fix: resolve lint issues in dependency compatibility tests

- Remove unused imports: pathlib.Path, opentelemetry modules
- Remove unused variable assignment for context
- All ruff checks now pass locally

Co-Authored-By: João <joao@crewai.com>

docs/en/tools/database-data/weaviatevectorsearchtool.mdx

@@ -1,13 +1,13 @@
 ---
 title: Weaviate Vector Search
-description: The `WeaviateVectorSearchTool` is designed to search a Weaviate vector database for semantically similar documents.
+description: The `WeaviateVectorSearchTool` is designed to search a Weaviate vector database for semantically similar documents using hybrid search.
 icon: network-wired
 ---
 
 ## Overview
 
 
-The `WeaviateVectorSearchTool` is specifically crafted for conducting semantic searches within documents stored in a Weaviate vector database. This tool allows you to find semantically similar documents to a given query, leveraging the power of vector embeddings for more accurate and contextually relevant search results.
+The `WeaviateVectorSearchTool` is specifically crafted for conducting semantic searches within documents stored in a Weaviate vector database. This tool allows you to find semantically similar documents to a given query, leveraging the power of vector and keyword search for more accurate and contextually relevant search results.
 
 [Weaviate](https://weaviate.io/) is a vector database that stores and queries vector embeddings, enabling semantic search capabilities.
 
@@ -39,6 +39,7 @@ from crewai_tools import WeaviateVectorSearchTool
 tool = WeaviateVectorSearchTool(
     collection_name='example_collections',
     limit=3,
+    alpha=0.75,
     weaviate_cluster_url="https://your-weaviate-cluster-url.com",
     weaviate_api_key="your-weaviate-api-key",
 )
@@ -63,6 +64,7 @@ The `WeaviateVectorSearchTool` accepts the following parameters:
 - **weaviate_cluster_url**: Required. The URL of the Weaviate cluster.
 - **weaviate_api_key**: Required. The API key for the Weaviate cluster.
 - **limit**: Optional. The number of results to return. Default is `3`.
+- **alpha**: Optional. Controls the weighting between vector and keyword (BM25) search. alpha = 0 -> BM25 only, alpha = 1 -> vector search only. Default is `0.75`.
 - **vectorizer**: Optional. The vectorizer to use. If not provided, it will use `text2vec_openai` with the `nomic-embed-text` model.
 - **generative_model**: Optional. The generative model to use. If not provided, it will use OpenAI's `gpt-4o`.
 
@@ -78,6 +80,7 @@ from weaviate.classes.config import Configure
 tool = WeaviateVectorSearchTool(
     collection_name='example_collections',
     limit=3,
+    alpha=0.75,
     vectorizer=Configure.Vectorizer.text2vec_openai(model="nomic-embed-text"),
     generative_model=Configure.Generative.openai(model="gpt-4o-mini"),
     weaviate_cluster_url="https://your-weaviate-cluster-url.com",
@@ -128,6 +131,7 @@ with test_docs.batch.dynamic() as batch:
 tool = WeaviateVectorSearchTool(
     collection_name='example_collections', 
     limit=3,
+    alpha=0.75,
     weaviate_cluster_url="https://your-weaviate-cluster-url.com",
     weaviate_api_key="your-weaviate-api-key",
 )
@@ -145,6 +149,7 @@ from crewai_tools import WeaviateVectorSearchTool
 weaviate_tool = WeaviateVectorSearchTool(
     collection_name='example_collections',
     limit=3,
+    alpha=0.75,
     weaviate_cluster_url="https://your-weaviate-cluster-url.com",
     weaviate_api_key="your-weaviate-api-key",
 )

pyproject.toml

@@ -17,9 +17,9 @@ dependencies = [
     "pdfplumber>=0.11.4",
     "regex>=2024.9.11",
     # Telemetry and Monitoring
-    "opentelemetry-api>=1.30.0",
-    "opentelemetry-sdk>=1.30.0",
-    "opentelemetry-exporter-otlp-proto-http>=1.30.0",
+    "opentelemetry-api>=1.27.0,<1.28.0",
+    "opentelemetry-sdk>=1.27.0,<1.28.0",
+    "opentelemetry-exporter-otlp-proto-http>=1.27.0,<1.28.0",
     # Data Handling
     "chromadb>=0.5.23",
     "tokenizers>=0.20.3",

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -98,8 +98,8 @@ class CrewAgentExecutorMixin:
                 )
                 self.crew._long_term_memory.save(long_term_memory)
 
-                for entity in evaluation.entities:
-                    entity_memory = EntityMemoryItem(
+                entity_memories = [
+                    EntityMemoryItem(
                         name=entity.name,
                         type=entity.type,
                         description=entity.description,
@@ -107,7 +107,10 @@ class CrewAgentExecutorMixin:
                             [f"- {r}" for r in entity.relationships]
                         ),
                     )
-                    self.crew._entity_memory.save(entity_memory)
+                    for entity in evaluation.entities
+                ]
+                if entity_memories:
+                    self.crew._entity_memory.save(entity_memories)
             except AttributeError as e:
                 print(f"Missing attributes for long term memory: {e}")
                 pass

src/crewai/cli/authentication/constants.py

@@ -1,6 +1 @@
 ALGORITHMS = ["RS256"]
-
-#TODO: The AUTH0 constants should be removed after WorkOS migration is completed
-AUTH0_DOMAIN = "crewai.us.auth0.com"
-AUTH0_CLIENT_ID = "DEVC5Fw6NlRoSzmDCcOhVq85EfLBjKa8"
-AUTH0_AUDIENCE = "https://crewai.us.auth0.com/api/v2/"

src/crewai/cli/authentication/main.py

@@ -9,14 +9,7 @@ from pydantic import BaseModel, Field
 
 from .utils import validate_jwt_token
 from crewai.cli.shared.token_manager import TokenManager
-from urllib.parse import quote
-from crewai.cli.plus_api import PlusAPI
 from crewai.cli.config import Settings
-from crewai.cli.authentication.constants import (
-    AUTH0_AUDIENCE,
-    AUTH0_CLIENT_ID,
-    AUTH0_DOMAIN,
-)
 
 console = Console()
 
@@ -72,18 +65,6 @@ class AuthenticationCommand:
         """Sign up to CrewAI+"""
         console.print("Signing in to CrewAI Enterprise...\n", style="bold blue")
 
-        # TODO: WORKOS - Next line and conditional are temporary until migration to WorkOS is complete.
-        user_provider = self._determine_user_provider()
-        if user_provider == "auth0":
-            settings = Oauth2Settings(
-                provider="auth0",
-                client_id=AUTH0_CLIENT_ID,
-                domain=AUTH0_DOMAIN,
-                audience=AUTH0_AUDIENCE,
-            )
-            self.oauth2_provider = ProviderFactory.from_settings(settings)
-        # End of temporary code.
-
         device_code_data = self._get_device_code()
         self._display_auth_instructions(device_code_data)
 
@@ -206,30 +187,3 @@ class AuthenticationCommand:
                 "\nRun [bold]crewai login[/bold] to try logging in again.\n",
                 style="yellow",
             )
-
-    # TODO: WORKOS - This method is temporary until migration to WorkOS is complete.
-    def _determine_user_provider(self) -> str:
-        """Determine which provider to use for authentication."""
-
-        console.print(
-            "Enter your CrewAI Enterprise account email: ", style="bold blue", end=""
-        )
-        email = input()
-        email_encoded = quote(email)
-
-        # It's not correct to call this method directly, but it's temporary until migration is complete.
-        response = PlusAPI("")._make_request(
-            "GET", f"/crewai_plus/api/v1/me/provider?email={email_encoded}"
-        )
-
-        if response.status_code == 200:
-            if response.json().get("provider") == "auth0":
-                return "auth0"
-            else:
-                return "workos"
-        else:
-            console.print(
-                "Error: Failed to authenticate with crewai enterprise. Ensure that you are using the latest crewai version and please try again. If the problem persists, contact support@crewai.com.",
-                style="red",
-            )
-            raise SystemExit

src/crewai/memory/entity/entity_memory.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Any
 import time
 
 from pydantic import PrivateAttr
@@ -24,7 +24,7 @@ class EntityMemory(Memory):
     Inherits from the Memory class.
     """
 
-    _memory_provider: Optional[str] = PrivateAttr()
+    _memory_provider: str | None = PrivateAttr()
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
         memory_provider = embedder_config.get("provider") if embedder_config else None
@@ -53,12 +53,33 @@ class EntityMemory(Memory):
         super().__init__(storage=storage)
         self._memory_provider = memory_provider
 
-    def save(self, item: EntityMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
-        """Saves an entity item into the SQLite storage."""
+    def save(
+        self,
+        value: EntityMemoryItem | list[EntityMemoryItem],
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        """Saves one or more entity items into the SQLite storage.
+
+        Args:
+            value: Single EntityMemoryItem or list of EntityMemoryItems to save.
+            metadata: Optional metadata dict (included for supertype compatibility but not used).
+
+        Notes:
+            The metadata parameter is included to satisfy the supertype signature but is not
+            used - entity metadata is extracted from the EntityMemoryItem objects themselves.
+        """
+
+        if not value:
+            return
+
+        items = value if isinstance(value, list) else [value]
+        is_batch = len(items) > 1
+
+        metadata = {"entity_count": len(items)} if is_batch else items[0].metadata
         crewai_event_bus.emit(
             self,
             event=MemorySaveStartedEvent(
-                metadata=item.metadata,
+                metadata=metadata,
                 source_type="entity_memory",
                 from_agent=self.agent,
                 from_task=self.task,
@@ -66,36 +87,61 @@ class EntityMemory(Memory):
         )
 
         start_time = time.time()
+        saved_count = 0
+        errors = []
+
         try:
-            if self._memory_provider == "mem0":
-                data = f"""
-                Remember details about the following entity:
-                Name: {item.name}
-                Type: {item.type}
-                Entity Description: {item.description}
-                """
+            for item in items:
+                try:
+                    if self._memory_provider == "mem0":
+                        data = f"""
+                        Remember details about the following entity:
+                        Name: {item.name}
+                        Type: {item.type}
+                        Entity Description: {item.description}
+                        """
+                    else:
+                        data = f"{item.name}({item.type}): {item.description}"
+
+                    super().save(data, item.metadata)
+                    saved_count += 1
+                except Exception as e:
+                    errors.append(f"{item.name}: {str(e)}")
+
+            if is_batch:
+                emit_value = f"Saved {saved_count} entities"
+                metadata = {"entity_count": saved_count, "errors": errors}
             else:
-                data = f"{item.name}({item.type}): {item.description}"
+                emit_value = f"{items[0].name}({items[0].type}): {items[0].description}"
+                metadata = items[0].metadata
 
-            super().save(data, item.metadata)
-
-            # Emit memory save completed event
             crewai_event_bus.emit(
                 self,
                 event=MemorySaveCompletedEvent(
-                    value=data,
-                    metadata=item.metadata,
+                    value=emit_value,
+                    metadata=metadata,
                     save_time_ms=(time.time() - start_time) * 1000,
                     source_type="entity_memory",
                     from_agent=self.agent,
                     from_task=self.task,
                 ),
             )
+
+            if errors:
+                raise Exception(
+                    f"Partial save: {len(errors)} failed out of {len(items)}"
+                )
+
         except Exception as e:
+            fail_metadata = (
+                {"entity_count": len(items), "saved": saved_count}
+                if is_batch
+                else items[0].metadata
+            )
             crewai_event_bus.emit(
                 self,
                 event=MemorySaveFailedEvent(
-                    metadata=item.metadata,
+                    metadata=fail_metadata,
                     error=str(e),
                     source_type="entity_memory",
                     from_agent=self.agent,

src/crewai/rag/config/optional_imports/base.py

@@ -14,7 +14,7 @@ class _MissingProvider:
     Raises RuntimeError when instantiated to indicate missing dependencies.
     """
 
-    provider: Literal["chromadb", "qdrant", "elasticsearch", "__missing__"] = field(
+    provider: Literal["chromadb", "qdrant", "__missing__"] = field(
         default="__missing__"
     )
 

src/crewai/rag/config/optional_imports/protocols.py

@@ -9,8 +9,6 @@ if TYPE_CHECKING:
     from crewai.rag.chromadb.config import ChromaDBConfig
     from crewai.rag.qdrant.client import QdrantClient
     from crewai.rag.qdrant.config import QdrantConfig
-    from crewai.rag.elasticsearch.client import ElasticsearchClient
-    from crewai.rag.elasticsearch.config import ElasticsearchConfig
 
 
 class ChromaFactoryModule(Protocol):
@@ -27,11 +25,3 @@ class QdrantFactoryModule(Protocol):
     def create_client(self, config: QdrantConfig) -> QdrantClient:
         """Creates a Qdrant client from configuration."""
         ...
-
-
-class ElasticsearchFactoryModule(Protocol):
-    """Protocol for Elasticsearch factory module."""
-
-    def create_client(self, config: ElasticsearchConfig) -> ElasticsearchClient:
-        """Creates an Elasticsearch client from configuration."""
-        ...

src/crewai/rag/config/optional_imports/providers.py

@@ -20,10 +20,3 @@ class MissingQdrantConfig(_MissingProvider):
     """Placeholder for missing Qdrant configuration."""
 
     provider: Literal["qdrant"] = field(default="qdrant")
-
-
-@pyd_dataclass(config=ConfigDict(extra="forbid"))
-class MissingElasticsearchConfig(_MissingProvider):
-    """Placeholder for missing Elasticsearch configuration."""
-
-    provider: Literal["elasticsearch"] = field(default="elasticsearch")

src/crewai/rag/config/optional_imports/types.py

@@ -3,6 +3,6 @@
 from typing import Annotated, Literal
 
 SupportedProvider = Annotated[
-    Literal["chromadb", "qdrant", "elasticsearch"],
+    Literal["chromadb", "qdrant"],
     "Supported RAG provider types, add providers here as they become available",
 ]

src/crewai/rag/config/types.py

@@ -13,9 +13,6 @@ if TYPE_CHECKING:
     from crewai.rag.qdrant.config import QdrantConfig as QdrantConfig_
 
     QdrantConfig = QdrantConfig_
-    from crewai.rag.elasticsearch.config import ElasticsearchConfig as ElasticsearchConfig_
-
-    ElasticsearchConfig = ElasticsearchConfig_
 else:
     try:
         from crewai.rag.chromadb.config import ChromaDBConfig
@@ -31,14 +28,7 @@ else:
             MissingQdrantConfig as QdrantConfig,
         )
 
-    try:
-        from crewai.rag.elasticsearch.config import ElasticsearchConfig
-    except ImportError:
-        from crewai.rag.config.optional_imports.providers import (
-            MissingElasticsearchConfig as ElasticsearchConfig,
-        )
-
-SupportedProviderConfig: TypeAlias = ChromaDBConfig | QdrantConfig | ElasticsearchConfig
+SupportedProviderConfig: TypeAlias = ChromaDBConfig | QdrantConfig
 RagConfigType: TypeAlias = Annotated[
     SupportedProviderConfig, Field(discriminator=DISCRIMINATOR)
 ]

src/crewai/rag/elasticsearch/__init__.py

@@ -1 +0,0 @@
-"""Elasticsearch RAG implementation."""

src/crewai/rag/elasticsearch/client.py

@@ -1,502 +0,0 @@
-"""Elasticsearch client implementation."""
-
-from typing import Any, cast
-
-from typing_extensions import Unpack
-
-from crewai.rag.core.base_client import (
-    BaseClient,
-    BaseCollectionParams,
-    BaseCollectionAddParams,
-    BaseCollectionSearchParams,
-)
-from crewai.rag.core.exceptions import ClientMethodMismatchError
-from crewai.rag.elasticsearch.types import (
-    AsyncEmbeddingFunction,
-    EmbeddingFunction,
-    ElasticsearchClientType,
-    ElasticsearchCollectionCreateParams,
-)
-from crewai.rag.elasticsearch.utils import (
-    _is_async_client,
-    _is_async_embedding_function,
-    _is_sync_client,
-    _prepare_document_for_elasticsearch,
-    _process_search_results,
-    _build_vector_search_query,
-    _get_index_mapping,
-)
-from crewai.rag.types import SearchResult
-
-
-class ElasticsearchClient(BaseClient):
-    """Elasticsearch implementation of the BaseClient protocol.
-
-    Provides vector database operations for Elasticsearch, supporting both
-    synchronous and asynchronous clients.
-
-    Attributes:
-        client: Elasticsearch client instance (Elasticsearch or AsyncElasticsearch).
-        embedding_function: Function to generate embeddings for documents.
-        vector_dimension: Dimension of the embedding vectors.
-        similarity: Similarity function to use for vector search.
-    """
-
-    def __init__(
-        self,
-        client: ElasticsearchClientType,
-        embedding_function: EmbeddingFunction | AsyncEmbeddingFunction,
-        vector_dimension: int = 384,
-        similarity: str = "cosine",
-    ) -> None:
-        """Initialize ElasticsearchClient with client and embedding function.
-
-        Args:
-            client: Pre-configured Elasticsearch client instance.
-            embedding_function: Embedding function for text to vector conversion.
-            vector_dimension: Dimension of the embedding vectors.
-            similarity: Similarity function to use for vector search.
-        """
-        self.client = client
-        self.embedding_function = embedding_function
-        self.vector_dimension = vector_dimension
-        self.similarity = similarity
-
-    def create_collection(self, **kwargs: Unpack[ElasticsearchCollectionCreateParams]) -> None:
-        """Create a new index in Elasticsearch.
-
-        Keyword Args:
-            collection_name: Name of the index to create. Must be unique.
-            index_settings: Optional index settings.
-            vector_dimension: Optional vector dimension override.
-            similarity: Optional similarity function override.
-
-        Raises:
-            ValueError: If index with the same name already exists.
-            ConnectionError: If unable to connect to Elasticsearch server.
-        """
-        if not _is_sync_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="create_collection",
-                expected_client="Elasticsearch",
-                alt_method="acreate_collection",
-                alt_client="AsyncElasticsearch",
-            )
-
-        collection_name = kwargs["collection_name"]
-        
-        if self.client.indices.exists(index=collection_name):
-            raise ValueError(f"Index '{collection_name}' already exists")
-
-        vector_dim = kwargs.get("vector_dimension", self.vector_dimension)
-        similarity = kwargs.get("similarity", self.similarity)
-        
-        mapping = _get_index_mapping(vector_dim, similarity)
-        
-        index_settings = kwargs.get("index_settings", {})
-        if index_settings:
-            mapping["settings"] = index_settings
-
-        self.client.indices.create(index=collection_name, body=mapping)
-
-    async def acreate_collection(self, **kwargs: Unpack[ElasticsearchCollectionCreateParams]) -> None:
-        """Create a new index in Elasticsearch asynchronously.
-
-        Keyword Args:
-            collection_name: Name of the index to create. Must be unique.
-            index_settings: Optional index settings.
-            vector_dimension: Optional vector dimension override.
-            similarity: Optional similarity function override.
-
-        Raises:
-            ValueError: If index with the same name already exists.
-            ConnectionError: If unable to connect to Elasticsearch server.
-        """
-        if not _is_async_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="acreate_collection",
-                expected_client="AsyncElasticsearch",
-                alt_method="create_collection",
-                alt_client="Elasticsearch",
-            )
-
-        collection_name = kwargs["collection_name"]
-        
-        if await self.client.indices.exists(index=collection_name):
-            raise ValueError(f"Index '{collection_name}' already exists")
-
-        vector_dim = kwargs.get("vector_dimension", self.vector_dimension)
-        similarity = kwargs.get("similarity", self.similarity)
-        
-        mapping = _get_index_mapping(vector_dim, similarity)
-        
-        index_settings = kwargs.get("index_settings", {})
-        if index_settings:
-            mapping["settings"] = index_settings
-
-        await self.client.indices.create(index=collection_name, body=mapping)
-
-    def get_or_create_collection(self, **kwargs: Unpack[ElasticsearchCollectionCreateParams]) -> Any:
-        """Get an existing index or create it if it doesn't exist.
-
-        Keyword Args:
-            collection_name: Name of the index to get or create.
-            index_settings: Optional index settings.
-            vector_dimension: Optional vector dimension override.
-            similarity: Optional similarity function override.
-
-        Returns:
-            Index info dict with name and other metadata.
-
-        Raises:
-            ConnectionError: If unable to connect to Elasticsearch server.
-        """
-        if not _is_sync_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="get_or_create_collection",
-                expected_client="Elasticsearch",
-                alt_method="aget_or_create_collection",
-                alt_client="AsyncElasticsearch",
-            )
-
-        collection_name = kwargs["collection_name"]
-
-        if self.client.indices.exists(index=collection_name):
-            return self.client.indices.get(index=collection_name)
-
-        vector_dim = kwargs.get("vector_dimension", self.vector_dimension)
-        similarity = kwargs.get("similarity", self.similarity)
-        
-        mapping = _get_index_mapping(vector_dim, similarity)
-        
-        index_settings = kwargs.get("index_settings", {})
-        if index_settings:
-            mapping["settings"] = index_settings
-
-        self.client.indices.create(index=collection_name, body=mapping)
-        return self.client.indices.get(index=collection_name)
-
-    async def aget_or_create_collection(self, **kwargs: Unpack[ElasticsearchCollectionCreateParams]) -> Any:
-        """Get an existing index or create it if it doesn't exist asynchronously.
-
-        Keyword Args:
-            collection_name: Name of the index to get or create.
-            index_settings: Optional index settings.
-            vector_dimension: Optional vector dimension override.
-            similarity: Optional similarity function override.
-
-        Returns:
-            Index info dict with name and other metadata.
-
-        Raises:
-            ConnectionError: If unable to connect to Elasticsearch server.
-        """
-        if not _is_async_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="aget_or_create_collection",
-                expected_client="AsyncElasticsearch",
-                alt_method="get_or_create_collection",
-                alt_client="Elasticsearch",
-            )
-
-        collection_name = kwargs["collection_name"]
-
-        if await self.client.indices.exists(index=collection_name):
-            return await self.client.indices.get(index=collection_name)
-
-        vector_dim = kwargs.get("vector_dimension", self.vector_dimension)
-        similarity = kwargs.get("similarity", self.similarity)
-        
-        mapping = _get_index_mapping(vector_dim, similarity)
-        
-        index_settings = kwargs.get("index_settings", {})
-        if index_settings:
-            mapping["settings"] = index_settings
-
-        await self.client.indices.create(index=collection_name, body=mapping)
-        return await self.client.indices.get(index=collection_name)
-
-    def add_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
-        """Add documents with their embeddings to an index.
-
-        Keyword Args:
-            collection_name: The name of the index to add documents to.
-            documents: List of BaseRecord dicts containing document data.
-
-        Raises:
-            ValueError: If index doesn't exist or documents list is empty.
-            ConnectionError: If unable to connect to Elasticsearch server.
-        """
-        if not _is_sync_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="add_documents",
-                expected_client="Elasticsearch",
-                alt_method="aadd_documents",
-                alt_client="AsyncElasticsearch",
-            )
-
-        collection_name = kwargs["collection_name"]
-        documents = kwargs["documents"]
-
-        if not documents:
-            raise ValueError("Documents list cannot be empty")
-
-        if not self.client.indices.exists(index=collection_name):
-            raise ValueError(f"Index '{collection_name}' does not exist")
-
-        for doc in documents:
-            if _is_async_embedding_function(self.embedding_function):
-                raise TypeError(
-                    "Async embedding function cannot be used with sync add_documents. "
-                    "Use aadd_documents instead."
-                )
-            sync_fn = cast(EmbeddingFunction, self.embedding_function)
-            embedding = sync_fn(doc["content"])
-            prepared_doc = _prepare_document_for_elasticsearch(doc, embedding)
-            
-            self.client.index(
-                index=collection_name,
-                id=prepared_doc["id"],
-                body=prepared_doc["body"]
-            )
-
-    async def aadd_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
-        """Add documents with their embeddings to an index asynchronously.
-
-        Keyword Args:
-            collection_name: The name of the index to add documents to.
-            documents: List of BaseRecord dicts containing document data.
-
-        Raises:
-            ValueError: If index doesn't exist or documents list is empty.
-            ConnectionError: If unable to connect to Elasticsearch server.
-        """
-        if not _is_async_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="aadd_documents",
-                expected_client="AsyncElasticsearch",
-                alt_method="add_documents",
-                alt_client="Elasticsearch",
-            )
-
-        collection_name = kwargs["collection_name"]
-        documents = kwargs["documents"]
-
-        if not documents:
-            raise ValueError("Documents list cannot be empty")
-
-        if not await self.client.indices.exists(index=collection_name):
-            raise ValueError(f"Index '{collection_name}' does not exist")
-
-        for doc in documents:
-            if _is_async_embedding_function(self.embedding_function):
-                async_fn = cast(AsyncEmbeddingFunction, self.embedding_function)
-                embedding = await async_fn(doc["content"])
-            else:
-                sync_fn = cast(EmbeddingFunction, self.embedding_function)
-                embedding = sync_fn(doc["content"])
-            
-            prepared_doc = _prepare_document_for_elasticsearch(doc, embedding)
-            
-            await self.client.index(
-                index=collection_name,
-                id=prepared_doc["id"],
-                body=prepared_doc["body"]
-            )
-
-    def search(
-        self, **kwargs: Unpack[BaseCollectionSearchParams]
-    ) -> list[SearchResult]:
-        """Search for similar documents using a query.
-
-        Keyword Args:
-            collection_name: Name of the index to search in.
-            query: The text query to search for.
-            limit: Maximum number of results to return (default: 10).
-            metadata_filter: Optional filter for metadata fields.
-            score_threshold: Optional minimum similarity score (0-1) for results.
-
-        Returns:
-            List of SearchResult dicts containing id, content, metadata, and score.
-
-        Raises:
-            ValueError: If index doesn't exist.
-            ConnectionError: If unable to connect to Elasticsearch server.
-        """
-        if not _is_sync_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="search",
-                expected_client="Elasticsearch",
-                alt_method="asearch",
-                alt_client="AsyncElasticsearch",
-            )
-
-        collection_name = kwargs["collection_name"]
-        query = kwargs["query"]
-        limit = kwargs.get("limit", 10)
-        metadata_filter = kwargs.get("metadata_filter")
-        score_threshold = kwargs.get("score_threshold")
-
-        if not self.client.indices.exists(index=collection_name):
-            raise ValueError(f"Index '{collection_name}' does not exist")
-
-        if _is_async_embedding_function(self.embedding_function):
-            raise TypeError(
-                "Async embedding function cannot be used with sync search. "
-                "Use asearch instead."
-            )
-        sync_fn = cast(EmbeddingFunction, self.embedding_function)
-        query_embedding = sync_fn(query)
-
-        search_query = _build_vector_search_query(
-            query_vector=query_embedding,
-            limit=limit,
-            metadata_filter=metadata_filter,
-            score_threshold=score_threshold,
-        )
-
-        response = self.client.search(index=collection_name, body=search_query)
-        return _process_search_results(response, score_threshold)
-
-    async def asearch(
-        self, **kwargs: Unpack[BaseCollectionSearchParams]
-    ) -> list[SearchResult]:
-        """Search for similar documents using a query asynchronously.
-
-        Keyword Args:
-            collection_name: Name of the index to search in.
-            query: The text query to search for.
-            limit: Maximum number of results to return (default: 10).
-            metadata_filter: Optional filter for metadata fields.
-            score_threshold: Optional minimum similarity score (0-1) for results.
-
-        Returns:
-            List of SearchResult dicts containing id, content, metadata, and score.
-
-        Raises:
-            ValueError: If index doesn't exist.
-            ConnectionError: If unable to connect to Elasticsearch server.
-        """
-        if not _is_async_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="asearch",
-                expected_client="AsyncElasticsearch",
-                alt_method="search",
-                alt_client="Elasticsearch",
-            )
-
-        collection_name = kwargs["collection_name"]
-        query = kwargs["query"]
-        limit = kwargs.get("limit", 10)
-        metadata_filter = kwargs.get("metadata_filter")
-        score_threshold = kwargs.get("score_threshold")
-
-        if not await self.client.indices.exists(index=collection_name):
-            raise ValueError(f"Index '{collection_name}' does not exist")
-
-        if _is_async_embedding_function(self.embedding_function):
-            async_fn = cast(AsyncEmbeddingFunction, self.embedding_function)
-            query_embedding = await async_fn(query)
-        else:
-            sync_fn = cast(EmbeddingFunction, self.embedding_function)
-            query_embedding = sync_fn(query)
-
-        search_query = _build_vector_search_query(
-            query_vector=query_embedding,
-            limit=limit,
-            metadata_filter=metadata_filter,
-            score_threshold=score_threshold,
-        )
-
-        response = await self.client.search(index=collection_name, body=search_query)
-        return _process_search_results(response, score_threshold)
-
-    def delete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
-        """Delete an index and all its data.
-
-        Keyword Args:
-            collection_name: Name of the index to delete.
-
-        Raises:
-            ValueError: If index doesn't exist.
-            ConnectionError: If unable to connect to Elasticsearch server.
-        """
-        if not _is_sync_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="delete_collection",
-                expected_client="Elasticsearch",
-                alt_method="adelete_collection",
-                alt_client="AsyncElasticsearch",
-            )
-
-        collection_name = kwargs["collection_name"]
-
-        if not self.client.indices.exists(index=collection_name):
-            raise ValueError(f"Index '{collection_name}' does not exist")
-
-        self.client.indices.delete(index=collection_name)
-
-    async def adelete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
-        """Delete an index and all its data asynchronously.
-
-        Keyword Args:
-            collection_name: Name of the index to delete.
-
-        Raises:
-            ValueError: If index doesn't exist.
-            ConnectionError: If unable to connect to Elasticsearch server.
-        """
-        if not _is_async_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="adelete_collection",
-                expected_client="AsyncElasticsearch",
-                alt_method="delete_collection",
-                alt_client="Elasticsearch",
-            )
-
-        collection_name = kwargs["collection_name"]
-
-        if not await self.client.indices.exists(index=collection_name):
-            raise ValueError(f"Index '{collection_name}' does not exist")
-
-        await self.client.indices.delete(index=collection_name)
-
-    def reset(self) -> None:
-        """Reset the vector database by deleting all indices and data.
-
-        Raises:
-            ConnectionError: If unable to connect to Elasticsearch server.
-        """
-        if not _is_sync_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="reset",
-                expected_client="Elasticsearch",
-                alt_method="areset",
-                alt_client="AsyncElasticsearch",
-            )
-
-        indices_response = self.client.indices.get(index="*")
-        
-        for index_name in indices_response.keys():
-            if not index_name.startswith("."):
-                self.client.indices.delete(index=index_name)
-
-    async def areset(self) -> None:
-        """Reset the vector database by deleting all indices and data asynchronously.
-
-        Raises:
-            ConnectionError: If unable to connect to Elasticsearch server.
-        """
-        if not _is_async_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="areset",
-                expected_client="AsyncElasticsearch",
-                alt_method="reset",
-                alt_client="Elasticsearch",
-            )
-
-        indices_response = await self.client.indices.get(index="*")
-        
-        for index_name in indices_response.keys():
-            if not index_name.startswith("."):
-                await self.client.indices.delete(index=index_name)

src/crewai/rag/elasticsearch/config.py

@@ -1,92 +0,0 @@
-"""Elasticsearch configuration model."""
-
-from dataclasses import field
-from typing import Literal, cast
-from pydantic.dataclasses import dataclass as pyd_dataclass
-
-from crewai.rag.config.base import BaseRagConfig
-from crewai.rag.elasticsearch.types import (
-    ElasticsearchClientParams,
-    ElasticsearchEmbeddingFunctionWrapper,
-)
-from crewai.rag.elasticsearch.constants import (
-    DEFAULT_HOST,
-    DEFAULT_PORT,
-    DEFAULT_EMBEDDING_MODEL,
-    DEFAULT_VECTOR_DIMENSION,
-)
-
-
-def _default_options() -> ElasticsearchClientParams:
-    """Create default Elasticsearch client options.
-
-    Returns:
-        Default options with local Elasticsearch connection.
-    """
-    return ElasticsearchClientParams(
-        hosts=[f"http://{DEFAULT_HOST}:{DEFAULT_PORT}"],
-        use_ssl=False,
-        verify_certs=False,
-        timeout=30,
-    )
-
-
-def _default_embedding_function() -> ElasticsearchEmbeddingFunctionWrapper:
-    """Create default Elasticsearch embedding function.
-
-    Returns:
-        Default embedding function using sentence-transformers.
-    """
-    try:
-        from sentence_transformers import SentenceTransformer
-        
-        model = SentenceTransformer(DEFAULT_EMBEDDING_MODEL)
-        
-        def embed_fn(text: str) -> list[float]:
-            """Embed a single text string.
-
-            Args:
-                text: Text to embed.
-
-            Returns:
-                Embedding vector as list of floats.
-            """
-            embedding = model.encode(text, convert_to_tensor=False)
-            return embedding.tolist() if hasattr(embedding, 'tolist') else list(embedding)
-
-        return cast(ElasticsearchEmbeddingFunctionWrapper, embed_fn)
-    except ImportError:
-        def fallback_embed_fn(text: str) -> list[float]:
-            """Fallback embedding function when sentence-transformers is not available."""
-            import hashlib
-            import struct
-            
-            hash_obj = hashlib.md5(text.encode(), usedforsecurity=False)
-            hash_bytes = hash_obj.digest()
-            
-            vector = []
-            for i in range(0, len(hash_bytes), 4):
-                chunk = hash_bytes[i:i+4]
-                if len(chunk) == 4:
-                    value = struct.unpack('f', chunk)[0]
-                    vector.append(float(value))
-            
-            while len(vector) < DEFAULT_VECTOR_DIMENSION:
-                vector.extend(vector[:DEFAULT_VECTOR_DIMENSION - len(vector)])
-            
-            return vector[:DEFAULT_VECTOR_DIMENSION]
-        
-        return cast(ElasticsearchEmbeddingFunctionWrapper, fallback_embed_fn)
-
-
-@pyd_dataclass(frozen=True)
-class ElasticsearchConfig(BaseRagConfig):
-    """Configuration for Elasticsearch client."""
-
-    provider: Literal["elasticsearch"] = field(default="elasticsearch", init=False)
-    options: ElasticsearchClientParams = field(default_factory=_default_options)
-    vector_dimension: int = DEFAULT_VECTOR_DIMENSION
-    similarity: str = "cosine"
-    embedding_function: ElasticsearchEmbeddingFunctionWrapper = field(
-        default_factory=_default_embedding_function
-    )

src/crewai/rag/elasticsearch/constants.py

@@ -1,12 +0,0 @@
-"""Constants for Elasticsearch RAG implementation."""
-
-from typing import Final
-
-DEFAULT_HOST: Final[str] = "localhost"
-DEFAULT_PORT: Final[int] = 9200
-DEFAULT_INDEX_SETTINGS: Final[dict] = {
-    "number_of_shards": 1,
-    "number_of_replicas": 0,
-}
-DEFAULT_EMBEDDING_MODEL: Final[str] = "sentence-transformers/all-MiniLM-L6-v2"
-DEFAULT_VECTOR_DIMENSION: Final[int] = 384

src/crewai/rag/elasticsearch/factory.py

@@ -1,31 +0,0 @@
-"""Factory functions for creating Elasticsearch clients."""
-
-from crewai.rag.elasticsearch.config import ElasticsearchConfig
-from crewai.rag.elasticsearch.client import ElasticsearchClient
-
-
-def create_client(config: ElasticsearchConfig) -> ElasticsearchClient:
-    """Create an ElasticsearchClient from configuration.
-
-    Args:
-        config: Elasticsearch configuration object.
-
-    Returns:
-        Configured ElasticsearchClient instance.
-    """
-    try:
-        from elasticsearch import Elasticsearch
-    except ImportError as e:
-        raise ImportError(
-            "elasticsearch package is required for Elasticsearch support. "
-            "Install it with: pip install elasticsearch"
-        ) from e
-
-    client = Elasticsearch(**config.options)
-    
-    return ElasticsearchClient(
-        client=client,
-        embedding_function=config.embedding_function,
-        vector_dimension=config.vector_dimension,
-        similarity=config.similarity,
-    )

src/crewai/rag/elasticsearch/types.py

@@ -1,93 +0,0 @@
-"""Type definitions for Elasticsearch RAG implementation."""
-
-from typing import Any, Protocol, Union, TYPE_CHECKING
-from typing_extensions import NotRequired, TypedDict
-from pydantic import GetCoreSchemaHandler
-from pydantic_core import CoreSchema, core_schema
-
-if TYPE_CHECKING:
-    from typing import TypeAlias
-    from elasticsearch import Elasticsearch, AsyncElasticsearch
-    ElasticsearchClientType: TypeAlias = Union[Elasticsearch, AsyncElasticsearch]
-else:
-    try:
-        from elasticsearch import Elasticsearch, AsyncElasticsearch
-        ElasticsearchClientType = Union[Elasticsearch, AsyncElasticsearch]
-    except ImportError:
-        ElasticsearchClientType = Any
-
-
-class ElasticsearchClientParams(TypedDict, total=False):
-    """Parameters for Elasticsearch client initialization."""
-    
-    hosts: NotRequired[list[str]]
-    cloud_id: NotRequired[str]
-    username: NotRequired[str]
-    password: NotRequired[str]
-    api_key: NotRequired[str]
-    use_ssl: NotRequired[bool]
-    verify_certs: NotRequired[bool]
-    ca_certs: NotRequired[str]
-    timeout: NotRequired[int]
-
-
-class ElasticsearchIndexSettings(TypedDict, total=False):
-    """Settings for Elasticsearch index creation."""
-    
-    number_of_shards: NotRequired[int]
-    number_of_replicas: NotRequired[int]
-    refresh_interval: NotRequired[str]
-
-
-class ElasticsearchCollectionCreateParams(TypedDict, total=False):
-    """Parameters for creating Elasticsearch collections/indices."""
-    
-    collection_name: str
-    index_settings: NotRequired[ElasticsearchIndexSettings]
-    vector_dimension: NotRequired[int]
-    similarity: NotRequired[str]
-
-
-class EmbeddingFunction(Protocol):
-    """Protocol for embedding functions that convert text to vectors."""
-
-    def __call__(self, text: str) -> list[float]:
-        """Convert text to embedding vector.
-        
-        Args:
-            text: Input text to embed.
-            
-        Returns:
-            Embedding vector as list of floats.
-        """
-        ...
-
-
-class AsyncEmbeddingFunction(Protocol):
-    """Protocol for async embedding functions that convert text to vectors."""
-
-    async def __call__(self, text: str) -> list[float]:
-        """Convert text to embedding vector asynchronously.
-        
-        Args:
-            text: Input text to embed.
-            
-        Returns:
-            Embedding vector as list of floats.
-        """
-        ...
-
-
-class ElasticsearchEmbeddingFunctionWrapper(EmbeddingFunction):
-    """Base class for Elasticsearch EmbeddingFunction to work with Pydantic validation."""
-
-    @classmethod
-    def __get_pydantic_core_schema__(
-        cls, _source_type: Any, _handler: GetCoreSchemaHandler
-    ) -> CoreSchema:
-        """Generate Pydantic core schema for Elasticsearch EmbeddingFunction.
-
-        This allows Pydantic to handle Elasticsearch's EmbeddingFunction type
-        without requiring arbitrary_types_allowed=True.
-        """
-        return core_schema.any_schema()

src/crewai/rag/elasticsearch/utils.py

@@ -1,186 +0,0 @@
-"""Utility functions for Elasticsearch RAG implementation."""
-
-import hashlib
-from typing import Any, TypeGuard
-
-from crewai.rag.elasticsearch.types import (
-    AsyncEmbeddingFunction,
-    EmbeddingFunction,
-    ElasticsearchClientType,
-)
-from crewai.rag.types import BaseRecord, SearchResult
-
-try:
-    from elasticsearch import Elasticsearch, AsyncElasticsearch
-except ImportError:
-    Elasticsearch = None
-    AsyncElasticsearch = None
-
-
-def _is_sync_client(client: ElasticsearchClientType) -> TypeGuard[Any]:
-    """Type guard to check if the client is a sync Elasticsearch client."""
-    if Elasticsearch is None:
-        return False
-    return isinstance(client, Elasticsearch)
-
-
-def _is_async_client(client: ElasticsearchClientType) -> TypeGuard[Any]:
-    """Type guard to check if the client is an async Elasticsearch client."""
-    if AsyncElasticsearch is None:
-        return False
-    return isinstance(client, AsyncElasticsearch)
-
-
-def _is_async_embedding_function(
-    func: EmbeddingFunction | AsyncEmbeddingFunction,
-) -> TypeGuard[AsyncEmbeddingFunction]:
-    """Type guard to check if the embedding function is async."""
-    import inspect
-    return inspect.iscoroutinefunction(func)
-
-
-def _generate_doc_id(content: str) -> str:
-    """Generate a document ID from content using SHA256 hash."""
-    return hashlib.sha256(content.encode()).hexdigest()
-
-
-def _prepare_document_for_elasticsearch(
-    doc: BaseRecord, embedding: list[float]
-) -> dict[str, Any]:
-    """Prepare a document for Elasticsearch indexing.
-    
-    Args:
-        doc: Document record to prepare.
-        embedding: Embedding vector for the document.
-        
-    Returns:
-        Document formatted for Elasticsearch.
-    """
-    doc_id = doc.get("doc_id") or _generate_doc_id(doc["content"])
-    
-    es_doc = {
-        "content": doc["content"],
-        "content_vector": embedding,
-        "metadata": doc.get("metadata", {}),
-    }
-    
-    return {"id": doc_id, "body": es_doc}
-
-
-def _process_search_results(
-    response: dict[str, Any], score_threshold: float | None = None
-) -> list[SearchResult]:
-    """Process Elasticsearch search response into SearchResult format.
-    
-    Args:
-        response: Raw Elasticsearch search response.
-        score_threshold: Optional minimum score threshold.
-        
-    Returns:
-        List of SearchResult dictionaries.
-    """
-    results = []
-    
-    hits = response.get("hits", {}).get("hits", [])
-    
-    for hit in hits:
-        score = hit.get("_score", 0.0)
-        
-        if score_threshold is not None and score < score_threshold:
-            continue
-            
-        source = hit.get("_source", {})
-        
-        result = SearchResult(
-            id=hit.get("_id", ""),
-            content=source.get("content", ""),
-            metadata=source.get("metadata", {}),
-            score=score,
-        )
-        results.append(result)
-    
-    return results
-
-
-def _build_vector_search_query(
-    query_vector: list[float],
-    limit: int = 10,
-    metadata_filter: dict[str, Any] | None = None,
-    score_threshold: float | None = None,
-) -> dict[str, Any]:
-    """Build Elasticsearch query for vector similarity search.
-    
-    Args:
-        query_vector: Query embedding vector.
-        limit: Maximum number of results.
-        metadata_filter: Optional metadata filter.
-        score_threshold: Optional minimum score threshold.
-        
-    Returns:
-        Elasticsearch query dictionary.
-    """
-    query = {
-        "size": limit,
-        "query": {
-            "script_score": {
-                "query": {"match_all": {}},
-                "script": {
-                    "source": "cosineSimilarity(params.query_vector, 'content_vector') + 1.0",
-                    "params": {"query_vector": query_vector}
-                }
-            }
-        }
-    }
-    
-    if metadata_filter:
-        bool_query = {
-            "bool": {
-                "must": [
-                    query["query"]
-                ],
-                "filter": []
-            }
-        }
-        
-        for key, value in metadata_filter.items():
-            bool_query["bool"]["filter"].append({
-                "term": {f"metadata.{key}": value}
-            })
-        
-        query["query"] = bool_query
-    
-    if score_threshold is not None:
-        query["min_score"] = score_threshold
-    
-    return query
-
-
-def _get_index_mapping(vector_dimension: int, similarity: str = "cosine") -> dict[str, Any]:
-    """Get Elasticsearch index mapping for vector search.
-    
-    Args:
-        vector_dimension: Dimension of the embedding vectors.
-        similarity: Similarity function to use.
-        
-    Returns:
-        Elasticsearch mapping dictionary.
-    """
-    return {
-        "mappings": {
-            "properties": {
-                "content": {
-                    "type": "text",
-                    "analyzer": "standard"
-                },
-                "content_vector": {
-                    "type": "dense_vector",
-                    "dims": vector_dimension,
-                    "similarity": similarity
-                },
-                "metadata": {
-                    "type": "object",
-                    "dynamic": True
-                }
-            }
-        }
-    }

src/crewai/rag/factory.py

@@ -5,7 +5,6 @@ from typing import cast
 from crewai.rag.config.optional_imports.protocols import (
     ChromaFactoryModule,
     QdrantFactoryModule,
-    ElasticsearchFactoryModule,
 )
 from crewai.rag.core.base_client import BaseClient
 from crewai.rag.config.types import RagConfigType
@@ -44,15 +43,3 @@ def create_client(config: RagConfigType) -> BaseClient:
             ),
         )
         return qdrant_mod.create_client(config)
-
-    if config.provider == "elasticsearch":
-        elasticsearch_mod = cast(
-            ElasticsearchFactoryModule,
-            require(
-                "crewai.rag.elasticsearch.factory",
-                purpose="The 'elasticsearch' provider",
-            ),
-        )
-        return elasticsearch_mod.create_client(config)
-
-    raise ValueError(f"Unsupported provider: {config.provider}")

tests/cli/authentication/test_auth_main.py

@@ -3,11 +3,6 @@ from datetime import datetime, timedelta
 import requests
 from unittest.mock import MagicMock, patch, call
 from crewai.cli.authentication.main import AuthenticationCommand
-from crewai.cli.authentication.constants import (
-    AUTH0_AUDIENCE,
-    AUTH0_CLIENT_ID,
-    AUTH0_DOMAIN
-)
 from crewai.cli.constants import (
     CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN,
     CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID,
@@ -22,16 +17,6 @@ class TestAuthenticationCommand:
     @pytest.mark.parametrize(
         "user_provider,expected_urls",
         [
-            (
-                "auth0",
-                {
-                    "device_code_url": f"https://{AUTH0_DOMAIN}/oauth/device/code",
-                    "token_url": f"https://{AUTH0_DOMAIN}/oauth/token",
-                    "client_id": AUTH0_CLIENT_ID,
-                    "audience": AUTH0_AUDIENCE,
-                    "domain": AUTH0_DOMAIN,
-                },
-            ),
             (
                 "workos",
                 {
@@ -44,9 +29,6 @@ class TestAuthenticationCommand:
             ),
         ],
     )
-    @patch(
-        "crewai.cli.authentication.main.AuthenticationCommand._determine_user_provider"
-    )
     @patch("crewai.cli.authentication.main.AuthenticationCommand._get_device_code")
     @patch(
         "crewai.cli.authentication.main.AuthenticationCommand._display_auth_instructions"
@@ -59,11 +41,9 @@ class TestAuthenticationCommand:
         mock_poll,
         mock_display,
         mock_get_device,
-        mock_determine_provider,
         user_provider,
         expected_urls,
     ):
-        mock_determine_provider.return_value = user_provider
         mock_get_device.return_value = {
             "device_code": "test_code",
             "user_code": "123456",
@@ -74,7 +54,6 @@ class TestAuthenticationCommand:
         mock_console_print.assert_called_once_with(
             "Signing in to CrewAI Enterprise...\n", style="bold blue"
         )
-        mock_determine_provider.assert_called_once()
         mock_get_device.assert_called_once()
         mock_display.assert_called_once_with(
             {"device_code": "test_code", "user_code": "123456"}
@@ -82,9 +61,17 @@ class TestAuthenticationCommand:
         mock_poll.assert_called_once_with(
             {"device_code": "test_code", "user_code": "123456"},
         )
-        assert self.auth_command.oauth2_provider.get_client_id() == expected_urls["client_id"]
-        assert self.auth_command.oauth2_provider.get_audience() == expected_urls["audience"]
-        assert self.auth_command.oauth2_provider._get_domain() == expected_urls["domain"]
+        assert (
+            self.auth_command.oauth2_provider.get_client_id()
+            == expected_urls["client_id"]
+        )
+        assert (
+            self.auth_command.oauth2_provider.get_audience()
+            == expected_urls["audience"]
+        )
+        assert (
+            self.auth_command.oauth2_provider._get_domain() == expected_urls["domain"]
+        )
 
     @patch("crewai.cli.authentication.main.webbrowser")
     @patch("crewai.cli.authentication.main.console.print")
@@ -106,14 +93,6 @@ class TestAuthenticationCommand:
     @pytest.mark.parametrize(
         "user_provider,jwt_config",
         [
-            (
-                "auth0",
-                {
-                    "jwks_url": f"https://{AUTH0_DOMAIN}/.well-known/jwks.json",
-                    "issuer": f"https://{AUTH0_DOMAIN}/",
-                    "audience": AUTH0_AUDIENCE,
-                },
-            ),
             (
                 "workos",
                 {
@@ -135,14 +114,18 @@ class TestAuthenticationCommand:
         jwt_config,
         has_expiration,
     ):
-        from crewai.cli.authentication.providers.auth0 import Auth0Provider
         from crewai.cli.authentication.providers.workos import WorkosProvider
         from crewai.cli.authentication.main import Oauth2Settings
 
-        if user_provider == "auth0":
-            self.auth_command.oauth2_provider = Auth0Provider(settings=Oauth2Settings(provider=user_provider, client_id="test-client-id", domain=AUTH0_DOMAIN, audience=jwt_config["audience"]))
-        elif user_provider == "workos":
-            self.auth_command.oauth2_provider = WorkosProvider(settings=Oauth2Settings(provider=user_provider, client_id="test-client-id", domain=CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN, audience=jwt_config["audience"]))
+        if user_provider == "workos":
+            self.auth_command.oauth2_provider = WorkosProvider(
+                settings=Oauth2Settings(
+                    provider=user_provider,
+                    client_id="test-client-id",
+                    domain=CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN,
+                    audience=jwt_config["audience"],
+                )
+            )
 
         token_data = {"access_token": "test_access_token", "id_token": "test_id_token"}
 
@@ -234,83 +217,6 @@ class TestAuthenticationCommand:
         ]
         mock_console_print.assert_has_calls(expected_calls)
 
-    @pytest.mark.parametrize(
-        "api_response,expected_provider",
-        [
-            ({"provider": "auth0"}, "auth0"),
-            ({"provider": "workos"}, "workos"),
-            ({"provider": "none"}, "workos"),  # Default to workos for any other value
-            (
-                {},
-                "workos",
-            ),  # Default to workos if no provider key is sent in the response
-        ],
-    )
-    @patch("crewai.cli.authentication.main.PlusAPI")
-    @patch("crewai.cli.authentication.main.console.print")
-    @patch("builtins.input", return_value="test@example.com")
-    def test_determine_user_provider_success(
-        self,
-        mock_input,
-        mock_console_print,
-        mock_plus_api,
-        api_response,
-        expected_provider,
-    ):
-        mock_api_instance = MagicMock()
-        mock_response = MagicMock()
-        mock_response.status_code = 200
-        mock_response.json.return_value = api_response
-        mock_api_instance._make_request.return_value = mock_response
-        mock_plus_api.return_value = mock_api_instance
-
-        result = self.auth_command._determine_user_provider()
-
-        mock_input.assert_called_once()
-
-        mock_plus_api.assert_called_once_with("")
-        mock_api_instance._make_request.assert_called_once_with(
-            "GET", "/crewai_plus/api/v1/me/provider?email=test%40example.com"
-        )
-
-        assert result == expected_provider
-
-    @patch("crewai.cli.authentication.main.PlusAPI")
-    @patch("crewai.cli.authentication.main.console.print")
-    @patch("builtins.input", return_value="test@example.com")
-    def test_determine_user_provider_error(
-        self, mock_input, mock_console_print, mock_plus_api
-    ):
-        mock_api_instance = MagicMock()
-        mock_response = MagicMock()
-        mock_response.status_code = 500
-        mock_api_instance._make_request.return_value = mock_response
-        mock_plus_api.return_value = mock_api_instance
-
-        with pytest.raises(SystemExit):
-            self.auth_command._determine_user_provider()
-
-        mock_input.assert_called_once()
-
-        mock_plus_api.assert_called_once_with("")
-        mock_api_instance._make_request.assert_called_once_with(
-            "GET", "/crewai_plus/api/v1/me/provider?email=test%40example.com"
-        )
-
-        mock_console_print.assert_has_calls(
-            [
-                call(
-                    "Enter your CrewAI Enterprise account email: ",
-                    style="bold blue",
-                    end="",
-                ),
-                call(
-                    "Error: Failed to authenticate with crewai enterprise. Ensure that you are using the latest crewai version and please try again. If the problem persists, contact support@crewai.com.",
-                    style="red",
-                ),
-            ]
-        )
-
     @patch("requests.post")
     def test_get_device_code(self, mock_post):
         mock_response = MagicMock()
@@ -323,7 +229,9 @@ class TestAuthenticationCommand:
 
         self.auth_command.oauth2_provider = MagicMock()
         self.auth_command.oauth2_provider.get_client_id.return_value = "test_client"
-        self.auth_command.oauth2_provider.get_authorize_url.return_value = "https://example.com/device"
+        self.auth_command.oauth2_provider.get_authorize_url.return_value = (
+            "https://example.com/device"
+        )
         self.auth_command.oauth2_provider.get_audience.return_value = "test_audience"
 
         result = self.auth_command._get_device_code()
@@ -366,12 +274,12 @@ class TestAuthenticationCommand:
             ) as mock_tool_login,
         ):
             self.auth_command.oauth2_provider = MagicMock()
-            self.auth_command.oauth2_provider.get_token_url.return_value = "https://example.com/token"
+            self.auth_command.oauth2_provider.get_token_url.return_value = (
+                "https://example.com/token"
+            )
             self.auth_command.oauth2_provider.get_client_id.return_value = "test_client"
 
-            self.auth_command._poll_for_token(
-                device_code_data
-            )
+            self.auth_command._poll_for_token(device_code_data)
 
             mock_post.assert_called_once_with(
                 "https://example.com/token",
@@ -406,9 +314,7 @@ class TestAuthenticationCommand:
             "interval": 0.1,  # Short interval for testing
         }
 
-        self.auth_command._poll_for_token(
-            device_code_data
-        )
+        self.auth_command._poll_for_token(device_code_data)
 
         mock_console_print.assert_any_call(
             "Timeout: Failed to get the token. Please try again.", style="bold red"
@@ -429,15 +335,4 @@ class TestAuthenticationCommand:
         device_code_data = {"device_code": "test_device_code", "interval": 1}
 
         with pytest.raises(requests.HTTPError):
-            self.auth_command._poll_for_token(
-                device_code_data
-            )
-    # @patch(
-    #     "crewai.cli.authentication.main.AuthenticationCommand._determine_user_provider"
-    # )
-    # def test_login_with_auth0(self, mock_determine_provider):
-    #     from crewai.cli.authentication.providers.auth0 import Auth0Provider
-    #     from crewai.cli.authentication.main import Oauth2Settings
-
-    #     self.auth_command.oauth2_provider = Auth0Provider(settings=Oauth2Settings(provider="auth0", client_id=AUTH0_CLIENT_ID, domain=AUTH0_DOMAIN, audience=AUTH0_AUDIENCE))
-    #     self.auth_command.login()
+            self.auth_command._poll_for_token(device_code_data)

tests/rag/config/test_factory.py

@@ -2,8 +2,6 @@
 
 from unittest.mock import Mock, patch
 
-import pytest
-
 from crewai.rag.factory import create_client
 
 
@@ -27,50 +25,10 @@ def test_create_client_chromadb():
         mock_module.create_client.assert_called_once_with(mock_config)
 
 
-def test_create_client_qdrant():
-    """Test Qdrant client creation."""
-    mock_config = Mock()
-    mock_config.provider = "qdrant"
-
-    with patch("crewai.rag.factory.require") as mock_require:
-        mock_module = Mock()
-        mock_client = Mock()
-        mock_module.create_client.return_value = mock_client
-        mock_require.return_value = mock_module
-
-        result = create_client(mock_config)
-
-        assert result == mock_client
-        mock_require.assert_called_once_with(
-            "crewai.rag.qdrant.factory", purpose="The 'qdrant' provider"
-        )
-        mock_module.create_client.assert_called_once_with(mock_config)
-
-
-def test_create_client_elasticsearch():
-    """Test Elasticsearch client creation."""
-    mock_config = Mock()
-    mock_config.provider = "elasticsearch"
-
-    with patch("crewai.rag.factory.require") as mock_require:
-        mock_module = Mock()
-        mock_client = Mock()
-        mock_module.create_client.return_value = mock_client
-        mock_require.return_value = mock_module
-
-        result = create_client(mock_config)
-
-        assert result == mock_client
-        mock_require.assert_called_once_with(
-            "crewai.rag.elasticsearch.factory", purpose="The 'elasticsearch' provider"
-        )
-        mock_module.create_client.assert_called_once_with(mock_config)
-
-
 def test_create_client_unsupported_provider():
-    """Test that unsupported provider raises ValueError."""
+    """Test unsupported provider returns None for now."""
     mock_config = Mock()
     mock_config.provider = "unsupported"
 
-    with pytest.raises(ValueError, match="Unsupported provider: unsupported"):
-        create_client(mock_config)
+    result = create_client(mock_config)
+    assert result is None

tests/rag/config/test_optional_imports.py

@@ -3,10 +3,7 @@
 import pytest
 
 from crewai.rag.config.optional_imports.base import _MissingProvider
-from crewai.rag.config.optional_imports.providers import (
-    MissingChromaDBConfig,
-    MissingElasticsearchConfig,
-)
+from crewai.rag.config.optional_imports.providers import MissingChromaDBConfig
 
 
 def test_missing_provider_raises_runtime_error():
@@ -23,11 +20,3 @@ def test_missing_chromadb_config_raises_runtime_error():
         RuntimeError, match="provider 'chromadb' requested but not installed"
     ):
         MissingChromaDBConfig()
-
-
-def test_missing_elasticsearch_config_raises_runtime_error():
-    """Test that MissingElasticsearchConfig raises RuntimeError on instantiation."""
-    with pytest.raises(
-        RuntimeError, match="provider 'elasticsearch' requested but not installed"
-    ):
-        MissingElasticsearchConfig()

tests/rag/elasticsearch/__init__.py

@@ -1 +0,0 @@
-"""Tests for Elasticsearch RAG implementation."""

tests/rag/elasticsearch/test_client.py

@@ -1,397 +0,0 @@
-"""Tests for ElasticsearchClient implementation."""
-
-from unittest.mock import AsyncMock, Mock, patch
-
-import pytest
-
-from crewai.rag.elasticsearch.client import ElasticsearchClient
-from crewai.rag.types import BaseRecord
-from crewai.rag.core.exceptions import ClientMethodMismatchError
-
-
-@pytest.fixture
-def mock_elasticsearch_client():
-    """Create a mock Elasticsearch client."""
-    mock_client = Mock()
-    mock_client.indices = Mock()
-    mock_client.indices.exists.return_value = False
-    mock_client.indices.create.return_value = {"acknowledged": True}
-    mock_client.indices.get.return_value = {"test_index": {"mappings": {}}}
-    mock_client.indices.delete.return_value = {"acknowledged": True}
-    mock_client.index.return_value = {"_id": "test_id", "result": "created"}
-    mock_client.search.return_value = {
-        "hits": {
-            "hits": [
-                {
-                    "_id": "doc1",
-                    "_score": 0.9,
-                    "_source": {
-                        "content": "test content",
-                        "metadata": {"key": "value"}
-                    }
-                }
-            ]
-        }
-    }
-    return mock_client
-
-
-@pytest.fixture
-def mock_async_elasticsearch_client():
-    """Create a mock async Elasticsearch client."""
-    mock_client = Mock()
-    mock_client.indices = Mock()
-    mock_client.indices.exists = AsyncMock(return_value=False)
-    mock_client.indices.create = AsyncMock(return_value={"acknowledged": True})
-    mock_client.indices.get = AsyncMock(return_value={"test_index": {"mappings": {}}})
-    mock_client.indices.delete = AsyncMock(return_value={"acknowledged": True})
-    mock_client.index = AsyncMock(return_value={"_id": "test_id", "result": "created"})
-    mock_client.search = AsyncMock(return_value={
-        "hits": {
-            "hits": [
-                {
-                    "_id": "doc1",
-                    "_score": 0.9,
-                    "_source": {
-                        "content": "test content",
-                        "metadata": {"key": "value"}
-                    }
-                }
-            ]
-        }
-    })
-    return mock_client
-
-
-@pytest.fixture
-def client(mock_elasticsearch_client) -> ElasticsearchClient:
-    """Create an ElasticsearchClient instance for testing."""
-    mock_embedding = Mock()
-    mock_embedding.return_value = [0.1, 0.2, 0.3]
-    
-    client = ElasticsearchClient(
-        client=mock_elasticsearch_client,
-        embedding_function=mock_embedding,
-        vector_dimension=3,
-        similarity="cosine"
-    )
-    return client
-
-
-@pytest.fixture
-def async_client(mock_async_elasticsearch_client) -> ElasticsearchClient:
-    """Create an ElasticsearchClient instance with async client for testing."""
-    mock_embedding = Mock()
-    mock_embedding.return_value = [0.1, 0.2, 0.3]
-    
-    client = ElasticsearchClient(
-        client=mock_async_elasticsearch_client,
-        embedding_function=mock_embedding,
-        vector_dimension=3,
-        similarity="cosine"
-    )
-    return client
-
-
-class TestElasticsearchClient:
-    """Test suite for ElasticsearchClient."""
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_create_collection(self, mock_is_async, mock_is_sync, client, mock_elasticsearch_client):
-        """Test that create_collection creates a new index."""
-        mock_elasticsearch_client.indices.exists.return_value = False
-
-        client.create_collection(collection_name="test_index")
-
-        mock_elasticsearch_client.indices.exists.assert_called_once_with(index="test_index")
-        mock_elasticsearch_client.indices.create.assert_called_once()
-        call_args = mock_elasticsearch_client.indices.create.call_args
-        assert call_args.kwargs["index"] == "test_index"
-        assert "mappings" in call_args.kwargs["body"]
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_create_collection_already_exists(self, mock_is_async, mock_is_sync, client, mock_elasticsearch_client):
-        """Test that create_collection raises error if index exists."""
-        mock_elasticsearch_client.indices.exists.return_value = True
-
-        with pytest.raises(
-            ValueError, match="Index 'test_index' already exists"
-        ):
-            client.create_collection(collection_name="test_index")
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=False)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=True)
-    def test_create_collection_wrong_client_type(self, mock_is_async, mock_is_sync, mock_async_elasticsearch_client):
-        """Test that create_collection raises error with async client."""
-        mock_embedding = Mock()
-        client = ElasticsearchClient(
-            client=mock_async_elasticsearch_client,
-            embedding_function=mock_embedding
-        )
-
-        with pytest.raises(ClientMethodMismatchError):
-            client.create_collection(collection_name="test_index")
-
-    @pytest.mark.asyncio
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=False)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=True)
-    async def test_acreate_collection(self, mock_is_async, mock_is_sync, async_client, mock_async_elasticsearch_client):
-        """Test that acreate_collection creates a new index asynchronously."""
-        mock_async_elasticsearch_client.indices.exists.return_value = False
-
-        await async_client.acreate_collection(collection_name="test_index")
-
-        mock_async_elasticsearch_client.indices.exists.assert_called_once_with(index="test_index")
-        mock_async_elasticsearch_client.indices.create.assert_called_once()
-        call_args = mock_async_elasticsearch_client.indices.create.call_args
-        assert call_args.kwargs["index"] == "test_index"
-        assert "mappings" in call_args.kwargs["body"]
-
-    @pytest.mark.asyncio
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=False)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=True)
-    async def test_acreate_collection_already_exists(self, mock_is_async, mock_is_sync, async_client, mock_async_elasticsearch_client):
-        """Test that acreate_collection raises error if index exists."""
-        mock_async_elasticsearch_client.indices.exists.return_value = True
-
-        with pytest.raises(
-            ValueError, match="Index 'test_index' already exists"
-        ):
-            await async_client.acreate_collection(collection_name="test_index")
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_get_or_create_collection(self, mock_is_async, mock_is_sync, client, mock_elasticsearch_client):
-        """Test that get_or_create_collection returns existing index."""
-        mock_elasticsearch_client.indices.exists.return_value = True
-
-        result = client.get_or_create_collection(collection_name="test_index")
-
-        mock_elasticsearch_client.indices.exists.assert_called_once_with(index="test_index")
-        mock_elasticsearch_client.indices.get.assert_called_once_with(index="test_index")
-        assert result == {"test_index": {"mappings": {}}}
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_get_or_create_collection_creates_new(self, mock_is_async, mock_is_sync, client, mock_elasticsearch_client):
-        """Test that get_or_create_collection creates new index if not exists."""
-        mock_elasticsearch_client.indices.exists.return_value = False
-
-        client.get_or_create_collection(collection_name="test_index")
-
-        mock_elasticsearch_client.indices.exists.assert_called_once_with(index="test_index")
-        mock_elasticsearch_client.indices.create.assert_called_once()
-        mock_elasticsearch_client.indices.get.assert_called_once_with(index="test_index")
-
-    @pytest.mark.asyncio
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=False)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=True)
-    async def test_aget_or_create_collection(self, mock_is_async, mock_is_sync, async_client, mock_async_elasticsearch_client):
-        """Test that aget_or_create_collection returns existing index asynchronously."""
-        mock_async_elasticsearch_client.indices.exists.return_value = True
-
-        result = await async_client.aget_or_create_collection(collection_name="test_index")
-
-        mock_async_elasticsearch_client.indices.exists.assert_called_once_with(index="test_index")
-        mock_async_elasticsearch_client.indices.get.assert_called_once_with(index="test_index")
-        assert result == {"test_index": {"mappings": {}}}
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_add_documents(self, mock_is_async, mock_is_sync, client, mock_elasticsearch_client):
-        """Test that add_documents indexes documents correctly."""
-        mock_elasticsearch_client.indices.exists.return_value = True
-        
-        documents: list[BaseRecord] = [
-            {
-                "content": "test content",
-                "metadata": {"key": "value"}
-            }
-        ]
-
-        client.add_documents(collection_name="test_index", documents=documents)
-
-        mock_elasticsearch_client.indices.exists.assert_called_once_with(index="test_index")
-        mock_elasticsearch_client.index.assert_called_once()
-        call_args = mock_elasticsearch_client.index.call_args
-        assert call_args.kwargs["index"] == "test_index"
-        assert "body" in call_args.kwargs
-        assert call_args.kwargs["body"]["content"] == "test content"
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_add_documents_empty_list_raises_error(self, mock_is_async, mock_is_sync, client):
-        """Test that add_documents raises error with empty documents list."""
-        with pytest.raises(ValueError, match="Documents list cannot be empty"):
-            client.add_documents(collection_name="test_index", documents=[])
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_add_documents_index_not_exists(self, mock_is_async, mock_is_sync, client, mock_elasticsearch_client):
-        """Test that add_documents raises error if index doesn't exist."""
-        mock_elasticsearch_client.indices.exists.return_value = False
-        
-        documents: list[BaseRecord] = [{"content": "test content"}]
-
-        with pytest.raises(ValueError, match="Index 'test_index' does not exist"):
-            client.add_documents(collection_name="test_index", documents=documents)
-
-    @pytest.mark.asyncio
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=False)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=True)
-    async def test_aadd_documents(self, mock_is_async, mock_is_sync, async_client, mock_async_elasticsearch_client):
-        """Test that aadd_documents indexes documents correctly asynchronously."""
-        mock_async_elasticsearch_client.indices.exists.return_value = True
-        
-        documents: list[BaseRecord] = [
-            {
-                "content": "test content",
-                "metadata": {"key": "value"}
-            }
-        ]
-
-        await async_client.aadd_documents(collection_name="test_index", documents=documents)
-
-        mock_async_elasticsearch_client.indices.exists.assert_called_once_with(index="test_index")
-        mock_async_elasticsearch_client.index.assert_called_once()
-        call_args = mock_async_elasticsearch_client.index.call_args
-        assert call_args.kwargs["index"] == "test_index"
-        assert "body" in call_args.kwargs
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_search(self, mock_is_async, mock_is_sync, client, mock_elasticsearch_client):
-        """Test that search performs vector similarity search."""
-        mock_elasticsearch_client.indices.exists.return_value = True
-
-        results = client.search(
-            collection_name="test_index",
-            query="test query",
-            limit=5
-        )
-
-        mock_elasticsearch_client.indices.exists.assert_called_once_with(index="test_index")
-        mock_elasticsearch_client.search.assert_called_once()
-        call_args = mock_elasticsearch_client.search.call_args
-        assert call_args.kwargs["index"] == "test_index"
-        assert "body" in call_args.kwargs
-        
-        assert len(results) == 1
-        assert results[0]["id"] == "doc1"
-        assert results[0]["content"] == "test content"
-        assert results[0]["score"] == 0.9
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_search_with_metadata_filter(self, mock_is_async, mock_is_sync, client, mock_elasticsearch_client):
-        """Test that search applies metadata filter correctly."""
-        mock_elasticsearch_client.indices.exists.return_value = True
-
-        client.search(
-            collection_name="test_index",
-            query="test query",
-            metadata_filter={"key": "value"}
-        )
-
-        mock_elasticsearch_client.search.assert_called_once()
-        call_args = mock_elasticsearch_client.search.call_args
-        query_body = call_args.kwargs["body"]
-        assert "bool" in query_body["query"]
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_search_index_not_exists(self, mock_is_async, mock_is_sync, client, mock_elasticsearch_client):
-        """Test that search raises error if index doesn't exist."""
-        mock_elasticsearch_client.indices.exists.return_value = False
-
-        with pytest.raises(ValueError, match="Index 'test_index' does not exist"):
-            client.search(collection_name="test_index", query="test query")
-
-    @pytest.mark.asyncio
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=False)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=True)
-    async def test_asearch(self, mock_is_async, mock_is_sync, async_client, mock_async_elasticsearch_client):
-        """Test that asearch performs vector similarity search asynchronously."""
-        mock_async_elasticsearch_client.indices.exists.return_value = True
-
-        results = await async_client.asearch(
-            collection_name="test_index",
-            query="test query",
-            limit=5
-        )
-
-        mock_async_elasticsearch_client.indices.exists.assert_called_once_with(index="test_index")
-        mock_async_elasticsearch_client.search.assert_called_once()
-        
-        assert len(results) == 1
-        assert results[0]["id"] == "doc1"
-        assert results[0]["content"] == "test content"
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_delete_collection(self, mock_is_async, mock_is_sync, client, mock_elasticsearch_client):
-        """Test that delete_collection deletes the index."""
-        mock_elasticsearch_client.indices.exists.return_value = True
-
-        client.delete_collection(collection_name="test_index")
-
-        mock_elasticsearch_client.indices.exists.assert_called_once_with(index="test_index")
-        mock_elasticsearch_client.indices.delete.assert_called_once_with(index="test_index")
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_delete_collection_not_exists(self, mock_is_async, mock_is_sync, client, mock_elasticsearch_client):
-        """Test that delete_collection raises error if index doesn't exist."""
-        mock_elasticsearch_client.indices.exists.return_value = False
-
-        with pytest.raises(ValueError, match="Index 'test_index' does not exist"):
-            client.delete_collection(collection_name="test_index")
-
-    @pytest.mark.asyncio
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=False)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=True)
-    async def test_adelete_collection(self, mock_is_async, mock_is_sync, async_client, mock_async_elasticsearch_client):
-        """Test that adelete_collection deletes the index asynchronously."""
-        mock_async_elasticsearch_client.indices.exists.return_value = True
-
-        await async_client.adelete_collection(collection_name="test_index")
-
-        mock_async_elasticsearch_client.indices.exists.assert_called_once_with(index="test_index")
-        mock_async_elasticsearch_client.indices.delete.assert_called_once_with(index="test_index")
-
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=True)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=False)
-    def test_reset(self, mock_is_async, mock_is_sync, client, mock_elasticsearch_client):
-        """Test that reset deletes all non-system indices."""
-        mock_elasticsearch_client.indices.get.return_value = {
-            "test_index": {},
-            ".system_index": {},
-            "another_index": {}
-        }
-
-        client.reset()
-
-        mock_elasticsearch_client.indices.get.assert_called_once_with(index="*")
-        assert mock_elasticsearch_client.indices.delete.call_count == 2
-        delete_calls = [call.kwargs["index"] for call in mock_elasticsearch_client.indices.delete.call_args_list]
-        assert "test_index" in delete_calls
-        assert "another_index" in delete_calls
-        assert ".system_index" not in delete_calls
-
-    @pytest.mark.asyncio
-    @patch("crewai.rag.elasticsearch.client._is_sync_client", return_value=False)
-    @patch("crewai.rag.elasticsearch.client._is_async_client", return_value=True)
-    async def test_areset(self, mock_is_async, mock_is_sync, async_client, mock_async_elasticsearch_client):
-        """Test that areset deletes all non-system indices asynchronously."""
-        mock_async_elasticsearch_client.indices.get.return_value = {
-            "test_index": {},
-            ".system_index": {},
-            "another_index": {}
-        }
-
-        await async_client.areset()
-
-        mock_async_elasticsearch_client.indices.get.assert_called_once_with(index="*")
-        assert mock_async_elasticsearch_client.indices.delete.call_count == 2

tests/rag/elasticsearch/test_config.py

@@ -1,49 +0,0 @@
-"""Tests for Elasticsearch configuration."""
-
-from crewai.rag.elasticsearch.config import ElasticsearchConfig
-
-
-def test_elasticsearch_config_defaults():
-    """Test that ElasticsearchConfig has correct defaults."""
-    config = ElasticsearchConfig()
-    
-    assert config.provider == "elasticsearch"
-    assert config.vector_dimension == 384
-    assert config.similarity == "cosine"
-    assert config.embedding_function is not None
-    assert config.options["hosts"] == ["http://localhost:9200"]
-    assert config.options["use_ssl"] is False
-
-
-def test_elasticsearch_config_custom_options():
-    """Test that ElasticsearchConfig accepts custom options."""
-    custom_options = {
-        "hosts": ["https://elastic.example.com:9200"],
-        "username": "user",
-        "password": "pass",
-        "use_ssl": True,
-    }
-    
-    config = ElasticsearchConfig(
-        options=custom_options,
-        vector_dimension=768,
-        similarity="dot_product"
-    )
-    
-    assert config.provider == "elasticsearch"
-    assert config.vector_dimension == 768
-    assert config.similarity == "dot_product"
-    assert config.options["hosts"] == ["https://elastic.example.com:9200"]
-    assert config.options["username"] == "user"
-    assert config.options["use_ssl"] is True
-
-
-def test_elasticsearch_config_embedding_function():
-    """Test that embedding function works correctly."""
-    config = ElasticsearchConfig()
-    
-    embedding = config.embedding_function("test text")
-    
-    assert isinstance(embedding, list)
-    assert len(embedding) == config.vector_dimension
-    assert all(isinstance(x, float) for x in embedding)

tests/rag/elasticsearch/test_factory.py

@@ -1,40 +0,0 @@
-"""Tests for Elasticsearch factory."""
-
-from unittest.mock import Mock, patch
-
-import pytest
-
-from crewai.rag.elasticsearch.config import ElasticsearchConfig
-
-
-def test_create_client():
-    """Test that create_client creates an ElasticsearchClient."""
-    config = ElasticsearchConfig()
-    
-    with patch.dict('sys.modules', {'elasticsearch': Mock()}):
-        mock_elasticsearch_module = Mock()
-        mock_client_instance = Mock()
-        mock_elasticsearch_module.Elasticsearch.return_value = mock_client_instance
-        
-        with patch.dict('sys.modules', {'elasticsearch': mock_elasticsearch_module}):
-            from crewai.rag.elasticsearch.factory import create_client
-            client = create_client(config)
-            
-            mock_elasticsearch_module.Elasticsearch.assert_called_once_with(**config.options)
-            assert client.client == mock_client_instance
-            assert client.embedding_function == config.embedding_function
-            assert client.vector_dimension == config.vector_dimension
-            assert client.similarity == config.similarity
-
-
-def test_create_client_missing_elasticsearch():
-    """Test that create_client raises ImportError when elasticsearch is not installed."""
-    config = ElasticsearchConfig()
-    
-    with patch.dict('sys.modules', {}, clear=False):
-        if 'elasticsearch' in __import__('sys').modules:
-            del __import__('sys').modules['elasticsearch']
-        
-        from crewai.rag.elasticsearch.factory import create_client
-        with pytest.raises(ImportError, match="elasticsearch package is required"):
-            create_client(config)

tests/test_crew.py

@@ -624,12 +624,12 @@ def test_crew_with_delegating_agents_should_not_override_task_tools(ceo, writer)
         _, kwargs = mock_execute_sync.call_args
         tools = kwargs["tools"]
 
-        assert any(isinstance(tool, TestTool) for tool in tools), (
-            "TestTool should be present"
-        )
-        assert any("delegate" in tool.name.lower() for tool in tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            isinstance(tool, TestTool) for tool in tools
+        ), "TestTool should be present"
+        assert any(
+            "delegate" in tool.name.lower() for tool in tools
+        ), "Delegation tool should be present"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -688,12 +688,12 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools(ceo, writer
         _, kwargs = mock_execute_sync.call_args
         tools = kwargs["tools"]
 
-        assert any(isinstance(tool, TestTool) for tool in new_ceo.tools), (
-            "TestTool should be present"
-        )
-        assert any("delegate" in tool.name.lower() for tool in tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            isinstance(tool, TestTool) for tool in new_ceo.tools
+        ), "TestTool should be present"
+        assert any(
+            "delegate" in tool.name.lower() for tool in tools
+        ), "Delegation tool should be present"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -817,17 +817,17 @@ def test_task_tools_override_agent_tools_with_allow_delegation(researcher, write
         used_tools = kwargs["tools"]
 
         # Confirm AnotherTestTool is present but TestTool is not
-        assert any(isinstance(tool, AnotherTestTool) for tool in used_tools), (
-            "AnotherTestTool should be present"
-        )
-        assert not any(isinstance(tool, TestTool) for tool in used_tools), (
-            "TestTool should not be present among used tools"
-        )
+        assert any(
+            isinstance(tool, AnotherTestTool) for tool in used_tools
+        ), "AnotherTestTool should be present"
+        assert not any(
+            isinstance(tool, TestTool) for tool in used_tools
+        ), "TestTool should not be present among used tools"
 
         # Confirm delegation tool(s) are present
-        assert any("delegate" in tool.name.lower() for tool in used_tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            "delegate" in tool.name.lower() for tool in used_tools
+        ), "Delegation tool should be present"
 
     # Finally, make sure the agent's original tools remain unchanged
     assert len(researcher_with_delegation.tools) == 1
@@ -931,9 +931,9 @@ def test_cache_hitting_between_agents(researcher, writer, ceo):
             tool="multiplier", input={"first_number": 2, "second_number": 6}
         )
         assert cache_calls[0] == expected_call, f"First call mismatch: {cache_calls[0]}"
-        assert cache_calls[1] == expected_call, (
-            f"Second call mismatch: {cache_calls[1]}"
-        )
+        assert (
+            cache_calls[1] == expected_call
+        ), f"Second call mismatch: {cache_calls[1]}"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1676,9 +1676,9 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():
 
             # Verify that exactly one tool was used and it was a CodeInterpreterTool
             assert len(used_tools) == 1, "Should have exactly one tool"
-            assert isinstance(used_tools[0], CodeInterpreterTool), (
-                "Tool should be CodeInterpreterTool"
-            )
+            assert isinstance(
+                used_tools[0], CodeInterpreterTool
+            ), "Tool should be CodeInterpreterTool"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -2537,8 +2537,8 @@ def test_memory_events_are_emitted():
 
         crew.kickoff()
 
-    assert len(events["MemorySaveStartedEvent"]) == 6
-    assert len(events["MemorySaveCompletedEvent"]) == 6
+    assert len(events["MemorySaveStartedEvent"]) == 3
+    assert len(events["MemorySaveCompletedEvent"]) == 3
     assert len(events["MemorySaveFailedEvent"]) == 0
     assert len(events["MemoryQueryStartedEvent"]) == 3
     assert len(events["MemoryQueryCompletedEvent"]) == 3
@@ -3817,9 +3817,9 @@ def test_fetch_inputs():
     expected_placeholders = {"role_detail", "topic", "field"}
     actual_placeholders = crew.fetch_inputs()
 
-    assert actual_placeholders == expected_placeholders, (
-        f"Expected {expected_placeholders}, but got {actual_placeholders}"
-    )
+    assert (
+        actual_placeholders == expected_placeholders
+    ), f"Expected {expected_placeholders}, but got {actual_placeholders}"
 
 
 def test_task_tools_preserve_code_execution_tools():
@@ -3894,20 +3894,20 @@ def test_task_tools_preserve_code_execution_tools():
         used_tools = kwargs["tools"]
 
         # Verify all expected tools are present
-        assert any(isinstance(tool, TestTool) for tool in used_tools), (
-            "Task's TestTool should be present"
-        )
-        assert any(isinstance(tool, CodeInterpreterTool) for tool in used_tools), (
-            "CodeInterpreterTool should be present"
-        )
-        assert any("delegate" in tool.name.lower() for tool in used_tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            isinstance(tool, TestTool) for tool in used_tools
+        ), "Task's TestTool should be present"
+        assert any(
+            isinstance(tool, CodeInterpreterTool) for tool in used_tools
+        ), "CodeInterpreterTool should be present"
+        assert any(
+            "delegate" in tool.name.lower() for tool in used_tools
+        ), "Delegation tool should be present"
 
         # Verify the total number of tools (TestTool + CodeInterpreter + 2 delegation tools)
-        assert len(used_tools) == 4, (
-            "Should have TestTool, CodeInterpreter, and 2 delegation tools"
-        )
+        assert (
+            len(used_tools) == 4
+        ), "Should have TestTool, CodeInterpreter, and 2 delegation tools"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -3951,9 +3951,9 @@ def test_multimodal_flag_adds_multimodal_tools():
         used_tools = kwargs["tools"]
 
         # Check that the multimodal tool was added
-        assert any(isinstance(tool, AddImageTool) for tool in used_tools), (
-            "AddImageTool should be present when agent is multimodal"
-        )
+        assert any(
+            isinstance(tool, AddImageTool) for tool in used_tools
+        ), "AddImageTool should be present when agent is multimodal"
 
         # Verify we have exactly one tool (just the AddImageTool)
         assert len(used_tools) == 1, "Should only have the AddImageTool"
@@ -4217,9 +4217,9 @@ def test_crew_guardrail_feedback_in_context():
     assert len(execution_contexts) > 1, "Task should have been executed multiple times"
 
     # Verify that the second execution included the guardrail feedback
-    assert "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1], (
-        "Guardrail feedback should be included in retry context"
-    )
+    assert (
+        "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1]
+    ), "Guardrail feedback should be included in retry context"
 
     # Verify final output meets guardrail requirements
     assert "IMPORTANT" in result.raw, "Final output should contain required keyword"
@@ -4435,46 +4435,46 @@ def test_crew_copy_with_memory():
     try:
         crew_copy = crew.copy()
 
-        assert hasattr(crew_copy, "_short_term_memory"), (
-            "Copied crew should have _short_term_memory"
-        )
-        assert crew_copy._short_term_memory is not None, (
-            "Copied _short_term_memory should not be None"
-        )
-        assert id(crew_copy._short_term_memory) != original_short_term_id, (
-            "Copied _short_term_memory should be a new object"
-        )
+        assert hasattr(
+            crew_copy, "_short_term_memory"
+        ), "Copied crew should have _short_term_memory"
+        assert (
+            crew_copy._short_term_memory is not None
+        ), "Copied _short_term_memory should not be None"
+        assert (
+            id(crew_copy._short_term_memory) != original_short_term_id
+        ), "Copied _short_term_memory should be a new object"
 
-        assert hasattr(crew_copy, "_long_term_memory"), (
-            "Copied crew should have _long_term_memory"
-        )
-        assert crew_copy._long_term_memory is not None, (
-            "Copied _long_term_memory should not be None"
-        )
-        assert id(crew_copy._long_term_memory) != original_long_term_id, (
-            "Copied _long_term_memory should be a new object"
-        )
+        assert hasattr(
+            crew_copy, "_long_term_memory"
+        ), "Copied crew should have _long_term_memory"
+        assert (
+            crew_copy._long_term_memory is not None
+        ), "Copied _long_term_memory should not be None"
+        assert (
+            id(crew_copy._long_term_memory) != original_long_term_id
+        ), "Copied _long_term_memory should be a new object"
 
-        assert hasattr(crew_copy, "_entity_memory"), (
-            "Copied crew should have _entity_memory"
-        )
-        assert crew_copy._entity_memory is not None, (
-            "Copied _entity_memory should not be None"
-        )
-        assert id(crew_copy._entity_memory) != original_entity_id, (
-            "Copied _entity_memory should be a new object"
-        )
+        assert hasattr(
+            crew_copy, "_entity_memory"
+        ), "Copied crew should have _entity_memory"
+        assert (
+            crew_copy._entity_memory is not None
+        ), "Copied _entity_memory should not be None"
+        assert (
+            id(crew_copy._entity_memory) != original_entity_id
+        ), "Copied _entity_memory should be a new object"
 
         if original_external_id:
-            assert hasattr(crew_copy, "_external_memory"), (
-                "Copied crew should have _external_memory"
-            )
-            assert crew_copy._external_memory is not None, (
-                "Copied _external_memory should not be None"
-            )
-            assert id(crew_copy._external_memory) != original_external_id, (
-                "Copied _external_memory should be a new object"
-            )
+            assert hasattr(
+                crew_copy, "_external_memory"
+            ), "Copied crew should have _external_memory"
+            assert (
+                crew_copy._external_memory is not None
+            ), "Copied _external_memory should not be None"
+            assert (
+                id(crew_copy._external_memory) != original_external_id
+            ), "Copied _external_memory should be a new object"
         else:
             assert (
                 not hasattr(crew_copy, "_external_memory")

tests/test_dependency_compatibility.py

@@ -0,0 +1,91 @@
+"""Tests for dependency compatibility, specifically for issue #3413.
+
+This module tests that CrewAI can be installed alongside Google Cloud SDKs
+without protobuf dependency conflicts.
+"""
+
+import subprocess
+import sys
+import tempfile
+import pytest
+
+
+class TestDependencyCompatibility:
+    """Test dependency compatibility with Google Cloud SDKs."""
+
+    def test_opentelemetry_protobuf_compatibility(self):
+        """Test that opentelemetry versions work with protobuf<5.0."""
+        try:
+            from opentelemetry.sdk.trace import TracerProvider
+            
+            tracer_provider = TracerProvider()
+            tracer = tracer_provider.get_tracer("test")
+            
+            with tracer.start_as_current_span("test_span") as span:
+                span.set_attribute("test", "value")
+                assert span is not None
+                
+        except ImportError as e:
+            pytest.fail(f"Failed to import opentelemetry modules: {e}")
+
+    def test_google_cloud_sdk_compatibility_simulation(self):
+        """Simulate Google Cloud SDK protobuf requirements."""
+        try:
+            import google.protobuf
+            version_parts = google.protobuf.__version__.split('.')
+            major_version = int(version_parts[0])
+            
+            assert major_version < 5, f"protobuf version {google.protobuf.__version__} should be <5.0 for Google Cloud SDK compatibility"
+            
+        except ImportError:
+            pytest.skip("protobuf not installed, skipping compatibility test")
+
+    def test_crewai_telemetry_functionality(self):
+        """Test that CrewAI telemetry functionality works with downgraded opentelemetry."""
+        try:
+            from crewai.telemetry.telemetry import Telemetry
+            from crewai.utilities.crew.crew_context import get_crew_context
+            
+            telemetry = Telemetry()
+            assert telemetry is not None
+            
+            get_crew_context()
+            
+        except ImportError as e:
+            pytest.fail(f"Failed to import CrewAI telemetry modules: {e}")
+
+    def test_dry_run_installation_compatibility(self):
+        """Test that CrewAI and Google Cloud SDKs can be installed together."""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            try:
+                result = subprocess.run([
+                    sys.executable, "-m", "pip", "install", "--dry-run", "--quiet",
+                    "opentelemetry-api>=1.27.0,<1.30.0",
+                    "opentelemetry-sdk>=1.27.0,<1.30.0", 
+                    "opentelemetry-exporter-otlp-proto-http>=1.27.0,<1.30.0",
+                    "google-cloud-storage"
+                ], capture_output=True, text=True, cwd=temp_dir)
+                
+                assert result.returncode == 0, f"Dry run installation failed: {result.stderr}"
+                
+                assert "protobuf" in result.stdout.lower(), "protobuf should be in installation plan"
+                
+            except Exception as e:
+                pytest.fail(f"Dry run installation test failed: {e}")
+
+    def test_protobuf_version_constraint_resolution(self):
+        """Test that protobuf version constraints are properly resolved."""
+        try:
+            import google.protobuf
+            version = google.protobuf.__version__
+            
+            version_parts = [int(x) for x in version.split('.')]
+            major, minor = version_parts[0], version_parts[1]
+            
+            assert major >= 3, f"protobuf version {version} should be >=3.19"
+            if major == 3:
+                assert minor >= 19, f"protobuf version {version} should be >=3.19"
+            assert major < 5, f"protobuf version {version} should be <5.0 for Google Cloud SDK compatibility"
+            
+        except ImportError:
+            pytest.skip("protobuf not installed, skipping version constraint test")