new cassette for test

pyproject.toml

@@ -26,7 +26,7 @@ dependencies = [
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
-    "chromadb>=0.5.18",
+    "chromadb>=0.5.23",
     "pdfplumber>=0.11.4",
     "openpyxl>=3.1.5",
     "blinker>=1.9.0",
@@ -38,7 +38,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.14.0"]
+tools = ["crewai-tools>=0.17.0"]
 agentops = ["agentops>=0.3.0"]
 fastembed = ["fastembed>=0.4.1"]
 pdfplumber = [
@@ -64,7 +64,7 @@ dev-dependencies = [
     "mkdocs-material-extensions>=1.3.1",
     "pillow>=10.2.0",
     "cairosvg>=2.7.1",
-    "crewai-tools>=0.14.0",
+    "crewai-tools>=0.17.0",
     "pytest>=8.0.0",
     "pytest-vcr>=1.0.2",
     "python-dotenv>=1.0.0",

src/crewai/agent.py

@@ -23,27 +23,19 @@ from crewai.utilities.converter import generate_model_description
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
+agentops = None
 
-def mock_agent_ops_provider():
-    def track_agent(*args, **kwargs):
+try:
+    import agentops  # type: ignore # Name "agentops" is already defined
+    from agentops import track_agent  # type: ignore
+except ImportError:
+
+    def track_agent():
         def noop(f):
             return f
 
         return noop
 
-    return track_agent
-
-
-agentops = None
-
-if os.environ.get("AGENTOPS_API_KEY"):
-    try:
-        from agentops import track_agent
-    except ImportError:
-        track_agent = mock_agent_ops_provider()
-else:
-    track_agent = mock_agent_ops_provider()
-
 
 @track_agent()
 class Agent(BaseAgent):

src/crewai/crew.py

@@ -1,6 +1,5 @@
 import asyncio
 import json
-import os
 import uuid
 import warnings
 from concurrent.futures import Future
@@ -49,12 +48,10 @@ from crewai.utilities.planning_handler import CrewPlanner
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
-agentops = None
-if os.environ.get("AGENTOPS_API_KEY"):
-    try:
-        import agentops  # type: ignore
-    except ImportError:
-        pass
+try:
+    import agentops  # type: ignore
+except ImportError:
+    agentops = None
 
 
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")

src/crewai/knowledge/source/base_knowledge_source.py

@@ -46,4 +46,5 @@ class BaseKnowledgeSource(BaseModel, ABC):
         Save the documents to the storage.
         This method should be called after the chunks and embeddings are generated.
         """
+        print("CHUNKS: ", self.chunks)
         self.storage.save(self.chunks)

src/crewai/knowledge/storage/knowledge_storage.py

@@ -124,43 +124,60 @@ class KnowledgeStorage(BaseKnowledgeStorage):
         documents: List[str],
         metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
     ):
-        if self.collection:
-            try:
-                if metadata is None:
-                    metadatas: Optional[OneOrMany[chromadb.Metadata]] = None
-                elif isinstance(metadata, list):
-                    metadatas = [cast(chromadb.Metadata, m) for m in metadata]
-                else:
-                    metadatas = cast(chromadb.Metadata, metadata)
-
-                ids = [
-                    hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents
-                ]
-
-                self.collection.upsert(
-                    documents=documents,
-                    metadatas=metadatas,
-                    ids=ids,
-                )
-            except chromadb.errors.InvalidDimensionException as e:
-                Logger(verbose=True).log(
-                    "error",
-                    "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
-                    "red",
-                )
-                raise ValueError(
-                    "Embedding dimension mismatch. Make sure you're using the same embedding model "
-                    "across all operations with this collection."
-                    "Try resetting the collection using `crewai reset-memories -a`"
-                ) from e
-            except Exception as e:
-                Logger(verbose=True).log(
-                    "error", f"Failed to upsert documents: {e}", "red"
-                )
-                raise
-        else:
+        if not self.collection:
             raise Exception("Collection not initialized")
 
+        try:
+            # Create a dictionary to store unique documents
+            unique_docs = {}
+
+            # Generate IDs and create a mapping of id -> (document, metadata)
+            for idx, doc in enumerate(documents):
+                doc_id = hashlib.sha256(doc.encode("utf-8")).hexdigest()
+                doc_metadata = None
+                if metadata is not None:
+                    if isinstance(metadata, list):
+                        doc_metadata = metadata[idx]
+                    else:
+                        doc_metadata = metadata
+                unique_docs[doc_id] = (doc, doc_metadata)
+
+            # Prepare filtered lists for ChromaDB
+            filtered_docs = []
+            filtered_metadata = []
+            filtered_ids = []
+
+            # Build the filtered lists
+            for doc_id, (doc, meta) in unique_docs.items():
+                filtered_docs.append(doc)
+                filtered_metadata.append(meta)
+                filtered_ids.append(doc_id)
+
+            # If we have no metadata at all, set it to None
+            final_metadata: Optional[OneOrMany[chromadb.Metadata]] = (
+                None if all(m is None for m in filtered_metadata) else filtered_metadata
+            )
+
+            self.collection.upsert(
+                documents=filtered_docs,
+                metadatas=final_metadata,
+                ids=filtered_ids,
+            )
+        except chromadb.errors.InvalidDimensionException as e:
+            Logger(verbose=True).log(
+                "error",
+                "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
+                "red",
+            )
+            raise ValueError(
+                "Embedding dimension mismatch. Make sure you're using the same embedding model "
+                "across all operations with this collection."
+                "Try resetting the collection using `crewai reset-memories -a`"
+            ) from e
+        except Exception as e:
+            Logger(verbose=True).log("error", f"Failed to upsert documents: {e}", "red")
+            raise
+
     def _create_default_embedding_function(self):
         from chromadb.utils.embedding_functions.openai_embedding_function import (
             OpenAIEmbeddingFunction,

src/crewai/tools/tool_usage.py

@@ -1,6 +1,5 @@
 import ast
 import datetime
-import os
 import time
 from difflib import SequenceMatcher
 from textwrap import dedent
@@ -15,12 +14,10 @@ from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
 from crewai.tools.tool_usage_events import ToolUsageError, ToolUsageFinished
 from crewai.utilities import I18N, Converter, ConverterError, Printer
 
-agentops = None
-if os.environ.get("AGENTOPS_API_KEY"):
-    try:
-        import agentops  # type: ignore
-    except ImportError:
-        pass
+try:
+    import agentops  # type: ignore
+except ImportError:
+    agentops = None
 
 OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o", "o1-preview", "o1-mini"]
 

src/crewai/utilities/evaluators/task_evaluator.py

@@ -1,4 +1,3 @@
-import os
 from typing import List
 
 from pydantic import BaseModel, Field
@@ -6,27 +5,17 @@ from pydantic import BaseModel, Field
 from crewai.utilities import Converter
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
+agentops = None
+try:
+    from agentops import track_agent  # type: ignore
+except ImportError:
 
-def mock_agent_ops_provider():
-    def track_agent(*args, **kwargs):
+    def track_agent(name):
         def noop(f):
             return f
 
         return noop
 
-    return track_agent
-
-
-agentops = None
-
-if os.environ.get("AGENTOPS_API_KEY"):
-    try:
-        from agentops import track_agent
-    except ImportError:
-        track_agent = mock_agent_ops_provider()
-else:
-    track_agent = mock_agent_ops_provider()
-
 
 class Entity(BaseModel):
     name: str = Field(description="The name of the entity.")

tests/agent_test.py

@@ -1595,19 +1595,15 @@ def test_agent_execute_task_with_ollama():
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_with_knowledge_sources():
     # Create a knowledge source with some content
-    content = "Brandon's favorite color is blue and he likes Mexican food."
-    string_source = StringKnowledgeSource(
-        content=content, metadata={"preference": "personal"}
-    )
+    content = "Brandon's favorite color is red and he likes Mexican food."
+    string_source = StringKnowledgeSource(content=content)
 
     with patch(
         "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
     ) as MockKnowledge:
         mock_knowledge_instance = MockKnowledge.return_value
         mock_knowledge_instance.sources = [string_source]
-        mock_knowledge_instance.query.return_value = [
-            {"content": content, "metadata": {"preference": "personal"}}
-        ]
+        mock_knowledge_instance.query.return_value = [{"content": content}]
 
         agent = Agent(
             role="Information Agent",
@@ -1628,4 +1624,4 @@ def test_agent_with_knowledge_sources():
         result = crew.kickoff()
 
         # Assert that the agent provides the correct information
-        assert "blue" in result.raw.lower()
+        assert "red" in result.raw.lower()

tests/cassettes/test_agent_with_knowledge_sources.yaml

@@ -1,6 +1,6 @@
 interactions:
 - request:
-    body: '{"input": ["Brandon''s favorite color is blue and he likes Mexican food."],
+    body: '{"input": ["Brandon''s favorite color is red and he likes Mexican food."],
       "model": "text-embedding-3-small", "encoding_format": "base64"}'
     headers:
       accept:
@@ -10,7 +10,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '138'
+      - '137'
       content-type:
       - application/json
       host:
@@ -32,19 +32,19 @@ interactions:
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.12.7
+      - 3.11.9
     method: POST
     uri: https://api.openai.com/v1/embeddings
   response:
     content: "{\n  \"object\": \"list\",\n  \"data\": [\n    {\n      \"object\":
-      \"embedding\",\n      \"index\": 0,\n      \"embedding\": \"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 def test_task_definition_based_on_dict():

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+    { name = "chromadb", specifier = ">=0.5.23" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.14.0" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.17.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },
     { name = "json-repair", specifier = ">=0.25.2" },
@@ -678,7 +678,7 @@ requires-dist = [
 [package.metadata.requires-dev]
 dev = [
     { name = "cairosvg", specifier = ">=2.7.1" },
-    { name = "crewai-tools", specifier = ">=0.14.0" },
+    { name = "crewai-tools", specifier = ">=0.17.0" },
     { name = "mkdocs", specifier = ">=1.4.3" },
     { name = "mkdocs-material", specifier = ">=9.5.7" },
     { name = "mkdocs-material-extensions", specifier = ">=1.3.1" },