=AsyncElasticsearch) + client_mock.return_value.hosts = ["http://localhost:9200"] + client_mock.return_value.indices = MagicMock(spec=AsyncIndicesClient) + # coroutines need to be mocked explicitly + client_mock.return_value.indices.exists_alias = AsyncMock() + client_mock.return_value.indices.put_mapping = AsyncMock() + client_mock.return_value.indices.exists = AsyncMock() + client_mock.return_value.indices.create = AsyncMock() + yield client_mock() + +@pytest.fixture +def async_es_client_fail() -> Generator[MagicMock, None, None]: + client_mock = MagicMock(spec=AsyncElasticsearch) + def raise_connection_error(*args, **kwargs): + raise ConnectionError("Failed to connect to Elasticsearch") + client_mock.return_value.update.side_effect = raise_connection_error + yield client_mock() + +@pytest.fixture +def index_config(): + return ElasticsearchIndexConfig( + store_index_name="langgraph-store", + ) + +@pytest.fixture +def store(factory_es_client, index_config): + return AsyncElasticsearchMemoryStore( + es_connection=factory_es_client, + index_config=index_config + ) \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/tests/cases/async/test_async_put.py b/libs/langgraph-elasticsearch/tests/cases/async/test_async_put.py new file mode 100644 index 0000000..384c327 --- /dev/null +++ b/libs/langgraph-elasticsearch/tests/cases/async/test_async_put.py @@ -0,0 +1,6 @@ +import pytest +from tests.cases.test_put import TestPutOperation + +@pytest.mark.asyncio +class TestSyncPutOperation(TestPutOperation): + pass \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/tests/cases/conftest.py b/libs/langgraph-elasticsearch/tests/cases/conftest.py new file mode 100644 index 0000000..5d8a0a7 --- /dev/null +++ b/libs/langgraph-elasticsearch/tests/cases/conftest.py @@ -0,0 +1,154 @@ +import pytest +from typing import Generator +from unittest.mock import MagicMock, Mock +from elasticsearch import Elasticsearch +from elasticsearch.client import IndicesClient +from langchain_openai import AzureOpenAIEmbeddings +from langchain_core.embeddings.fake import FakeEmbeddings + +from langgraph.store.elasticsearch.base import ElasticsearchMemoryStore +from langgraph.store.elasticsearch.config import ElasticsearchIndexConfig +from elastic_transport import ConnectionError +from langchain_elasticsearch._utilities import DistanceStrategy +from langchain_elasticsearch import DenseVectorStrategy, DistanceMetric +from testcontainers.elasticsearch import ElasticSearchContainer + +@pytest.fixture +def factory_es_client(request): + if request.node.get_closest_marker("fail"): + return request.getfixturevalue("sync_es_client_fail") + if request.node.get_closest_marker("integration"): + return request.getfixturevalue("sync_es_client") + if request.node.get_closest_marker("mock"): + return request.getfixturevalue("sync_es_client") + + + + +@pytest.fixture +def sync_es_client(request) -> Generator[MagicMock, None, None]: + if request.node.get_closest_marker("integration"): + client = Elasticsearch( + basic_auth=("elastic","elastic"), + hosts=["http://localhost:9200"], + verify_certs=False + ) + yield client + else: + client_mock = MagicMock(spec=Elasticsearch) + client_mock.return_value.hosts = ["http://localhost:9200"] + client_mock.return_value.indices = MagicMock(spec=IndicesClient) + client_mock.return_value.indices.exists_alias = Mock() + client_mock.return_value.indices.put_mapping = Mock() + client_mock.return_value.indices.exists = Mock() + client_mock.return_value.indices.create = Mock() + yield client_mock() + + # with ElasticSearchContainer('elasticsearch:8.9.2') as container: + # client = Elasticsearch([container.get_url()]) + # yield client + +@pytest.fixture +def sync_es_client_fail() -> Generator[MagicMock, None, None]: + client_mock = MagicMock(spec=Elasticsearch) + def raise_connection_error(*args, **kwargs): + raise ConnectionError("Failed to connect to Elasticsearch") + client_mock.return_value.update.side_effect = raise_connection_error + yield client_mock() + +@pytest.fixture +def index_config(request): + if request.node.get_closest_marker("embed"): + return ElasticsearchIndexConfig( + store_index_name="langgraph-store", + vector_index_name="langgraph-vectorstore", + dims=3072, + distance_strategy=DistanceStrategy.COSINE, + strategy=DenseVectorStrategy( + distance=DistanceMetric.COSINE, + ), + embed=FakeEmbeddings( + size=50, + ) if not request.node.get_closest_marker("fail") else None, + fields=["data"], + ) + + return ElasticsearchIndexConfig( + store_index_name="langgraph-store", + ) + +@pytest.fixture +def store(factory_es_client, index_config): + return ElasticsearchMemoryStore( + es_connection=factory_es_client, + index_config=index_config + ) + +@pytest.fixture +def async_store(factory_es_client, index_config): + return ElasticsearchMemoryStore( + es_connection=factory_es_client, + index_config=index_config + ) + + + + + + + + + + + + +# Mocks +@pytest.fixture +def mock_get(sync_es_client_fx): + sync_es_client_fx.get.return_value = { + "_index": "langgraph-store", + "_id": "test/foo/key1", + "_source": { + "namespace": "test/foo", + "key": "key1", + "value": { + "data": "value1" + }, + "data": "value1", + "created_at": "2021-10-01T00:00:00Z", + "updated_at": "2021-10-01T00:00:00Z" + } + } + +@pytest.fixture +def mock_search(sync_es_client_fx): + sync_es_client_fx.search.return_value = { + "_index": "langgraph-store", + "_id": "test/foo/key1", + "_source": { + "namespace": "test/foo", + "key": "key1", + "value": { + "data": "value1" + }, + "data": "value1", + "created_at": "2021-10-01T00:00:00Z", + "updated_at": "2021-10-01T00:00:00Z" + } + } + +@pytest.fixture +def mock_listname(sync_es_client_fx): + sync_es_client_fx.search.return_value = { + 'hits': { + 'hits': [ + { + '_index': 'langgraph-store', + '_id': 'test/foo/key1', + '_source': { + 'namespace': 'test/foo', + } + } + ] + } + } \ No newline at end of file diff --git a/libs/langgraph-elasticsearch/tests/cases/test_put.py b/libs/langgraph-elasticsearch/tests/cases/test_put.py new file mode 100644 index 0000000..b89f31a --- /dev/null +++ b/libs/langgraph-elasticsearch/tests/cases/test_put.py @@ -0,0 +1,121 @@ +from langgraph.store.base import PutOp +import pytest + +from langgraph.store.elasticsearch.queries.base import namespace_to_text, text_to_namespace +from elastic_transport import ConnectionError +import logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) +mocks = [ + { + "_index": "langgraph-store", + "_id": "test/foo/key1", + "_source": { + "namespace": "test/foo", + "key": "key1", + "value": { + "data": "value1" + }, + "created_at": "2021-10-01T00:00:00Z", + "updated_at": "2021-10-01T00:00:00Z" + } + }, + { + "_index": "langgraph-store", + "_id": "test/foo/key2", + "_source": { + "namespace": "test/foo", + "key": "key2", + "value": { + "data": "value2" + }, + "created_at": "2021-10-01T00:00:00Z", + "updated_at": "2021-10-01T00:00:00Z" + } + } +] + +@pytest.mark.parametrize( + "index_name", + "langgraph-store", +) +@pytest.mark.mock +class TestPutOperation: + @pytest.mark.parametrize( + "op", + PutOp(namespace=("test", "foo"), key="key1", value={"data": "value1"}), + ) + def test_one_put(self, factory_es_client, store, request, op, index_name): + store.batch(op) + _, _, kwargs = factory_es_client.update.mock_calls[0] + + assert kwargs["index"] == index_name + assert kwargs["id"] == text_to_namespace(op.namespace + (op.key,)) + assert kwargs["body"]["upsert"]["namespace"] == namespace_to_text(op.namespace) + assert kwargs["body"]["upsert"]["key"] == op.key + assert kwargs["body"]["upsert"]["value"] == op.value + assert kwargs["body"]["doc"]["value"] == op.value + + @pytest.mark.parametrize( + "ops", + [ + PutOp(namespace=("test", "foo"), key="key1", value={"data": "value1"}), + PutOp(namespace=("test", "foo"), key="key2", value={"data": "value2"}), + ], + ) + def test_multiple_puts(self, factory_es_client, store, ops, index_name): + ops = [ + PutOp(namespace=("test", "foo"), key="key1", value={"data": "value1"}), + PutOp(namespace=("test", "foo"), key="key2", value={"data": "value2"}), + ] + + store.batch(ops) + for idx, calls in enumerate(factory_es_client.update.mock_calls): + _, _, kwargs = calls + assert kwargs["index"] == mocks[idx]["_index"] + assert kwargs["id"] == mocks[idx]["_id"] + assert kwargs["body"]["upsert"]["namespace"] == mocks[idx]["_source"]["namespace"] + assert kwargs["body"]["upsert"]["key"] == mocks[idx]["_source"]["key"] + assert kwargs["body"]["upsert"]["value"] == mocks[idx]["_source"]["value"] + assert kwargs["body"]["doc"]["value"] == mocks[idx]["_source"]["value"] + + @pytest.mark.fail + def test_fail_connection(self, store, ops, index_name): + ops = [ + PutOp(namespace=("test", "foo"), key="key1", value={"data": "value1"}), + ] + with pytest.raises(ConnectionError): + store.batch(ops) + +@pytest.mark.integration +class TestIntegrationPutOperation(TestPutOperation): + def test_one_put(self, factory_es_client, store, request): + mock = mocks[0] + op = [ + PutOp( + namespace=text_to_namespace(mock["_source"]["namespace"]), + key=mock["_source"]["key"], + value=mock["_source"]["value"],) + ] + + store.batch(op) + retrieved = factory_es_client.get(index=mock["_index"], id=mock["_id"]) + + assert retrieved["_index"] == mock["_index"] + assert retrieved["_id"] == mock["_id"] + assert retrieved["_source"]["namespace"] == mock["_source"]["namespace"] + assert retrieved["_source"]["key"] == mock["_source"]["key"] + assert retrieved["_source"]["value"] == mock["_source"]["value"] + +@pytest.mark.embed +class TestPutOperationWithEmbeddings(TestPutOperation): + @pytest.mark.fail + def test_fail(self, store): + ops = [ + PutOp(namespace=("test", "foo"), key="key1", value={"data": "value1"}), + ] + with pytest.raises(ConnectionError): + store.batch(ops) + + + diff --git a/libs/langgraph-elasticsearch/tests/translator_test.py b/libs/langgraph-elasticsearch/tests/translator_test.py new file mode 100644 index 0000000..1466102 --- /dev/null +++ b/libs/langgraph-elasticsearch/tests/translator_test.py @@ -0,0 +1,96 @@ +import pytest +from langchain_core.structured_query import Comparator, Comparison, Operation, Operator, StructuredQuery +from langgraph.translator import ElasticsearchTranslator, ExtendedStructuredQuery + +@pytest.fixture +def translator(): + return ElasticsearchTranslator() + +def test_format_func_operator(translator): + assert translator._format_func(Operator.AND) == "must" + assert translator._format_func(Operator.OR) == "should" + assert translator._format_func(Operator.NOT) == "must_not" + +def test_format_func_comparator(translator): + assert translator._format_func(Comparator.EQ) == "term" + assert translator._format_func(Comparator.GT) == "gt" + assert translator._format_func(Comparator.GTE) == "gte" + assert translator._format_func(Comparator.LT) == "lt" + assert translator._format_func(Comparator.LTE) == "lte" + assert translator._format_func(Comparator.CONTAIN) == "wildcard" + assert translator._format_func(Comparator.LIKE) == "match" + assert translator._format_func(Comparator.IN) == "terms" + assert translator._format_func(Comparator.NIN) == "must_not_terms" + +def test_str_to_comparator(translator): + assert translator.str_to_comparator("$eq") == Comparator.EQ + assert translator.str_to_comparator("$ne") == Comparator.NE + assert translator.str_to_comparator("$gt") == Comparator.GT + assert translator.str_to_comparator("$gte") == Comparator.GTE + assert translator.str_to_comparator("$lt") == Comparator.LT + assert translator.str_to_comparator("$lte") == Comparator.LTE + assert translator.str_to_comparator("$contain") == Comparator.CONTAIN + assert translator.str_to_comparator("$like") == Comparator.LIKE + assert translator.str_to_comparator("$in") == Comparator.IN + assert translator.str_to_comparator("$nin") == Comparator.NIN + +def test_visit_operation(translator): + operation = Operation(operator=Operator.AND, arguments=[ + Comparison(attribute="age", comparator=Comparator.GT, value=30), + Comparison(attribute="name", comparator=Comparator.LIKE, value="John") + ]) + result = translator.visit_operation(operation) + expected = { + "bool": { + "must": [ + {"range": {"age": {"gt": 30}}}, + {"match": {"name": {"query": "John", "fuzziness": "AUTO"}}} + ] + } + } + assert result == expected + +def test_visit_comparison(translator): + comparison = Comparison(attribute="age", comparator=Comparator.GT, value=30) + result = translator.visit_comparison(comparison) + expected = {"range": {"age": {"gt": 30}}} + assert result == expected + + comparison = Comparison(attribute="name", comparator=Comparator.LIKE, value="John") + result = translator.visit_comparison(comparison) + expected = {"match": {"name": {"query": "John", "fuzziness": "AUTO"}}} + assert result == expected + + comparison = Comparison(attribute="tags", comparator=Comparator.IN, value=["python", "elasticsearch"]) + result = translator.visit_comparison(comparison) + expected = {"terms": {"tags": ["python", "elasticsearch"]}} + assert result == expected + + comparison = Comparison(attribute="tags", comparator=Comparator.NIN, value=["python", "elasticsearch"]) + result = translator.visit_comparison(comparison) + expected = {"bool": {"must_not_terms": [{"terms": {"tags": ["python", "elasticsearch"]}}]}} + assert result == expected + +def test_visit_structured_query(translator): + structured_query = ExtendedStructuredQuery( + query="test query", + filter=Operation(operator=Operator.AND, arguments=[ + Comparison(attribute="age", comparator=Comparator.GT, value=30), + Comparison(attribute="name", comparator=Comparator.LIKE, value="John") + ]), + limit=10 + ) + query, result = translator.visit_structured_query(structured_query) + expected = { + "query": { + "bool": { + "must": [ + {"range": {"age": {"gt": 30}}}, + {"match": {"name": {"query": "John", "fuzziness": "AUTO"}}} + ] + } + }, + "size": 10 + } + assert query == "test query" + assert result == expected \ No newline at end of file