linted

docs/concepts/knowledge.mdx

@@ -51,12 +51,41 @@ crew = Crew(
     tasks=[task],
     verbose=True,
     process=Process.sequential,
-    knowledge={"sources": [string_source], "metadata": {"preference": "personal"}}, # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
+    knowledge_sources=[string_source], # Enable knowledge by adding the sources here.
 )
 
 result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
 ```
 
+## Appending Knowledge Sources To Individual Agents
+Sometimes you may want to append knowledge sources to an individual agent. You can do this by setting the `knowledge` parameter in the `Agent` class.
+
+```python
+agent = Agent(
+    ...
+    knowledge_sources=[
+        StringKnowledgeSource(
+            content="Users name is John. He is 30 years old and lives in San Francisco.",
+            metadata={"preference": "personal"},
+        )
+    ],
+)
+```
+
+## Agent Level Knowledge Sources
+
+You can also append knowledge sources to an individual agent by setting the `knowledge_sources` parameter in the `Agent` class.
+
+```python
+string_source = StringKnowledgeSource(
+    content="Users name is John. He is 30 years old and lives in San Francisco.",
+    metadata={"preference": "personal"},
+)
+agent = Agent(
+    ...
+    knowledge_sources=[string_source],
+)
+```
 
 ## Embedder Configuration
 
@@ -70,10 +99,7 @@ string_source = StringKnowledgeSource(
 )
 crew = Crew(
     ...
-    knowledge={
-        "sources": [string_source],
-        "metadata": {"preference": "personal"},
-        "embedder_config": {"provider": "openai", "config": {"model": "text-embedding-3-small"}},
-    },
+    knowledge_sources=[string_source],
+    embedder_config={"provider": "ollama", "config": {"model": "nomic-embed-text:latest"}},
 )
 ```

src/crewai/agent.py

@@ -1,7 +1,7 @@
 import os
 import shutil
 import subprocess
-from typing import Any, List, Literal, Optional, Union
+from typing import Any, List, Literal, Optional, Union, Dict
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
@@ -10,6 +10,8 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.cli.constants import ENV_VARS
 from crewai.llm import LLM
+from crewai.knowledge.knowledge import Knowledge
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.task import Task
 from crewai.tools import BaseTool
@@ -19,6 +21,7 @@ from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_F
 from crewai.utilities.converter import generate_model_description
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
+from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
 
 
 def mock_agent_ops_provider():
@@ -65,6 +68,7 @@ class Agent(BaseAgent):
             allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
             tools: Tools at agents disposal
             step_callback: Callback to be executed after each step of the agent execution.
+            knowledge_sources: Knowledge sources for the agent.
     """
 
     _times_executed: int = PrivateAttr(default=0)
@@ -122,9 +126,21 @@ class Agent(BaseAgent):
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
     )
+    embedder_config: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Embedder configuration for the agent.",
+    )
+    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
+        default=None,
+        description="Knowledge sources for the agent.",
+    )
+    _knowledge: Optional[Knowledge] = PrivateAttr(
+        default=None,
+    )
 
     @model_validator(mode="after")
     def post_init_setup(self):
+        self._set_knowledge()
         self.agent_ops_agent_name = self.role
         unaccepted_attributes = [
             "AWS_ACCESS_KEY_ID",
@@ -232,6 +248,21 @@ class Agent(BaseAgent):
             self.cache_handler = CacheHandler()
         self.set_cache_handler(self.cache_handler)
 
+    def _set_knowledge(self):
+        try:
+            if self.knowledge_sources:
+                knowledge_agent_name = f"{self.role.replace(' ', '_')}"
+                if isinstance(self.knowledge_sources, list) and all(
+                    isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
+                ):
+                    self._knowledge = Knowledge(
+                        sources=self.knowledge_sources,
+                        embedder_config=self.embedder_config,
+                        collection_name=knowledge_agent_name,
+                    )
+        except (TypeError, ValueError) as e:
+            raise ValueError(f"Invalid Knowledge Configuration: {str(e)}")
+
     def execute_task(
         self,
         task: Task,
@@ -286,17 +317,21 @@ class Agent(BaseAgent):
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
-        # Integrate the knowledge base
-        if self.crew and self.crew.knowledge:
-            knowledge_snippets = self.crew.knowledge.query([task.prompt()])
-            valid_snippets = [
-                result["context"]
-                for result in knowledge_snippets
-                if result and result.get("context")
-            ]
-            if valid_snippets:
-                formatted_knowledge = "\n".join(valid_snippets)
-                task_prompt += f"\n\nAdditional Information:\n{formatted_knowledge}"
+        if self._knowledge:
+            agent_knowledge_snippets = self._knowledge.query([task.prompt()])
+            if agent_knowledge_snippets:
+                agent_knowledge_context = extract_knowledge_context(
+                    agent_knowledge_snippets
+                )
+                if agent_knowledge_context:
+                    task_prompt += agent_knowledge_context
+
+        if self.crew:
+            knowledge_snippets = self.crew.query_knowledge([task.prompt()])
+            if knowledge_snippets:
+                crew_knowledge_context = extract_knowledge_context(knowledge_snippets)
+                if crew_knowledge_context:
+                    task_prompt += crew_knowledge_context
 
         tools = tools or self.tools or []
         self.create_agent_executor(tools=tools, task=task)

src/crewai/crew.py

@@ -28,6 +28,7 @@ from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.knowledge.knowledge import Knowledge
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.memory.user.user_memory import UserMemory
 from crewai.process import Process
 from crewai.task import Task
@@ -202,10 +203,13 @@ class Crew(BaseModel):
         default=[],
         description="List of execution logs for tasks",
     )
-    knowledge: Optional[Dict[str, Any]] = Field(
-        default=None, description="Knowledge for the crew. Add knowledge sources to the knowledge object."
+    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
+        default=None,
+        description="Knowledge sources for the crew. Add knowledge sources to the knowledge object.",
+    )
+    _knowledge: Optional[Knowledge] = PrivateAttr(
+        default=None,
     )
-
 
     @field_validator("id", mode="before")
     @classmethod
@@ -282,11 +286,22 @@ class Crew(BaseModel):
 
     @model_validator(mode="after")
     def create_crew_knowledge(self) -> "Crew":
-        if self.knowledge:
+        """Create the knowledge for the crew."""
+        if self.knowledge_sources:
             try:
-                self.knowledge = Knowledge(**self.knowledge) if isinstance(self.knowledge, dict) else self.knowledge
-            except (TypeError, ValueError) as e:
-                raise ValueError(f"Invalid knowledge configuration: {str(e)}")
+                if isinstance(self.knowledge_sources, list) and all(
+                    isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
+                ):
+                    self._knowledge = Knowledge(
+                        sources=self.knowledge_sources,
+                        embedder_config=self.embedder,
+                        collection_name="crew",
+                    )
+
+            except Exception as e:
+                self._logger.log(
+                    "warning", f"Failed to init knowledge: {e}", color="yellow"
+                )
         return self
 
     @model_validator(mode="after")
@@ -942,6 +957,11 @@ class Crew(BaseModel):
         result = self._execute_tasks(self.tasks, start_index, True)
         return result
 
+    def query_knowledge(self, query: List[str]) -> Union[List[Dict[str, Any]], None]:
+        if self._knowledge:
+            return self._knowledge.query(query)
+        return None
+
     def copy(self):
         """Create a deep copy of the Crew."""
 

src/crewai/knowledge/knowledge.py

@@ -5,8 +5,8 @@ from pydantic import BaseModel, ConfigDict, Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
-from crewai.utilities.logger import Logger
 from crewai.utilities.constants import DEFAULT_SCORE_THRESHOLD
+
 os.environ["TOKENIZERS_PARALLELISM"] = "false"  # removes logging from fastembed
 
 
@@ -18,24 +18,33 @@ class Knowledge(BaseModel):
         storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
         embedder_config: Optional[Dict[str, Any]] = None
     """
+
     sources: List[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
     embedder_config: Optional[Dict[str, Any]] = None
+    collection_name: Optional[str] = None
 
-    def __init__(self, embedder_config: Optional[Dict[str, Any]] = None, **data):
+    def __init__(
+        self,
+        collection_name: str,
+        sources: List[BaseKnowledgeSource],
+        embedder_config: Optional[Dict[str, Any]] = None,
+        storage: Optional[KnowledgeStorage] = None,
+        **data,
+    ):
         super().__init__(**data)
-        self.storage = KnowledgeStorage(embedder_config=embedder_config or None)
-
-        try:
-            for source in self.sources:
-                source.add()
-        except Exception as e:
-            Logger(verbose=True).log(
-                "warning",
-                f"Failed to init knowledge: {e}",
-                color="yellow",
+        if storage:
+            self.storage = storage
+        else:
+            self.storage = KnowledgeStorage(
+                embedder_config=embedder_config, collection_name=collection_name
             )
+        self.sources = sources
+        self.storage.initialize_knowledge_storage()
+        for source in sources:
+            source.storage = self.storage
+            source.add()
 
     def query(
         self, query: List[str], limit: int = 3, preference: Optional[str] = None
@@ -52,3 +61,8 @@ class Knowledge(BaseModel):
             score_threshold=DEFAULT_SCORE_THRESHOLD,
         )
         return results
+
+    def _add_sources(self):
+        for source in self.sources:
+            source.storage = self.storage
+            source.add()

src/crewai/knowledge/source/base_knowledge_source.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import List, Dict, Any
+from typing import List, Dict, Any, Optional
 
 import numpy as np
 from pydantic import BaseModel, ConfigDict, Field
@@ -18,6 +18,7 @@ class BaseKnowledgeSource(BaseModel, ABC):
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
     metadata: Dict[str, Any] = Field(default_factory=dict)
+    collection_name: Optional[str] = Field(default=None)
 
     @abstractmethod
     def load_content(self) -> Dict[Any, str]:

src/crewai/knowledge/source/string_knowledge_source.py

@@ -1,4 +1,4 @@
-from typing import List
+from typing import List, Optional
 
 from pydantic import Field
 
@@ -9,6 +9,7 @@ class StringKnowledgeSource(BaseKnowledgeSource):
     """A knowledge source that stores and queries plain text content using embeddings."""
 
     content: str = Field(...)
+    collection_name: Optional[str] = Field(default=None)
 
     def model_post_init(self, _):
         """Post-initialization method to validate content."""

src/crewai/knowledge/storage/knowledge_storage.py

@@ -3,12 +3,16 @@ import io
 import logging
 import chromadb
 import os
+
+import chromadb.errors
 from crewai.utilities.paths import db_storage_path
-from typing import Optional, List
-from typing import Dict, Any
+from typing import Optional, List, Dict, Any, Union
 from crewai.utilities import EmbeddingConfigurator
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
 import hashlib
+from chromadb.config import Settings
+from chromadb.api import ClientAPI
+from crewai.utilities.logger import Logger
 
 
 @contextlib.contextmanager
@@ -35,9 +39,16 @@ class KnowledgeStorage(BaseKnowledgeStorage):
     """
 
     collection: Optional[chromadb.Collection] = None
+    collection_name: Optional[str] = "knowledge"
+    app: Optional[ClientAPI] = None
 
-    def __init__(self, embedder_config: Optional[Dict[str, Any]] = None):
-        self._initialize_app(embedder_config or {})
+    def __init__(
+        self,
+        embedder_config: Optional[Dict[str, Any]] = None,
+        collection_name: Optional[str] = None,
+    ):
+        self.collection_name = collection_name
+        self._set_embedder_config(embedder_config)
 
     def search(
         self,
@@ -67,43 +78,75 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             else:
                 raise Exception("Collection not initialized")
 
-    def _initialize_app(self, embedder_config: Optional[Dict[str, Any]] = None):
-        import chromadb
-        from chromadb.config import Settings
-
-        self._set_embedder_config(embedder_config)
-
+    def initialize_knowledge_storage(self):
+        base_path = os.path.join(db_storage_path(), "knowledge")
         chroma_client = chromadb.PersistentClient(
-            path=f"{db_storage_path()}/knowledge",
+            path=base_path,
             settings=Settings(allow_reset=True),
         )
 
         self.app = chroma_client
 
         try:
-            self.collection = self.app.get_or_create_collection(name="knowledge")
+            collection_name = (
+                f"knowledge_{self.collection_name}"
+                if self.collection_name
+                else "knowledge"
+            )
+            if self.app:
+                self.collection = self.app.get_or_create_collection(
+                    name=collection_name, embedding_function=self.embedder_config
+                )
+            else:
+                raise Exception("Vector Database Client not initialized")
         except Exception:
             raise Exception("Failed to create or get collection")
 
     def reset(self):
         if self.app:
             self.app.reset()
+        else:
+            base_path = os.path.join(db_storage_path(), "knowledge")
+            self.app = chromadb.PersistentClient(
+                path=base_path,
+                settings=Settings(allow_reset=True),
+            )
+            self.app.reset()
 
     def save(
-        self, documents: List[str], metadata: Dict[str, Any] | List[Dict[str, Any]]
+        self,
+        documents: List[str],
+        metadata: Union[Dict[str, Any], List[Dict[str, Any]]],
     ):
         if self.collection:
-            metadatas = [metadata] if isinstance(metadata, dict) else metadata
+            try:
+                metadatas = [metadata] if isinstance(metadata, dict) else metadata
 
-            ids = [
-                hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents
-            ]
+                ids = [
+                    hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents
+                ]
 
-            self.collection.upsert(
-                documents=documents,
-                metadatas=metadatas,
-                ids=ids,
-            )
+                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:
             raise Exception("Collection not initialized")
 

src/crewai/knowledge/utils/knowledge_utils.py

@@ -0,0 +1,12 @@
+from typing import Any, Dict, List
+
+
+def extract_knowledge_context(knowledge_snippets: List[Dict[str, Any]]) -> str:
+    """Extract knowledge from the task prompt."""
+    valid_snippets = [
+        result["context"]
+        for result in knowledge_snippets
+        if result and result.get("context")
+    ]
+    snippet = "\n".join(valid_snippets)
+    return f"Additional Information: {snippet}" if valid_snippets else ""

tests/agent_test.py

@@ -3,20 +3,19 @@
 import os
 from unittest import mock
 from unittest.mock import patch
-
 import pytest
 
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
 from crewai.tools import tool
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
 from crewai.tools.tool_usage_events import ToolUsageFinished
 from crewai.utilities import RPMController
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.utilities.events import Emitter
 
 
@@ -1584,21 +1583,22 @@ def test_agent_with_knowledge_sources():
     string_source = StringKnowledgeSource(
         content=content, metadata={"preference": "personal"}
     )
-    
 
-    with patch('crewai.knowledge.storage.knowledge_storage.KnowledgeStorage') as MockKnowledge:
+    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, "metadata": {"preference": "personal"}}
+        ]
+
         agent = Agent(
             role="Information Agent",
             goal="Provide information based on knowledge sources",
             backstory="You have access to specific knowledge sources.",
             llm=LLM(model="gpt-4o-mini"),
+            knowledge_sources=[string_source],
         )
 
         # Create a task that requires the agent to use the knowledge
@@ -1613,4 +1613,3 @@ def test_agent_with_knowledge_sources():
 
         # Assert that the agent provides the correct information
         assert "blue" in result.raw.lower()
-

tests/cassettes/test_agent_with_knowledge_sources.yaml

@@ -1,4 +1,415 @@
 interactions:
+- request:
+    body: '{"input": ["Brandon''s favorite color is blue and he likes Mexican food."],
+      "model": "text-embedding-3-small", "encoding_format": "base64"}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '138'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.52.1
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.52.1
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.11.9
+    method: POST
+    uri: https://api.openai.com/v1/embeddings
+  response:
+    body:
+      string: !!binary |
+        H4sIAAAAAAAAA1R6SxOyurrmfP+KVWtKn5KLkGTNuIuABAFRu7q6QJGLIAKSQE6d/96l367T3RMH
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