diff --git a/.env.template b/.env.template index 7fd3ba9e8..89ac06830 100644 --- a/.env.template +++ b/.env.template @@ -28,11 +28,10 @@ EMBEDDING_ENDPOINT="" EMBEDDING_API_VERSION="" EMBEDDING_DIMENSIONS=3072 EMBEDDING_MAX_TOKENS=8191 +EMBEDDING_BATCH_SIZE=36 # If embedding key is not provided same key set for LLM_API_KEY will be used #EMBEDDING_API_KEY="your_api_key" -# Note: OpenAI support up to 2048 elements and Gemini supports a maximum of 100 elements in an embedding batch, -# Cognee sets the optimal batch size for OpenAI and Gemini, but a custom size can be defined if necessary for other models -#EMBEDDING_BATCH_SIZE=2048 + # If using BAML structured output these env variables will be used BAML_LLM_PROVIDER=openai @@ -248,10 +247,10 @@ LITELLM_LOG="ERROR" #LLM_PROVIDER="ollama" #LLM_ENDPOINT="http://localhost:11434/v1" #EMBEDDING_PROVIDER="ollama" -#EMBEDDING_MODEL="avr/sfr-embedding-mistral:latest" +#EMBEDDING_MODEL="nomic-embed-text:latest" #EMBEDDING_ENDPOINT="http://localhost:11434/api/embeddings" -#EMBEDDING_DIMENSIONS=4096 -#HUGGINGFACE_TOKENIZER="Salesforce/SFR-Embedding-Mistral" +#EMBEDDING_DIMENSIONS=768 +#HUGGINGFACE_TOKENIZER="nomic-ai/nomic-embed-text-v1.5" ########## OpenRouter (also free) ######################################################### diff --git a/.github/actions/cognee_setup/action.yml b/.github/actions/cognee_setup/action.yml index 1326f2d81..4017d524b 100644 --- a/.github/actions/cognee_setup/action.yml +++ b/.github/actions/cognee_setup/action.yml @@ -41,4 +41,4 @@ runs: EXTRA_ARGS="$EXTRA_ARGS --extra $extra" done fi - uv sync --extra api --extra docs --extra evals --extra codegraph --extra ollama --extra dev --extra neo4j $EXTRA_ARGS + uv sync --extra api --extra docs --extra evals --extra codegraph --extra ollama --extra dev --extra neo4j --extra redis $EXTRA_ARGS diff --git a/.github/workflows/e2e_tests.yml b/.github/workflows/e2e_tests.yml index 3fe7a7992..9582a3f3b 100644 --- a/.github/workflows/e2e_tests.yml +++ b/.github/workflows/e2e_tests.yml @@ -1,4 +1,6 @@ name: Reusable Integration Tests +permissions: + contents: read on: workflow_call: @@ -264,3 +266,68 @@ jobs: EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} run: uv run python ./cognee/tests/test_edge_ingestion.py + + + + run_concurrent_subprocess_access_test: + name: Concurrent Subprocess access test + runs-on: ubuntu-latest + defaults: + run: + shell: bash + services: + postgres: + image: pgvector/pgvector:pg17 + env: + POSTGRES_USER: cognee + POSTGRES_PASSWORD: cognee + POSTGRES_DB: cognee_db + options: >- + --health-cmd pg_isready + --health-interval 10s + --health-timeout 5s + --health-retries 5 + ports: + - 5432:5432 + + redis: + image: redis:7 + ports: + - 6379:6379 + options: >- + --health-cmd "redis-cli ping" + --health-interval 5s + --health-timeout 3s + --health-retries 5 + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: '3.11.x' + extra-dependencies: "postgres redis" + + - name: Run Concurrent subprocess access test (Kuzu/Lancedb/Postgres) + env: + ENV: dev + LLM_MODEL: ${{ secrets.LLM_MODEL }} + LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} + LLM_API_KEY: ${{ secrets.LLM_API_KEY }} + LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} + EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} + EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} + EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} + EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} + GRAPH_DATABASE_PROVIDER: 'kuzu' + CACHING: true + SHARED_KUZU_LOCK: true + DB_PROVIDER: 'postgres' + DB_NAME: 'cognee_db' + DB_HOST: '127.0.0.1' + DB_PORT: 5432 + DB_USERNAME: cognee + DB_PASSWORD: cognee + run: uv run python ./cognee/tests/test_concurrent_subprocess_access.py \ No newline at end of file diff --git a/.github/workflows/examples_tests.yml b/.github/workflows/examples_tests.yml index 4eaaa0386..57bc88157 100644 --- a/.github/workflows/examples_tests.yml +++ b/.github/workflows/examples_tests.yml @@ -110,6 +110,81 @@ jobs: EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} run: uv run python ./examples/python/dynamic_steps_example.py + test-temporal-example: + name: Run Temporal Tests + runs-on: ubuntu-22.04 + steps: + - name: Check out repository + uses: actions/checkout@v4 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: '3.11.x' + + - name: Run Temporal Example + env: + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + LLM_MODEL: ${{ secrets.LLM_MODEL }} + LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} + LLM_API_KEY: ${{ secrets.LLM_API_KEY }} + LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} + EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} + EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} + EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} + EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} + run: uv run python ./examples/python/temporal_example.py + + test-ontology-example: + name: Run Ontology Tests + runs-on: ubuntu-22.04 + steps: + - name: Check out repository + uses: actions/checkout@v4 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: '3.11.x' + + - name: Run Ontology Demo Example + env: + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + LLM_MODEL: ${{ secrets.LLM_MODEL }} + LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} + LLM_API_KEY: ${{ secrets.LLM_API_KEY }} + LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} + EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} + EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} + EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} + EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} + run: uv run python ./examples/python/ontology_demo_example.py + + test-agentic-reasoning: + name: Run Agentic Reasoning Tests + runs-on: ubuntu-22.04 + steps: + - name: Check out repository + uses: actions/checkout@v4 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: '3.11.x' + + - name: Run Agentic Reasoning Example + env: + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + LLM_MODEL: ${{ secrets.LLM_MODEL }} + LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} + LLM_API_KEY: ${{ secrets.LLM_API_KEY }} + LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} + EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} + EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} + EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} + EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} + run: uv run python ./examples/python/agentic_reasoning_procurement_example.py + test-memify: name: Run Memify Example runs-on: ubuntu-22.04 diff --git a/.github/workflows/test_different_operating_systems.yml b/.github/workflows/test_different_operating_systems.yml index 6eb5744f3..64f1a14f9 100644 --- a/.github/workflows/test_different_operating_systems.yml +++ b/.github/workflows/test_different_operating_systems.yml @@ -9,7 +9,7 @@ on: python-versions: required: false type: string - default: '["3.10.x", "3.11.x", "3.12.x"]' + default: '["3.10.x", "3.12.x", "3.13.x"]' secrets: LLM_PROVIDER: required: true @@ -193,6 +193,13 @@ jobs: with: python-version: ${{ matrix.python-version }} + - name: Path setup + if: ${{ matrix.os }} == 'windows-latest' + shell: bash + run: | + PATH=$(printf '%s' "$PATH" | tr ':' $'\n' | grep -vi '/git/usr/bin' | paste -sd: -) + export PATH + - name: Run Soft Deletion Tests env: ENV: 'dev' diff --git a/.github/workflows/test_suites.yml b/.github/workflows/test_suites.yml index 2f1bdebf0..5c1597a93 100644 --- a/.github/workflows/test_suites.yml +++ b/.github/workflows/test_suites.yml @@ -85,7 +85,7 @@ jobs: needs: [basic-tests, e2e-tests] uses: ./.github/workflows/test_different_operating_systems.yml with: - python-versions: '["3.10.x", "3.11.x", "3.12.x"]' + python-versions: '["3.10.x", "3.11.x", "3.12.x", "3.13.x"]' secrets: inherit # Matrix-based vector database tests diff --git a/README.md b/README.md index a1eebae73..305bffdfe 100644 --- a/README.md +++ b/README.md @@ -71,7 +71,7 @@ Build dynamic memory for Agents and replace RAG using scalable, modular ECL (Ext ## Get Started -Get started quickly with a Google Colab notebook , Deepnote notebook or starter repo +Get started quickly with a Google Colab notebook , Deepnote notebook or starter repo ## About cognee @@ -224,12 +224,12 @@ We now have a paper you can cite: ```bibtex @misc{markovic2025optimizinginterfaceknowledgegraphs, - title={Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning}, + title={Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning}, author={Vasilije Markovic and Lazar Obradovic and Laszlo Hajdu and Jovan Pavlovic}, year={2025}, eprint={2505.24478}, archivePrefix={arXiv}, primaryClass={cs.AI}, - url={https://arxiv.org/abs/2505.24478}, + url={https://arxiv.org/abs/2505.24478}, } ``` diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index 65394f1ec..0f14683f9 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -41,6 +41,7 @@ async def add( extraction_rules: Optional[Dict[str, Any]] = None, tavily_config: Optional[BaseModel] = None, soup_crawler_config: Optional[BaseModel] = None, + data_per_batch: Optional[int] = 20, ): """ Add data to Cognee for knowledge graph processing. @@ -235,6 +236,7 @@ async def add( vector_db_config=vector_db_config, graph_db_config=graph_db_config, incremental_loading=incremental_loading, + data_per_batch=data_per_batch, ): pipeline_run_info = run_info diff --git a/cognee/api/v1/cognify/cognify.py b/cognee/api/v1/cognify/cognify.py index c3045f00a..0fa345176 100644 --- a/cognee/api/v1/cognify/cognify.py +++ b/cognee/api/v1/cognify/cognify.py @@ -44,6 +44,7 @@ async def cognify( graph_model: BaseModel = KnowledgeGraph, chunker=TextChunker, chunk_size: int = None, + chunks_per_batch: int = None, config: Config = None, vector_db_config: dict = None, graph_db_config: dict = None, @@ -51,6 +52,7 @@ async def cognify( incremental_loading: bool = True, custom_prompt: Optional[str] = None, temporal_cognify: bool = False, + data_per_batch: int = 20, ): """ Transform ingested data into a structured knowledge graph. @@ -105,6 +107,7 @@ async def cognify( Formula: min(embedding_max_completion_tokens, llm_max_completion_tokens // 2) Default limits: ~512-8192 tokens depending on models. Smaller chunks = more granular but potentially fragmented knowledge. + chunks_per_batch: Number of chunks to be processed in a single batch in Cognify tasks. vector_db_config: Custom vector database configuration for embeddings storage. graph_db_config: Custom graph database configuration for relationship storage. run_in_background: If True, starts processing asynchronously and returns immediately. @@ -209,10 +212,18 @@ async def cognify( } if temporal_cognify: - tasks = await get_temporal_tasks(user, chunker, chunk_size) + tasks = await get_temporal_tasks( + user=user, chunker=chunker, chunk_size=chunk_size, chunks_per_batch=chunks_per_batch + ) else: tasks = await get_default_tasks( - user, graph_model, chunker, chunk_size, config, custom_prompt + user=user, + graph_model=graph_model, + chunker=chunker, + chunk_size=chunk_size, + config=config, + custom_prompt=custom_prompt, + chunks_per_batch=chunks_per_batch, ) # By calling get pipeline executor we get a function that will have the run_pipeline run in the background or a function that we will need to wait for @@ -228,6 +239,7 @@ async def cognify( graph_db_config=graph_db_config, incremental_loading=incremental_loading, pipeline_name="cognify_pipeline", + data_per_batch=data_per_batch, ) @@ -238,6 +250,7 @@ async def get_default_tasks( # TODO: Find out a better way to do this (Boris's chunk_size: int = None, config: Config = None, custom_prompt: Optional[str] = None, + chunks_per_batch: int = 100, ) -> list[Task]: if config is None: ontology_config = get_ontology_env_config() @@ -256,6 +269,9 @@ async def get_default_tasks( # TODO: Find out a better way to do this (Boris's "ontology_config": {"ontology_resolver": get_default_ontology_resolver()} } + if chunks_per_batch is None: + chunks_per_batch = 100 + default_tasks = [ Task(classify_documents), Task(check_permissions_on_dataset, user=user, permissions=["write"]), @@ -269,20 +285,20 @@ async def get_default_tasks( # TODO: Find out a better way to do this (Boris's graph_model=graph_model, config=config, custom_prompt=custom_prompt, - task_config={"batch_size": 10}, + task_config={"batch_size": chunks_per_batch}, ), # Generate knowledge graphs from the document chunks. Task( summarize_text, - task_config={"batch_size": 10}, + task_config={"batch_size": chunks_per_batch}, ), - Task(add_data_points, task_config={"batch_size": 10}), + Task(add_data_points, task_config={"batch_size": chunks_per_batch}), ] return default_tasks async def get_temporal_tasks( - user: User = None, chunker=TextChunker, chunk_size: int = None + user: User = None, chunker=TextChunker, chunk_size: int = None, chunks_per_batch: int = 10 ) -> list[Task]: """ Builds and returns a list of temporal processing tasks to be executed in sequence. @@ -299,10 +315,14 @@ async def get_temporal_tasks( user (User, optional): The user requesting task execution, used for permission checks. chunker (Callable, optional): A text chunking function/class to split documents. Defaults to TextChunker. chunk_size (int, optional): Maximum token size per chunk. If not provided, uses system default. + chunks_per_batch (int, optional): Number of chunks to process in a single batch in Cognify Returns: list[Task]: A list of Task objects representing the temporal processing pipeline. """ + if chunks_per_batch is None: + chunks_per_batch = 10 + temporal_tasks = [ Task(classify_documents), Task(check_permissions_on_dataset, user=user, permissions=["write"]), @@ -311,9 +331,9 @@ async def get_temporal_tasks( max_chunk_size=chunk_size or get_max_chunk_tokens(), chunker=chunker, ), - Task(extract_events_and_timestamps, task_config={"chunk_size": 10}), + Task(extract_events_and_timestamps, task_config={"batch_size": chunks_per_batch}), Task(extract_knowledge_graph_from_events), - Task(add_data_points, task_config={"batch_size": 10}), + Task(add_data_points, task_config={"batch_size": chunks_per_batch}), ] return temporal_tasks diff --git a/cognee/infrastructure/databases/cache/__init__.py b/cognee/infrastructure/databases/cache/__init__.py new file mode 100644 index 000000000..d96c77658 --- /dev/null +++ b/cognee/infrastructure/databases/cache/__init__.py @@ -0,0 +1,2 @@ +from .get_cache_engine import get_cache_engine +from .config import get_cache_config diff --git a/cognee/infrastructure/databases/cache/cache_db_interface.py b/cognee/infrastructure/databases/cache/cache_db_interface.py new file mode 100644 index 000000000..0c0b578f8 --- /dev/null +++ b/cognee/infrastructure/databases/cache/cache_db_interface.py @@ -0,0 +1,42 @@ +from abc import ABC, abstractmethod +from contextlib import contextmanager + + +class CacheDBInterface(ABC): + """ + Abstract base class for distributed cache coordination systems (e.g., Redis, Memcached). + Provides a common interface for lock acquisition, release, and context-managed locking. + """ + + def __init__(self, host: str, port: int, lock_key: str): + self.host = host + self.port = port + self.lock_key = lock_key + self.lock = None + + @abstractmethod + def acquire_lock(self): + """ + Acquire a lock on the given key. + Must be implemented by subclasses. + """ + pass + + @abstractmethod + def release_lock(self): + """ + Release the lock if it is held. + Must be implemented by subclasses. + """ + pass + + @contextmanager + def hold_lock(self): + """ + Context manager for safely acquiring and releasing the lock. + """ + self.acquire() + try: + yield + finally: + self.release() diff --git a/cognee/infrastructure/databases/cache/config.py b/cognee/infrastructure/databases/cache/config.py new file mode 100644 index 000000000..b399e0259 --- /dev/null +++ b/cognee/infrastructure/databases/cache/config.py @@ -0,0 +1,39 @@ +from pydantic_settings import BaseSettings, SettingsConfigDict +from functools import lru_cache + + +class CacheConfig(BaseSettings): + """ + Configuration for distributed cache systems (e.g., Redis), used for locking or coordination. + + Attributes: + - shared_kuzu_lock: Shared kuzu lock logic on/off. + - cache_host: Hostname of the cache service. + - cache_port: Port number for the cache service. + - agentic_lock_expire: Automatic lock expiration time (in seconds). + - agentic_lock_timeout: Maximum time (in seconds) to wait for the lock release. + """ + + caching: bool = False + shared_kuzu_lock: bool = False + cache_host: str = "localhost" + cache_port: int = 6379 + agentic_lock_expire: int = 240 + agentic_lock_timeout: int = 300 + + model_config = SettingsConfigDict(env_file=".env", extra="allow") + + def to_dict(self) -> dict: + return { + "caching": self.caching, + "shared_kuzu_lock": self.shared_kuzu_lock, + "cache_host": self.cache_host, + "cache_port": self.cache_port, + "agentic_lock_expire": self.agentic_lock_expire, + "agentic_lock_timeout": self.agentic_lock_timeout, + } + + +@lru_cache +def get_cache_config(): + return CacheConfig() diff --git a/cognee/infrastructure/databases/cache/get_cache_engine.py b/cognee/infrastructure/databases/cache/get_cache_engine.py new file mode 100644 index 000000000..92186f877 --- /dev/null +++ b/cognee/infrastructure/databases/cache/get_cache_engine.py @@ -0,0 +1,59 @@ +"""Factory to get the appropriate cache coordination engine (e.g., Redis).""" + +from functools import lru_cache +from cognee.infrastructure.databases.cache.config import get_cache_config + +from cognee.infrastructure.databases.cache.cache_db_interface import CacheDBInterface + +config = get_cache_config() + + +@lru_cache +def create_cache_engine( + cache_host: str, + cache_port: int, + lock_key: str, + agentic_lock_expire: int = 240, + agentic_lock_timeout: int = 300, +): + """ + Factory function to instantiate a cache coordination backend (currently Redis). + + Parameters: + ----------- + - cache_host: Hostname or IP of the cache server. + - cache_port: Port number to connect to. + - lock_key: Identifier used for the locking resource. + - agentic_lock_expire: Duration to hold the lock after acquisition. + - agentic_lock_timeout: Max time to wait for the lock before failing. + + Returns: + -------- + - CacheDBInterface: An instance of the appropriate cache adapter. :TODO: Now we support only Redis. later if we add more here we can split the logic + """ + if config.caching: + from cognee.infrastructure.databases.cache.redis.RedisAdapter import RedisAdapter + + return RedisAdapter( + host=cache_host, + port=cache_port, + lock_name=lock_key, + timeout=agentic_lock_expire, + blocking_timeout=agentic_lock_timeout, + ) + else: + return None + + +def get_cache_engine(lock_key: str) -> CacheDBInterface: + """ + Returns a cache adapter instance using current context configuration. + """ + + return create_cache_engine( + cache_host=config.cache_host, + cache_port=config.cache_port, + lock_key=lock_key, + agentic_lock_expire=config.agentic_lock_expire, + agentic_lock_timeout=config.agentic_lock_timeout, + ) diff --git a/cognee/infrastructure/databases/cache/redis/RedisAdapter.py b/cognee/infrastructure/databases/cache/redis/RedisAdapter.py new file mode 100644 index 000000000..70c8de9bb --- /dev/null +++ b/cognee/infrastructure/databases/cache/redis/RedisAdapter.py @@ -0,0 +1,49 @@ +import redis +from contextlib import contextmanager +from cognee.infrastructure.databases.cache.cache_db_interface import CacheDBInterface + + +class RedisAdapter(CacheDBInterface): + def __init__(self, host, port, lock_name, timeout=240, blocking_timeout=300): + super().__init__(host, port, lock_name) + self.redis = redis.Redis(host=host, port=port) + self.timeout = timeout + self.blocking_timeout = blocking_timeout + + def acquire_lock(self): + """ + Acquire the Redis lock manually. Raises if acquisition fails. + """ + self.lock = self.redis.lock( + name=self.lock_key, + timeout=self.timeout, + blocking_timeout=self.blocking_timeout, + ) + + acquired = self.lock.acquire() + if not acquired: + raise RuntimeError(f"Could not acquire Redis lock: {self.lock_key}") + + return self.lock + + def release_lock(self): + """ + Release the Redis lock manually, if held. + """ + if self.lock: + try: + self.lock.release() + self.lock = None + except redis.exceptions.LockError: + pass + + @contextmanager + def hold_lock(self): + """ + Context manager for acquiring and releasing the Redis lock automatically. + """ + self.acquire() + try: + yield + finally: + self.release() diff --git a/cognee/infrastructure/databases/graph/get_graph_engine.py b/cognee/infrastructure/databases/graph/get_graph_engine.py index 1861aa15c..1ea61d29f 100644 --- a/cognee/infrastructure/databases/graph/get_graph_engine.py +++ b/cognee/infrastructure/databases/graph/get_graph_engine.py @@ -162,5 +162,5 @@ def create_graph_engine( raise EnvironmentError( f"Unsupported graph database provider: {graph_database_provider}. " - f"Supported providers are: {', '.join(list(supported_databases.keys()) + ['neo4j', 'kuzu', 'kuzu-remote', 'memgraph', 'neptune', 'neptune_analytics'])}" + f"Supported providers are: {', '.join(list(supported_databases.keys()) + ['neo4j', 'kuzu', 'kuzu-remote', 'neptune', 'neptune_analytics'])}" ) diff --git a/cognee/infrastructure/databases/graph/kuzu/adapter.py b/cognee/infrastructure/databases/graph/kuzu/adapter.py index 7b772097f..3f0fb0c57 100644 --- a/cognee/infrastructure/databases/graph/kuzu/adapter.py +++ b/cognee/infrastructure/databases/graph/kuzu/adapter.py @@ -4,7 +4,7 @@ import os import json import asyncio import tempfile -from uuid import UUID +from uuid import UUID, uuid5, NAMESPACE_OID from kuzu import Connection from kuzu.database import Database from datetime import datetime, timezone @@ -23,9 +23,14 @@ from cognee.infrastructure.engine import DataPoint from cognee.modules.storage.utils import JSONEncoder from cognee.modules.engine.utils.generate_timestamp_datapoint import date_to_int from cognee.tasks.temporal_graph.models import Timestamp +from cognee.infrastructure.databases.cache.config import get_cache_config logger = get_logger() +cache_config = get_cache_config() +if cache_config.shared_kuzu_lock: + from cognee.infrastructure.databases.cache.get_cache_engine import get_cache_engine + class KuzuAdapter(GraphDBInterface): """ @@ -39,12 +44,20 @@ class KuzuAdapter(GraphDBInterface): def __init__(self, db_path: str): """Initialize Kuzu database connection and schema.""" + self.open_connections = 0 + self._is_closed = False self.db_path = db_path # Path for the database directory self.db: Optional[Database] = None self.connection: Optional[Connection] = None - self.executor = ThreadPoolExecutor() - self._initialize_connection() + if cache_config.shared_kuzu_lock: + self.redis_lock = get_cache_engine( + lock_key="kuzu-lock-" + str(uuid5(NAMESPACE_OID, db_path)) + ) + else: + self.executor = ThreadPoolExecutor() + self._initialize_connection() self.KUZU_ASYNC_LOCK = asyncio.Lock() + self._connection_change_lock = asyncio.Lock() def _initialize_connection(self) -> None: """Initialize the Kuzu database connection and schema.""" @@ -209,9 +222,13 @@ class KuzuAdapter(GraphDBInterface): params = params or {} def blocking_query(): + lock_acquired = False try: + if cache_config.shared_kuzu_lock: + self.redis_lock.acquire_lock() + lock_acquired = True if not self.connection: - logger.debug("Reconnecting to Kuzu database...") + logger.info("Reconnecting to Kuzu database...") self._initialize_connection() result = self.connection.execute(query, params) @@ -225,12 +242,47 @@ class KuzuAdapter(GraphDBInterface): val = val.as_py() processed_rows.append(val) rows.append(tuple(processed_rows)) + return rows except Exception as e: logger.error(f"Query execution failed: {str(e)}") raise + finally: + if cache_config.shared_kuzu_lock and lock_acquired: + try: + self.close() + finally: + self.redis_lock.release_lock() - return await loop.run_in_executor(self.executor, blocking_query) + if cache_config.shared_kuzu_lock: + async with self._connection_change_lock: + self.open_connections += 1 + logger.info(f"Open connections after open: {self.open_connections}") + try: + result = blocking_query() + finally: + self.open_connections -= 1 + logger.info(f"Open connections after close: {self.open_connections}") + return result + else: + result = await loop.run_in_executor(self.executor, blocking_query) + return result + + def close(self): + if self.connection: + del self.connection + self.connection = None + if self.db: + del self.db + self.db = None + self._is_closed = True + logger.info("Kuzu database closed successfully") + + def reopen(self): + if self._is_closed: + self._is_closed = False + self._initialize_connection() + logger.info("Kuzu database re-opened successfully") @asynccontextmanager async def get_session(self): @@ -1557,44 +1609,6 @@ class KuzuAdapter(GraphDBInterface): logger.error(f"Failed to delete graph data: {e}") raise - async def clear_database(self) -> None: - """ - Clear all data from the database by deleting the database files and reinitializing. - - This method removes all files associated with the database and reinitializes the Kuzu - database structure, ensuring a completely empty state. It handles exceptions that might - occur during file deletions or initializations carefully. - """ - try: - if self.connection: - self.connection = None - if self.db: - self.db.close() - self.db = None - - db_dir = os.path.dirname(self.db_path) - db_name = os.path.basename(self.db_path) - file_storage = get_file_storage(db_dir) - - if await file_storage.file_exists(db_name): - await file_storage.remove_all() - logger.info(f"Deleted Kuzu database files at {self.db_path}") - - # Reinitialize the database - self._initialize_connection() - # Verify the database is empty - result = self.connection.execute("MATCH (n:Node) RETURN COUNT(n)") - count = result.get_next()[0] if result.has_next() else 0 - if count > 0: - logger.warning( - f"Database still contains {count} nodes after clearing, forcing deletion" - ) - self.connection.execute("MATCH (n:Node) DETACH DELETE n") - logger.info("Database cleared successfully") - except Exception as e: - logger.error(f"Error during database clearing: {e}") - raise - async def get_document_subgraph(self, data_id: str): """ Get all nodes that should be deleted when removing a document. diff --git a/cognee/infrastructure/databases/graph/memgraph/memgraph_adapter.py b/cognee/infrastructure/databases/graph/memgraph/memgraph_adapter.py deleted file mode 100644 index 3612e3277..000000000 --- a/cognee/infrastructure/databases/graph/memgraph/memgraph_adapter.py +++ /dev/null @@ -1,1116 +0,0 @@ -"""Memgraph Adapter for Graph Database""" - -import json -from cognee.shared.logging_utils import get_logger, ERROR -import asyncio -from textwrap import dedent -from typing import Optional, Any, List, Dict, Type, Tuple -from contextlib import asynccontextmanager -from uuid import UUID -from neo4j import AsyncSession -from neo4j import AsyncGraphDatabase -from neo4j.exceptions import Neo4jError -from cognee.infrastructure.engine import DataPoint -from cognee.infrastructure.databases.graph.graph_db_interface import GraphDBInterface -from cognee.modules.storage.utils import JSONEncoder -from cognee.infrastructure.databases.exceptions.exceptions import NodesetFilterNotSupportedError - -logger = get_logger("MemgraphAdapter", level=ERROR) - - -class MemgraphAdapter(GraphDBInterface): - """ - Handles interaction with a Memgraph database through various graph operations. - - Public methods include: - - get_session - - query - - has_node - - add_node - - add_nodes - - extract_node - - extract_nodes - - delete_node - - delete_nodes - - has_edge - - has_edges - - add_edge - - add_edges - - get_edges - - get_disconnected_nodes - - get_predecessors - - get_successors - - get_neighbours - - get_connections - - remove_connection_to_predecessors_of - - remove_connection_to_successors_of - - delete_graph - - serialize_properties - - get_model_independent_graph_data - - get_graph_data - - get_nodeset_subgraph - - get_filtered_graph_data - - get_node_labels_string - - get_relationship_labels_string - - get_graph_metrics - """ - - def __init__( - self, - graph_database_url: str, - graph_database_username: Optional[str] = None, - graph_database_password: Optional[str] = None, - driver: Optional[Any] = None, - ): - # Only use auth if both username and password are provided - auth = None - if graph_database_username and graph_database_password: - auth = (graph_database_username, graph_database_password) - - self.driver = driver or AsyncGraphDatabase.driver( - graph_database_url, - auth=auth, - max_connection_lifetime=120, - ) - - @asynccontextmanager - async def get_session(self) -> AsyncSession: - """ - Manage a session with the database, yielding the session for use in operations. - """ - async with self.driver.session() as session: - yield session - - async def query( - self, - query: str, - params: Optional[Dict[str, Any]] = None, - ) -> List[Dict[str, Any]]: - """ - Execute a provided query on the Memgraph database and return the results. - - Parameters: - ----------- - - - query (str): The Cypher query to be executed against the database. - - params (Optional[Dict[str, Any]]): Optional parameters to be used in the query. - (default None) - - Returns: - -------- - - - List[Dict[str, Any]]: A list of dictionaries representing the result set of the - query. - """ - try: - async with self.get_session() as session: - result = await session.run(query, params) - data = await result.data() - return data - except Neo4jError as error: - logger.error("Memgraph query error: %s", error, exc_info=True) - raise error - - async def has_node(self, node_id: str) -> bool: - """ - Determine if a node with the given ID exists in the database. - - Parameters: - ----------- - - - node_id (str): The ID of the node to check for existence. - - Returns: - -------- - - - bool: True if the node exists; otherwise, False. - """ - results = await self.query( - """ - MATCH (n) - WHERE n.id = $node_id - RETURN COUNT(n) > 0 AS node_exists - """, - {"node_id": node_id}, - ) - return results[0]["node_exists"] if len(results) > 0 else False - - async def add_node(self, node: DataPoint): - """ - Add a new node to the database with specified properties. - - Parameters: - ----------- - - - node (DataPoint): The DataPoint object representing the node to add. - - Returns: - -------- - - The result of the node addition, including its internal ID and node ID. - """ - serialized_properties = self.serialize_properties(node.model_dump()) - - query = """ - MERGE (node {id: $node_id}) - ON CREATE SET node:$node_label, node += $properties, node.updated_at = timestamp() - ON MATCH SET node:$node_label, node += $properties, node.updated_at = timestamp() - RETURN ID(node) AS internal_id, node.id AS nodeId - """ - - params = { - "node_id": str(node.id), - "node_label": type(node).__name__, - "properties": serialized_properties, - } - return await self.query(query, params) - - async def add_nodes(self, nodes: list[DataPoint]) -> None: - """ - Add multiple nodes to the database in a single operation. - - Parameters: - ----------- - - - nodes (list[DataPoint]): A list of DataPoint objects representing the nodes to - add. - - Returns: - -------- - - - None: None. - """ - query = """ - UNWIND $nodes AS node - MERGE (n {id: node.node_id}) - ON CREATE SET n:node.label, n += node.properties, n.updated_at = timestamp() - ON MATCH SET n:node.label, n += node.properties, n.updated_at = timestamp() - RETURN ID(n) AS internal_id, n.id AS nodeId - """ - - nodes = [ - { - "node_id": str(node.id), - "label": type(node).__name__, - "properties": self.serialize_properties(node.model_dump()), - } - for node in nodes - ] - - results = await self.query(query, dict(nodes=nodes)) - return results - - async def extract_node(self, node_id: str): - """ - Retrieve a single node based on its ID. - - Parameters: - ----------- - - - node_id (str): The ID of the node to retrieve. - - Returns: - -------- - - The node corresponding to the provided ID, or None if not found. - """ - results = await self.extract_nodes([node_id]) - - return results[0] if len(results) > 0 else None - - async def extract_nodes(self, node_ids: List[str]): - """ - Retrieve multiple nodes based on their IDs. - - Parameters: - ----------- - - - node_ids (List[str]): A list of IDs for the nodes to retrieve. - - Returns: - -------- - - A list of nodes corresponding to the provided IDs. - """ - query = """ - UNWIND $node_ids AS id - MATCH (node {id: id}) - RETURN node""" - - params = {"node_ids": node_ids} - - results = await self.query(query, params) - - return [result["node"] for result in results] - - async def delete_node(self, node_id: str): - """ - Delete a node from the database based on its ID. - - Parameters: - ----------- - - - node_id (str): The ID of the node to delete. - - Returns: - -------- - - None. - """ - sanitized_id = node_id.replace(":", "_") - - query = "MATCH (node: {{id: $node_id}}) DETACH DELETE node" - params = {"node_id": sanitized_id} - - return await self.query(query, params) - - async def delete_nodes(self, node_ids: list[str]) -> None: - """ - Delete multiple nodes from the database based on their IDs. - - Parameters: - ----------- - - - node_ids (list[str]): A list of IDs for the nodes to delete. - - Returns: - -------- - - - None: None. - """ - query = """ - UNWIND $node_ids AS id - MATCH (node {id: id}) - DETACH DELETE node""" - - params = {"node_ids": node_ids} - - return await self.query(query, params) - - async def has_edge(self, from_node: UUID, to_node: UUID, edge_label: str) -> bool: - """ - Check if a directed edge exists between two nodes identified by their IDs. - - Parameters: - ----------- - - - from_node (UUID): The ID of the source node. - - to_node (UUID): The ID of the target node. - - edge_label (str): The label of the edge to check. - - Returns: - -------- - - - bool: True if the edge exists; otherwise, False. - """ - query = """ - MATCH (from_node)-[relationship]->(to_node) - WHERE from_node.id = $from_node_id AND to_node.id = $to_node_id AND type(relationship) = $edge_label - RETURN COUNT(relationship) > 0 AS edge_exists - """ - - params = { - "from_node_id": str(from_node), - "to_node_id": str(to_node), - "edge_label": edge_label, - } - - records = await self.query(query, params) - return records[0]["edge_exists"] if records else False - - async def has_edges(self, edges): - """ - Check for the existence of multiple edges based on provided criteria. - - Parameters: - ----------- - - - edges: A list of edges to verify existence for. - - Returns: - -------- - - A list of boolean values indicating the existence of each edge. - """ - query = """ - UNWIND $edges AS edge - MATCH (a)-[r]->(b) - WHERE id(a) = edge.from_node AND id(b) = edge.to_node AND type(r) = edge.relationship_name - RETURN edge.from_node AS from_node, edge.to_node AS to_node, edge.relationship_name AS relationship_name, count(r) > 0 AS edge_exists - """ - - try: - params = { - "edges": [ - { - "from_node": str(edge[0]), - "to_node": str(edge[1]), - "relationship_name": edge[2], - } - for edge in edges - ], - } - - results = await self.query(query, params) - return [result["edge_exists"] for result in results] - except Neo4jError as error: - logger.error("Memgraph query error: %s", error, exc_info=True) - raise error - - async def add_edge( - self, - from_node: UUID, - to_node: UUID, - relationship_name: str, - edge_properties: Optional[Dict[str, Any]] = None, - ): - """ - Add a directed edge between two nodes with optional properties. - - Parameters: - ----------- - - - from_node (UUID): The ID of the source node. - - to_node (UUID): The ID of the target node. - - relationship_name (str): The type/label of the relationship to create. - - edge_properties (Optional[Dict[str, Any]]): Optional properties associated with - the edge. (default None) - - Returns: - -------- - - The result of the edge addition operation, including relationship details. - """ - - exists = await asyncio.gather(self.has_node(str(from_node)), self.has_node(str(to_node))) - - if not all(exists): - return None - - serialized_properties = self.serialize_properties(edge_properties or {}) - - query = dedent( - f"""\ - MATCH (from_node {{id: $from_node}}), - (to_node {{id: $to_node}}) - WHERE from_node IS NOT NULL AND to_node IS NOT NULL - MERGE (from_node)-[r:{relationship_name}]->(to_node) - ON CREATE SET r += $properties, r.updated_at = timestamp() - ON MATCH SET r += $properties, r.updated_at = timestamp() - RETURN r - """ - ) - - params = { - "from_node": str(from_node), - "to_node": str(to_node), - "relationship_name": relationship_name, - "properties": serialized_properties, - } - - return await self.query(query, params) - - async def add_edges(self, edges: list[tuple[str, str, str, dict[str, Any]]]) -> None: - """ - Batch add multiple edges between nodes, enforcing specified relationships. - - Parameters: - ----------- - - - edges (list[tuple[str, str, str, dict[str, Any]]): A list of tuples containing - specifications for each edge to add. - - Returns: - -------- - - - None: None. - """ - query = """ - UNWIND $edges AS edge - MATCH (from_node {id: edge.from_node}) - MATCH (to_node {id: edge.to_node}) - CALL merge.relationship( - from_node, - edge.relationship_name, - { - source_node_id: edge.from_node, - target_node_id: edge.to_node - }, - edge.properties, - to_node, - {} - ) YIELD rel - RETURN rel""" - - edges = [ - { - "from_node": str(edge[0]), - "to_node": str(edge[1]), - "relationship_name": edge[2], - "properties": { - **(edge[3] if edge[3] else {}), - "source_node_id": str(edge[0]), - "target_node_id": str(edge[1]), - }, - } - for edge in edges - ] - - try: - results = await self.query(query, dict(edges=edges)) - return results - except Neo4jError as error: - logger.error("Memgraph query error: %s", error, exc_info=True) - raise error - - async def get_edges(self, node_id: str): - """ - Retrieve all edges connected to a specific node identified by its ID. - - Parameters: - ----------- - - - node_id (str): The ID of the node for which to retrieve connected edges. - - Returns: - -------- - - A list of tuples representing the edges connected to the node. - """ - query = """ - MATCH (n {id: $node_id})-[r]-(m) - RETURN n, r, m - """ - - results = await self.query(query, dict(node_id=node_id)) - - return [ - (result["n"]["id"], result["m"]["id"], {"relationship_name": result["r"][1]}) - for result in results - ] - - async def get_disconnected_nodes(self) -> list[str]: - """ - Identify nodes in the graph that do not belong to the largest connected component. - - Returns: - -------- - - - list[str]: A list of IDs representing the disconnected nodes. - """ - query = """ - // Step 1: Collect all nodes - MATCH (n) - WITH COLLECT(n) AS nodes - - // Step 2: Find all connected components - WITH nodes - CALL { - WITH nodes - UNWIND nodes AS startNode - MATCH path = (startNode)-[*]-(connectedNode) - WITH startNode, COLLECT(DISTINCT connectedNode) AS component - RETURN component - } - - // Step 3: Aggregate components - WITH COLLECT(component) AS components - - // Step 4: Identify the largest connected component - UNWIND components AS component - WITH component - ORDER BY SIZE(component) DESC - LIMIT 1 - WITH component AS largestComponent - - // Step 5: Find nodes not in the largest connected component - MATCH (n) - WHERE NOT n IN largestComponent - RETURN COLLECT(ID(n)) AS ids - """ - - results = await self.query(query) - return results[0]["ids"] if len(results) > 0 else [] - - async def get_predecessors(self, node_id: str, edge_label: str = None) -> list[str]: - """ - Retrieve all predecessors of a node based on its ID and optional edge label. - - Parameters: - ----------- - - - node_id (str): The ID of the node to find predecessors for. - - edge_label (str): Optional edge label to filter predecessors. (default None) - - Returns: - -------- - - - list[str]: A list of predecessor node IDs. - """ - if edge_label is not None: - query = """ - MATCH (node)<-[r]-(predecessor) - WHERE node.id = $node_id AND type(r) = $edge_label - RETURN predecessor - """ - - results = await self.query( - query, - dict( - node_id=node_id, - edge_label=edge_label, - ), - ) - - return [result["predecessor"] for result in results] - else: - query = """ - MATCH (node)<-[r]-(predecessor) - WHERE node.id = $node_id - RETURN predecessor - """ - - results = await self.query( - query, - dict( - node_id=node_id, - ), - ) - - return [result["predecessor"] for result in results] - - async def get_successors(self, node_id: str, edge_label: str = None) -> list[str]: - """ - Retrieve all successors of a node based on its ID and optional edge label. - - Parameters: - ----------- - - - node_id (str): The ID of the node to find successors for. - - edge_label (str): Optional edge label to filter successors. (default None) - - Returns: - -------- - - - list[str]: A list of successor node IDs. - """ - if edge_label is not None: - query = """ - MATCH (node)-[r]->(successor) - WHERE node.id = $node_id AND type(r) = $edge_label - RETURN successor - """ - - results = await self.query( - query, - dict( - node_id=node_id, - edge_label=edge_label, - ), - ) - - return [result["successor"] for result in results] - else: - query = """ - MATCH (node)-[r]->(successor) - WHERE node.id = $node_id - RETURN successor - """ - - results = await self.query( - query, - dict( - node_id=node_id, - ), - ) - - return [result["successor"] for result in results] - - async def get_neighbors(self, node_id: str) -> List[Dict[str, Any]]: - """ - Get both predecessors and successors of a node. - - Parameters: - ----------- - - - node_id (str): The ID of the node to find neighbors for. - - Returns: - -------- - - - List[Dict[str, Any]]: A combined list of neighbor node IDs. - """ - predecessors, successors = await asyncio.gather( - self.get_predecessors(node_id), self.get_successors(node_id) - ) - - return predecessors + successors - - async def get_node(self, node_id: str) -> Optional[Dict[str, Any]]: - """Get a single node by ID.""" - query = """ - MATCH (node {id: $node_id}) - RETURN node - """ - results = await self.query(query, {"node_id": node_id}) - return results[0]["node"] if results else None - - async def get_nodes(self, node_ids: List[str]) -> List[Dict[str, Any]]: - """Get multiple nodes by their IDs.""" - query = """ - UNWIND $node_ids AS id - MATCH (node {id: id}) - RETURN node - """ - results = await self.query(query, {"node_ids": node_ids}) - return [result["node"] for result in results] - - async def get_connections(self, node_id: UUID) -> list: - """ - Retrieve connections for a given node, including both predecessors and successors. - - Parameters: - ----------- - - - node_id (UUID): The ID of the node for which to retrieve connections. - - Returns: - -------- - - - list: A list of connections associated with the node. - """ - predecessors_query = """ - MATCH (node)<-[relation]-(neighbour) - WHERE node.id = $node_id - RETURN neighbour, relation, node - """ - successors_query = """ - MATCH (node)-[relation]->(neighbour) - WHERE node.id = $node_id - RETURN node, relation, neighbour - """ - - predecessors, successors = await asyncio.gather( - self.query(predecessors_query, dict(node_id=str(node_id))), - self.query(successors_query, dict(node_id=str(node_id))), - ) - - connections = [] - - for neighbour in predecessors: - neighbour = neighbour["relation"] - connections.append((neighbour[0], {"relationship_name": neighbour[1]}, neighbour[2])) - - for neighbour in successors: - neighbour = neighbour["relation"] - connections.append((neighbour[0], {"relationship_name": neighbour[1]}, neighbour[2])) - - return connections - - async def remove_connection_to_predecessors_of( - self, node_ids: list[str], edge_label: str - ) -> None: - """ - Remove specified connections to the predecessors of the given node IDs. - - Parameters: - ----------- - - - node_ids (list[str]): A list of node IDs from which to remove predecessor - connections. - - edge_label (str): The label of the edges to remove. - - Returns: - -------- - - - None: None. - """ - query = f""" - UNWIND $node_ids AS nid - MATCH (node {id: nid})-[r]->(predecessor) - WHERE type(r) = $edge_label - DELETE r; - """ - - params = {"node_ids": node_ids, "edge_label": edge_label} - - return await self.query(query, params) - - async def remove_connection_to_successors_of( - self, node_ids: list[str], edge_label: str - ) -> None: - """ - Remove specified connections to the successors of the given node IDs. - - Parameters: - ----------- - - - node_ids (list[str]): A list of node IDs from which to remove successor - connections. - - edge_label (str): The label of the edges to remove. - - Returns: - -------- - - - None: None. - """ - query = f""" - UNWIND $node_ids AS id - MATCH (node:`{id}`)<-[r:{edge_label}]-(successor) - DELETE r; - """ - - params = {"node_ids": node_ids} - - return await self.query(query, params) - - async def delete_graph(self): - """ - Completely delete the graph from the database, removing all nodes and edges. - - Returns: - -------- - - None. - """ - query = """MATCH (node) - DETACH DELETE node;""" - - return await self.query(query) - - def serialize_properties(self, properties=dict()): - """ - Convert property values to a suitable representation for storage. - - Parameters: - ----------- - - - properties: A dictionary of properties to serialize. (default dict()) - - Returns: - -------- - - A dictionary of serialized properties. - """ - serialized_properties = {} - - for property_key, property_value in properties.items(): - if isinstance(property_value, UUID): - serialized_properties[property_key] = str(property_value) - continue - - if isinstance(property_value, dict): - serialized_properties[property_key] = json.dumps(property_value, cls=JSONEncoder) - continue - - serialized_properties[property_key] = property_value - - return serialized_properties - - async def get_model_independent_graph_data(self): - """ - Fetch nodes and relationships without any specific model filtering. - - Returns: - -------- - - A tuple containing nodes and edges as collections. - """ - query_nodes = "MATCH (n) RETURN collect(n) AS nodes" - nodes = await self.query(query_nodes) - - query_edges = "MATCH (n)-[r]->(m) RETURN collect([n, r, m]) AS elements" - edges = await self.query(query_edges) - - return (nodes, edges) - - async def get_graph_data(self): - """ - Retrieve all nodes and edges from the graph, including their properties. - - Returns: - -------- - - A tuple containing lists of nodes and edges. - """ - query = "MATCH (n) RETURN ID(n) AS id, labels(n) AS labels, properties(n) AS properties" - - result = await self.query(query) - - nodes = [ - ( - record["id"], - record["properties"], - ) - for record in result - ] - - query = """ - MATCH (n)-[r]->(m) - RETURN ID(n) AS source, ID(m) AS target, TYPE(r) AS type, properties(r) AS properties - """ - result = await self.query(query) - edges = [ - ( - record["source"], - record["target"], - record["type"], - record["properties"], - ) - for record in result - ] - - return (nodes, edges) - - async def get_nodeset_subgraph( - self, node_type: Type[Any], node_name: List[str] - ) -> Tuple[List[Tuple[int, dict]], List[Tuple[int, int, str, dict]]]: - """ - Throw an error indicating that node set filtering is not supported. - - Parameters: - ----------- - - - node_type (Type[Any]): The type of nodes to filter. - - node_name (List[str]): A list of node names to filter. - """ - raise NodesetFilterNotSupportedError - - async def get_filtered_graph_data(self, attribute_filters): - """ - Fetch nodes and relationships based on specified attribute filters. - - Parameters: - ----------- - - - attribute_filters: A list of criteria to filter nodes and relationships. - - Returns: - -------- - - A tuple containing filtered nodes and edges. - """ - where_clauses = [] - for attribute, values in attribute_filters[0].items(): - values_str = ", ".join( - f"'{value}'" if isinstance(value, str) else str(value) for value in values - ) - where_clauses.append(f"n.{attribute} IN [{values_str}]") - - where_clause = " AND ".join(where_clauses) - - query_nodes = f""" - MATCH (n) - WHERE {where_clause} - RETURN ID(n) AS id, labels(n) AS labels, properties(n) AS properties - """ - result_nodes = await self.query(query_nodes) - - nodes = [ - ( - record["id"], - record["properties"], - ) - for record in result_nodes - ] - - query_edges = f""" - MATCH (n)-[r]->(m) - WHERE {where_clause} AND {where_clause.replace("n.", "m.")} - RETURN ID(n) AS source, ID(m) AS target, TYPE(r) AS type, properties(r) AS properties - """ - result_edges = await self.query(query_edges) - - edges = [ - ( - record["source"], - record["target"], - record["type"], - record["properties"], - ) - for record in result_edges - ] - - return (nodes, edges) - - async def get_node_labels_string(self): - """ - Retrieve a string representation of all unique node labels in the graph. - - Returns: - -------- - - A string containing unique node labels. - """ - node_labels_query = """ - MATCH (n) - WITH DISTINCT labels(n) AS labelList - UNWIND labelList AS label - RETURN collect(DISTINCT label) AS labels; - """ - node_labels_result = await self.query(node_labels_query) - node_labels = node_labels_result[0]["labels"] if node_labels_result else [] - - if not node_labels: - raise ValueError("No node labels found in the database") - - node_labels_str = "[" + ", ".join(f"'{label}'" for label in node_labels) + "]" - return node_labels_str - - async def get_relationship_labels_string(self): - """ - Retrieve a string representation of all unique relationship types in the graph. - - Returns: - -------- - - A string containing unique relationship types. - """ - relationship_types_query = ( - "MATCH ()-[r]->() RETURN collect(DISTINCT type(r)) AS relationships;" - ) - relationship_types_result = await self.query(relationship_types_query) - relationship_types = ( - relationship_types_result[0]["relationships"] if relationship_types_result else [] - ) - - if not relationship_types: - raise ValueError("No relationship types found in the database.") - - relationship_types_undirected_str = ( - "{" - + ", ".join(f"{rel}" + ": {orientation: 'UNDIRECTED'}" for rel in relationship_types) - + "}" - ) - return relationship_types_undirected_str - - async def get_graph_metrics(self, include_optional=False): - """ - Calculate and return various metrics of the graph, including mandatory and optional - metrics. - - Parameters: - ----------- - - - include_optional: Specify whether to include optional metrics in the results. - (default False) - - Returns: - -------- - - A dictionary containing calculated graph metrics. - """ - - try: - # Basic metrics - node_count = await self.query("MATCH (n) RETURN count(n)") - edge_count = await self.query("MATCH ()-[r]->() RETURN count(r)") - num_nodes = node_count[0][0] if node_count else 0 - num_edges = edge_count[0][0] if edge_count else 0 - - # Calculate mandatory metrics - mandatory_metrics = { - "num_nodes": num_nodes, - "num_edges": num_edges, - "mean_degree": (2 * num_edges) / num_nodes if num_nodes > 0 else 0, - "edge_density": (num_edges) / (num_nodes * (num_nodes - 1)) if num_nodes > 1 else 0, - } - - # Calculate connected components - components_query = """ - MATCH (n:Node) - WITH n.id AS node_id - MATCH path = (n)-[:EDGE*0..]-() - WITH COLLECT(DISTINCT node_id) AS component - RETURN COLLECT(component) AS components - """ - components_result = await self.query(components_query) - component_sizes = ( - [len(comp) for comp in components_result[0][0]] if components_result else [] - ) - - mandatory_metrics.update( - { - "num_connected_components": len(component_sizes), - "sizes_of_connected_components": component_sizes, - } - ) - - if include_optional: - # Self-loops - self_loops_query = """ - MATCH (n:Node)-[r:EDGE]->(n) - RETURN COUNT(r) - """ - self_loops = await self.query(self_loops_query) - num_selfloops = self_loops[0][0] if self_loops else 0 - - # Shortest paths (simplified for Kuzu) - paths_query = """ - MATCH (n:Node), (m:Node) - WHERE n.id < m.id - MATCH path = (n)-[:EDGE*]-(m) - RETURN MIN(LENGTH(path)) AS length - """ - paths = await self.query(paths_query) - path_lengths = [p[0] for p in paths if p[0] is not None] - - # Local clustering coefficient - clustering_query = """ - /// Step 1: Get each node with its neighbors and degree - MATCH (n:Node)-[:EDGE]-(neighbor) - WITH n, COLLECT(DISTINCT neighbor) AS neighbors, COUNT(DISTINCT neighbor) AS degree - - // Step 2: Pair up neighbors and check if they are connected - UNWIND neighbors AS n1 - UNWIND neighbors AS n2 - WITH n, degree, n1, n2 - WHERE id(n1) < id(n2) // avoid duplicate pairs - - // Step 3: Use OPTIONAL MATCH to see if n1 and n2 are connected - OPTIONAL MATCH (n1)-[:EDGE]-(n2) - WITH n, degree, COUNT(n2) AS triangle_count - - // Step 4: Compute local clustering coefficient - WITH n, degree, - CASE WHEN degree <= 1 THEN 0.0 - ELSE (1.0 * triangle_count) / (degree * (degree - 1) / 2.0) - END AS local_cc - - // Step 5: Compute average - RETURN AVG(local_cc) AS avg_clustering_coefficient - """ - clustering = await self.query(clustering_query) - - optional_metrics = { - "num_selfloops": num_selfloops, - "diameter": max(path_lengths) if path_lengths else -1, - "avg_shortest_path_length": sum(path_lengths) / len(path_lengths) - if path_lengths - else -1, - "avg_clustering": clustering[0][0] if clustering and clustering[0][0] else -1, - } - else: - optional_metrics = { - "num_selfloops": -1, - "diameter": -1, - "avg_shortest_path_length": -1, - "avg_clustering": -1, - } - - return {**mandatory_metrics, **optional_metrics} - - except Exception as e: - logger.error(f"Failed to get graph metrics: {e}") - return { - "num_nodes": 0, - "num_edges": 0, - "mean_degree": 0, - "edge_density": 0, - "num_connected_components": 0, - "sizes_of_connected_components": [], - "num_selfloops": -1, - "diameter": -1, - "avg_shortest_path_length": -1, - "avg_clustering": -1, - } diff --git a/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py b/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py index 520295ed2..365d02979 100644 --- a/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py +++ b/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py @@ -1067,7 +1067,7 @@ class Neo4jAdapter(GraphDBInterface): query_nodes = f""" MATCH (n) WHERE {where_clause} - RETURN ID(n) AS id, labels(n) AS labels, properties(n) AS properties + RETURN n.id AS id, labels(n) AS labels, properties(n) AS properties """ result_nodes = await self.query(query_nodes) @@ -1082,7 +1082,7 @@ class Neo4jAdapter(GraphDBInterface): query_edges = f""" MATCH (n)-[r]->(m) WHERE {where_clause} AND {where_clause.replace("n.", "m.")} - RETURN ID(n) AS source, ID(m) AS target, TYPE(r) AS type, properties(r) AS properties + RETURN n.id AS source, n.id AS target, TYPE(r) AS type, properties(r) AS properties """ result_edges = await self.query(query_edges) diff --git a/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py index e34ab5d9d..c2acd516e 100644 --- a/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py @@ -1,8 +1,17 @@ -from cognee.shared.logging_utils import get_logger +import os +import logging from typing import List, Optional from fastembed import TextEmbedding import litellm -import os +from tenacity import ( + retry, + stop_after_delay, + wait_exponential_jitter, + retry_if_not_exception_type, + before_sleep_log, +) + +from cognee.shared.logging_utils import get_logger from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine from cognee.infrastructure.databases.exceptions import EmbeddingException from cognee.infrastructure.llm.tokenizer.TikToken import ( @@ -57,6 +66,13 @@ class FastembedEmbeddingEngine(EmbeddingEngine): enable_mocking = str(enable_mocking).lower() self.mock = enable_mocking in ("true", "1", "yes") + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def embed_text(self, text: List[str]) -> List[List[float]]: """ Embed the given text into numerical vectors. diff --git a/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py index ccef7bfcd..03ce86bee 100644 --- a/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py @@ -1,15 +1,21 @@ import asyncio +import logging + from cognee.shared.logging_utils import get_logger from typing import List, Optional import numpy as np import math +from tenacity import ( + retry, + stop_after_delay, + wait_exponential_jitter, + retry_if_not_exception_type, + before_sleep_log, +) import litellm import os from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine from cognee.infrastructure.databases.exceptions import EmbeddingException -from cognee.infrastructure.llm.tokenizer.Gemini import ( - GeminiTokenizer, -) from cognee.infrastructure.llm.tokenizer.HuggingFace import ( HuggingFaceTokenizer, ) @@ -19,10 +25,6 @@ from cognee.infrastructure.llm.tokenizer.Mistral import ( from cognee.infrastructure.llm.tokenizer.TikToken import ( TikTokenTokenizer, ) -from cognee.infrastructure.databases.vector.embeddings.embedding_rate_limiter import ( - embedding_rate_limit_async, - embedding_sleep_and_retry_async, -) litellm.set_verbose = False logger = get_logger("LiteLLMEmbeddingEngine") @@ -76,8 +78,13 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): enable_mocking = str(enable_mocking).lower() self.mock = enable_mocking in ("true", "1", "yes") - @embedding_sleep_and_retry_async() - @embedding_rate_limit_async + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def embed_text(self, text: List[str]) -> List[List[float]]: """ Embed a list of text strings into vector representations. diff --git a/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py index e79ba3f6a..2882b679a 100644 --- a/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py @@ -3,8 +3,16 @@ from cognee.shared.logging_utils import get_logger import aiohttp from typing import List, Optional import os - +import litellm +import logging import aiohttp.http_exceptions +from tenacity import ( + retry, + stop_after_delay, + wait_exponential_jitter, + retry_if_not_exception_type, + before_sleep_log, +) from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine from cognee.infrastructure.llm.tokenizer.HuggingFace import ( @@ -69,7 +77,6 @@ class OllamaEmbeddingEngine(EmbeddingEngine): enable_mocking = str(enable_mocking).lower() self.mock = enable_mocking in ("true", "1", "yes") - @embedding_rate_limit_async async def embed_text(self, text: List[str]) -> List[List[float]]: """ Generate embedding vectors for a list of text prompts. @@ -92,7 +99,13 @@ class OllamaEmbeddingEngine(EmbeddingEngine): embeddings = await asyncio.gather(*[self._get_embedding(prompt) for prompt in text]) return embeddings - @embedding_sleep_and_retry_async() + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def _get_embedding(self, prompt: str) -> List[float]: """ Internal method to call the Ollama embeddings endpoint for a single prompt. diff --git a/cognee/infrastructure/databases/vector/embeddings/config.py b/cognee/infrastructure/databases/vector/embeddings/config.py index 24f724151..56cd79678 100644 --- a/cognee/infrastructure/databases/vector/embeddings/config.py +++ b/cognee/infrastructure/databases/vector/embeddings/config.py @@ -24,11 +24,10 @@ class EmbeddingConfig(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="allow") def model_post_init(self, __context) -> None: - # If embedding batch size is not defined use 2048 as default for OpenAI and 100 for all other embedding models if not self.embedding_batch_size and self.embedding_provider.lower() == "openai": - self.embedding_batch_size = 2048 + self.embedding_batch_size = 36 elif not self.embedding_batch_size: - self.embedding_batch_size = 100 + self.embedding_batch_size = 36 def to_dict(self) -> dict: """ diff --git a/cognee/infrastructure/files/utils/guess_file_type.py b/cognee/infrastructure/files/utils/guess_file_type.py index edd2d89b0..dcdd68cad 100644 --- a/cognee/infrastructure/files/utils/guess_file_type.py +++ b/cognee/infrastructure/files/utils/guess_file_type.py @@ -124,6 +124,12 @@ def guess_file_type(file: BinaryIO) -> filetype.Type: """ file_type = filetype.guess(file) + # If file type could not be determined consider it a plain text file as they don't have magic number encoding + if file_type is None: + from filetype.types.base import Type + + file_type = Type("text/plain", "txt") + if file_type is None: raise FileTypeException(f"Unknown file detected: {file.name}.") diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/anthropic/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/anthropic/adapter.py index 2d88a8271..bf19d6e86 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/anthropic/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/anthropic/adapter.py @@ -1,19 +1,24 @@ +import logging from typing import Type from pydantic import BaseModel +import litellm import instructor +from cognee.shared.logging_utils import get_logger +from tenacity import ( + retry, + stop_after_delay, + wait_exponential_jitter, + retry_if_not_exception_type, + before_sleep_log, +) -from cognee.infrastructure.llm.exceptions import MissingSystemPromptPathError from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( LLMInterface, ) -from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( - rate_limit_async, - sleep_and_retry_async, -) - -from cognee.infrastructure.llm.LLMGateway import LLMGateway from cognee.infrastructure.llm.config import get_llm_config +logger = get_logger() + class AnthropicAdapter(LLMInterface): """ @@ -35,8 +40,13 @@ class AnthropicAdapter(LLMInterface): self.model = model self.max_completion_tokens = max_completion_tokens - @sleep_and_retry_async() - @rate_limit_async + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py index 510d29ce8..1187e0cad 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py @@ -12,11 +12,18 @@ from cognee.infrastructure.llm.exceptions import ContentPolicyFilterError from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( LLMInterface, ) -from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( - rate_limit_async, - sleep_and_retry_async, +import logging +from cognee.shared.logging_utils import get_logger +from tenacity import ( + retry, + stop_after_delay, + wait_exponential_jitter, + retry_if_not_exception_type, + before_sleep_log, ) +logger = get_logger() + class GeminiAdapter(LLMInterface): """ @@ -58,8 +65,13 @@ class GeminiAdapter(LLMInterface): self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON) - @sleep_and_retry_async() - @rate_limit_async + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py index 917599d4d..8bbbaa2cc 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py @@ -12,11 +12,18 @@ from cognee.infrastructure.llm.exceptions import ContentPolicyFilterError from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( LLMInterface, ) -from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( - rate_limit_async, - sleep_and_retry_async, +import logging +from cognee.shared.logging_utils import get_logger +from tenacity import ( + retry, + stop_after_delay, + wait_exponential_jitter, + retry_if_not_exception_type, + before_sleep_log, ) +logger = get_logger() + class GenericAPIAdapter(LLMInterface): """ @@ -58,8 +65,13 @@ class GenericAPIAdapter(LLMInterface): self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON) - @sleep_and_retry_async() - @rate_limit_async + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py index c4e51b70b..78a3cbff5 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py @@ -1,20 +1,23 @@ import litellm import instructor from pydantic import BaseModel -from typing import Type, Optional -from litellm import acompletion, JSONSchemaValidationError +from typing import Type +from litellm import JSONSchemaValidationError from cognee.shared.logging_utils import get_logger from cognee.modules.observability.get_observe import get_observe -from cognee.infrastructure.llm.exceptions import MissingSystemPromptPathError from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( LLMInterface, ) -from cognee.infrastructure.llm.LLMGateway import LLMGateway from cognee.infrastructure.llm.config import get_llm_config -from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( - rate_limit_async, - sleep_and_retry_async, + +import logging +from tenacity import ( + retry, + stop_after_delay, + wait_exponential_jitter, + retry_if_not_exception_type, + before_sleep_log, ) logger = get_logger() @@ -47,8 +50,13 @@ class MistralAdapter(LLMInterface): api_key=get_llm_config().llm_api_key, ) - @sleep_and_retry_async() - @rate_limit_async + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: @@ -99,31 +107,3 @@ class MistralAdapter(LLMInterface): logger.error(f"Schema validation failed: {str(e)}") logger.debug(f"Raw response: {e.raw_response}") raise ValueError(f"Response failed schema validation: {str(e)}") - - def show_prompt(self, text_input: str, system_prompt: str) -> str: - """ - Format and display the prompt for a user query. - - Parameters: - ----------- - - text_input (str): Input text from the user to be included in the prompt. - - system_prompt (str): The system prompt that will be shown alongside the user input. - - Returns: - -------- - - str: The formatted prompt string combining system prompt and user input. - """ - if not text_input: - text_input = "No user input provided." - if not system_prompt: - raise MissingSystemPromptPathError() - - system_prompt = LLMGateway.read_query_prompt(system_prompt) - - formatted_prompt = ( - f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n""" - if system_prompt - else None - ) - - return formatted_prompt diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/ollama/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/ollama/adapter.py index 314cb79d8..9c3d185aa 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/ollama/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/ollama/adapter.py @@ -1,4 +1,6 @@ import base64 +import litellm +import logging import instructor from typing import Type from openai import OpenAI @@ -7,11 +9,17 @@ from pydantic import BaseModel from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( LLMInterface, ) -from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( - rate_limit_async, - sleep_and_retry_async, -) from cognee.infrastructure.files.utils.open_data_file import open_data_file +from cognee.shared.logging_utils import get_logger +from tenacity import ( + retry, + stop_after_delay, + wait_exponential_jitter, + retry_if_not_exception_type, + before_sleep_log, +) + +logger = get_logger() class OllamaAPIAdapter(LLMInterface): @@ -47,8 +55,13 @@ class OllamaAPIAdapter(LLMInterface): OpenAI(base_url=self.endpoint, api_key=self.api_key), mode=instructor.Mode.JSON ) - @sleep_and_retry_async() - @rate_limit_async + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: @@ -90,7 +103,13 @@ class OllamaAPIAdapter(LLMInterface): return response - @rate_limit_async + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def create_transcript(self, input_file: str) -> str: """ Generate an audio transcript from a user query. @@ -123,7 +142,13 @@ class OllamaAPIAdapter(LLMInterface): return transcription.text - @rate_limit_async + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def transcribe_image(self, input_file: str) -> str: """ Transcribe content from an image using base64 encoding. diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py index 527f64d75..305b426b8 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py @@ -7,6 +7,15 @@ from openai import ContentFilterFinishReasonError from litellm.exceptions import ContentPolicyViolationError from instructor.core import InstructorRetryException +import logging +from tenacity import ( + retry, + stop_after_delay, + wait_exponential_jitter, + retry_if_not_exception_type, + before_sleep_log, +) + from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( LLMInterface, ) @@ -14,19 +23,13 @@ from cognee.infrastructure.llm.exceptions import ( ContentPolicyFilterError, ) from cognee.infrastructure.files.utils.open_data_file import open_data_file -from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( - rate_limit_async, - rate_limit_sync, - sleep_and_retry_async, - sleep_and_retry_sync, -) from cognee.modules.observability.get_observe import get_observe from cognee.shared.logging_utils import get_logger -observe = get_observe() - logger = get_logger() +observe = get_observe() + class OpenAIAdapter(LLMInterface): """ @@ -97,8 +100,13 @@ class OpenAIAdapter(LLMInterface): self.fallback_endpoint = fallback_endpoint @observe(as_type="generation") - @sleep_and_retry_async() - @rate_limit_async + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: @@ -148,10 +156,7 @@ class OpenAIAdapter(LLMInterface): InstructorRetryException, ) as e: if not (self.fallback_model and self.fallback_api_key): - raise ContentPolicyFilterError( - f"The provided input contains content that is not aligned with our content policy: {text_input}" - ) from e - + raise e try: return await self.aclient.chat.completions.create( model=self.fallback_model, @@ -186,8 +191,13 @@ class OpenAIAdapter(LLMInterface): ) from error @observe - @sleep_and_retry_sync() - @rate_limit_sync + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) def create_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: @@ -231,7 +241,13 @@ class OpenAIAdapter(LLMInterface): max_retries=self.MAX_RETRIES, ) - @rate_limit_async + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def create_transcript(self, input): """ Generate an audio transcript from a user query. @@ -263,7 +279,13 @@ class OpenAIAdapter(LLMInterface): return transcription - @rate_limit_async + @retry( + stop=stop_after_delay(128), + wait=wait_exponential_jitter(2, 128), + retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError), + before_sleep=before_sleep_log(logger, logging.DEBUG), + reraise=True, + ) async def transcribe_image(self, input) -> BaseModel: """ Generate a transcription of an image from a user query. diff --git a/cognee/infrastructure/loaders/LoaderEngine.py b/cognee/infrastructure/loaders/LoaderEngine.py index 87a008660..6b62f7641 100644 --- a/cognee/infrastructure/loaders/LoaderEngine.py +++ b/cognee/infrastructure/loaders/LoaderEngine.py @@ -105,7 +105,6 @@ class LoaderEngine: async def load_file( self, file_path: str, - file_stream: Optional[Any], preferred_loaders: Optional[List[str]] = None, **kwargs, ): diff --git a/cognee/infrastructure/loaders/external/advanced_pdf_loader.py b/cognee/infrastructure/loaders/external/advanced_pdf_loader.py index 7bab8cac6..6d1412b77 100644 --- a/cognee/infrastructure/loaders/external/advanced_pdf_loader.py +++ b/cognee/infrastructure/loaders/external/advanced_pdf_loader.py @@ -14,14 +14,6 @@ from cognee.infrastructure.loaders.external.pypdf_loader import PyPdfLoader logger = get_logger(__name__) -try: - from unstructured.partition.pdf import partition_pdf -except ImportError as e: - logger.info( - "unstructured[pdf] not installed, can't use AdvancedPdfLoader, will use PyPdfLoader instead." - ) - raise ImportError from e - @dataclass class _PageBuffer: @@ -88,6 +80,8 @@ class AdvancedPdfLoader(LoaderInterface): **kwargs, } # Use partition to extract elements + from unstructured.partition.pdf import partition_pdf + elements = partition_pdf(**partition_kwargs) # Process elements into text content diff --git a/cognee/modules/pipelines/operations/pipeline.py b/cognee/modules/pipelines/operations/pipeline.py index b59a171f7..e15e9e505 100644 --- a/cognee/modules/pipelines/operations/pipeline.py +++ b/cognee/modules/pipelines/operations/pipeline.py @@ -35,6 +35,7 @@ async def run_pipeline( vector_db_config: dict = None, graph_db_config: dict = None, incremental_loading: bool = False, + data_per_batch: int = 20, ): validate_pipeline_tasks(tasks) await setup_and_check_environment(vector_db_config, graph_db_config) @@ -50,6 +51,7 @@ async def run_pipeline( pipeline_name=pipeline_name, context={"dataset": dataset}, incremental_loading=incremental_loading, + data_per_batch=data_per_batch, ): yield run_info @@ -62,6 +64,7 @@ async def run_pipeline_per_dataset( pipeline_name: str = "custom_pipeline", context: dict = None, incremental_loading=False, + data_per_batch: int = 20, ): # Will only be used if ENABLE_BACKEND_ACCESS_CONTROL is set to True await set_database_global_context_variables(dataset.id, dataset.owner_id) @@ -77,7 +80,7 @@ async def run_pipeline_per_dataset( return pipeline_run = run_tasks( - tasks, dataset.id, data, user, pipeline_name, context, incremental_loading + tasks, dataset.id, data, user, pipeline_name, context, incremental_loading, data_per_batch ) async for pipeline_run_info in pipeline_run: diff --git a/cognee/modules/pipelines/operations/run_tasks.py b/cognee/modules/pipelines/operations/run_tasks.py index 4a0c77309..ecc2f647b 100644 --- a/cognee/modules/pipelines/operations/run_tasks.py +++ b/cognee/modules/pipelines/operations/run_tasks.py @@ -24,7 +24,6 @@ from cognee.modules.pipelines.operations import ( log_pipeline_run_complete, log_pipeline_run_error, ) -from .run_tasks_with_telemetry import run_tasks_with_telemetry from .run_tasks_data_item import run_tasks_data_item from ..tasks.task import Task @@ -60,6 +59,7 @@ async def run_tasks( pipeline_name: str = "unknown_pipeline", context: dict = None, incremental_loading: bool = False, + data_per_batch: int = 20, ): if not user: user = await get_default_user() @@ -89,24 +89,29 @@ async def run_tasks( if incremental_loading: data = await resolve_data_directories(data) - # Create async tasks per data item that will run the pipeline for the data item - data_item_tasks = [ - asyncio.create_task( - run_tasks_data_item( - data_item, - dataset, - tasks, - pipeline_name, - pipeline_id, - pipeline_run_id, - context, - user, - incremental_loading, + # Create and gather batches of async tasks of data items that will run the pipeline for the data item + results = [] + for start in range(0, len(data), data_per_batch): + data_batch = data[start : start + data_per_batch] + + data_item_tasks = [ + asyncio.create_task( + run_tasks_data_item( + data_item, + dataset, + tasks, + pipeline_name, + pipeline_id, + pipeline_run_id, + context, + user, + incremental_loading, + ) ) - ) - for data_item in data - ] - results = await asyncio.gather(*data_item_tasks) + for data_item in data_batch + ] + + results.extend(await asyncio.gather(*data_item_tasks)) # Remove skipped data items from results results = [result for result in results if result] diff --git a/cognee/modules/pipelines/operations/run_tasks_data_item.py b/cognee/modules/pipelines/operations/run_tasks_data_item.py index 94fc631a8..152e72d7f 100644 --- a/cognee/modules/pipelines/operations/run_tasks_data_item.py +++ b/cognee/modules/pipelines/operations/run_tasks_data_item.py @@ -115,9 +115,8 @@ async def run_tasks_data_item_incremental( data_point = ( await session.execute(select(Data).filter(Data.id == data_id)) ).scalar_one_or_none() - data_point.pipeline_status[pipeline_name] = { - str(dataset.id): DataItemStatus.DATA_ITEM_PROCESSING_COMPLETED - } + status_for_pipeline = data_point.pipeline_status.setdefault(pipeline_name, {}) + status_for_pipeline[str(dataset.id)] = DataItemStatus.DATA_ITEM_PROCESSING_COMPLETED await session.merge(data_point) await session.commit() diff --git a/cognee/modules/pipelines/operations/run_tasks_distributed.py b/cognee/modules/pipelines/operations/run_tasks_distributed.py index 95cdb0266..3fce3763d 100644 --- a/cognee/modules/pipelines/operations/run_tasks_distributed.py +++ b/cognee/modules/pipelines/operations/run_tasks_distributed.py @@ -88,6 +88,7 @@ async def run_tasks_distributed( pipeline_name: str = "unknown_pipeline", context: dict = None, incremental_loading: bool = False, + data_per_batch: int = 20, ): if not user: user = await get_default_user() diff --git a/cognee/tasks/storage/index_data_points.py b/cognee/tasks/storage/index_data_points.py index 362412657..902789c80 100644 --- a/cognee/tasks/storage/index_data_points.py +++ b/cognee/tasks/storage/index_data_points.py @@ -1,6 +1,6 @@ -from cognee.shared.logging_utils import get_logger +import asyncio -from cognee.infrastructure.databases.exceptions import EmbeddingException +from cognee.shared.logging_utils import get_logger from cognee.infrastructure.databases.vector import get_vector_engine from cognee.infrastructure.engine import DataPoint @@ -33,18 +33,23 @@ async def index_data_points(data_points: list[DataPoint]): indexed_data_point.metadata["index_fields"] = [field_name] index_points[index_name].append(indexed_data_point) - for index_name_and_field, indexable_points in index_points.items(): - first_occurence = index_name_and_field.index("_") - index_name = index_name_and_field[:first_occurence] - field_name = index_name_and_field[first_occurence + 1 :] - try: - # In case the amount of indexable points is too large we need to send them in batches - batch_size = vector_engine.embedding_engine.get_batch_size() - for i in range(0, len(indexable_points), batch_size): - batch = indexable_points[i : i + batch_size] - await vector_engine.index_data_points(index_name, field_name, batch) - except EmbeddingException as e: - logger.warning(f"Failed to index data points for {index_name}.{field_name}: {e}") + tasks: list[asyncio.Task] = [] + batch_size = vector_engine.embedding_engine.get_batch_size() + + for index_name_and_field, points in index_points.items(): + first = index_name_and_field.index("_") + index_name = index_name_and_field[:first] + field_name = index_name_and_field[first + 1 :] + + # Create embedding requests per batch to run in parallel later + for i in range(0, len(points), batch_size): + batch = points[i : i + batch_size] + tasks.append( + asyncio.create_task(vector_engine.index_data_points(index_name, field_name, batch)) + ) + + # Run all embedding requests in parallel + await asyncio.gather(*tasks) return data_points diff --git a/cognee/tasks/storage/index_graph_edges.py b/cognee/tasks/storage/index_graph_edges.py index b7bf7a2b9..4fa8cfc75 100644 --- a/cognee/tasks/storage/index_graph_edges.py +++ b/cognee/tasks/storage/index_graph_edges.py @@ -1,3 +1,5 @@ +import asyncio + from cognee.modules.engine.utils.generate_edge_id import generate_edge_id from cognee.shared.logging_utils import get_logger from collections import Counter @@ -76,15 +78,20 @@ async def index_graph_edges( indexed_data_point.metadata["index_fields"] = [field_name] index_points[index_name].append(indexed_data_point) + # Get maximum batch size for embedding model + batch_size = vector_engine.embedding_engine.get_batch_size() + tasks: list[asyncio.Task] = [] + for index_name, indexable_points in index_points.items(): index_name, field_name = index_name.split(".") - # Get maximum batch size for embedding model - batch_size = vector_engine.embedding_engine.get_batch_size() - # We save the data in batches of {batch_size} to not put a lot of pressure on the database + # Create embedding tasks to run in parallel later for start in range(0, len(indexable_points), batch_size): batch = indexable_points[start : start + batch_size] - await vector_engine.index_data_points(index_name, field_name, batch) + tasks.append(vector_engine.index_data_points(index_name, field_name, batch)) + + # Start all embedding tasks and wait for completion + await asyncio.gather(*tasks) return None diff --git a/cognee/tests/subprocesses/reader.py b/cognee/tests/subprocesses/reader.py new file mode 100644 index 000000000..df54a63e4 --- /dev/null +++ b/cognee/tests/subprocesses/reader.py @@ -0,0 +1,25 @@ +import asyncio +import time +from cognee.infrastructure.databases.graph.kuzu.adapter import KuzuAdapter + +# This will create the test.db if it doesn't exist + + +async def main(): + adapter = KuzuAdapter("test.db") + result = await adapter.query("MATCH (n:Node) RETURN COUNT(n)") + print(f"Reader: Found {result[0][0]} nodes") + result = await adapter.query("MATCH (n:Node) RETURN COUNT(n)") + print(f"Reader: Found {result[0][0]} nodes") + result = await adapter.query("MATCH (n:Node) RETURN COUNT(n)") + print(f"Reader: Found {result[0][0]} nodes") + result = await adapter.query("MATCH (n:Node) RETURN COUNT(n)") + print(f"Reader: Found {result[0][0]} nodes") + result = await adapter.query("MATCH (n:Node) RETURN COUNT(n)") + print(f"Reader: Found {result} nodes") + result = await adapter.query("MATCH (n:Node) RETURN COUNT(n)") + print(f"Reader: Found {result[0][0]} nodes") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/cognee/tests/subprocesses/simple_cognify_1.py b/cognee/tests/subprocesses/simple_cognify_1.py new file mode 100644 index 000000000..cf4d65c88 --- /dev/null +++ b/cognee/tests/subprocesses/simple_cognify_1.py @@ -0,0 +1,31 @@ +import asyncio +import cognee +from cognee.shared.logging_utils import setup_logging, INFO +from cognee.api.v1.search import SearchType + + +async def main(): + await cognee.cognify(datasets=["first_cognify_dataset"]) + + query_text = ( + "Tell me what is in the context. Additionally write out 'FIRST_COGNIFY' before your answer" + ) + search_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, + query_text=query_text, + datasets=["first_cognify_dataset"], + ) + + print("Search results:") + for result_text in search_results: + print(result_text) + + +if __name__ == "__main__": + logger = setup_logging(log_level=INFO) + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + loop.run_until_complete(main()) + finally: + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/cognee/tests/subprocesses/simple_cognify_2.py b/cognee/tests/subprocesses/simple_cognify_2.py new file mode 100644 index 000000000..6de5035ec --- /dev/null +++ b/cognee/tests/subprocesses/simple_cognify_2.py @@ -0,0 +1,31 @@ +import asyncio +import cognee +from cognee.shared.logging_utils import setup_logging, INFO +from cognee.api.v1.search import SearchType + + +async def main(): + await cognee.cognify(datasets=["second_cognify_dataset"]) + + query_text = ( + "Tell me what is in the context. Additionally write out 'SECOND_COGNIFY' before your answer" + ) + search_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, + query_text=query_text, + datasets=["second_cognify_dataset"], + ) + + print("Search results:") + for result_text in search_results: + print(result_text) + + +if __name__ == "__main__": + logger = setup_logging(log_level=INFO) + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + loop.run_until_complete(main()) + finally: + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/cognee/tests/subprocesses/writer.py b/cognee/tests/subprocesses/writer.py new file mode 100644 index 000000000..27d00caba --- /dev/null +++ b/cognee/tests/subprocesses/writer.py @@ -0,0 +1,32 @@ +import asyncio +import time +import uuid +from cognee.modules.data.processing.document_types import PdfDocument +from cognee.infrastructure.databases.graph.kuzu.adapter import KuzuAdapter + + +def create_node(name): + document = PdfDocument( + id=uuid.uuid4(), + name=name, + raw_data_location=name, + external_metadata="test_external_metadata", + mime_type="test_mime", + ) + return document + + +async def main(): + adapter = KuzuAdapter("test.db") + nodes = [create_node(f"Node{i}") for i in range(5)] + + print("Writer: Starting...") + await adapter.add_nodes(nodes) + + print("writer finished...") + + time.sleep(10) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/cognee/tests/tasks/descriptive_metrics/metrics_test_utils.py b/cognee/tests/tasks/descriptive_metrics/metrics_test_utils.py index 911d9c33b..579a499fd 100644 --- a/cognee/tests/tasks/descriptive_metrics/metrics_test_utils.py +++ b/cognee/tests/tasks/descriptive_metrics/metrics_test_utils.py @@ -1,7 +1,6 @@ from typing import List from cognee.infrastructure.engine import DataPoint from cognee.tasks.storage.add_data_points import add_data_points -from cognee.infrastructure.databases.graph.get_graph_engine import create_graph_engine import cognee from cognee.infrastructure.databases.graph import get_graph_engine import json @@ -64,7 +63,6 @@ async def create_connected_test_graph(): async def get_metrics(provider: str, include_optional=True): - create_graph_engine.cache_clear() cognee.config.set_graph_database_provider(provider) graph_engine = await get_graph_engine() await graph_engine.delete_graph() diff --git a/cognee/tests/tasks/descriptive_metrics/neo4j_metrics_test.py b/cognee/tests/tasks/descriptive_metrics/neo4j_metrics_test.py index 2ca9e9f7e..8d7a6ab02 100644 --- a/cognee/tests/tasks/descriptive_metrics/neo4j_metrics_test.py +++ b/cognee/tests/tasks/descriptive_metrics/neo4j_metrics_test.py @@ -1,7 +1,12 @@ -from cognee.tests.tasks.descriptive_metrics.metrics_test_utils import assert_metrics import asyncio +async def main(): + from cognee.tests.tasks.descriptive_metrics.metrics_test_utils import assert_metrics + + await assert_metrics(provider="neo4j", include_optional=False) + await assert_metrics(provider="neo4j", include_optional=True) + + if __name__ == "__main__": - asyncio.run(assert_metrics(provider="neo4j", include_optional=False)) - asyncio.run(assert_metrics(provider="neo4j", include_optional=True)) + asyncio.run(main()) diff --git a/cognee/tests/test_concurrent_subprocess_access.py b/cognee/tests/test_concurrent_subprocess_access.py new file mode 100644 index 000000000..de03ed254 --- /dev/null +++ b/cognee/tests/test_concurrent_subprocess_access.py @@ -0,0 +1,84 @@ +import os +import asyncio +import cognee +import pathlib +import subprocess + +from cognee.shared.logging_utils import get_logger + +logger = get_logger() + +""" +Test: Redis-based Kùzu Locking Across Subprocesses + +This test ensures the Redis shared lock correctly serializes access to the Kùzu +database when multiple subprocesses (writer/reader and cognify tasks) run in parallel. +If this test fails, it indicates the locking mechanism is not properly handling +concurrent subprocess access. +""" + + +async def concurrent_subprocess_access(): + data_directory_path = str( + pathlib.Path( + os.path.join(pathlib.Path(__file__).parent, ".data_storage/concurrent_tasks") + ).resolve() + ) + cognee_directory_path = str( + pathlib.Path( + os.path.join(pathlib.Path(__file__).parent, ".cognee_system/concurrent_tasks") + ).resolve() + ) + + subprocess_directory_path = str( + pathlib.Path(os.path.join(pathlib.Path(__file__).parent, "subprocesses/")).resolve() + ) + + writer_path = subprocess_directory_path + "/writer.py" + reader_path = subprocess_directory_path + "/reader.py" + + cognee.config.data_root_directory(data_directory_path) + cognee.config.system_root_directory(cognee_directory_path) + + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + + writer_process = subprocess.Popen([os.sys.executable, str(writer_path)]) + + reader_process = subprocess.Popen([os.sys.executable, str(reader_path)]) + + # Wait for both processes to complete + writer_process.wait() + reader_process.wait() + + logger.info("Basic write read subprocess example finished") + + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + + text = """ + This is the text of the first cognify subprocess + """ + await cognee.add(text, dataset_name="first_cognify_dataset") + + text = """ + This is the text of the second cognify subprocess + """ + await cognee.add(text, dataset_name="second_cognify_dataset") + + first_cognify_path = subprocess_directory_path + "/simple_cognify_1.py" + second_cognify_path = subprocess_directory_path + "/simple_cognify_2.py" + + first_cognify_process = subprocess.Popen([os.sys.executable, str(first_cognify_path)]) + + second_cognify_process = subprocess.Popen([os.sys.executable, str(second_cognify_path)]) + + # Wait for both processes to complete + first_cognify_process.wait() + second_cognify_process.wait() + + logger.info("Database concurrent subprocess example finished") + + +if __name__ == "__main__": + asyncio.run(concurrent_subprocess_access()) diff --git a/cognee/tests/test_memgraph.py b/cognee/tests/test_memgraph.py deleted file mode 100644 index d0d968fc4..000000000 --- a/cognee/tests/test_memgraph.py +++ /dev/null @@ -1,105 +0,0 @@ -import os - -import pathlib -import cognee -from cognee.infrastructure.files.storage import get_storage_config -from cognee.modules.search.operations import get_history -from cognee.modules.users.methods import get_default_user -from cognee.shared.logging_utils import get_logger -from cognee.modules.search.types import SearchType - - -logger = get_logger() - - -async def main(): - cognee.config.set_graph_database_provider("memgraph") - data_directory_path = str( - pathlib.Path( - os.path.join(pathlib.Path(__file__).parent, ".data_storage/test_memgraph") - ).resolve() - ) - cognee.config.data_root_directory(data_directory_path) - cognee_directory_path = str( - pathlib.Path( - os.path.join(pathlib.Path(__file__).parent, ".cognee_system/test_memgraph") - ).resolve() - ) - cognee.config.system_root_directory(cognee_directory_path) - - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - - dataset_name = "cs_explanations" - - explanation_file_path_nlp = os.path.join( - pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" - ) - await cognee.add([explanation_file_path_nlp], dataset_name) - - explanation_file_path_quantum = os.path.join( - pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" - ) - - await cognee.add([explanation_file_path_quantum], dataset_name) - - await cognee.cognify([dataset_name]) - - from cognee.infrastructure.databases.vector import get_vector_engine - - vector_engine = get_vector_engine() - random_node = (await vector_engine.search("Entity_name", "Quantum computer"))[0] - random_node_name = random_node.payload["text"] - - search_results = await cognee.search( - query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name - ) - assert len(search_results) != 0, "The search results list is empty." - print("\n\nExtracted sentences are:\n") - for result in search_results: - print(f"{result}\n") - - search_results = await cognee.search(query_type=SearchType.CHUNKS, query_text=random_node_name) - assert len(search_results) != 0, "The search results list is empty." - print("\n\nExtracted chunks are:\n") - for result in search_results: - print(f"{result}\n") - - search_results = await cognee.search( - query_type=SearchType.SUMMARIES, query_text=random_node_name - ) - assert len(search_results) != 0, "Query related summaries don't exist." - print("\nExtracted results are:\n") - for result in search_results: - print(f"{result}\n") - - search_results = await cognee.search( - query_type=SearchType.NATURAL_LANGUAGE, - query_text=f"Find nodes connected to node with name {random_node_name}", - ) - assert len(search_results) != 0, "Query related natural language don't exist." - print("\nExtracted results are:\n") - for result in search_results: - print(f"{result}\n") - - user = await get_default_user() - history = await get_history(user.id) - - assert len(history) == 8, "Search history is not correct." - - await cognee.prune.prune_data() - data_root_directory = get_storage_config()["data_root_directory"] - assert not os.path.isdir(data_root_directory), "Local data files are not deleted" - - await cognee.prune.prune_system(metadata=True) - from cognee.infrastructure.databases.graph import get_graph_engine - - graph_engine = await get_graph_engine() - nodes, edges = await graph_engine.get_graph_data() - assert len(nodes) == 0 and len(edges) == 0, "Memgraph graph database is not empty" - - -if __name__ == "__main__": - import asyncio - - asyncio.run(main()) diff --git a/cognee/tests/unit/infrastructure/databases/cache/test_cache_config.py b/cognee/tests/unit/infrastructure/databases/cache/test_cache_config.py new file mode 100644 index 000000000..cb5086a70 --- /dev/null +++ b/cognee/tests/unit/infrastructure/databases/cache/test_cache_config.py @@ -0,0 +1,87 @@ +"""Tests for cache configuration.""" + +import pytest +from cognee.infrastructure.databases.cache.config import CacheConfig, get_cache_config + + +def test_cache_config_defaults(): + """Test that CacheConfig has the correct default values.""" + config = CacheConfig() + + assert config.caching is False + assert config.shared_kuzu_lock is False + assert config.cache_host == "localhost" + assert config.cache_port == 6379 + assert config.agentic_lock_expire == 240 + assert config.agentic_lock_timeout == 300 + + +def test_cache_config_custom_values(): + """Test that CacheConfig accepts custom values.""" + config = CacheConfig( + caching=True, + shared_kuzu_lock=True, + cache_host="redis.example.com", + cache_port=6380, + agentic_lock_expire=120, + agentic_lock_timeout=180, + ) + + assert config.caching is True + assert config.shared_kuzu_lock is True + assert config.cache_host == "redis.example.com" + assert config.cache_port == 6380 + assert config.agentic_lock_expire == 120 + assert config.agentic_lock_timeout == 180 + + +def test_cache_config_to_dict(): + """Test the to_dict method returns all configuration values.""" + config = CacheConfig( + caching=True, + shared_kuzu_lock=True, + cache_host="test-host", + cache_port=7000, + agentic_lock_expire=100, + agentic_lock_timeout=200, + ) + + config_dict = config.to_dict() + + assert config_dict == { + "caching": True, + "shared_kuzu_lock": True, + "cache_host": "test-host", + "cache_port": 7000, + "agentic_lock_expire": 100, + "agentic_lock_timeout": 200, + } + + +def test_get_cache_config_singleton(): + """Test that get_cache_config returns the same instance.""" + config1 = get_cache_config() + config2 = get_cache_config() + + assert config1 is config2 + + +def test_cache_config_extra_fields_allowed(): + """Test that CacheConfig allows extra fields due to extra='allow'.""" + config = CacheConfig(extra_field="extra_value", another_field=123) + + assert hasattr(config, "extra_field") + assert config.extra_field == "extra_value" + assert hasattr(config, "another_field") + assert config.another_field == 123 + + +def test_cache_config_boolean_type_validation(): + """Test that boolean fields accept various truthy/falsy values.""" + config1 = CacheConfig(caching="true", shared_kuzu_lock="yes") + assert config1.caching is True + assert config1.shared_kuzu_lock is True + + config2 = CacheConfig(caching="false", shared_kuzu_lock="no") + assert config2.caching is False + assert config2.shared_kuzu_lock is False diff --git a/docker-compose.yml b/docker-compose.yml index 9f0e199a0..43d9b2607 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -129,6 +129,30 @@ services: networks: - cognee-network + redis: + image: redis:7-alpine + container_name: redis + profiles: + - redis + ports: + - "6379:6379" + networks: + - cognee-network + volumes: + - redis_data:/data + command: [ "redis-server", "--appendonly", "yes" ] + + + redisinsight: + image: redislabs/redisinsight:latest + container_name: redisinsight + restart: always + ports: + - "5540:5540" + networks: + - cognee-network + + networks: cognee-network: name: cognee-network @@ -136,3 +160,4 @@ networks: volumes: chromadb_data: postgres_data: + redis_data: diff --git a/notebooks/neptune-analytics-example.ipynb b/notebooks/neptune-analytics-example.ipynb index e80ea4dcb..c85ccf58a 100644 --- a/notebooks/neptune-analytics-example.ipynb +++ b/notebooks/neptune-analytics-example.ipynb @@ -83,16 +83,16 @@ ] }, { - "metadata": {}, "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import os\n", "import pathlib\n", "from cognee import config, add, cognify, search, SearchType, prune, visualize_graph\n", "from dotenv import load_dotenv" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -106,7 +106,9 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# load environment variables from file .env\n", "load_dotenv()\n", @@ -145,9 +147,7 @@ " \"vector_db_url\": f\"neptune-graph://{graph_identifier}\", # Neptune Analytics endpoint with the format neptune-graph://\n", " }\n", ")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -159,19 +159,19 @@ ] }, { - "metadata": {}, "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Prune data and system metadata before running, only if we want \"fresh\" state.\n", "await prune.prune_data()\n", "await prune.prune_system(metadata=True)" - ], - "outputs": [], - "execution_count": null + ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## Setup data and cognify\n", "\n", @@ -180,7 +180,9 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# Add sample text to the dataset\n", "sample_text_1 = \"\"\"Neptune Analytics is a memory-optimized graph database engine for analytics. With Neptune\n", @@ -205,9 +207,7 @@ "\n", "# Cognify the text data.\n", "await cognify([dataset_name])" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -215,14 +215,16 @@ "source": [ "## Graph Memory visualization\n", "\n", - "Initialize Memgraph as a Graph Memory store and save to .artefacts/graph_visualization.html\n", + "Initialize Neptune as a Graph Memory store and save to .artefacts/graph_visualization.html\n", "\n", "![visualization](./neptune_analytics_demo.png)" ] }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# Get a graphistry url (Register for a free account at https://www.graphistry.com)\n", "# url = await render_graph()\n", @@ -235,9 +237,7 @@ " ).resolve()\n", ")\n", "await visualize_graph(graph_file_path)" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -250,19 +250,19 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# Completion query that uses graph data to form context.\n", "graph_completion = await search(query_text=\"What is Neptune Analytics?\", query_type=SearchType.GRAPH_COMPLETION)\n", "print(\"\\nGraph completion result is:\")\n", "print(graph_completion)" - ], - "outputs": [], - "execution_count": null + ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## SEARCH: RAG Completion\n", "\n", @@ -271,19 +271,19 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# Completion query that uses document chunks to form context.\n", "rag_completion = await search(query_text=\"What is Neptune Analytics?\", query_type=SearchType.RAG_COMPLETION)\n", "print(\"\\nRAG Completion result is:\")\n", "print(rag_completion)" - ], - "outputs": [], - "execution_count": null + ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## SEARCH: Graph Insights\n", "\n", @@ -291,8 +291,10 @@ ] }, { - "metadata": {}, "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Search graph insights\n", "insights_results = await search(query_text=\"Neptune Analytics\", query_type=SearchType.GRAPH_COMPLETION)\n", @@ -302,13 +304,11 @@ " tgt_node = result[2].get(\"name\", result[2][\"type\"])\n", " relationship = result[1].get(\"relationship_name\", \"__relationship__\")\n", " print(f\"- {src_node} -[{relationship}]-> {tgt_node}\")" - ], - "outputs": [], - "execution_count": null + ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## SEARCH: Entity Summaries\n", "\n", @@ -316,8 +316,10 @@ ] }, { - "metadata": {}, "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Query all summaries related to query.\n", "summaries = await search(query_text=\"Neptune Analytics\", query_type=SearchType.SUMMARIES)\n", @@ -326,13 +328,11 @@ " type = summary[\"type\"]\n", " text = summary[\"text\"]\n", " print(f\"- {type}: {text}\")" - ], - "outputs": [], - "execution_count": null + ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## SEARCH: Chunks\n", "\n", @@ -340,8 +340,10 @@ ] }, { - "metadata": {}, "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "chunks = await search(query_text=\"Neptune Analytics\", query_type=SearchType.CHUNKS)\n", "print(\"\\nChunk results are:\")\n", @@ -349,9 +351,7 @@ " type = chunk[\"type\"]\n", " text = chunk[\"text\"]\n", " print(f\"- {type}: {text}\")" - ], - "outputs": [], - "execution_count": null + ] } ], "metadata": { diff --git a/poetry.lock b/poetry.lock index 120de4f7f..c974a0b43 100644 --- a/poetry.lock +++ b/poetry.lock @@ -7,7 +7,7 @@ description = "Accelerate" optional = true python-versions = ">=3.9.0" groups = ["main"] -markers = "extra == \"docs\"" +markers = "extra == \"docs\" or extra == \"docling\"" files = [ {file = "accelerate-1.10.1-py3-none-any.whl", hash = "sha256:3621cff60b9a27ce798857ece05e2b9f56fcc71631cfb31ccf71f0359c311f11"}, {file = "accelerate-1.10.1.tar.gz", hash = "sha256:3dea89e433420e4bfac0369cae7e36dcd6a56adfcfd38cdda145c6225eab5df8"}, @@ -315,7 +315,7 @@ description = "ANTLR 4.9.3 runtime for Python 3.7" optional = true python-versions = "*" groups = ["main"] -markers = "extra == \"docs\"" +markers = "extra == \"docling\" or extra == \"docs\"" files = [ {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"}, ] @@ -354,6 +354,35 @@ files = [ {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"}, ] +[[package]] +name = "apscheduler" +version = "3.11.0" +description = "In-process task scheduler with Cron-like capabilities" +optional = true +python-versions = ">=3.8" +groups = ["main"] +markers = "extra == \"scraping\"" +files = [ + {file = 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